Tu,| I \I I'll I . meals \\\\\\\\ l\\\\\ MlCHIGAN STATE \ —l\\\\\\\\\\\\\\\\\\\\\\\\\\ :\'\\\\\\\\\i\‘\i\\i L (“4“ ~ ‘:—-—q‘ _ MIMIY Mulligan But. {Immanrrtlty' K ~__ J i This is to certify that the dissertation entitled POPULATION PRESSURE, DEFORESTATION, LAND DEGRADATION AND POPULATION REDISTRIBUTION IN THE PLAN SIERRA REGION OF THE CORDILLERA CENTRAL, Date DOMINICAN REPUBLIC presented by Richard Alan Sambrook has been accepted towards fulfillment of the requirements for Mdegree in _Geagxaphy_ / /Z;Z,z 7/ 4M /'/ Major pl‘fasor July 15, 1992 MS U is an Affirmative Action/Equal Opportunity Institution 0-12771 PLACE IN RETURN BOX to remove this checkout from your record. TO AVOID FINES return on or before date due. SE? 2 S 1993 ’ 9 3.5.9“ 15199;: ' i} i. "‘ MSU Is An Affirmative Action/Equal Opportunity Institution chS-DJ POPULATION PRESSURE, DEFORESTATION, LAND DEGRADATION AND POPULATION REDISTRIBUTION IN THE PLAN SIERRA REGION OF THE CORDILLERA CENTRAL, DOMINICAN REPUBLIC By Richard Alan Sambrook A DISSERTATION Submitted to Michigan State University in partial fulfillment of the requirement for the degree DOCTOR OF PHILOSOPHY Department of Geography 1992 a /— m ~/ ABSTRACT POPULATION PRESSURE, DEFORESTATION, LAND DEGRADATION AND POPULATION REDISTRIBUTION IN THE PLAN SIERRA REGION OF THE CORDILLERA CENTRAL, DOMINICAN REPUBLIC By Richard Alan Sambrook Accelerated population growth, deforestation, and land degradation are a major concern of developmentplannersinthe‘l'hirdWorld. Environmentalexpertsstudythewttingoftreesto determinetheextentaswellastheimpactofforestconvcrsiononsuchphysiealprocessesaserosion and stream and reservoir silting. However, a major social-human dimension interfaces with these physicalprocessesandposesmanyquestions. Whatefl'ect,ifany,docspopulationpressurehaveon deforestation, the intensity of agricultural practices, and population redistribution? Do these relationshipschangcovertime? Despitetheobviousimpouanceofthesequeriesfewresearchershave attempted to explain interrelationships between two or more of these processes WithinthefiameworkofawnceptualmodeLasefiesofreseuchhypotheseswasfomuhted to define the nature ofthe relationships between these principal themes: agricultural intensity, deforestation, human carrying capacity, land degradation, population pressure, and population redistribution. Thisresearchwaswndudedflflospafialscakathepdifical'seaiom'thesmdlest mganizedmhbebwthemmidpahtyfmwhichDommkancemmdamispubfisheimdthemd farmhousehold. Inteml-ratiodatafromthenationalcensusandfamlevelsamplesumyswere evaluatedbymeansofbivariateregressionandsimplecorrelafionanalysis Results confirm a strong positive relationship between population premure and out-migration atthepoliticalsectionscale. OnthefarmleveLamoderatelystrongpositivcrelationshipisestablished bumpopflafimpressmeanddefmmAdmihrsuengthumdflnkagekeuabfished betweendeforestationandlanddegradation. Incontrast,onlyonlargerfarmsisevidenceofaweak pmhiwnhbnshipwnfirmedbefleenpopuhthnprmmandBmempianmeumudayiammd intensity. IiekeflscalingwasempbyedtoasessWolpat’swnceptof'placeufilirrinamd environment. Mingswhodcfinethehocwpafionasfarmmgrevealaposifivemehfionbetween landdegradationandthededsiontomigrate. Therdeoflanddegradatiomanecological'push'factor, increaseswithage. Fmafly,weakpositivewrrchfimsamongthevariablesage,educafion,ocwpafion WandmiyafimdesfimfimsmdiutematmpuhfimmdktribudonkdgnifimNyMdeiw than anticipated of a 'late transitional society,“ within the context of Zelinsky’s mobility transition model. Cornish! by RICHARD ALAN SAMBROOK 1992 Dedicatedto BverettABenjamenSambrook Born Marchm,1987 on. C ”linls SantiagodelosCaballeros D .. R If ACKNOWLEDGEMENTS Anumbuofindividuakneedtobencogniudfmthernuibufimstowdthenmfid ccmpletionofth'ndissertation. Mysinceregratitude'nexpressedtothcmembersofmydoetoral committee:Dr.RobertN.ThomasChair;Dr.DavidJ.Campbell;Dr.BruceW.Pigozzi;andDr.Jack F.Williams,alloftheGeographyDepartment. Dr.LeeM.James,formerChairoftheForestr-y Department, served as a committee member and Dean’s representative from the Graduate School. Dr. JudyMOkomChahdtheDepamdeeogaphy,yadoudyprmfledoffiwspaumtheUman PlanningBuildingaftamyreturnfirmtheDominicanRepublie. Mmchmuldnahawbeenpossibkfithomthegemomhelpofmymdividmkm theDominicanRepublic. GeographerRafaelEmilioYunéninn-oducedmetotheproblcmsof defueaafiomhnddeyadadonmdpopuhdmmemmePhnSimaregimdtheCadflbra Central Hisencomagememmdguidanceduringthefmmafivephasesofthisresearchwere invaluable. RedaNuneeoftherfifidaUniversidadCatdliaMadreyMaesminSamiagodelos Cahaflamwuagradomhostmdwnstamwmceofsupponmdspifimdmspirafimdufingmy twenty-twomonthstayintheDominicenRepublic. DomingoFrandsqagraduateoftheSociology DepartmentatPUCMM,workeddoselywithmeduringthefieldworkphaseofthereaearch. Hewas mmmmmmumdwmmmmmmmm householdinterviews. PedroJuan del Rosarioofthe Center for UrbanandRegionStudies(CEUR) ofiaedmymefidsumfionsfacmducfingmveyreseuchmthePhnSierraregim. Frekye OfivqataficartogapherofCEURhelpedwithforestcovermapcompilation. Dr.EduardoLuna, famaiydtheMflhemafiaBduafimDeparmaPUWudnedwhhprehmharydm compilationandatat’nticalanalysis. Sexuammmamsmmomlymm mMaerialphotography,maps,andnmplennvcyduacdlededbyhisuafl. DomingoFortuna, MmembumchugedtheahphaouchiwsandmppmgsedimuPhnSieuamveryhelpful Fmafly,lwishtoexpressmythankstothefollowingpeoplein8antoDomingo:Seh'orPiehardoofthe DominicanOficeofNationalStatisties(ONE);SefiorJos€IoaquinHungriaMorell,Directorofthe PmAmericanImfitmedGeogaphyandmstmy,and;Sefi'mOrhndoAdmsoftheNadmd MywifeStephaniermnSmbrmhdefignedthehyomandqpedthefinflverdmofthe fieldquestionnaire. Sheaificaflyreviewedthepreieflandpflotsamplesuneyrespmseeprepared basemapsforaectionleveldataanalysis,andcompiledforestcovermaps. Inaddition,myspouse activdyparfidpatedintheprocessingandcompiladonofquesfionnairedata Fmally,Stephanie melfisflyrwiewdmbmdrahsoffiediseflafimandmademmyuwfidmggesfiomfmimmofing ShamyrumfiomthebominianRepublkthefonowingmdifidudsmerhmyyafitude. MnMdTaybroftheCompmuWqudMichiganUniversitywnedasaSASmham andgreatlycontributedtothestatisticalanalysisofthedata. DrJothattonoftheComputerCenter uMhmiUmmdtyfadfimedmeUansferofSASdauandproymfiksmOxfmehhwhkh allowedmetocompletetheresearch. Severalofthemapsfoundintheearlierehaptersofthe dissertation were prepared by the Graphics Department at Central Michigan University. The majority ofthemapsfoundinthelatterchaptersot'thetextwereprcparedbytheCenterforCartographic ResearehandSpatialAnalysisatMichiganStateUniversity. Thesemapsaretobeincludedina'series duficiesamendybemgpreparedbymeaummmdsekdmembenofhkdoaadcomminee. DuringthelasttwoyearsinOxt’ord.Dr.RichardV.SmithandDr.JohnC.lGink,Chairofthc DeparmdeeoyaphymdPhnnmghwbeenespedaflywpanwofmyresearchmmresum LatinAmeriea. FmaflyJoweaspedalthankstoMrsPegyChristianforhelpingmepmthefinal draftofthisdocumenttogether. TABLE OF CONTENTS Preface: Contents and Objectives of Dissertation Introduction Research Goals Relevance of the Research Research Organization of the Field Work Chapter 1: Population Dynamics and Forest Resources in the Dominican Republic Introduction Population Redistribution Overview of Forest Cover Overview of Deforestation LandUse Chapter 2: Population Pressure and Environmental Degradation in Developing Countries Introduction The Concept of Carrying Capacity The Concept of Population Pressure Deforestation in Developing Countries Deforestation and Environmental Degradation Conceptual Models of Population Growth Models of Population Pressure and Environmental Degradation Population Redistribution Chapter 3: Statement of Problem, Framework of Analysis, and Hypotheses Statement of Problem Model of Population Pressure in Plan Sierra Research Hypotheses Chapter 4: The Plan Sierra Study Area, Data Sources, Measurements, and Methods of Analysis Introduction Study Area Data Sources Measures of Population Pressure and Carrying Capacity The 1987 Plan Sierra Farm Household Sample Sample Survey Design Methods of Analysis 8888 55838 32888883 43 51 51 53 62 Chapter 5: Results of Analysis, Part 1: Population Pressure and Land Usage in the Cordillera Central Introduction 63 Assessment of Section Level Population Pressure 63 Assessment of Deforestation in the Sierra, 1960-1980 67 Farm Level Trends in Population Pressure and Deforestation 69 Resolution of Hypotheses 1A - 28 76 Chapter 6: Results of Analysis, Part 2: Motivations for Migration and Destinations Introduction 1m Assessment of Population Redistribution, 1960-1981 103 Farm Household Level Trends in Population Redistribution 108 Out-migrants mg In-migrants 111 Return Migrants 117 Potential Migrants 121 Non-migrants 121 Resolution of Hypotheses 3A - GB 122 Chapter 7: Summary of Results, Conclusions, and Recommendations for Further Research Introduction 146 Summarized Results with Conclusions and Implications 147 Critique of the Conceptual Model 161 Recommendations for Future Study 166 Appendix A: Variable Directories 168 Appendix B: Sample Survey Field Questionnaire 178 Bibliography 216 Table 1.1 Table 1.2 Table 1.3 Table 4.1 Table 5.1 Table 5.2 Table 5.3 Table 5.4 Table 6.1 Table 6.2 Table 6.3 Table 6.4 Table 6.5 Table 6.6 Table 6.7 Table 6.8 Table 6.9 Table 6.10 Table 6.11 Table 6.12 Table 6.13 LIST OF TABLES Area of Holdridge Life Zones in the Dominican Republic - 12 Land Cover/Use in the Dominican Republic and the Plan Sierra Study Area . 19 Yearly Forest Conversion Rates for Political Sections in Plan Sierra ................. 20 Seven Groups Obtained from the Multiplicity Survey (Aragon 1984) ............... 61 Significant Results of Bivariate Regression (Hypothesis 1A and 1A‘1) ............. 81 Significant Results of Bivariate Regression (Hypotheses 13‘1, 18‘2, 18‘3, and 18‘4) 94 Significant Results of Bivariate Regression (Hypothesis 2A) 98 Significant Results of Bivariate Regression (Hypothesis 28) 98 Average Yearly Net Migration for the Plan Sierra Section Level Study Area ...105 Out-migrants Reason for Departure 1 11 Classification of Out-migrant Destinations 1 1 5 Classification of ln-migrant Source Areas 1 L5 In-migrants Reason for Leaving Source Area 1 16 In-migrants Source of Information 119 Classification of Re-migrant Source Areas 119 Return Migrants Reason for Departure 120 Non-migrants Reason for Staying 1m Significant Results of Bivariate Regression (Hypotheses 3A, 38, and 4) .......... 128 Significant Results of Bivariate Correlation Analysis (Hypotheses 5A, 58, 5C, 6A, 6B) 115 Return Migrants Reason for Staying 144 Potential Migrants Reason for Leaving 144 Figure 1.1 Figure 1.2 Figure 1.3 Figure 2.1 Figure 2.2 Figure 2.3 Figure 3.1 Figure 4.1 Figure 4.2 Figure 4.3 Figure 5.1 Figure 5.2 Figure 5.3 Figure 5.4 Figure 5.5 Figure 5.6 Figure 5.7 Figure 5.8 Figure 5.9 Figure 5.10 Figure 5.11 LIST OF FIGURES Crude Population Density by Province, the Dominican Republic, 1981 ............. 06 Migration Streams in the Dominican Republic 08 Holdridge’s Life Zones in the Dominican Republic 11 Newman and Matzke’s Concept of ”Optimum Population" 25 Newman and Matzke’s Alternative Carrying Capacity Consequences ................ 32 Bernard’s Population Growth Hypothetical Man-Land Systems 35 Model of Responses to Population Pressure in the Plan Sierra Region ............. 41 Hydrogeographic Divisions in the Dominican Republic 52 The Sierra Study Area, Cordillera Central, the Dominican Republic. ................ 54 Political Divisions of the Sierra Study Area, Cordillera Central 54 Population Pressure, Sierra Study Area, 1960 65 Population Pressure, Sierra Study Area, 1971 65 Population Pressure, Sierra Study Area, 1977 66 Population Pressure, Sierra Study Area, 1981 66 Forest Cover, Sierra Study Area, 1960 68 Forest Cover, Sierra Study Area, 1980 68 Decrease in Forest Cover, 1960 to 1980 84 Residuals from Regression, Forest Cover (Y) with Population Pressure (X), 1960 84 Residuals from Regression, Forest Cover (Y) with Population Pressure (X), 1971 86 Residuals from Regression, Forest Cover (Y) with Population Pressure (X), 1980 86 Parque Educativo de J anico 101 Figure 6.1 Figure 6.2 Figure 6.3 Figure 6.4 Figure 6.5 Figure 7.1 Figure 7.2 Net Migration, Sierra Study Area, 1960 to 1971 107 Net Migration, Sierra Study Area, 1971 to 1971 to 1980 with Deforestation Rate (X), Hypothesis 3A with Population Pressure (X), Hypothesis 4, Equation 1 with Population Pressure (X), Hypothesis 4, Equation 2 Revised and Expanded Model of Social Responses 107 Residuals from Regression, In-migration 1970 to 1981 (Y) 124 Residuals from Regression, Out-migration 1960 to 1970 (Y) 132 Residuals from Regression, Out-migration 1970 to 1981 (Y) 132 162 to Population Pressure CEUR’s Model of the Dominican Small Farmer and the Environment. ........... 165 Preface Contents and Objectives Introduction Since the end of the Second World War (1945), explosive population growth in developing areas of the world has stimulated accelerated deforestation. Rapid conversion of primary forest has takenplacetoprovidefor:theexpansionoflandsforfoodproductiomfuelwoodandcharcoal production for household cooking and heating; and logwood and construction timber, with some commercial lumber exports intended specifically to generate foreign exchange. In a few countries, change in natural forest canopy has been carefully managed to facilitate long-term socioeconomic development. Unfortunately, in most localities of the Third World, rapid tree removal, in conjunction with poor silviculture and poor soil conservation practices, results in large-scale environmental degradation. Enensivesoilbssprimafilyinthefomofguflyingmdmasswasfingsubjedsmal residents to decreased food production and deprives them of vital natural vegetation growth, especially in ecolop'callysensitiveareas; allcombine tofurtherincrease population pressurcontheland. In developing areas where agroforestry or appropriate silviculture and farming systems lead to sIIstainableccosystemslanddegradationisminimized. hsomeeaseshumancanyingcapacitymaybe increased to generate immigration. In contrast, in areas where land degradation negatively impacts biomass productivity, rapid deforestation may ultimately lead to a decrease in population-supporting capacity. Subsequently, out-migration may occur as a major social response to these dynamic interrelated processes. An explanation ofthese competing social responses to population pressure and their associated impacts on both the physical environment and population redistribution are worthy of Swill analysis Accekratedpopuhfimgrowthdefmeflafionandhnddegradafimueamajawncernd developmentplannersintheDominicanRepublic(Antoniniandothers1974, 1975;Hartshornand others1981;KemphandHernandee1987;Lang1988). Environmentalexpertshavestudiedtheextent uweflutheimpadddefmefiafimmsuchphydmlprowsesuamionandmemmdreservoir silting (Hartshorn and others 1981). However, a major social-human dimension interfaces with these Walphysiealprowssesandposesmanyrelatedquestions. Whatefl'ect,ifany,doespopulation pressure have on deforestation, land degradation, and ultimately, population redistribution? Do these relationships Chang over time? Moreover, how are incipient agricultural intensification practices (in response to pervasive population pressure) related to environmental change? Has population density dreadyexceededtheunyingcapadtyofthefiadifionalhflslopefarmmgsystem,especiallyinmore marg'nalmountainousareas? Ifso,istheruralenvironmentoftheDominicanRepublicbecominga degradedphcemfiveomwhkhmbngermfisfiesthe'plawufifitfofUadidmdfarming households? Comequendy,doeshnddegradafimaaasaresomce'push'faamwhichmyuiggera decisiontomigrate? Ifso,whomoveswhereandwhy? Moreimportantly,perhaps,whydosome peoplechoosetostaybehind? F'mally,whataretheshortandlong—termimplications,bothnational MdehaeafingnmberdecdogialrefitgeesfledngUadifionflmaflhrmumof theDominieanRepublic? ResearchGoals Thepfimobjediwofthisdisseflafimistoanswutheserdfledrewuchquesfiommd quainthelinkambetweenthesedynamicproeessesandvariables. Toaccomplishthistaska wnceptudmodelkproposedthatwiflseneuthethemefialfimemrkfuthereseuch. Thismodel draws heavily from conventional population-economic, people-environment, and behavrorabmrgratron theory. Withintheframework ofthe conceptual modeLaseries ofresearch hypothesesisformulated mdefimthewmeofmhfimshipsandamodafimsbememtheprmdpdthemesenmmedmthk WagiadmdmtendfiafiommrrfingupadwfiefmmhnddegaMmpuhfion RelevanceofReaearch DesphetheohdousimpwunceofthewthemesmtheThdeanfewreseucheminthe masoddsdenwshawauemptedtoexplammemmedthemtmehfiomhipbawenMN moreoftheseprowssesorvariables. Thisdissertationattemptstobothcritieallyexaminetheserelated promhadnglemptudmoddandemphiuflyuwfithemoddhareprewflafiwdenbphg country(theDominicanRepublic)spatiallyattwodifl'erentscalesofanalysis(thepoliticalsectionand thefamhousehold)andtemporaflyoverapproximatelyathiflyyeufime&ame. MWundfleldWork Thepresmufimofmkreseuchkmganhedmtowwnchaptersfoflowingabriefprefaw whichdetailsthecontentsandintentofthedissertation. Thefirstchapterfunctionsasanhistorical overview of population distribution, growth, and patterns of internal and external migration, changing MofmnndfmeaandvegaafimmrmnthuseanddegadadmmmeDMMmRepubhe Chaptammaaifialmvkwdamrowhtewbcdonsdpubfishedrmmpemmingtome problem. 'I‘hisliterattneeanbegroupedintothreegeneraltheoreticalbodies. OneispeOple- environmenttheoryorhuman-culturalecology. Withinthisbodyliethesodalcausesofland degradafiomviewedfiommepempecfiwdmaflfarmmamgersAndherkewnomicmwry, specificallythatdealingwithpopulationandresourcerelationships. Includedinthisbodyarethe mptsofhumanmrryhgupadtymopflafimpreumeandagfimkmdmtensification Lastis behfimdmdcmwnfionflmigrafionfiemymeflainhgtopercepfiomof‘phwufihtfmdme propensity to migrate as well as to resource push factors or environmental stresses that actually trigger ChapterThreemUoduwstheproposedmnceptudmodelmasenesasthethemefical framework for this research. As noted, the model attempts to link conventional population-economic, people-environment, and behavioral-migration theory in order to explain the processes addressed in therelatedresearchquestions. Thesflcngthoftheassodafionsorlinearrelationshipsamong variablespairedbyhypothesesthatpredictthedirectionoftherelationshipsproposedinthemodcl, wfllbetestedbymeansofsimplewnehfionorbivariateregressionanalysis. ThefounhchaptamifiaflychmdefiusmePhnSierraregionoftheCudflbraCenudm thestudyarea. Thebdanceofthechaptufifldiswssinordendefimfimsofkeyvariablestemsmd equafimsfiefieflsmy,hdudhgsmpfingshflegyandqmsfimnahedefign;andmethodsd analysis. hthefihhandsinhchapterammmaryresuhsofboththefieldquesfimmiresimpb' binrideregrusbnandwnehfimanalysismdkansedmmdummthemrmhnhmthe conceptual model and their contributions to prevailing theory. Chapter Five focuses on results related mpopuhfimpresmreandresuhamhnduse;ChaptaShmthemofivafiomfmmigrafionmd destinationchoiws. Thelastchapterdiswssesthecondusionsandimpoflantimplicafionsofthis dissertationandmakesrecommendationsforfutureresearch. FollowingthaLthefieldsample quesfimmheandnfiabkdhedmiesfmthemiomdahsebmlyzedinthisdissemfimue includedasappendiws. FieldworkfmthisdissenafimwuwnduaedovermenendedfisittotheDominiean RepubhqwhichlastedappronimatelyfromAugust 15,1986throughMay15,1988. Theformal queflionnairewasadmhistaeddmmgthemmthdFebrmryw,withmepflasampk,pre-testing andtrainingofinterviewersaccomplishedinlanuaryofthesameyear. Chapter One Population Dynamics and Forest Resources in the Dominican Republic Introduction The Dominican Republic has experienced rapid population growth, like most developing countries since the end of the Second World War which apparently has stimulated deforestation and population redistribution. The relationship between accelerated population growth and rapid depletion ofnamralresourcesonthelslandoinspaniolawasfirstreportedbyCristin1950andDyerin1954. From 1965 through 1975 the rate of natural increase ranged from a high of 3.24%, for the five-year period 1965 to 1970, to 3.09% for the five-year period 1970 to 1975 (Loaiza C. 19%). However, the average intercensal natural growth rate from 1970 through 1981 was 2.9%, indicating a gradual decline in the natural growth process. Specific reference to population growth and increased forest conversion in the Dominican Republic resulted from the United Nation’s Food and Agriculture Organization (FAO) baseline studies conducted during the early 1970s (United Nations 1973; Antonini and others 1975). The accelerated population growth generated a number of social, demographic, economic and ecological problems. For example, 1'hebabyboomofthe’50sand’60sputtheeconomicallyactivepopulation (15-64yearsofage) inthe minority(49%)in 1978.1nthatsameyear48%ofthepopulationwasunder15yearsof age. Inthepastfewyearsthebirthrate(per1m0)hasrisento45—thehighestinthe Caribbeanregion. Infantmortalityof83perllllllivebirthsisoneofthehighestin1atin America (Hartshorn and others 1981, 11). Bytheyear 1980theDominicanpopulationhadsoaredtoover5.6million. Crudebirthratesslowed, somewhat, to 34.6 per 1000 and total fertility rates fell below five (4.8), demonstrating the success of large-scale familyplanningefforts. Crudedeathrates declinedto9.1 per 1000mm: lifeexpectancyat birth averaging 60.3 years. BythewlylMtheDommieanRepublkahadywasomofthemoademelypopuhted countriesinLatinAmerica (crudedensity), averagingover 115 persons per square kilometer (Figure 1.1). Mfigmereafisfically,shouldbesdmuchhigher,umuchofthehndmmeDominian 5 on 55 83 .H Q 8. 2 on .3. 2. o. 8— I 322 En e: I :5. .8 8:288 <2 32 disses neefioa 2: .855 3 £38 8:238 oeeO 2 cases RepuflieisnaconsideredsuimblefmagricuhmemanshornandahemmBLfl). Estimatesof physiological density for the republic were set at 267 inhabitants per square kilometer, based on arable landmeasuresmartshornandothers1981,11). The majority of the population is concentrated in urban corridors of the central lowland valleys, which generally separate the country into three parts. Low population densities are found along the mountainous border with the Republic of Haiti and at the extreme eastern end of the island. ThehighestpopulationconcenfiafionsinthecountryarefoundintheNafionalDistfia,whichcontains thecapitalcityofSantoDomingo. In1970,thisareahadsurpassed550personspersquarekilometer; by 193) the density in the district soared to 1050 persons per square kilometer, an increase of over ninety percent. The provinces of Santiago, Valverde, and San Pedro de Macoris demonstrate similar growth patterns (Beetstra 1983). In 1990, the population of the Dominican Republic broke the seven millimmark;roughlyathirdofthepeopleliveintheNationalDistrict,over675,0001iveinthe provinceofSantiago. Population Redistribution Historically, population redistribution in the Dominican Republic has been characterized by threegeneralmigrationstreams. Thefirsaandmostimportantintermsofnumbersofinternal migrants,isdefinedasnualoN—miyafimfiomhighhndareastowudthecenfialvalleymban corridors (Figure 1.2). The Sierra region of the Cordillera Central and mountainous southwest (Sierra Barouco) are the most important migant source areas for these flows (Ramirez, Tactuk and Bretdn 1977). A second important external migration stream originates from the mountainous Haitian border amawhenbothlegalmdflkgalimmigrmmgravimtetowudwastalvinagesanddfies Thirdly,there isafineralmovementofpeoplefromsmallcoastalcityareastowardthelargerurbancentersfoundin thelowlandvalleycorridorsoftherepublic. Fmally,andmostrecently,thereisawelldefined WfiowofDomhkanemigranutotheUnhedSmtesspedfimflymNewYkahy. WuhintheDommicanRepuHithetwopdmarymbandesfinafimsuethecaphaLSm Domingo,batedabngtheCaribbemman,andthemajangimdcemeromefiagodebs .r CabafleronloeatedattheterminusofthewesternCibaovalley. Migrationpatternstothese so: zEEeIeSo AS .389 293.. a. s 8228 2:3 "858 Q COO-3.00320.- HI DZNGwa 258.50 a: 43825 . I: o c f 3.1.8 223” % fall/v (3:! Essex SEES 2.. e seesaw 8:33 S can destinations difier in a number of important ways. Traditionally, the movement to Santo Domingo is characterized as step-wise migration, where migrants “stepped” their way up through the urban hierarchy sequentially over a number of moves. For example, recent studies determine that slightly less than two-thirds (62%) of the in-migrants to the Capital city are from urban centers with populations greater than five thousand people; fifty-six (56%) percent of the in-migrants are from the North—Cibao Region, presumably from the city of Santiago (Ramirez 1981). Although some migrants from the north coast still move step-wise fashion to Santiago, in anticipationofafuturemovetothecapitaLmost migrationflowstoSantiagonowappeartobe primarily 'direct" rather than the step-wise pattern. Recent research indicates the in-migrants are not from the smaller cities in the hinterlands but come directly (59%) from the rural political sections and parishes of the countryside (Ramirez 1981). Explanations for these direct moves include: close proximity ofrural source areas; general economic problems and overall lack of opportunities in the intermediate sized cities; the traditionally high standards of living and employment opportunities in Santiago; and finally changing perceptions toward the capital which is losing its popularity as an internal destination (Duarte 198), 1986; Grasmuck 1984). As mentioned before, Santiago de los Caballeros has traditionally been a widely-chosen temporary home for migrants who have eventually taken up permanent residence in Santo Domingo. Additionally, Santiago’spopularityisgrowingbothasafinaldestination andasa'stagingarea" for international migration. Migrants from the mountainous areas surrounding the city frequently move to Santiago prior to migrating to the United States. The international out-flow of Dominicans, especially to the United States, is both noteworthy andwelldoatmented(Ugalde,BeanandCardenas1979;MorrisonandSinkin 1982). Foracountryof mlywvenmflbnhhabhmtstheDommkanRepubficrmhmnthuammmueafmimmiyafion mtheUniedStaeuthkkmutandhgwhenmewnsidasthebwkwkofrehgeemigrafimmd comparatively small population base. Moreover, Dominicans are well known to manipulate US. immigration laws to their advantage through the extended family system (Bryce-Laporte 1979). 10 Consequently, the process of'chain migration“ has been established for almost thirtyyears, facilitating the eventual movement of Dominican family members to the United States. Population redistribution is considered the primary demographic response to accelerated population growth in developing countries. Consequently, it is likely that the dynamic migration streams in the Dominican Republic may be attributed to increasing population pressure on natural resources over the past forty-five years. On the other hand, traditional rural societies also employ a number of economic strategies in response to pervasive population pressure on food production . resources. Cunomufly,mnversimofprimaryfmestvegaafimtoagficuhmdhndiswnsideredm important initial response. The following section will assess recent changes within major vegetation complemsoftheDominieanRepublic. OverviewofForectCover ThemmrdvegetafimdtheDommicanRepubhcisdasrifiedawmdingtotheHoldfidge systemoflifezones. Thissystemisbasedupontwoindependentdimaficparameterameanannual rainfall and bio-temperature, where bio-temperature "differs from regular temperature in that it substituteszeroforallunitvaluesabove30C.andbelow0C. Forexample,bio—temperatureinthe Dominican lowlandsislower thanthe standard temperature" (Hartshorn and others 1981, 13). Nine ecological zones were delimited by the OAS’s natural resources inventory in 1967 (Figure 1.3),basedonafirstorderclassificationO-lartshorn and others 1981). Thetwomajor life m-mbuopialmoinfmenandmonpkaldryfmea—cmerabomsinyeigMperwmofme mfaceareaoftherepublic. However,thesubtropieallowermontanewetlifezone—coveringabout sevenpercentofthesurfacearea-iscitedasbeingthemostimportantforthefuturedevelopmentof thecountry;itcontainstheheadwatersofthemajorriversintheCordilleraCentraLwhicharecritical fuhrigafimmthebwerweuernCibaovaflcymdthegemrafionofhydroeleaficpowerforthemajor ur'bancenterinSanfiagodelosCaballerosGlanshornandotherslfiLl). halwvendthenheHddridgeUeronesuefoundmvaryingdeg-euintheCordflkra Central (Table 1.1). The subtropical dry forest—second most extensive in the country (N.72%)- is {OudmthewumfibaovafleyandefleaivdymrsthehwerRioYaqmdelNMewuershed 11 at: is 2.38: 138:!» D «in ix ace—so! sale.— Telugu 522 «a? .5on 839. saga :23. 59! 23:8! sole... Tux—nag» IOSN 0—: z a... .u. 2.3 5...... .o 3.2.82. con-.5 o... .2 23 225.. s s 3.8.... a. 2.x... .0 .c 3.2.8.: as: .8 38> o... .2 3.2.83 :55 a: .n 3.2.8:. 25> 9.... d Sou .3836 .a s ...8 .32 .~ 2.3 as... 880 .u Sou 828.. as.» .. 23.320 cinnamooao€>z (mm lama-:0 2300 0:25: 1‘ 3330”— 50.588 05 E 88ng 03983036»: 3. use... 53 YaquedelNortewatershed. 'I’hisisparticularlytrueofthePlanSierramanagementarea,whichis largelynestedwithintheProvinceofSantiago,withanextensionintotheeasternmostportionofthe Province of Santiago Rodriguez (Figure 4.2 & 4.3). The management area contains thirty-four political “sections“intheirentirety. Thepolitical“section“isthesmallestadministrativeunitbelowthe provincial level for which published census data is regularly available, and fortunately, within this mountainous region, the political boundaries have not changed since the 1950 national cemus. Consequently,thethirtyfourpoliticalsectiomwithinthePlanSierraregionallowforthecompilation of comparable variables for three national censuses, 1960, 1970, and 1981; “sections“ therefore serve as onespatiallevelofanalysisinthisdissertation. DntaSonrces A recent publication by the Population Studies Center at the University of Texas evaluates all theavaflableDominicancensusmaterialsindetaflGarson1987). Populationandagriculturaldatafor theDominicanRepublicareavailablefromtheNafionalStafisficsOffice(ONE)inSantoDorningoat mmmmdmmmmmwmwmmmmmmmny theparish. ThefirstDominicancenwswasukeninmeyeu192ll,foflowedbydecenmdmusesm 1940, 1950, 1960, and 1970. Results fiom the 1981 population census are currently available, with detailed maps and statistics published for the Province of Santiago and the Federal District. Unfortunately, as of Fall 1990, the results of the 1981 agricultural census were still generally unavailablewithonlyalimitednumberofnationalleveltablesinprint. Incontrast,(Antoniniand others1975)haveobservedthatthe1950censusisthemostaccurateandpopulationprojectionsoften arebasedonthisyear. OnthenafionaLregionalandprovindalleveLsummarydemographiceconomic daumdprojeaionsmepubflshedmaquasi-yearlybasisas'stafisficalbriefs.“ Owingtothehreguhrwverageandrekaseofthenflbnalcemushkfoflmatethflthe DomhicanDeparMemofAykukmdSEAhmhmizedanumberofhomehoflsmysfmmd umwhkhmmemderthejufisdiabndwoperafiwhmmafimdresourcemmgememprojeaa ProjectBaoandPlanSierraareamongthese. Inlieuofthe1981nationalagriculturalcensus,detailed 54 Figure 4.2 The Sierra Study Area, Cordillera Central, the Dominican Republic Morita Cristi D 0 so Figure 4.3 Political Divisions of the Sierra Study Area, Cordillera Central, the miniean Republic — Provincial Limit Municipal Limit — Sectional Limit -— Major Roads [:Isonnudozuasonum 55 farmdataareavailableforanalysisfortheyearl983fiomthearchivesatthePlanSierraheadquarters inSanJosédelasMatas(AbeuandI-lernandezl983). Nanudresomcemvemofiesofienprofidefmeawvermformafiommudlymmapfmat thenationaland,occasionally,theregionallevel. ResourceinventoriesoftheDominicanRepublicalso areavailableforanumberofyears. Ofthese,the1967OASnaturalresourwsinventoryisclearlythe most comprehensive, including sixteen color maps at a scale of 1:250,” and numerous tables. A detailedmmbinafionhndwver-usemapofthePhnSienamanagemengionwaspreparedin1983 byBelgiangeographer,FranzGielfus. A frequent problem, encountered in comparing the natural resources inventories, is definition ofkeytermsandchangeofmapscale. Thevariousnationalandinternationalagenciesthathavemade WmiesdmeDomimanRepubficoftmmeddifieremderhfmhndmdasfifiafimmkk particularlytrueofforestcover. ForenmpleFAOrequhedaminimumoftwentypercentcrown chsure-twentyperwmofmegomdsurhwmkshadedbymeUem-fmfaeudesignafim. At the other extreme, the 1980 Comprehensive Resources Inventory and Evaluation Survey (CRIBS) of the Dominican Republic used seventy-five percent crown closure. Finally, the medium scale format used for the 1967 OAS national level analysis (1950111)) is generally inadequate for precise measurementatlargerregionalscakasuchasthewatershedlevel. In order tominimize these problems, standardized large scale (15041”) tepographicsheets wereemployedastheoverlaymappingbaseinconjunctionwithrelerencehndcover-usemapsand aerialphotographyasasecondaryinformationsource,whichallowedforestcovermeasurestobemade attwopointsintime(1960andl9&l). Forestcoverdefinitionwasbaseduponaminimummeasureof fifty-percent crown closure; thisisthe standard conventionusedbythe US. Army Map Service, the agencyresponsibkfmmappingtheDomimcanRepublicatthelmscale. FfirlycompkteurhlmragedmeDominimnRepubficefiasumdsubcam), (194,“), (150411)), and (insane) for a representative sample of years. Antonini used imagery from 1948,1958and1966inananalysisofthelaguaBaowatershed(Mommandotheml974,1975). The CRIBSprrjedflMkhiganMeUniwrshyMSWdoamemedchanguhhndcommtheOwe 56 watershedwithimageryfrom1946,1963and1983. AtthePlanSierraagr-iculturalreseardrstationin San Josede las Matas, imageryis available rot reference purposes at scales of(1:20,0m) and (1:24.010) fortheyearsl946,1963and1980. Agrowinginventoryofaerialphotographyandotherimagery prmdykbemguchiwdbymeCenmfaUrbanaMRegimdSmdies(CEUR),houwdqunme CatholicUniversityinSantiago. Aerialphotoyaphyandtopographicmapsalsoareavailablefor purchase fi'om the National Cartographic Institute in Santo Domingo. Mensaree of Population Pressure and Carrying Capaclty h1966anmtermfionalsymposiummpopuhfionpressmemresomws(PPR)washeld, where it was observed that little consensus existed regarding definition or measurement: ThebeastwecaflPPR(populafionpressmemresomces)istoohrge,ambiguomand ambulatory to be simply catalogued. It is bigger than we suspected previously; it has too many appendages, angles, and wrinkles for comfort, and it may travel in herds. We have succeeded only in demonstrating that it is truly multidirectional, that it involves relationships among many different sets of variables—ecological, social, technological, psychological, and historical It is impossible to produce a simple, universal definition that subsumes all the many kinds of pressuresituationsobservedatdifierentplacesandindifierentperiods. Butweareall uneasily aware that it exists (Zelinsky and others 1970, 581-582). Fortunately, despite its ambiguities the study of population pressure has continued to function as a conceptual framework for population-resource relationships (Bernard 1977, 47). Moreover, regarding wryingapadeemesgeoyaphersnowdaim,“mmhermethodmeflediwlyamsmme productivepotenfialoftheenvironmeminnhfiontoauremMpotenfialhummuses“(Berwd, CampbellandThom1989,4m). Inpart,thismaylargelybeduetothefactthat“carryingcapacityis conceptually inseparable from density“ (Igbozurike 1981, 147). Bydefimfiomassessmenmdpopdafionpresmemuladafiomofwryingmpadtyrequhea densitymeasureoftherelationshipbetweenpeopleandaunitareaofthelandresourcebasethatmust supportthembyprovidingsubsistence. Populationdensity'nrathersimplydepictedbasedonthe formula(D-N/A),whereDsymbofimthepopuhfiondensity,Ndefinesthepopuhtionfathe designatedtimeperiodand(A) representsthespatialdimensionagbozurike1981, 147). Population dahmathemafiuflymafipflfledhthkhshionmmenmppedmaflybymechmopkthmemod. Mtechniqmproduwsuwfiubmnmer“audedenfitrwpresemafimsbeammewdmiqm 57 mmesm(mrufisfic)equaldismbufimdpopuhfimmroughommemhmmappedwhkhohen producesabruptandartificialboundariesbetweendataclassintervals. “Surprisindy,thissimple expresdmofaudepoprflafimdendtykdearlyfiemoflpopflarfommusemgeoyaphk, demographicandotherfiteratmeasweflasfihsesandwaflmps“(lgbmike198h 147). Attempts tohnmowmewchniqumreprewnfingpopuhfimdendwbydmymeuicmappingwerefim proposedbyWrightin1936,andalthoughhismethodmaystiflbecriticizedforgivinggreateremphasis toarearatherthanpeoph,hhasserved“uaspfingboardfmtheemergenceofahrgegrmpofdensity measures referred to as physiological densities, or, less commonly, economic densities“ (Igbozurike 198L147). 1hecmwptudbasisfmmppinghummpopuhfiombasedm“physiologicd'ntherthan “aude'densuyrummthepremkeMmmeruhsficmmmesdpopuhfimpressmemyONybe basedonthelandareauponwhichthepopulafiondependsforfoodproduction. lnthismanner populationdensityisoftenafunctionofpeopletotheunitareaofarablelandthatmustsupportthem (Turnerandothersl977). However,inabroaderperspective, these[physidogialmeasmes]rehtepopuhfimsbetomemmmerewmmstomespedfic portionoflandorwaterinwhichthcresourcesarelocated,toparticularresourceexploitation systemaandewntodeyeesdsuccesanaimdmresomceufifinthnaaivifiuameanued bycurrent standards ofliving (Igbozurike 1981, 147). In the early 1980s, major methodological improvements were introduced by Bernard and Thom (1981) and recently reiterated by Bernard, Campbell and Thom (1989), who assessed population pressure andcarryingcapacitiesatthesub—distridlevelinKenya. Theynote,“examplesatthesub- prwmddscabdemmsuaeMewnudngpruemaudeecdogimLhnduse,yieldpdceand demogaphicmwecanmakemeaningfulestimates“ (BernardandThom1981, 338). Thesebeliefs wemfiramppmtedbyPater(l970)bawdupmfieflmeuchenmimngpopuhfionprusunm resources(PPR)inMachakosDistrict,Kenya. Theproposedcalculationsofminimumfarmsize(l)to dct“mincafl'yinscapadw(2)mdmcflPOP‘Il-fimmeeonmmflmfl)"climb”: S '((rf“P¢)+fl)divid°de(Y“8) (1) where, S - minimumfarmsizeinhanewssarytosustainan average household each year. y = averageyieldperhaoffoodcropsinmillionsof kilocaloriesper annum. pc = percentofcaloriesderivedfi'omcrops. rf :- total subsistence food crop requirement for an average household in millions of kilocalories per annum. s -= season rl :- livestock requirement in he per family. C = (A-aw) dividedbyS (2) where, C = carrying capacity in number of households A a total area of location in ha. aw: areaoflocationinhaneithersuitablefor grazingnorcultivation. S = minimum farmsizenecessarytosustainanaverage household (computed from first formula) For the population pressure formulation (3), the actual number of households is divided by the carrying capacity(C). Theresidfingrafioisanefieaiwmeasmeofpopulafimpressmemhndresomceswith vflmsmermemdkafingmamemrryingapadtyofmefmmmgsystemmhomholdshubeen exceeded;inwnfias§valuesbelowoneindicatethatmsscapadtyremains Asafurtherrefinement, Mm. aspafiaflymmeexphdtexpresdmofmewdaarequuedwnwrsimofhouseholdstopemm and computation of surplus and deficit densities. The population pressure ratio provides a compmafiveindexofthedegreeofpressmeoverthestudymeamerwdandThom 1981, 397) Animportantcontributionintheirformulationliesinthefactthatthecarryingcapacitymodel maybebaseduponfullandpartialsubsistence. Forfullsubsistenceanassumptionismadethat 100% d“basicmbfinenceneeds“ueprwidedfiomthehndavaflabktouchhousehddAsthk assumption,inmostcasesisnotreafisficmdwasamajmaifidsmofemfierfomuhfionsBemard, Campbell, and'l'homhaveprovidedapartialsubsistenceoption. Asaresultoffieldsurveys,estimates 59 forpartialsubsistencewereobtainedandaveraged. Forexample,ifaveragehouseholdsobtained eightypercent(80%)subsistenceneedslocallyandtwentypercent(m%) fi'omthemarketplacethen twentypercent(M)ofthelandrequhedforeachhouseholdcouldbededuaedfiomtheminimum farmsize(variableS,Formula1). Noticealso,thatthe(aw)variable(Formuh2)measureslandnotsuitableforgrazingor cultivation. Thismeasmeaccountsfmhndthatisbothphysicaflymdsodaflynotsrutablefor agriculturalproduction. Achangeinthe(aw)variablecouldalsobeanefi'ectivemeasureof emn'ronmentaldegradationovertime. Ultimately,agenerallackofamsibilitytoland,onaper householdbasiscorddgreaflyafi'edthepopulafionpressurewithinanarea Consequently,formulas twoandthreeabmewereempbyedmthemseuudyofthePhnSiemregimdmeCmdiflen Cmflahuabafisfmdaemhhg“wdim“kwlpopuhfimmessmbasedupmtheunyingmpadty ofthetraditionalhillslopefarmingsystem. 'I‘helQ‘IPlanSierr-aFarrnHousekoldSample Householdbvddauakowemobtahedmtheuemdagfimhmedemographymcmomics, andeducation,hymeansofasamplesurvey. 'l'heconventioninsurveyresearchhastothispointbeen mwmww-anmnmarmmmmmm Hmmdmmktechniquemuchmfmmafimabompreefisfingmdoewnomkandenfirmmemd cmdifionsmthemdmmceueaspuhapspenainhgtotheausesofom-migrafionism Inorder wacqmreaddifimddaufaom-migafimuduwmmaprowdmhmnahulfiplidqdedgr“ wasusedinthisresearch. Aragon(1984)noteshowthistechniqueprobesmutualaidandsurvival strategiesbaseduponcommunicationthroughkinshipties: hmkwayukposfibktoobtammusingmuhipfidtymyswithheadsdhomehddswho can provide crucial information about the spatial mobility of their relatives, making possible therewnsnuaionofpanofthetotdflowtowhichtheinfommtsthemselvesbebng An interview schedule was designed to 1) register a detailed migration history of heads of homehoflsand2)doamempandmemigafionhistothhehrehfim(psnntssiblhg spouseandchildren),frominformationprovidedbytheheadofhouseholdabouttheplawsof binhmdpresemresidmceofthmemhfivuandthehpasagebymeplaceofimerview. Onwthehfomafimisproceswdhwiflbepmsibktoidenfifymngrwpsofmdividmk relatedtotheplaceofinterview(Aragonl984,l). 60 ThesevengroupsrelatedtotheplaceofinteniewarelistedinTable(4.l). Amoredetaileddiscussion oftheufifityofmulfipfidtysmveydesiprsisfoundinthePapemoftheEast-WestPopulationlnstitute (GoldsteinandGoldstein1981). Gufiefimsfmmyandqwsfimnfiredefimspedficaflyrehtedtoagricukmemd migrafimhbw-mwmewmuieswemobtaimdfiomatenpubfishedbythehtemfimallabom Organization’s (ILO) World Employment Programme (Bilsborrow, Oberai and Standing 1984). The aubusrwommmdMnewremrchqwsfimnfiresbepaflmedafiermcmsfiflsmyhsflmem thathavebeenthoroughlyfieldtested. Systematicareasofinterest,forexampleagriculture,are presented as questionnaire modules with specific types of questions being grouped into “submodulesf ForthePlanSienafamlevelsumyinstrumentanagr-iadmrehousehold characteristics,and migration module was designed, employing a select number of questionnaire “submodules' in modified form(AppendixB). Sal-PIGSWDGUI ThePlanSierrasub-regionoftheCordilleraCentral,locatedintheupperdrainagebasinof theRioYaquedelNoneservesasthepopulation(esfimateduniverseoflOSMinlWDforthis sample. Within the Plan Sierra management project, political sections were ranked according to a nmpkmdexdpopuhfionwusmeofimafledwhyfidogicdpopuhfimdendtfmemrdmm 1981). Fromtherank ordering, threepoliticalsectionswereselected fromeachoffollowingthree groups:kssthanaverage,awrage,andgreaathanavaagepoptflafimpressme. Ineachofthe mpkuflsasystemficmfignedrandomamphwashkentomhimiubmfiondbiasmthe selectionoffarmhouseholds. Thesamplingwasbasedonthelocationofresidentialunitstructures,» initiallyidentifiedonthemostrecent(l981)largescalecemusmaps. Statisticalsamplingtableswere consultedtodeterminethenumberofrespondentsforeachsampleunit. Forexample,ataselected wnfidenabwldnmeq-fiwmwntfaeuhpohficd“waim“ammimmoffifiyhomholdswue sampled. MensundthatheaificdnumberofhomehddsmthisuseWJmthemmty-fiveand fiveoptionwuenceededbysmy-fiixhouseholds(8heskin1985,35). 01. 61 Table 4.1 SevenGroupsObtainedfi'omtheMultiplicitySurvey (Arise-11984) MigrantGroups Borninhouseholdand noteinigrated. Borninhousehold, emigratedandreturned. Bornoutsideofhousehold, untildateofinterview. Borninhouseholdand Bornoutsideofhousehold, immigratedandleft totheplaceofbirth. ortoanotherplace. Bornoutsideofhousehold andnotemigrated. Bornoutsideofhousehold andeinigratedtoaplace difi’erentthanthehousehold. Information (Households) Indirect Information (Relatives) Methods of Analysls Thkresearchempbyssimphwndafimandbivafiaeregressimanflysistodaamheme precise nature of the relationships under investigation. These quantitative methods were selected because they are both statistically powerful and appropriate for the analysis of ordinal and interval- ratiodata. Themraflobjeaivesofsimpbbindmeregressimandyskunbesummafiudmfoflows (1) to determine whether or not a relationship exists between two variables, a criterion (dependent) and predictor (independent) variable; (2) to describe the precise nature of the relationship, should one existintheformofanequation;and(3)toassesstheaccuracyordegreeofpredictionoftheequation (Kachigan 1986). Inthefirststageofquanfificafiomthevariousdatasetspreparedforthisresearchwere subjected to a “univariate' analfiis using commercial (SAS Version 5) statistical software. This routine produees a multitude of diagnostic statistics for individual variables, including sample n, mean, standard deviation, skewness, kurtosis, etc. Histograms and normal probability plots provide for ample assessment of data normality. Based on this evidence, base ten log transformations occasionally were applied to selected key variables. A large number of frequency distribution tables were produced from the household questionnaire data. Commercial (SAS) software also was employed to produce simple bivariate coeficients and residuals, via the regression procedure. For each regression equation, a scatter plot was generated to verify the linearity or near-linearity of the relationship. Within the regression procedure, supplementary “residual probability plots“ and related statistics were evaluated to ensure that other assumptions of the linear regression model were upheld. To the best of my knowledge, all of the variousdamsembmhmdyzedmddkcussedmmefoflowingchapwrwmplyndththemumpfimsof thelinearregressionmodel: linearity,normafity,meansofconditionaldistribufionsareequaltozero, homoscedascity, and lack of measurement error (Johnston 1978; Kachigan 1986; Poole and O’Farrell 1970). Chapter Five Results of Analysis, Part 1: Population Pressure and Land Usage in the Cordillera Central Introduction Thischapterbeginswithanovewiewofthemaijendsinpopuhfionpressmeand deforestationobservedonthe“politicalsection“scale. 'I'hetimeframeappliedtothislevelofanalysis extends from 1960 (as the benchmark year for this study) until 1983, the most recent year for which data are available for the entire Plan Sierra management area. A description of the contents and fithsoffielWhmhoumhoMquesfimnfirefoummdufingadkamdmdhmbWIUmdsh population pressure, the intensity of innovation in agriculture, deforestation and land degradation. A reviewoftheresuhspenmmngtomefirflmmofreseuchhypotheseswndudesmechapter. SectionandfarmlevelfiendsinpopulafionredistribufimaresubsequendyinuOducedinchaptersix, generating a consideration of migration subgroups: out-migrants, in-migrants, return migrants, potentialmigrantsandnon-migrants. Chaptersixcontinueswithadiscussionoftheresultsofthe quantitative analysis for the remaining four sets of hypotheses. Assessment of Section Level Population Pressure Measures of population pressure on maximum carrying capacity were determined for five separateyearsonthepoliticalsectionlevel. ’l'hreeoftheyears,1960,1971andl981representdata fromtheDominicannationalcensus;thethirdandfifthyears(19‘fland1983)reflectdatagatheredfor special surveys conducted in the Plan Sierra management area. Carrying capacity and population pressuremeasuresarebaseduponthefollowing:1)theamountofarablelandineachpoliticalsection, asdeterminedbyaninternationalresearchteamundertheauspiccsoftheDominicanDepartmentof Agriculture (Grupo de Trabajo “La Sierra’ 1977); 2) a minimum farm size ofeightytareas (five heaares)mqmmpponuamagehomehdddudskpersom(admandchfldrw),bawd upmfiefiadifimdhifldopefumingsyflemandenfirmmenulpaenfifldmeregim(6mpode Trabajo'laSierra’1977;Yun£n1986),andfinally;3)theactualnumberofpersonsandestimated 63 64 number of households recorded for each of the sample years. Population pressure is measured as the rafiodthenmberofhouwhddstomeurrfingapadw,exprmdm1m%popmfimmppmfing capacity when converted to a percentage. Despitedgnifimenmofmtmafimalemigrafimandmtemdom-migrafimfiomthe PhnSienaaudyuempopMafimprusunnthepdifiulwdimsaktendstomyaduafly from1960uptotheyear1977. ’I’histrendisfollowedbyaslightdeclineinmeanpopulationpressure in1981andareturntothe1977densitylevelsby1983. Forexample,startingintheyearl960,mean population pressure stood at just below forty-five percent (44.1%) of carrying capacity, rangingfrom a lowofslightlybelowsixpercent(5.6%)toahighof168.5%,indicatingoverpopulationinthepolitical SectionBaitoaatthefareasternendofthestudyareaWigureSJ). Essenfiaflythispanemisduphcatedmlmeherethestafisficsrangefiomalowof approximatelysixpercent (5.5%) toahighof165.5% (Figure 5.2). Despiteaminordecreaseinthe mafimumpopflafionpressmekwlrewrdedfmSeaimBahmbemeenthewmfimflmmyeara mean section level population pressure increased to almost one-half (49.1%) of population supporting capacity. Bythisdaetwoseaiommahmdeebu’)exhibhpopmafimdensitieswerthelm% thresholdforcarryingcapacity. WiththenotableexceptionofPedregaLthereisageneraltrendtoward increasingdensityinmostpoliticalsections. Sixyearlater(l977)threepoliticalsectiom(Baitoaand CebiiintheeastandPalmarejointhewest)refiectpopulafiondensitiesover1m%ofcarryingcapacity, andmmpopuhfionmusmeracbshshigheflrecudedkvdfljuflbebwfifiyfiwwwm(546%) ofanyinsmpadtymsmS-il By 1981, population pressure ranged from a low of a fraction under seven percent (6.8%) to a noticeablehighof192.7%ofcanyingcapadty(Baitoa)butwithaslighdylowermeanofjustoverfifty percent (52.1%). Incontrast,populationpressureinPedregal continuedtodecline,aswellasseveral kohtedpdifialwcfionsfmmmpb:Cebu,GwaAbaqummanmuejoandElRubh,whkh haddeclineddrastically(Figure5.4). In1983,p0pulationpressureonlandresourwsrangedfi'oma newhwoffiwpercem(5.1%)toapprofimudythesamehighdendtyasexperiencedm1981,andthe meanpopuhfionpressmeraemahnostfifiy-fivepercem(542%)ofmnyingcapadty. By1983,three 65 Figure 5.1 Population Pressure, Sierra Study Area, 1960 Percent of Carrying Capacity - 121.9 to 192.7 - 59.1 to 1055 - 31910 57.9 5.210 29.4 t NO DATA Figure 5.2 Population Pressure, Sierra Study Area, 1971 Percent of Carrying Capacity - 121.9 to 192.7 - 59.1 to 1053 - 3100: 579 m 52 ,0 29.4 a NO DATA 66 Figure 5.3 Population Pressure, Sierra Study Area, 1977 Percent of Carrying Capacity - 1219 to 192.7 - 59.1 to 1053 - 310 to 579 52 to 294 * NO DATA Figure 5.4 Population Pressure, Sierra Study Area, 1981 Par'cent oi Carrying Capacity 121.9 to 192.7 - - 59.1 to 105.8 - E23 31.0 to 57.9 67 political sections (Baitoa, Mesetas and Sabana Iglesia) in the eastern portion of the study area illustrate population densities over 100% of population supporting capacity. In summary, between 1960 and 1983 mean levels of population pressure on the political section level rose approximately ten percent (10%) while maximum population pressure levels for individual political sections rose about (25%) twenty-five percent. A linear settlement pattern is commonly found in rural areas of the Dominican Republic, where settlement tends to take place initially along major transportation lines connecting villages and cities and subsequently diffuses perpendicularly away from these lines of communication and transport (Antonini and others 1975). This general pattern is reflected in the population pressure maps for the abovementionedyears. Fmenmplehighpoptflafiondensifiesarefoundinthepolificalsectionsof BahoaSabanaIglesiaMemmsCebiiGumamdPedregamechaflwntmnmrfiomdmemdn roadleadingfromtheupperCibaoVafleytothenodalviflagesoerinico,inSeaionMesetas,andSan JosEdelasMatasintheSectionofPedregal. AseessmentofDefoiestatlonintheSleri-n,l960tol980 Theyear19605ewesasthebasefineforforestwvermeasmesalsocalaflatedonapofifical sectionbasis (Figure55). AtthattimethePlanSierrastudyareawaslargelyforestedwithpolitical section level percentages ranging from a low ofsixteen percent forest cover in Sabana Iglesia in the far eastern portion to over ninety-five percent (97%) forest cover in one of the far western sections ('I’oma). A mean of seventy percent (70%) forest cover indicates that this area was essentially a forested frontier in 1960. For example, twenty-five out of thirty-two political sections demonstrate forest cover over fifty percent (50%) oftheir surface area. In addition, the forest cover map for 1960 also denotes the traditional linear settlement process with the incipient diffusion of settlers into the forested upland areas. Agricultural setdementoverthetwentyyearpuiodasrefleaedbydeforestafiomgenerafly hasemhedhdoseprofimitytofinesoffimpoflafionmdmaradidpauemfiomthehrger wflemeNnodu(SabmlglefiaJ£nkoaMSqu€dehsMams),wimmehigheaperwmamd deforestationgraduallyadvancingtowardtheperipheryofthePlanSierrastudyarea(Figure5.6). 68 Figure 5.5 Forest Cover, Sierra Study Area, 1960 Provlncla Santiago ri uez Percentage Cover - 76 to 100 - 51 to 75 - 261050 f: o to 25 t No DATA Figure 5.6 Forest Cover, Sierra Study Area, 1980 Provincla Santiago Provincln Santiago Rodriguez Percentage Cover -76t0100 -51to75 -26t050 " o to 25 * NO DATA Provincla Santlago 69 PMagefaeawvuchmgentunryfiommdertwperwm(Sabamlgleda)mtheeauemmou pmfimofthestudyamatosfighdylessthanwventy-fiWpercemmodeo)inthewest(Figure5.7),with amean deforestation rate ofthirtyfivepercent (35%)since1960. FaeumverchangemesmPhnSienaakorefleaamoderaionofmedefmesmfimprocem. The1971censusindicatesthatbetween1960tol971theeasterntwo—thirdsofthestudyregion,anarea includingthemunicipios ofJinicoandSanJosédehsMatasdemonstrateameanforestcoverchange of 021.24%), equal to (4.94%) annually. Yearly forest conversion rates range from a low of (+0.4%) mLomadeCmdto(62%)mCWaAbajqrewahngmempfionaflyhighmeofdefmeaafim. Averageyeadyfmestconversionratesfatheenfiretwentyyearperiodflable1.3)alsoreflectslight increases in forest cover (+0.4%) and a lower annual maximum deforestation rate (3.7%). The twenty yearaveragedeforestationrateequalssomewhatlessthantwopercent(1.74%)peryear,slightlylower thanpreviousreadings. Famievel'l‘rendsinPopnlatlonPressurenndDeforestation hudertoauessthefimurehfionshipsbdweenpopuhfionpressmeanddefaesufion, hrmhgsyuemmtenntymndhnddegadafimmumdividudhousehoflsahthefimm (module)ofthequestionnairewasdesignedtoprobethesethemes. Thesurveyinstrumentwas systematically administered to approximately 450 rural households. From this group, 435 questionnaheswerefoundsuitableforcompilationanddataprowssing. Theprincipalfarmer,the mdividudwhokmpmsibbfammgflndamhmmanagememdedsbnsmnrgetednmemah respondent. Popuhfionpressmewudueminedbyfirucakuhfingmnyingmpadtymmindividudfum bafisummmgmaveragehomehddduofshpersommmaveragefarmduofeightymeas The mmdflxweu(85heaares)perpersmwuwmparedtotheadualrafiodfarmfiuand numberofpersonsinthehousehold. ThePlanSierrafarmsamplerevealedameanpopulation premed(8557%)eigluy1kpawm&myhgapadty,mdiafingmeamagehrmmmthe sampleareawassomewhatbelowthetheoreticalsupportingcapacity. In1987,farmlevelpopulation 70 preemrenngedfimabwdapprofimudy(25%)mmyfiwperwmmahighd250%dmnying «purity. TheaveragefarmmthePhnSiemumplemrdymumntaimamemofeightymweas (82.15) andranpsinsizefi'omtwotomtareascntomhectares). Generally,farmsizeslessthan one-halfhectarearereferred toas“microfimdios.“ Most farmers (72.5%)claimtoowntheirownland, withtheaveragefarmerholdingtitletoapproximatelysixtyfivetareas. Ownershiprangesinsizefrom aslittleastwouptoflllltareas. Inaddifion,thesveragefarmerrentsahnostseven(6.9)tareasfiom otherhndhddersaccounfingforabomdghteenperwm(183%)ofthefarmstead Theamountof farmlandrentedrangedashighathareas. F'mally,alargeamormtofagiiculturallandisoccupied bywmehrmerswimombgalfidetomship;mkpracfiwkfiequendyrdenedmusqmtfing andisbecomingaseriousprobleminruralareas: Problemshrmalueashavebroughtabonmaeaseofsquanerstobothmfionaland privatelands. Whileinl979therewereonlyfiveinvasionsofprivatepropefly,30invasions hadtakenplaceinthefirstninemonthsof1980. lnvasionoflandisnotaminordisorder,but an organized confrontation involving hundreds of squatter families; some 5000 peasant householdswereinvolvedin1980...Ifthepresenttrendinlandinvasioncontinuesfitwillpose seriouspdenfialfmpoflficdmnfimhfionwitthommiansodetyaianshornmdmhem 1981,86). InthecaseofthePlanSierrasamplearea,theaveragefarmerillegallyoccupiesameanofslightlyover ten(10.15)ureasmafinkmmethanninepercemOftheareadesignatedasfamhnd Theamountof hmhndocurpiedmthishshbnrmgesfimnmemuhighulmmeukiny-fomheames). OntheaveragefarminthePlanSierrasamplestudyareatherewerefifty—five(54.3)tareasof landinproductionin1987,accomfingforslighflymorethanseventy-fivepercemoftheaveragefarm size. Approximately twelve (11.98) tareas are usedfor crops, alittle over forty (42.3) tareas, on average,fmyazingpmpmesand&ebahnwofaboNthifly(2834)weasuemfallow. Theaverage smudproduaionpermreameasmedmkiloy’msfmbmhmimflandaopproduaionkm momm(m285hpmspuhedue);meupperfimhdtherangek3,7462kflogmsperwu (58,440.72kilograinsperhectare). 'l'heaveragefarmhouseholdderivesabouteighty-sixpercentdits subsistencefoodconsrrmptionfiomthefarm. 71 OfthethreegeneraldassesoffamhndinPhnSierradoperavineandflaLmost(69.36%) ofthefoodproductiontakesplaceontheflomaWrslopeland. Theaverage’farmcontainsameanof slightlyoversixtyvfive(65.24)tareasinthisclass. 'I‘hesecondcategory,landsinravines,awountfor sfighflymaethanninepercentofthefamhndandaveragesomewhatlessthanfive(4.56)tareasper farm. Iasfly,thehigMydedred“fiaPhndscomprkeafinkmmemantwentypermefmeuabb hndmthisueaandavengedighflymmethantwebemreuperfam.Antonimobserves Dommkanhrmemgemraflydasflyhndmtothreemtegmiesm“somefimesrdmedm as “lo pendiente,“ are hill slopes; 'hoya“ refers literally to hollows or intervales; and “llama“ plains. “Haida“hndsamoflesspdeMialagriaflfiudvdueandwarraMmaddifionaldeafing aside from occasional weeding during the cultivation period. Higher valued “boys“ and “lines“ may provide the added incentive to complete land clearance by removing tree stumps and roots,butattimeseven“llanos“sitesremainunimproved. Thoughthemajorityofpeasants umhandtodssuchmthemchetehoeammddiggingflkkommyoccasiondlyseeos— drawnplowsbeingusedonhighervaluedlands(Antoninil971,3). Principal farmers were asked to estimate the average amount of new land (forested) put into productioneachyear.FarmlevelforestconversionrangedfromnonewlandsclearedtoahighoflSO A Mmemfarmkveldefmeuationngiamddighdymmethanme-haflheduemabomm tareasannually. ThisvaluempresenBadgnifiamdeaeaseinthemnualrateoffaestwnversionfm agriculturefiomtheearlyl970s,estimatedbyWerge(1974)attwentytareasperyearforthe traditional hillslope farming system in the Sierra. Forests are converted by traditional methods; for sin-ple, over forty percent of the farmers (41.0%) practice slash-and-burn using a machete to clear thehndAhrgermajority(57.6%)dahntouseacombinafionofoxenandmachetes Afew(1.4%) haveaccesstotraaorsandmodemmachineryforclearingthelandoftrees. Theprmdpdfmmemwenaskedmevaluatewhetheranathenmdividualmesoffaeu Whadmaeasedmdeaeuedomthepantmyemlhemajmuy(458%)mdicawmam changehastakenplaceintheirpatternoffarmleveldeforestation. Afewfarmersreportslight increasesintheirpractbsofforestconversion(ll.5%)andevenfewer(8.7%)reportthat deforestationhadsubstantiallyinaeased. Iargernumbersoffarmersindicatethattheirforest conversionbehaviornfiedsdightandmbstanfialdeaeues,(l7.4%)and(165%) respectively,since 1977. Twocondusimsmaybedrawnfiomthuerespomeneuherfarmersgeneraflyanmder 72 reportingtheamountofnewforestlandclearedeachyearorfarmlevelratesofdeforestation deacasedrapidlyfiomtheemlytothehtelmwuhdecfiningfmeuwveritself. Principal farmers were asked to estimate the average annual amount of arable and grazing landlostfromproduaionasaresultofenvironmentaldegadation. Thepurposewastoobtaina simple fieldmeasurebothdefinedby, andmeaningfulto,thepersonmost affectedbylanddegradation: Such simple field measures will never be fully respectable amongst the more technologically minded of the scientific fraternity, and quite valid criticisms can be made of the accuracy of measurements so obtained. However, the very strength of field measurements lies in the possibility of taking large numbers of them cheaply, with only semi-skilled technical assistance, andgivingresultsthatareprobablymore meaningfulandvisuallyimpressivetothefarmerand the extension worker than some super-sophisticated experimental facility at a distant research station. There would appear to be a lot of scope for development in this area of measurement oflanddegradationbecauseincrcasinglyitistheuserofthelandwhomustbeconvincedthat degradation is a problem (Stocking 1987, 57). The average farmer claims to be losing approximately two tareas (mean of 1.95 tareas) of land from productioneachyearasaconsequenceofdeterioratingsoilconditions. Thisequatestoaboutthree percentofthefarmsteadbeinglostfromfoodproductionannually. Whenaskedaboutspecificreasons forthechangesinlanduseoverathirdcitedproblemswithpoorsoils(33.7%). Almosttenpercent offered that the land was no longer productive. Moreover, over two-thirds (69.3%) of the principal farmemdaimedthattheirneighbmsarebsinghndfiomproducfionforfikereasms Similar observationsweremadeintheearly1980swhere: Withhisrudimentarytechnologythesmallfarmerisforcedtoextractasmuchaspossible from his land; this can easily lead to overutiliration and soil degradation and as soils deteriorate productivity decreases. The rudimentary technology the peasants use does not necessarily lead to deterioration of the soil and/or other natural resources. The regions where peasant technology is causing the greatest ecological damage are the steep slopes of the Cordillera Central unsuitable for seasonal crops, particularly beans. Even if a peasant is in a positiontotakesoilconservationmeasmeshisindividualefiortislostwhentheproblemsare widespread and afiect whole regions (Hartshorn and others 1981, 86). Most farmers (85.4%) in the Plan Sierra study area agree that, overall, the environmental conditions for agriculture have deteriorated since 1977. The majority (48.2%) respond that the conditions for agriculture have deteriorated considerably, while others (37.2%) support the contention thatconditionshavedeterioratedtosomedegree. Lessthanfivepercent (4.6%)indicatetherehas beennochangeintheconditionsforagiiculture. Approximatelytenpercentbelievedtheconditionsin 73 1987weremuecmdudwtoagiaumre,whhamymaflperwm(28%)befiefingmucmdifiom hadimprovedconsiderably. ThesemoreoptimisficassessmentaperhapsrefledthesucwssesofPlan Sierrasoilconservationprograms. Regardmgspedficreumsfmthedauiorafingwndifimsalmoamhhds(6l.7%)dthe prhépdhrmmdtetheindpiefldrwghtuthemdthdrewhgicflprobhmsflthough maemdogialdmdonNsupmerMMcfimficwndifionsuedgnifimflydrkrmanmmyor thhtyyeanagmthephysicalabsenceoftreesmakescondifiomappeardrier. Mostsmallfarmers generallyareunawareoftherolethattreesplayinthehydrologiccycle. Neverthelesaovertwenty percent(21.1%)redisticaflybefievethatthehndisoverworkedmddmosttenperceflsingleom npiddeforestafionasthecameforthemajasoflkissandtheherosionproblema Thenappearstobeawiderangedopinionmmgprmdpdhmemregardingdiangemme condifionoftheforestovertheperiodfioml977tol987. One-third(33.3%)enpresstheopinionthat thecondifionoftheforesthasdetedoratedtoalargedegreeunadditional(28.7%)ofi'erthatthe forestshavedeterioratedatleastsome. Iessthantenpercentbelievetherehasbeennochange. Slightlymorethantwentypercent(21.3%)indicatethattheconditionoftheforesthasimproved somewhat,andsboNeightpercem(76%)uguethatcmdifiomhavehrgelyhnproveddufingthe decadeinquestion. ThesemoreOpfimisficassessmentsmostlikelyrefleatheminorsimssesof nfmedafimprojedswithinthePhnSiemmmagemMueaandmjmefioflsoumideofthe mnagememueaespedaflyabngthemajahighwaybemeenthedtiesdlaVegamdSaMO Domingo. Prhdpalfamemwenaskedanumberofspedficqrwstionspeflahingtoagriudnud intensification. Inordertophceintemificafimvdthinthecmceptualfiamewukofthennan-scale agricultrnalistoftheSierrathebasicfarmunitmustfirstbeidentified: The Hparcela,“aslash-and-burnfarm[field]formerlycalleda conuco,“ scrvesasthebasic agriculturalproductionunitformostDominicanpeasants. Varyingwidelybutgenerally approximatingsix'tareas,“ thisunitprovidesthebasisforfamilysubsistenceandsuppliesan additionalcashincomeacauedfiomseasonalcashcropsales. Thepcasantfarmshis“parcela“ mingdmplehandtodsmdemployingayicuhmdpraaiwsbunedfiomhisfmefathus (Antoninil971,3). 74 Themodebfiomhadifimflmmmdhtenfitymfiedsdwfiequnqwithwhkhfieflsmaflowed torestinfallow.Antoninifound, Iandrotationispracticedthroughoutthenorthwest. 'I'hepeasantgaugesthetillageperiodby physical site characteristics. Thus “parcelss' [individual field plots] located in the “haida' are cultivated solely for one year and then placed in fallow for five to eight years. During this latter period, the forest is allowed to regenerate. “Hoya' and 'llanos“ sites are often treated dmflmly—adfivfledfmtwotothreeyumandthenphmedmgifineampangohgrassand used as pasture for two to four years (Antonini 1971, 4). Werge (1974)foundtheaveragesmaflfamermahhinsfivemsixwarcehflmrotafimeachyemin additiontoalargeamountoflandinpasture,where: The Dominicanfarmerhasawidevarietybutasmall number ofanimals. Mostare unimproved breeds. Two distinct classes are noted: “patio,“ yard animals; and the “potrero' or pastured stock. On the average farm, yard animals include chickens, a few turkeys, ducks and perhapsasowandapairofsucklingpigs. 'I'hefowlareraisedasscavengerswithinthe farmhouseyardandpigsarekeptinnearbysites. Alimitedamountofcormcassavaandsweet potatoesinadditiontothekitchenrefuseareusedforanimalfeed. Cattle,horsesanddonkeys are restricted to small pastures which maybe seeded inguinea grass but often are unimproved. These animals together with goats are also placed in abandorwd and worked-out “parcelas' and permitted to graze. The land rotation system associated with slash-and-burn agriadnuegivestheDominicanhmermpkoppMunhymshifigrazingueujunuhe staggersfarmproducfionfiomfieldtofield(Antonini1971,7). Furthermore,dufingtheagriadtmalyeuthefarmerexploimanequalamoumdddhndasmwhnd putintofoodproduction. Asthefaflowfimefortheindividualfieldplotsisahematedtheyproduce enoughtosatisfythevarioussubsistenceneedsofthefarmhousehold. Forexample,the“parcelas“ fromlastyearproduce“yuca“and“yautia“whilethosethatareseveralyearsoldproduce(guineos) bananasand(platanos)cookingbananas. 'I‘hefieldplotsusedmostintensivelyarethoseclosesttothe Wwfimmm Bywfinsmwwhsmdphnfinsnmymmpmfibhthe small farmerminimizestheriskof crop failure (Werge 1974, 54-55). Finally, however,Antonini cautions: TheDommkanpeasamfacesthechaflengeofwningafivdihoodbmdenedwithuchak farmingpradicesthatretudanytypeofhrge-scaleinaeaseinfamprodudiou Arapidly expandingpopuhtionhasmaasedpresmemthehndnecessimfingachangeowrfiom extensive slash-and-burn agriculture to more intensive cultivation. The present day tendency mshihtogrowingcashaopsmdpmchaseflaplefoodmmmodhieshasmadethepeuam inaeasinglydependemmthewmphxprice-muketnruameofnnhfiuauafingdemand itemsastobacco(Antonini1971,9). 75 ThelargenumberoffarmersinthePlanSierrasamplcarea(40.6%)admittedtheirfieldsare mahflowwefmpefiodsbsmanomyeannfledingahighkwldhnduseandhbmmtendty. Anadditionalnumber(28.7%)indicatethattheirfieldsarerotatedinafallowstateforperiodsgreater thanayearbutlessthantwoyears. Almosttwenty-fivepercentindicatethatahushfallow“systemis inplace,wherefieldslieinfallowfromtwotofouryears. Afewfarmersclaimedtohavelongerfallow lengthsoffiornfivetosevenyears(4.4%)andeighttotwelveyears(2.5%),approachingthetraditional fmeufaflowsystemofenensive“dashandburn“agriadmre,asdesaibedabow. ltappearsthatthe Whngthsobsemdmddesaibedmthewlylmespeddlymerquespraaimdm momtainshpeaweremlongermaintainedbyPhnSienafamersinthelatem Perhapsthis parfiaflyaccountsfortherelafivelyhighratesoffarmlevelhnddegradafiou However, most principal farmers (40.6%) believe that fallowing practises have not changed overthedecadesince1977. Almostequalnumbersoffarmersindicatethatfallowlenghshaveeither increasedordecreased. Forenmple,roughlytwefly~fivepercentdaimfaflowlengthshaveincreased; almostsixteen(15.8%)percentsaythatfallowlengthsinaeasedafittleandsomewhatmorethanten percent(10.6%)sayfallowlengthsincreasedalot. Ontheotherhandoverthirtypercentdesignate Mhflowcychsmdeaeafingdmoflsineen(158%)perwmmyhflowpuiodsuedeaeafinga littleandfifteenpercent(15.l%)sayfallowperiodsaredecreasingalot. ThemajorityofthefarmersinthePlanSierrasamplestudyareaarenotusingmostofthe modern food production technology that is available. For example, almost ninety-five percent (94.7%) sampledindicateadependenceuponrainfallfortheircrops. Thefarmerswhouseirrigation techniques generally pump water to the fields through pipes (2.5%) or use a variety of other strategies. Almost thirty percent (29.8%) ofthe farmers sampled said they had used chemical fertilizers over the precedingtwelvemonths. Most Dominicanfarmers, moreover,havenotinvestedinmajorlandimprovements. Slightly overninetypercent(90.4%)admittheydonotattempttoleveltheirlandpiiortocultivation. Aneven higherpercentage(9L1%)donotusetenadngforerosionconfiohhowever,alittlemorethanhalf (562%)plaMUeesingroupstofunaionusoilanchoratherebyndudngerosiom Roughly 76 twenty—fiveperceflofthefamemrevedtheyusesomespedaflyfleatedseed(20.4%)wkhafew (6.6%)indicatingtheirfrequentuse. Anumberofinstitutionalfactorsmayhelptoexplainthe apparentlyslowrateoftechnologicalintensification: Thesmallfarmerstillreliesonrudiinentaryagriculturaltechnology. Heusesbasictoolsand little if any mechanization or fertilizer. In the Dominican Republic fertilizers are used primarilybythecashcropexportsector. Atthesametimeseveralstudieshaveshowna positive and open attitude of small farmers to modern technology, including fertilizer, but they donotuseitbecausetheycannotafiordit. Loaninginstitutionsgenerallyconsiderthesmall farmers a risky credit subject; hence they have a hard time acquiring credit for production (Hartshornandothersl981,85). ResolutiononypotheeeslA-ZB According to conventional population-economic theory, deforestafiominordertoconvertnew hndmtofoodmoducfionkwndderedtheprhnaqmddmpmwmmaadngpopflafionprme oncarryingcapacity. However,thepredsenauneoftherehfionshipbetweenpopuhtionpressureand deforestationgenerallyisunknownorlargelymisunderstood. Moreover,competingtheoriesinthe popflafimhteramreahnepropmeahmuesoddrespmsestopewasiwwpflafimprme irrespectiveofdeforestation. Thisdissertationwillcontributetopopulation-economicandpeople- envhmmemthemybyexplaimngthenatureofthkmhfimshipwuhmausenudyfiomthe DominicanRepublic. Forexample,ssthePlanSierraregionoftheCordilleraCenUaliscommonly dtedfabdhaccekratedrflesddefmeflafimudhighkwhdpopuhfimprusmehkbgimlm predictapositivelinearrelationshipbetweenthesevariables. Regardless,nullhypothesis(1A)takes thefollowingform: Hethereismfimarnhfionshipbdweenthemhfionhwrnspondingvduuofpopdafion pressure and forest conversion. Hence, regression coefficient (b s 0.0) is equal to zero. 'I'healternativehypothesiscounters: HLthereisaposifiwfineurehfimshipbemeenthemiafionmcmrespmdingvfluesof populationpressureandforestconversion. Hencedefaestationdependsdirealyupon populationpressure,thereforeregressioncoefficient(b> 0.0)isgreaterthanzero. Thedpifianwmrejecfimhvdfmthepahdhypothesumspredaemmedunmety-fiwpuwm (95%). T7 Atthe'pdifialwcfion“sak,thebweamb-bvdbebwme'profinda“fawhkhpubflshed cemusdflmgemraflyanflabhamansmbsduafisfiamecompfledtodefimmemnuedthe hnearrdafiomhipbetweenchanmsinpopuhfionpresnrmandforestcoverfiom 1960tol980. Pofifial“wdim“bwlpopflafimpreumekdefimdutherafiooffienumberdfamhmmehofls (bawdmawnshflofSQSpasmspuhomehddhothemafimmhomeholdunyingapadty, calmlated fortheyears 1960, 1971, 1981.,andstandardizedasapercent (PR1960,PR1971, PR1980). FammphthevadabbPRlMdefinuwdionkwlmfiflionhpopflafimpresmefmtheyeu 1960,measuiedasapercentageofmsximumhouseholdcarryingcapacity. Therefore,seventy-five paceflpopidafimpressmemdiatesthaawcfimhuadensuyvdmequdmmmeqmmhsof theoreticalcapacity. Nen,arelatedsetofvadablesmeasmethevariafioninpopuhfionpressureover the time frames 1960 to 1971 (016071), 1971 to 1981(CH7181), and 1960 to 1981(C116081). For mphthenfiabkaiéomdefimwcfimbwlvafiafimmmeperwntagemangempopmfion pressmedensityoverthetwenty-oneyearfimepeiiodfiom1960tol98L Ukewisestandudizedfmeamvumeasmesmthepohfiulseaimkvdweremyedfathe yearsl960,1971and1980(FOCéO,FOC‘/1,FOC80). Forestcoverestimatesfortheyears1960and mmdetemmedbyphmmeukmemodsbamdupmefisfingfmeuhndmmpsmielfim 1%2),supplementedwithphotointerpretation. Forestcoverstatisticsfortheyearl971alsowere obtainedfiomtheagriculturalcensus(DominicanRepublic1975). Foreachoftheyearathe meumedmwmdfmeuwverwasesfimfledmheaaraandrecadednapercemageofthetad surfaceareaineachpoliticalsection. Inaddifiomarelatedsetofvariablesmeasuresthepercentage change inforestcoverforthefollowing time frames: 1960to 1971(CVCHGO71);1971to 1981 (CVCH7180); and 1960to 1980(CVCHétfll). Rewhsofdmpbbimhteregrudonandyskeflabfishaweakpofifiwrehfimshipbaween populafimpressmeandmuddefaeuafimmthepohfimlsecfimsabGlYPJAEq.LTabk 5.1). Anequafimthatprobunfiafimmthefinearnhfimshipbuweenthekeynrhblesmthe Wyyeufimefimepoduesafignifiamcmlflmodemelywakpodfiwrehfimshipo - .359) beMentheprediaamrhbb(CIm1)mdtheauaionvuiablemeuufingdefaeaafim 78 (CVCHGW). Inthisinstance,theregressioncoefficient(b- .48)isgreaterthanzerowitha significantt-test value (t - 2.11). However,theadjustedrsquare valueof(.1m) indicatesthatonlyten percemdthevariafimmdefmeuafimisexphinedbymaeasesmpopuhfimprusmemthe “politicalsection'scale. Thisdissenmonakownuibutumen'sfingpopuhfimeconomictheaybydefimngthe namedtherdafimshipbaweenpopdafimprumreandddmeaafimuthehrmhomhoflkwl AcwrdhgmBrookfieflanchifie(l987),wmmehendmdmewddupeasdmewpmand Mrfinkapswhhhighermmagememmmunbemadeutbmdividudhmmedfimmafing) scaleasaresultofbroadbasedcasestudies. Therefore,averageyearlyfarmlevelratesof deforestationfl‘CLRD = numberofforestedtareasclearedhredeterminedfiomtheresponsesof prindpalfarmerabasedonforestclearingpractiwssincelm. Inordertocompensateforvariationin mmmeyearlyawragenumberdweudefmededkcompuedwhhtheaaudhrmduand standardizedasapercent(PTCLRD - percentforestedtareascleared). Asnotedearlier,allvariables werembjededtane“mlyskmadertouseumedisuibufim(numafity)ddauvflms VuhbhsmmmibitedaskemddkuihuthdupmamobabMpbgmemmmedprbrto furtherstatisticalanalysis. Typically,abasetenlogtransformationwasenecutedtonormalizethe variable,therefore(I.PTCLRD - logofpercentforestedtareascleared). BmldingmmemahodobgyeaabfishedbyBamrdCampbenandThom(l981;1989),the fdbfingwwsoddmeamekpropmedwherehrmkvdpopuhfimprmureMPRBDkdefimd umerafioofmeaaudhmkwlphysidogimldemitytothehousehddmrryingupadty. Assuming ammimumfamsiuofeightymreasbuedmtheuadifimflhifldopehmhgsynemmthem Sieflaregiomawmamagehouwhddsizeddxpersommemmd(3333)mmkproduad whichdefinestheminimumsubsistenceareaperperson. Todeterminethecarryingcapacityofthe homehddtheadudfarmdnkdividedbythetheaefialmbsktenwdenfityperperm Thefarm bvdpopuhfimprunnerafiokakuluedbydividingtheadudhousehdddnmmeabythe carryingcapadty,asindicatedinthefollowingformula: FPPR87 - household /(farm size/13.333) (4) 79 Abueimbgfiandmmafimwureqmredtowrreafm(mafiu)pmifiw¢ewnessmmedefiwd datavalues. Simphbivafiateregressimanflydswnfirmsamoderflelysfimgposifiwrdafionship (r - .721)betweenBase10logmeasuresoffarmlevelpopulationpressure(l.FPPR87)andtherateof deforestationafl‘CIRD). Thestatisticsfortheenfirefarmsample,significantatthe(.tlll1)level indicatethattheregressioncoefficientw- .604)isgreaterthanzerowithahight-testvalue (t - 18.766). Moreover,thecoefficientofdetermination(rsquare *3 .58))revealsthatapproximately fifiytwopermefmewfiafionmthebgvdueoffmeawnwrdmisexphmedbythebgvflmda newfarmlevelmeasureofpopulationpressurea-IYP.1AEq.2,Table5.1). Inordertoassessthe efieddhmdummekeymriablesmewfirehouwhoflsmpkwasparfifiomdmtothreeywps hrgefummdefineduthmeopenfimshrgerfianflflmeuuptomwmwhikmedium sindfarmsrangefromgreaterthaneightytareasuptomweas. Thesmallestofthesesize groupings,farmsthatrangefiomaminimumoftwouptoeightyhreasproducesasignificam correlationcoefficientof(r a .529)atthe(.(ll)l) level (HYP.1AEq. 3, Table 5.1). Inthiscase,about twenty-eight percent of the variation in deforestation is explained by farm level population pressure, withafairlylargesub-sample(n = 233). lnaddifiomtheregressioncoefficientw - .256)alsois greaterthanzerowithasignificantt—testvalue(t - 9.57). Theregressioncoefficientsalonesugpst thatthestrengthofthepositiverelationshipbetweenpopidafionpressureandratesofdeforestation shouldincreasewithfarmsize. However,nomeaningfulresultswererealizedforthemediumand largefarmgroupsattherequiredconfidencelevel. ’I‘hismaybeattributedtothecomparativelylow sub-samplesizes,at(n -= 57)and(n - 3l)respeaively. Neverthelesssignificantresultsprovide convincing evidence for a moderately strong positive relationship between population pressure and defmeaafiommdudingmdhmueholdswnmmmgksmmtheufiifiondmmimumfarmsized eidrtytareasrequiredforsubsistence. Accordhgtocmvenfimflpopuhtionewnomictheayfidhfmeflwverandtheamouflof fallowlandvaryinresponsetoincreasesinpopulationpressure. Thesechangesweredocumentedin thehrmhousehddquesfimnahemmdermaeueamwnfiabkmasmethedefueuafimhflow 80 ratioCl'DEFAL87). ThisstafisticisdefinedastherafioofthenumberoftareasclearedffCLRD)to thenumberoftareasinfallowCl'FALbOW). Conceptually,avalueclosetozeroindicatesatraditional faedhflwhmmgsyumwherethemkankfivdymanmoumdfmeuhndbeingdeared comparedtoalargeamountoflandinlongtermfallow. Acomputedvalueofoneindicatesthatan equalamountofareaonthevafiousfamparcelsisbeingusedforeachpurpose. Valuesgreaterthan onesignalthatmpiddefmestationismkingphcemdthemoumOfbngtemfaflowhndisrapidly decreasing. 1hisvariabkrehtestotheaopfaflowrafioasfirflpropowdbmeemp(l965)butby definition,ismoresensitivetochangesinforestcover. Thisisanimportantmeasureconceptually becauseitisnotinfluencedbyvariationinfarmsize. Aposifiverehfionshipisprediaedbetweenfarmlevdpopuhfionpressmeandthe deforestation-fallowratio. Undersocialconditionsoflowpopulationpressureonewouldexpecttosee rdafivelysmaflamouanffmestedhndbeingdeuedmdhrgeamomofhndinfaflow.As popflafimpreumemaeasesdefmeuafionwmaccelaateandhndmeamfaflowwmdecfim. Resuhsofdmpkbivafiaemgressbnamlysk(r-546),wnfimamodemdysuongpmifiw rehfionshipbetweenthesevafiablessignificantatthe(.(ml)fortheentirefarmhouseholdsample (HYPlAEq.4,Table5.l). hthisinstancetheregr'essioncoemcientisgreaterthanzerow-ml) withasignificantt-testvalue(t=7.90). Thisdissertationalsoprovidesmeaningfulevidenceonthe mdifidudfamkvelmmmaeaseshpopdafionpressmehavetheefieaofdeaeasingthe deforestation-fallow ratio. Consequently, based on the above results, the null hypothesis of “no nhfionship“bemunpopmfimpressmemddefmeaafimshomdbenjeaedandmeahemfiw hypothesis (HI) acceptedatthe(.05) levelof significance Iineuregresdmamlysisemployingnuimalkvddmfmawbdgroupofdevdoping mfiiesdnadysuppmflasflongpodfiwnhfimshipbeMenpopflafimgthaMdefmafim (AllenandBarnes1985). This researchdemonstratesthatthelinearrelationshipalsoholdsonthe farmhorueholdscale. Onthe“politicalsection“level,however,resultsconfirmaweakpositive nhfimshipbefleenchanmsmpopuhfimprusmeandrauddefmmfimowratwemyyeufime frame. Thismaybetheresukofmeasurememenamdetaminingeuherpopuhfionpressmea 81 um an a: man Hm EN? :85. aged. 89: Rad v86 awed 8.3 8.3 3.8 mud. end new “man. #25. how. “was; Q 680m «95. E... “$3: Q :08 ~95. N2..- 985—.— Q 800..— ug. wen. Ema—nu..— Q inwar— fig. mun. Edna—nu..— Q 950:1— fig. “Nb. Emma—.— Q 240:..— 38. own. #8030 Q 86058 .38.— .. ...a> .1:— Q don— AHeS vs S moans—Ea ao§0§0¢ 03.8.3 ..o 3303— 333m fin 030,—. m... ...m a... ...m .a ...m ..<. E: 3.. ...m n... ...m «a. ...m .e ...m S E: 82 forestcoverchangeatthisscale. Forexample,thereliabilityoftheforestcoverstatisticsfromthe1971 Dominican agricultural census are questionable (Larson 1986). Perhapsasefiesofshficforestwveresfimatesknminedmerfimeasasunogatemeasureof deforestation) may also be significantly related with variation in population pressure. This related research question (lA') requiresthe formulation ofnewhypotheses, as follows: Ho, themismhnearnhfimshipbeflmenthevariafimhwrrespmdingvduesofpopulafion pressure and forest cover. Hence, the regression eoeficient (b = 0.0) is equal to zero. ’I'healternativehypothesiscounters: HI, there is an inverse linear relationship between the variation in corresponding values of population pressure and forest cover. Hence, changes in forest cover depends inversely upon population pressure therefore the regression coefficient (b < 0.0) islessthan zero. Thekeyvariables.asdefinedabove,forthesmaflseriesofdataonthepolificalsectionlevelare employed. Theyincludestandardizedforestcovermeasuresfortheyears1960, 197Land1980 (FOCGO, F0071, F0080) and standardized measures of population pressure (PR1960, PR1971, PR1981). TheresulmoffimplebimhtemgrudmambsisrewflahigMysignifimmnhfionshipfm eachoftheyearsinquestion. Startingwiththebaseyearl960,anegativeregressioncoefident (b=-690)estabfishesthat(b)islessthanzero,withasignifimMHestvalm(t=-.7.06). A mehfimmeflideflof(r=-.782)Mbfishesasflmghwrserehfimshipbemeenpopuhfim prasmandfmeflwvenhtfismhighvflwsdpopuhfimpressmeuespafiaflyamdfledwuh lowforestcovervalues. Thesquareofthecorrelafionwelfidem(rsquare=.612)indicatesthatsiny- one (61%) percent of the variation in the criterionvariable (forest cover) is explainedbythevariation inthepredictorvariable(populationpressure). FromtheequationGlYPlA‘lEq.l,Table5.l),we findthnfmeveryomperwmmaeawmpopmafimprusmthaeismMeddeaeasedom one—halfofonepereentforestcover. ‘l'heinterceptvalue(97.3)indicatesthepercentageofforestcover wewouldexpecttosee,inl960,ifpopulationpressurewerezero(lohnstonl978,28). Zeropopulation pressurewouldindicatethatthepofificalsecfionwuanunpopulatedfronfier. 83 AmapoftheflandudindredduakhomtheregudmeqmfionWIguresmhdiatestha fuedmwuhrgelymder—MdedhfiehighlypopflfledSedhnflSahmlflesh(wflamhg thedtdeabmaIgleda)mdmmechaflybatedSeaideerbaBuenawhichwntaimme villageofSanJose’delasMatas. Forestcoverwasconsiderablyover-predietedintheeasternSectionof GumaandweaemSediondRodeqwherebmhpofidmlwcfimswerehrgelyfmeuedhlm. MMm—predicfimsoffmeflmrmewenhtheweflemSecfimsdPflmuejoandElCadqm, andinthecentrallylocatedSectionoflasPiedras. lngeneraLtheregressionmodelover—estimates {Mmmwmmmwmmmammmmmmemmmmédc lasMatasandSabanalglesia. Ontheotherhandtheregressionmodelisunder-estimatingforest coverinseveral'sections'alreadyidentifiedastheforestedh'ontier. TheSectionoquamais espedaflynaewmhy,sfineighUperwmfmededmdwmtheemMngeutmfiwwnbmem panermitclearlywasanhnpatantindpientfiontierdestinationinlm. ShnfluresuhswemobtainedfmtheyeamlfllandlmwheremIWLaminmse nhfimshipbflweenpopuhfimpremureandfaeflmrkindimtedbyamgafiwregessim coeficient(b- -.480)againsignificantatthe(.(ll)l)levelwithat-testvalueof(t c -548). In addition,acorrelationcoeflicientof(r--.720)alsorevealsamoderatelystronginverserelationship (HYP.1A‘lEq.2,Table5.l). Inthiscase,thesquareofthecorrelationeoefficient(rsquare-.518) dropssomewhatomepdntwhereapprouimudyfifiym(52%)perwmdthewhfimmthe aiterionnriableisexplainedbytheprediauvafiablewopuhfionpressure). Byl971,therefore,a owpucmtmaeasempopuhfimprmekdhedlyrehtedmsfighdyleummame-hafldme percentdecreaseinforestcover. Amapoftheflandudhedredduakfiomtheregeubneqmfionflgmesmmdkatum fmeawmmtheweuemSecfimstodeqfwomqmdespedaflythehrgeSeaionome Grandganmer-prediaedhhhoughlessthmwouandudmmmtheasedeGrandeand lessthanone-and-one-halfstandarderrorsintheothers). ThelarprSectionsOicomeandMata Grande)waenmhrgdyhamibbnmkfimeandmheqmflymreparfiaflyhcumedhto theBermudaNationalForest. 'l'hereakoareminorover-predictionsofforestcoverintheeastern 84 Figure 5.7 Decrease in Forest Cover, 1960 to 1980 Percent Decrease -76to100 -51to75 -26t050 0,0 25 * NO DATA Figure 5.8 Residuals from Regression, Forest Cover (Y) with Population Pressure (X), 1960 85 SectionsofCeinandBaitoa. Minorunder-predictionsofforestcoverareexperiencedinthehighly popnluedeaaemSecfimsoflApeLSahmlgledaaMJamymndmmewnndSecfideerba Buena. Onwagaimtheregressimmoddkgeurfllyoverwimafingfmeamrmsebaedpofifiml sedionsneartheeast-westfinearbandofwflbmeflmdmmimpmtamwfi'eeprodudimareainthe smnheut(Jamy);hwnUasgthereg-esdmequafimkundercsfimafingfmeamrmfimfia areas,especiallythehrgerwestemsecfimsthatmmbeingpufiaflyinwrporatedintoproteaedfmest reserves. Bylmmemgthdmehmrrdafimshipbetwewmpulafimpressmandfmeam onthepdhialsedimhmldedmestoamoderueaameumusumedbytheregresdonweffidem (b--.2‘70)stillhighlysignificantatthe(.m01)level,withat-testvalueof(t--3.96). Thecorrelation coeficientoflr--.567)indicatesamoderatelystronginverserelationship(I-IYP.1A‘lEq.3,Table 5.1). Aouepercentincreaseinpopulationpressurein l980isinversely relatedwithslightlymorethan one-quarterofonepereentlossinforestcover. Bytheearly19805,thepredictorvariable(population pressure),ahne,issfiflaccomfingfmthhty4wmrcemofthenfiafimmthefmeawver(aherion) variable,atleastonthepoliticalsectionlevel. AmapofthestandardizedresidualsfiomtheregressionequafionfingunilO)indicatesthat fmeucoverkwnsiderablyover-prediaedintwoweuemSeaimsElCadquedeoma ‘l'helarger Section,TomaksfiflratherisdatedandalsofmapanoftheBemudezNafimalFaesgitis thereforesomewhatpraeaedfiomthefllefieasofpopuhfimpresmue,unfleaedbymerseventy percentforestcoveraslateasl980. ElCadquewasoversixtypercentforestedbutdidnothave prdededfmestdaMhperhapsnpresenfimWedfimfiermwevhmmflhureqmru furtherfieldinvestigation. TheeastemSectionofSabanalglesiaillustratesaminorover-prediction (whhhomandom-hafluandudenms)wrrespmdingwuhadigmmaeasemfmeawvudnce 1971. ltappeamthflpopflafimredkuibufimmthewentyyearperbd(1960-1980)fiomthe impaumemigrafimwuramdSabamlgledahaddeaeasedthewaimkwlprmemhnd resources. ThehrgeSeaimdeGrandekemfiderablyundaprediaedwetweenone-and-me- halfandtwostandarderrors)bytheregressionequationfor1980. ’I‘hisSectionexperiencedaforest 86 Figure 5.9 Residuals from Regression, Forest Cover (Y) with Population Pressure (X), 1971 .15 m -2.0 Less Then an " "0 WA Figure 5.10 Residuals from Regression, Forest Cover (Y) with Population Pressure (X), 1980 Reuduele From Regression +2.0 and ADOVB -1.0 to 45 .15 to -2.0 Less Then -2.0 * "0 WA 87 overlossofoverfiftypercentsince1960,duetointensesettlementinthelowernortheasternportionof thewcfiommdwnenflymfiemfiomflbgaltespmmthepraeaedresemoftheBemudez NationalForest. TheintefimseaionsonerbaBuenaandPedregaliflumatetheanddpatedmder- predictionsinforestcoverfoundinthemorehighlypopulatedruralareasnearvillagesandsmallcities. Thisdissemfimprofidesefidenwmthewcfionflkvelflmthededinemthesuengthd relationship between population pressure and deforestation over time. In 1960, the Plan Sierra study areawaslargelyaforestedfrontier,withmuchoftheoriginaltreecoverstillintact;bythelatel980s, thismountainousareawaslargelydeforestedandinadegradedstate. Commonsenseandempirical evidence(Sachflul974)uguethflacwssibihtytothefmestsphysmimpthrokhhndm change.Themmeaccessiblefmesmuefikelytobewnvenedeufierandnafasturuethanthemore inacwssible. likewisegentlyforestedslopesarelikelytobeconvertedtofarmlandearlierandata WarmthmfleeperdopesAddifimafly,Dominicenhmemoflenusefmeawmposifimnm indicator of soil qualityand,therefore,certainforest complexes/typesareremoved more quicklythan others. hcombimfiommeseresuIGmnaewmthymfigItofrecentfindingsfiomreseuchmaher mouflahommfiromentgwhichquufionmeprimerokofpopuhfimmusmemmedefmm processespedanymmmeadvancedstagesofdefmeuafimavesmdfittlmfii). Baseduponthe Plan Sierracasestudy,itisapparemthatthesuengthoftherelafionshipbetweenpoptflafimpressure anddeforestationchangessignificantlyovertime(twentytothirtyyears),whenmeasuredatlarger scdesdmdysksuchmmepofidcslseaionmthebominkankepubficWhenmemedmthe mdividudhwwhddmmerehfionsfipappemtobemmgerwhhmamhrmdmahhougha moderately strongpositiverelationshipwasestablishedfor farms smallerthaneightytareas. Perhaps thedeclineinstrengthofthisrehfionship,temporafly,indicatestherisingimpatanceofoneormme alternativeresponsestopervasivepopulationpressurc. Bflsbarowmopmuthusodetdrupmsestosustahedpopuhfimprmeevolwoverfimq yadingfiomemnmktoecmmkdemographktoadufiwlydemogaphkrupmmmflsbmrw 1987). FuthecesestudyfiomthePhnSierrarep‘onoftheCmdflkraCenUaLitappeanthu acwkruedfueuwnmsimfmfwdandfuelwoodproduabnhubeentheprimaryrapmnm 88 Mainedpopulationpressure,atleastfrom1960t01987. Neverthelesswiththegreatlyreducedlevels of forestcoverexperienced throughoutthe19805, perhaps nowwe should, accordingtotheconceptual hmemkmopmedbyBflsbom(m,upedtoneevMemdaheMempmstopopmafim pressure. Theahemaechohesuepropmedmthemptudmodeldetaibdmchapterthreeand areexaminedbelow. Research hypothesis (18) examines several of the alternative responses to population pressure,whicharecollectivelydefinedasagriatlturalintensificetion. Theseresponsesincludethe mtensitydhndmdhbmmputsmnmafimsmmpummtothehndremmwbasemdmnmfimsm or inputs of new food production skills (Boserup 1965, 1981; Brookfield 1971, 1984; Brookfield and Blaikie 1987). According to conventional population-economic theory, ”the primary purpose of intensificationisthesubstitutionoftheseinputsforland,soastogainmoreproductionfromagiven area, use it more frequently, and hence make possible a greater concentration of production” (Brookfield 1971, 51). According to the model proposed in this dissertation, agricultural intensification logically will be exercised by traditional farmers in response to increasing population pressure, irrespecfiveofiorasanalternativetoJorestconversion. hmdertoapmaisethembmaspedsofagiadmdmtensifiufiomtheorigindruearch hypothesiswillbcspecifiedinthefollowingwaystoaccountforthedistinctionbetweentheterms intensityandintensification. Inthisresearch,staticmeasuresofagfimltmalintensityareemployedin lieuofmeasuresthatassesschangesinfarmingpracticesovertime. Thefirstmodifiedhypothesis (lB‘l)willexaminethemorecommonnotionoflanduseintensity,asfirstproposedbyBoserup (1965). Ho, thereismrelationshipbetweenthevariafioninwnespmdingvduesofpopuhfim pressure and land use intensity. Hence, the regression coefficient (b a 0.0) is equal to zero. Thealternativehypothesisofiers: H1, thereisapmifiwrehfimshipbflweenthewfiafiminwrrespondingvdmofpopuhfim pressureandlanduseintensity. Hence,landuseintensitydependsuponpopulationpressure, thereforetheregression coeflicieutis(b > 0.0) greaterthanzero. 89 Thewwndmodifiedhypomeds(m‘2)wmmmmemerehfimshipbeMpopmafimprusmeaM intensityofinnovativefarmingskills,where: Hothereismnhfionshipbetwenthevafiafimincmrespmdingvfluesofpopuhtion pressureandintensityofinnovativefarmingskills. Therefore,theregressioncoefident (b - 0.0)isequaltozero. Thealternativehypothesisoffers: HLthemisapodfiverebfimshipbetweenthemhfiminwnespmdhgvfluuofpopuhfion pressureandtheintensityofinnovativefarmingskills. Hence,theintensityofinnovative hrmingskiflsdependsupmpopuhfimpressmqthaefaetheregresskmwefidemis (b > 0.0) greater than zero. Themhdmodifiedhypmhesk(m‘3)fiflenmimmenhfiomhipbemeenpopmfimmmem intemityofinvestmentsinthe landresourcebase, referred tobyBrookfield andBlaikie (1987) as landesque capital. Ho,thercismrehfimshipbemeenthemiafiminwrrespmdingvaluesofpoptflafim pressnreandintensityofinnovafivehnprovemcntsinthehndresomcebase. Hence,the regressioncoemcienflb = 0.0)isequaltozero. The alternative hypothesis ofiers: H1, thereisapodfiwrehfimshipbetweenthemfiafimmcmrespondingvfluesofpopuhfim pressmeandintensityofinnovafiveimprovementsinthelandresomcebase. Hence,the mtensityofmveamemmhndesqueapitddependsupmpopdafimprmethaefmethe regressioncoefficientis(b > 0.0)greaterthanzero. Thefomhmodifiedhypmhesk(m‘4)wiflmmmemerehfimshipbeMenmpuhfimwmmd agiwhmdmtendtymthefmmofwmbimdmnmfiwskflkmdimprmemmmthehMresomce base. However,thenullhypothesisisofthefollowingform: Hqthenismrehfionshipbetwcenthevariationincorrespondingvaluesof poptflafionpressmemdagfimhmalintensityfinthefmmofwmbinedinnovafive skillsandimprovementstothelandresourcebase. Hence,theregressioncoefficient (b - 0.0)isequal to zero. The alternative hypothesis offers: HLtherekaposidwnhfimshipbdweenthemfiafimmwrrespmdingvfluaofpopuhtion preumeandagricultmalintensityfintheformofmmbmedinnovafiveskilkand improvements tothelandresource base. Hence,thecombinedintensityofinnovativeskills andinvesmmhhndesqueapuddependsupmpopuhfimprmetherefaethe rcgressioncoeficientisgreateflb > 0.0)thanzero. 90 memmerejeabnadpifiamkvdfamepahsdhypahemmpreduamhedanwfiw (95%)me Remhsofsimpbbimhtereyesfimandyfismme'pofidmlseaim'kveluemeuingfuL hawva,hmdim&nwsthedhecfimofthenhfimshipbawewthekeymhblukthemme fromthatpredictedinthemodifiedresearchhypothesesflableSl). Datatoassesstherelationship bampopmmmeandhtendfiafimpracficu(ayiaflmdimM)mmlyavfihbkfa theyear1983. NeverthelesssignificaMresuhsueproducedfiomfomequafimsmmstequafim 0m.lB‘1Eq.1)enmhesmefimunhfimshipbeMewpopflafimprmaPR1983)andhnd useintensity. lnthiscase,landuseiNensityisdefinedbythevafiable(CULT83),whichisa aandardizedwrfimoftheaop/faflowrafiquredefimdbyhrwandbodink(l979). Results hdicdethflamweaHr-mnodfiwnhfimshipkeaablkhedbamtheprediaaand aiterionnfiabbqwhaethengesdmmefidemwsml)kmmmwhhat4wvmw (t=1.62)significantatthe(.0576)level. 'I'hesecondequationaiYP.lB‘2Eq.l)probesthecausal nhfionshipbemeenamrmdbgdpopflafimprmmeafkmsfiandimprmedfmdpoducfim skins(sxn1.ssa). Thevariable(SKnJ.883)measmesthepercentageoffamspersecfionthatmade useoffertilizersandinsectiddesin1983. However,incontrasttotheresearchhypothesis,resultsof bimhtewgremionandysisnwdamodemelynrmgmwmenhfimshin-dénbemthe predictorandcriterionvariables. Theregessioncoeficient(b~-37.99)isbssthanzerowithat~test value(t--3.T7)significantatthe(.(xm)level. likewkeathirdequstionGiYP.lB'3£q.l)probes therdafiomhipbeMeenpopuhfimpressmeaPRmMndhvesmmhhndesquempud (INNOVA83). hth'mcase,intensifiedhndresmucemanagememisdefinedbythepercentageof hmspersedionthflmdehmfiwuseofhfigationandermionmnfldmethodshflfl. Results offimpkbimfifieregresfimmflyflsakoreveflamoderfldysflmgmverserdafimhipk--.409), wheretheregresdoncoeflidefl(b--l659)klessthanzero,withat4eflvalw(t--2.69), significantatthe(.m58)level. 'l'hefourthequationtYP.IB‘4Bq.l)combinesthetwointensity measmu(SKn1583+mNOVA83-1BCH83),madcrmcmreNetheusummarymhfimwhh pepulationpressure. Thiscombinedvariableequafionproduwsthesuongestinverserehtionship 91 (r--.584),havingaregressioncoefficient(b--537)hssthanzero,withat-testvalueof(t=- 4.132), significant at the (M2) level. hdiredwnbafltocmvenfionflpopulafionmnomictheorymmemp1965J981),the ruuhsmdiutemammepofifialseaimmmkvekdpopuhfimpressmmmmefikelytobe relfledwhhthemtendtyofmnmafimmhmingpraaiwsthanhighpopuhfionprusme. Ifwecan assumelowerlevelsofpopulationpressurearefoundonthehrgerfamgitislikelythatvariationin famsizeaggregatedmmewaimkvdacmmtsmpamfmmemmnhdmshipbaweenmekey variables. Theintensityofagfiwkmdmnmfimappeantobemkingphcefithhpdifialseaiom whhahighnumberofhrgufamswhkhexperiencecomparafiwlybwerbwhdpopuhfim pressure. Theseresuhsdmindicatethflfadmsdherthanpopulafimpressmesuchuawessm infamafimanduphd(agfiwkmdbam)dhealyawomtfmmausinghtensityofmwvafiw farmingtechnology. Perhapsaclearersenseoftherehtionshipbetweenpopulationpressureand agriculturalintensitymaybegainedattheindividualfarmlevel. Thefoflowingwfiableswereaefledtomusmefiemfiomaspedsofayiafltmdflenfityn thefarmhouseholdscale. Improvementsinfarmingskills(SKILLSS7)areevaluatedinasummary memundmefoflowinguseofspeddwedsphnnngmrmweedmgmeofmwaiddeandused fertilizer. Individuals farms range in value from zero to five, if all of the enhanced farming techniques wereinusewithintwelvemonthspriortothel987interview. Inaddifioninvesunentsorinnovationsin theresourcebaseGandesquecapital)alsoaredeterminedinasummarymeasure(AGINOV87)ofthe following:kvefingofhndfmphnfingmeofirfigafionbuildingoftenawsandphnfingofm» soil anchors. Bothofthese measures are summed (AGINOV87 + SKILLS87 - AGIN'I'87) toassess theircombinedvariationcorrelatedwithpopulationpressure. undmeintensityismeasuredby vuhdminthepucemageofweasaoppedmOPhndafiequencydmlfivnionrafio (AGFREQ),asdefinedbyDoolittleand'Iurner(l979). Resuksofsimpbbivuiatewgeaimmlyflsfutheenfirefarmkvdmmpkmsomcwha agriculturalintensity. Iandmeflensitykamjmemeptiomwhereahighlysignifiaflnhfionsbipis 92 supportedwithpopulationpressme. Forenmple,astrongpositiverelatiomhip(r - .764)‘isevident buweenhmbvelpopuladmprusmeaflPRKDandaaandudizedmeasmeofaoppedhnd (U’I‘C‘ROP - logofpercenttareascropped). Here,theregressioncoefficient(b- 587)isgreater thanzerqwithahight-testvalueofa-24.40),significsntatthe(.m01)level,where(n-423). The squueofmecmrehfimweffidemindiatesmatmfifiyeightpucemdthevafiafimhthe pacenhgeoffummaoppedmnbeesphinedbynfiafimhpopuhfimpmflflfim‘lEq. 2). Inaddition,amoderatelystrongpositiverelationship(r - .537)existsbetweenfarmlevel popuhfionpressmeaFPPREDandah'equeneyofathivafionmeame(AGFREQ). Here,the regressioncoemcienub- .370)alsoisgreaterthanzem,withat-testvalue(t :- 13.04)significantat the(.m01)level. hthishflance,almodtwenty-ninepacefloftheinaeueshtherafio(htenshy)of aoppedhndomfalbwhndmeaplahedbypopflafimprmeflflfifl‘lfiqfiflabksz). Thenhfimshipsbemnpopflafimpresmeandinnomfivemeasmesoffamintenshyue notasclearlydefined. Whentheenfirefarmhousehddsampleisenminednosignificantreladonship mbeeflabfishedbeWeenpopflafimprmeandefihermtmdwdmwhmhgskflkfilflum orhndresomceimprovementsmcmovsnatthedesignatedconfidencelevel Neverthelessifthe logofaveragefarmoutputfoodproductionpertarea(AVPRODPI)isusedasamrogatemeasureof combinedagricuhmdintensity,amodemepmifivenhfimshipiseuablished(r- .406). Inthiscase theregressioncoefficient(b - .422)'ngreaterthanzero,withat-testvalueof(t -9.075)significantat the(.(ll)1)level(HYP.IB‘2Eq.2). Similarresultswereencomteredwithintwofarmsizesub— groups,thosefumsthflnnpdhsizefiomfmtytoeightytueuandthmerangingfiommerflflto 2000mm Thenhfionshipsbetweenpopuhfimpreuurcandmmnfiwmeamesoffarmmtensiym somewhatmoreclearlydefinedwhenvariationinfarmsizeisexamined. Onthesmallestsizedfam thoseranginginsizefiomtwotofatytareas,aweakpositiverehtionship(r - .122)canbeestablished betweenpopuhfionpresmeandabgmeasmeofinnovafivefamingakiflsaSKnS). Inth'ninstance, thereyudmweffidemisgreuuthanmwhhaueddgnifiamuthewzsmkwl Atthe medimhmsizesakfihowhrmrangingfiomdghtymmwaweakposhiwrehfimship 93 (r - .330)isestablishedbetweenpopulationpressureandthelogofSKILLS. Inthiscese,the regresdmweffidemakoisyeaerthmzerqwhhameasignifiamuthecmlmhvel Finally,on thehrgestfarmsthoseyeaterthanfll)hreas,aweakpositiverehfimship(r = .271)isestablished baweenpopuhfimpresmemdmemtendtyofmvesMenmhhndesqmupimHAGINOVW). As naedabmtheregressimwemdentisgreaathmwowithaueusignificamatthe(.0519)level. Thesereamsmthemfiomhmbvehdiflamdkeaimfiomthefindingsfoundmthepofifial sectionscale. Itksumglyevidemmathepodfiwrehfimshipbetweenpopuhfionprmeandhnduse intensityissignificantlyestablishedatthefarmhouseholdscale. Thenullhypothesisa-Io)ofno relationshipbetween populationpressureandlanduseintensity, for modified hypothesis (18‘1), therefmeshouflbenjeaedmdthedtemfiwhypahedsmlhcwmeduthewflkwlof significance. hconuasgsignificampodfiwmhfionshipsbetweenpopuhfimpressunandthemore mmfiwfmdagriadfluflmtenshymthehomehoubstuchuimprowdfarmmgskflkand Wenuhhndesqmmpflduemkwhenwmpuedwiththesuengthofthemhfimship establishedwithlanduseintensity. Onlywhenthefarmaveragefoodproductionpertareaisexamined, usmogakmeumeofimprowdfammgskiflsktheremhdiafimoftheprediaedposifiw relationship for the entire farm sample. However, the variable (AVPRODP’I‘) most likely reflects the mmbinedmtensityofmaeascdhnquimprondfamingskiflsandimprmemenmmtheland resourcebase. Whentheenfirehouseholdmmpbispaififiowdbyfamsimwukpoéfiwrdafimshipsue enabfishedbemmenpopuhfimpressunandhnonfiwmasmesdayiadmdmm,makafl threeofthefamsiugtonpstminismallandlargefarms. Baseduponthestatisticallysignificantfarm mbmpkkwlresidmabm,thenuflhypmhesesalo)ofmnhfimshipbetweenpopuhfim pressmeandhtemhydhmhgsfillsfmmodifiedhypdhesu(18‘2)andbawuupopflafim pressureandintensityofinvestmentsinlandesquecspitaLmodified hypothesis (18‘3),andthe wmbiudmeme(m‘4)dmmvafiwayiammdmmndtyshouldbenjeae¢andtheahemfiw hypothesesO-Il)acceptedattheninetyfivepercentlevel. we 3.9 am Sun. on Exam mm and? no EH6 hum mead NS. 9.3 an cash. 5* chad «9. hand an ~86 8.8 88. 8: 88 8.: as. 8.8 88. ea 88. a: mac. 3 88. 88 g 9.9 88. 8“ a8. 88 2.8 a see §§ $§§§ RH. 35. .— Bfimmmd Q §O< M853.— Q 9309—. hwy—mg Q $>OZ~C< 93mm: Q mw<>OZZ~ bazaars Q g3 hwy—mam..— Q as Bde Q tflomgs 83%.: Q WED—m Emma—m..— Q mum—€03 hwy—ms Q 306:..— mwaHM—md Q £50 .a> .3 Q smog are .95 as: .5: 88:5 Sussex 23.35 8 338m seam fin 033. N... ...m z. .5 v.3 E: «s ...m as em 2: E: z. .5 2. an «s em 2. .am as: E: n... . as .am 2. ...m 5: as: 95 Awmparisonofthewnflidhgresuhsbetweenpofificelseaimmdfmkvelanalyses mdiatesthammdfaaommherthmpopiflafimpressuremmhtedtomaeasingkvekof innovative agricultural intensity. These factors most likely include access to information about innovative technology, acwss to financial credit, and out-migration (brain drain) of likely innovators, amongothers. Thesefindingstendtowppmtmemfimdmcvdufimaqproyemionofmddresponsesto populationpressureovertime,asconceivedbyBilsborrow(1987). InthecasestudyfromthePlan SierraregimoffieCmdflemCenfiaLmewogeabndwddrespmsaamtomafilykufoflowx 1) deforestation, to place new land into food production; 2) farm labor and land use intensification, manifestedbyreduaionoffanowlengthsasweflasfallowareasandtoafimitedextentthereduction dUadifimdgrafingueasassodaedwimmueaseshuoppedhndfiflheramumgligibk mtenfifiufimdagicflfluflsfillammflefledbysomefimheduseoffinprmdwedsfmflhersand improved cultivation practices, and; 4) very limited innovations and investments in landesque capital, manifestedbytheuseofirrigation,theplantingoftreesassoilanchors,levelingoffieldsandthe buildingofterracesonsteepslopes. Mostoftheprincipalfarmers,espcciallyonsmallerfarms,employthefirsttwochoicesin response to increasing population pressure, whereas the third and fourth strategies are employed only toamodestdegree. Onthehrgerfarmsundercomparafivewndifionsofbwerpopulationpressme, iswhereinvestmentinlandesquecapitalistakingplace. Thismaybeareflectionofhigher wdoewmmkstamsmtheewnomiesofscalethNHeusmflyreafindwithhrgerfamfizes Perhaps.inthenearfuture(1990s)whenthefirstchoice(forestconversion)isnolongeraviableoption for traditional hillslope farmers, we may logically expect to see greater efforts to improve food productionskillsandpermanentinvestmentsinthelandresourcebase. Consequently,additionalfield studiesueinmder,atfiveyeuintemls,andshouldbephnnedforl992andl997. Hypahesis(2A)bufldsupmthewnceptualfiamewmkeaabfishedabmeandprenfling people-environment theory, by attempting to define the nature ofthe relationship between deforestationandfarmlevellanddegradation. Basedoncontradictoryresearchfi'omdeveloping 96 mmfiiegespedaflyfimewifimomtfinombiomesrapidmdmimmagedfmeucmmdmappeam mbedirealycurehwdwhhequaflyacwbruedanduwonudbdmvhmmentddegradafim, primarilyintheformofsoilerosionandmasswastingavesandPitt1987). Accordingtocompeting popdafionewnomkandpeopkcnfirmmemmeay,mweflmnsemchbyamdemkfmmew twoprm(defmestafimmdhnddegrsdafim)uenamwssafilymhtedmaammdeflea manner (Brookfield and Blaikie 1987). Therefore, the initial null hypothesis assumes the following form: Hqthemisnofineurehfimshipbetweenthevafiafimincmrespondingvaluesof deforestation and farm level environmental (land) degradation. Hence, the regression coefficient(b=0.0)isequaltozero. Thealternativehypothesisposes: HLthereisaposifivenhfionshipbetweenthevafiafiminconespondingvalwsof deforestation and farm level environmental (land) degradation. Hence, farm level land degradation depends upon deforestation, therefore the regression coefiicient (b > 0.0) is greater than zero. The sigiificance or rejection level for this hypothesis was predetermined at the ninety-five percent (.05) level. Sufisficalmalysiswaswnduaeduthefmmhousehddscalewhewaandudiudhmmd log) values for annual rates of forest conversion (LPTCLRD) were directly related with annual rates of land degradation (LPTCHNG). Percentagefigureswereemployedinorder tocontrol forvariation in farmsize;abaseienlog&ansfmmationwasrequiredtonormalizebothdauvalues. Inthisanalysis, farmleveldegradationwasdefinedastheaverageyearlyrateofarablelandlostfromfoodproduction asaresultoferosionalprocesses. AdheringtoguidelincssanctionedbyBrookfieldandBlaikie(l987), landmanagers(inthiscasestudytheprindpalfarmem)werenquestedtoesfimatethemoumd uabkhnddegradedmefinedmfammaassignedmebwrrankdnm-produaiwhndfiom amongtheirparcels)andefi’ectivelylostfiomfoodproduaionsincel977. Resuhsofsimplebivuiateregresdmanflysisdemmstraeamodaudymmgpaifiw nhfimship(r-559)bemeenmeakerhnmhbb(hnddegaduion)andtheprediammhbk (deforestation). Theregressioncoeficientisgreaterthanwow- .548),withat-testvalue 97 (t -8.825)significantatthe(.(l!)l)fortheentirehouseholdsample. Inthisinstance,appron°mately thirty-one(31%)percentofthevariafionintheaitefimvafiableisexphinedmaccoufledfmbythe predictorvariableO-IYP.ZAEq.1,Table5.3). Asomewhatweakerpositiverelationshipisrealized whenthehouseholdsampleispartitionedintofourgroupsbyfarmsize. Thesmallerfarmgroups, thosethatrangefromtwotofortytareasandthosethatrangefromfortytoeightytareasreflect meanindul correlation coefficients of (r . .446) and (r = .433) respectively (HYP. 2A Eqs. 2 & 3, Table53). Forthemini-farmstheregressioncoemcientisgreaterthanzerow- .4m),withat-test valueof(t =455)significantatthe(.tll)l)level. Inaddition,theregressioncoefficientforthe equationfi’omthesmallfarmsalsoisgreatertbanzerows .670),withat-testvalue(t - 2.93) significantatthe(.m3’7)level. Incontrast,nomeaningfulrelationshipsareestablishedforthemedium andlargefarmgroupsatthedesignatedconfidenceinterval. Basedonanevaluationoftheregression wefidenmmuappeamthamemengthdmenhfionshipbetwendefaemfionawhnd degradationincreaseswithfarmsize. Consequently,basedontheseresults,thenullhypothesis(I-Io)of norelationship shouldberejectedandthealternative hypothesis (H1) acceptedatthe(.05) levelof significance. Theseresults support personal field observationsthatcomparativelyfewefl'ortsarebeing madetoimprovetraditionalhillsidefarmingpractices. Occasionallynewforestplotsarehaphazardly clearedmisohtedfmeuedueasfiequendymwryueepsbpesfmtheflkgalshmtmmproduaion ofmarijuanaorotherhighlyvaluableashcrop. Aftertheinitialharvest,fieldsoftenareabandoned mdexpmedtowindmdwatererosiomutheenfiepremummmemmsucwssfuflyfimceamove outofthecountry. ilighratesofdeforestafioncombinedfithinaeasingaop—faflowrafiosin wnjmaionwithmeuukabsenwofinvesmminhndesqmmphdmimprmemmmfarmmg skillscontributetoacceleratedlanddegradation. Tocontinueonthisslowcoursetoimprovedfarm doomthemflhndsapeofthebomhkankepubficmtheameagwcdogialdkananowbdng experiencedinthemountainousenvironmentsofneighboringHaiti. 5239 um m3 n86. Rafi. 8nd. «ed 2%.: 8.3 mend 93 88. New- 5285 a. 02:85 I. em a... 88. an- 383 a 02:85 me em 84 88. 8a.- an. e 02:03.— : em Rd 2.8. ca. 8285 e 02:05 a... em 8 E: a so... c .a> as a. son EN 8325 cassava 283m so 3.33. .5588 ...m 3.5 v2 88. 8... 9:25 e @255 9.. ...m 8.“ 88. 8... 050:.— e 02:85 as ...m 8.8 «8: 8n 950......— e 02:05 as ...m <~ E: a do..— .— ua> .2.— Q den 5 834885 373.50% ogm Ho $183— E3553 fine—ash. 99 hmasgfiypmlwsis(23)anemptsmedabfishafinkagebaweeninmfiwagricuhmd Wandfarmlevellanddegradation. Aeoordingtopopulation-economictheory,ouemay uddpuemereismhmmhfionshipbemnhaufinghtendtyofagiaflmrdhnmfimmd reducfimshassodaedhnddeg’adafiondmtohvesmmhhndesqmaphdandimprowd famingsldflgbothleadingtobeaeroveraflmanagememoftheagmecosystem. Regardlesstheinitial nullhypothesisisoftheform: Hqthenismrehfimshipbetweenthenrhfioninearespmdingvduesofintenfityof agriculturalinnovationandlanddegradation. Hence,theregressioneoeficientis(b-0.0) equaltozero. Thealternativehypothesisstates: HLthereisamgafiwnhfimshipbuweenthewfiafionhmespondingvduuofintenshy ofagriculmralinnovationandlanddegadation. Hemhnddeg’adationinverselydepends upmmeintensityofagfiaflnudhnmfiommerefmew<0mthereguémwefidemis yeaterthanzero. Thesignificanceorrejectionlevelforthispairofhypotheseswaslikewise,predeterminedatninety-five pereent(.05)level. Results of simple bivariate regression analyses generally confirm that the relationship between mehtensityofhnomfiomhagriMMeandfambwlhnddeg-adafimmhwrseindhecfiom www.mmwengthnleastmhrgerfarmswithinthePhnSiemfarmsamph. Contradictory results were demonstrated, however, within the entire household sample. A positive regression eoeficieutwasderived(b=.640),withat-testvalue(t-2.132)signifieantatthe(.0174),fi’oman equadmthatregessesbgvduesofhtemhyofhmfiwinmmaAGlNVMWthnd deyadationGIYP.ZBEq.l,Table5.4). Thecorrelationcoemcient(r=.l83)definesaweakpositive relatiomhipbetweenthevariables. Ontheothahandaweakerinverserelationship(r--.108)was reafiudbemeenhgvduesdhtensiqdhnwafiwhrmhgsfiflsasmmdwagadafim Inthiscase,theregressioneoeflieientislessthanzero(b--355),withat-testvalue(t--1.576) sipificant atthe (.0582) level (HYP.ZBEq.2,Table 5.4). Mmeomdusiwresuhsueprodtwedwhenthkrehfimshipkenminedbythediflmmfam sizegroups. Famphmequafimfiomthemediumsizedhmthosemgingfiomeightymm 100 magakoprodueesauegativeregressioncoeficient (b- -2.02),withat-testvalue(t - ~2.l7) sipificantatthe(.0172)level(HYP.ZBEq.3). Aeorrelationeoeficientoflr- -.32’7)indieatesa mmhsumgermwrsemhfimshipbemmeprediamnrhbkasmandtheaherion variableOJ’I'CHNG). Asomewhatweakercorrelationeoefident(r= -.242)wasprodueedforthe landesqueeapitalvariableaAGINOV87). Inthisinstance,theregression eoeficientaa - -.023)also islessthquwithat-testvalueo =- -1.56)significantatthe(.0636)level. Ineonjunetion,these remhsmdieflemamemengthofthemvuserdafimshipbemeenidemmfive huprovementsinayiwkmeandlanddegradafionmayinaeuewithfarmsize. Nevenhelesnthe ratherweaknatureofthemengthoftheserehfionshipssuggeststhatmiablesotherthmthemtensity dmnwafivempummtotheUadifimdhflkbpefummgsyuemmmvemelyrdatedwithhnd degradation. Basedmtheabweruukgthenuflhypahesisofmrehfiomhipshouldbenjededand theahernafivehypothesisall)aeeeptedatthe(.05)levelofsignifieance. TheseresulmfunhermppoflthenofimthatfammamgemmtheleSienaregimofthe CmdiflenCenudgemmflymdomgfiubmmmimhemeimpaddaeeekrafinghnddegadafimm thefaeeofpervasivepopulationpressure. Th'nimpressionalsoissupportedbypersonalfield Wdespitethefomdingduayiwhmdschodmthewgimflcemudflnieowith eduufimdmrkshopsmdwmkingmodekofhrigafimsystemgtmmwmomphnfingand agroforestry tree-crop management strategies (Figure 5.11). Unfortunately, in the immediate slopes smromdingthemafldtydhinieothuemnumuomrewflenmpbsdgflfiuanddumpsthfl couldhaveeasilybeenprevented. Perhapsalongertimeframeoreasieraceesstoaeditforland immemkmwssaqbefaemetechnkalmmfimsdkphyednthemkwthandekewhew inthePhnSienamanagemeflueaJifl‘memorethmoughlymtothermdhinterhnd hsummary,thischapterhasehbafledthespafiflpanemofpopulafimpreuureand deforestationobservedonthe'politieelsection'scale. hadditiomadesaiptionoftheeontentsofthe Imammhddqmfiomdrefoflmdmdudmgadmdfamkvdmhpopuhfion pressure-yiadturalintensityfieforestationandlanddegadation. Subsequently,areviewofthe resultspertainingtothefirsttwosetsofresearchhypothesesemned. Inthenextehnpter,sectionand coo—u. «as! . . t... .. u rawhii... . . . .41.... ...............\ . .fi. )1. . .r \\ ... . a...“ ..‘...J.w . 33.. . u . . (.\ .. . ......\\........¢n... \Wf .. .... . I . . .... ..u . s. . ..v . . .. . ......le . .. . . 1.. H... . I. .w....... ....Ai... I\.....L+V. \...\J.... ...\ .... .d... Is. .4 . . .... . . . ..r/ ............ L JJ..... ... h .... .... A\\ «3...... . .. &rwl¢. brnéuflfiea; G “v.1... , .. . .. . . ....... .. .....JX. Ev. \ .. . ._ . . .. .. ......Iu.... . .. . ..a l . W! ...J).... l%%§.i 35... ...... u........3.ea..._... . .... «v. 3.8.3. . . . . \ f ..\.I. .rl. 1|- 4. .ll|. .0: . . . \. .. . . u { ~ 101 I) ’ '.( “N. 3’) n 5 E i. iii-3‘“ \ . .... )3. . ..l..u.., . l../ . I .T....\ . Fun. . . .. .//- . {(93.0% ....lv... (....-. .2 I .../.... -..r é ....r....u. ..L'ltwll'r- 58.5%. cc 9583m— 25.5. :.m 2:9.— 102 hrmkvdmmpopuhfimredimibufimmmuwuwdgenerafingadmnofmigmfim Ww-Mmh-mmmmpflwfiflwmdm-W Chant" skcmdudeswithadaaikdwviewoftheqmfitafiwanflysisfmthenmamingfomwuof hypotheses. ResuksofAnalysisPartZ: MotivationsforMigrationandDestinations introduction mmmerudMMmmwbwdprevfifingpauemdpopnhfimredisuibufimmthe politicalseetionleveLstartingwiththeyearlQGO. Severalstandardmethodsfordetermining populationredistributionareeonsidered. Adiscussionoffarmleveltrendsinmigrationfollowgbased upmubuhfiomofhomehddquesfiomakesadmmktewdmwleaedrmdparkhesofpdhicd sectionsinthePlanSierraregionduringthewinteroleB’Lwhiehspecifieallydetaildemographicand wdoewnomicchmadefisfiesofthemigrafimsub—groupsidenfifiedmthkdissemdm. Thesegroups incl“dc:Om-misrants.ill-migrants,rctnmmisrants.potcnfialmisrmts.andnon‘miermtr» '1'th wududeswifiadismsdmdfiemfldempkflvarhteregesfimanddmpbmehfimanflyfis fortheremaininghypotheses. Assessment of Population Redlstrlhutlon 1960-1981 ThePlanSierraregionoftheCordilleraCentraliseommonlyregardedasamajorsource regionforbothinternalandinternationalmigration. Twomapswerepreparedtoillustratenet migrationbetweenl9603ndl970andagainfrom 1970t019810nthepolitiealsectionseele,speeifieally for the Plan Sierra management area. Three methods are employed to determine net migration for a designatedareaandtimefi'amenheyaretheresidual,thesurvivalratioandnationalgromhrate methods. Althoughtheresidualmethodisthemmtwmmodyusedfadevelopingwmfliegage spedficbiflhanddeflhrewrdsuediffimhtoohdnandmybedquufimabkrdhbilfiymme politicalseetionlevel. memmwafifiedageduamarytoalalhtesurvivalrafiosahoue notgenerally availableintheDominicanRepublic (Pitchardo 1987). Consequently,netmigrationwas detammedbymeamofthemmrdngmemethodudesaibedbySundinguWJD, Foranyueapopuhtiongrowthwillbemoreorlessthanthenationalaverage. It'uassumed thatnundmaeasekutheumeratemaflmandthuifthepopulafimgrmhmem anyareaisgreaterthantheaverageratetherehasbeennetin—miyationinthepefiodifless thantheaveragethentherehasbeennetwt-migration. Ouitesimply,thenetmigrationrate, mdmikesfimfledfiomthefollowingwherenrefentothenationalfigme: 103 104 ml = «Pi - PLO/Pin " (Pl: - PhD/Pawn» X 100 where, p - Population tabeginningoftimeperiod t+1=endofthetimeperiod Moreover,therateofnaturalincreasewasadjustedforfour(fiveyear)intervalsbetween1960and 1980,aeeordingtoDominiennnafionaleensmealcula60nsGminC.1983). Subsequently,directional migafionWmemladfledbyZficha’guidefineawhae:meomWrneequakthe nmberofofl-migmbhthefimehtavfldifidedhyfiepopuhfimflthebeginnmgofmefime htermLumdlymulupliedbyaemstamm,md;them-miyafimmeequthenmbudm- mummdmemnddividedbymepopmmhebegimingomedmemm multipliedbyaeonstant(Zuiche519m,5). NamigrafimfmtheMmIWOperiodranpsfiomahighofmsevemyfivepereem (nu-1%) immigration to a low ofsixty percent (450.51%) out-migrationwith a mean ofnegative twenty-five percent (-25.5%),indieatinganoverallnegative&end(’l‘able a1). Thefuwesternpolitical WMPalmuejodemmmmamyhighrmdmomiyafimdsflghflymmrfiwwm smdem-Wmmmmhmmdcwammmm Rubio. However, Diferencia, RodeoandJunenlitoAbajoexperienwdeomiderableturnover, wmympamdmmhdmbadmmkmpammmme shnflarhendfmtheseaionsdlmfitoAbajomdlasPheaagmthewuthempmfimdthe Cordillera Central (Figure 6.1). Reasons often cited for out-migraine include: (1) limited eeeeesto hndfmmulymnflofledbythehmbuhdmfly;(2)paceiwdempbymemoppmtmhieshhrge mbmeemeeeindudinguewvakcamndamemiedimmmmmm 1965-660ASoeaipation(Antoniniandothersl975). mmmmmmdmmmwmkmm eMy-fiveyeupeeiodbegiminginmnmmnimdmms. lnaddition,SadItleroflersan mwmiaamwmwmmwwemwmm Dicayagua Abajo Franco Bido La Guama J agua Abajo- Janey Juncalito Abajo Loma de Corral Mesetas Pinalito Celestina Cuesta Abajo La Diferencia El Rubio Eugenio Perdomo Guama Inoa Jicome Las Piedras Las Plaeetas Los Montones Abajo Mata Grande Pedrcoal Yerba Buena Palmarejo Toma El Mamoncito Gurab Cadque Rodeo 105 Table 6.1 Average Yearly Net Migration for the Sierra Section Level Study Area 1960/1970 -3.9 4.1 -3.4 -2.7 +3.1 4.1 ~33 -3.1 -3.9 -3.0 -6.0 4.2 -3.1 -1.8 -1.8 + 1.2 -6.3 + 0.6 -3.9 -2.4 -1.1 ~2.7 -3.5 -3.1 -0.3 -3.9 4.9 4.0 +7.6 4.1 -2.1 -12 -1.1 -6.0 Source: Ofieina Nacional de Estadi’stica (ONE). 1970/1981 4.? -2.1 + 1.6 -1.6 -5.3 -3.9 -0.9 -3.8 -1.1 +0.0 +0.4 -0.9 -1.7 -2.7 -3.4 4.8 +4.5 4.0 -0.8 -3.7 -3.1 -3.7 -0.0 -2.3 -3.7 -1.2 -5.4 -1.6 -7.0 -0.6 -3.8 -5.0 ~2.9 + 1.4 1% operations initially constructed roads into formerly inaccessible forested frontier areas and, subsequently, small-scale farmers utilizedtheseroutes tosettlethenewlyopenedareas (Sachtler 1974). Out-migration within the southwestern third of the Plan Sierra study area probably reflects government attemptstoestablish protedednationalreservesintheregion. Tothisendtheyprohibitedin- migrationwhileforcing others to leavealanshornandothers1981; Breton 1978). Population redistribution for the 1970 to 1981 period demonstrates the same general patterns asabovewithsomenotableexceptionsfigureél). Netmigrationrangesfromahighofalmostfifty percent immigration (+4934%) to a lowofsliditlymore than seventy-sixpercent out-migration (46.92%), with a mean of negative twenty-five percent (-24.2%), also indicating an overall regional trendtowardout-migration. Twenty-nineofthepoliticalsectionsexperiencedlossesinpopulation, withfivesections(Palmarejo,Pedregal,Cebti,GuraboandCuestaAbajo)indicatingalossofoverfifty percentinpopulationgrowth. OdyfompofifimlsedimsWifereungodeanbanaIgledaandJuneafitoAbajo) demonstratethatin-migrationtookplace. Forexample,Diferencia,alargepoliticalsectioninthe westernthirdofthestudyuesshowsaposifiwh-migrafionrateareversalmpanemfiomtheearlier timeperiod. Mrevemdprobablyrepresentsflbgalmpassimmhepraededrmofthe ammnmrm'uamaeiompammmmemmnmm mig'afiomwhkhmdudeslmymmmanme-hafldompemmmaease,chraaerizemof ush-aopagiwhme(wfiwplanufims)m¢afiammifidphasedom-miyafimdmhgthewfier (1960-70) period, arenowattraaingmigrants. Perhaps of greater significance are the reversals represented by even more dramatic chanps innetmigration. ForenmplqthepofificalsecfimsofPalmmjoandCebfibothplacedmthesecond andmhdpodfiwqmrfiksmpediwh,dufingmel%0ml9709erb¢chmaaefifingdgnifiamm litigation. hwnuasgbythe1970m19816mefimethesemmepdificalseaiomplacedmthethird negativequartile,representingconsiderableom-migration. 1‘wootherpditicalseetions(ElRubioand CuestaAbajo)revealasimilartransition,althoughtoalesserextent. 107 Figure 6.1 Net Migration, Sierra Study Area, 1960 to 1971 Provlncla Santlago Rodriguez Per 100lPopulation 0 to -25 0 to 25 -26 to -so - - 26 to so -51 to -75 - - 51 to 76 Provlncla Santlago * NO DATA Figure 6.2 Net Migration, Sierra Study Area, 1971 to 1980 Provincla Santiago Rodriguez o to -25 -26to-50 - - 26t050 -51 to -75 - - 51 to 76 Provlncla Santiago * NO DATA 108 Maeover,aflfwrpofifimlwaimsakodisphydgnifiam“tuofdefmeuafimdufingthe twentyyearsfrom1960t01980. Itappearsthatthesemigrationdirectionreversalsrepresentformer forested frontier nodes of traditional agricultural settlement that experienced rapid deforestation and m-migrafiomfoflowedperhapsbyhnddeyadafimandsubseqmmreducfimsmpopmafim mppmfingupadtxmmtfikdy,misacfimdfimflelykdmwkcfiwom-migrafimtomhafiombr aresssndcitiesoftheDominieanRepublic. Neverthehss,accordingtoBrookfieldandBlaikie(1987) abeflauphnafionoftheseprowssumybeschievedflthehmhmhofllevdbymmsofa detailedcssestudy,interviewingprincipalfsrmers. Fem Household Level fiends In Population Redistribution hmdertoassessrewm(l9n-l987)popuhfimredkuibufimatmexdeofthemdividud homhofiapmbn(moduk)ofthequesfimnahewudedimtedtomethemeofmiyafim. Asnoted whafihemrwymsuumemwusystemafiullysdmmkteredtoapprofimatelyfiOmdfam households. Fromthisgroup,435questionnaireswerefoundsuitable fordatacompilationand processing. Fromthesequesfimmheesomempersmswereumpledmpresenfingzmuuks (personsagetwelveyearsandolder)and770children. Themajorityofpersonsweremale(51.52%) sndsgesrangedfromlessthanoneyesroldtolOSyears. Householdsrangedinsizefromoneto eighteenpersonswithameansizeofsixandone-third(6.33)persons;themeanof458sdultmembers Wumamageddighflymfwudm-hflddfiwbmhoflfifiamdamdevhfim ofabouttwoandme—quaner(226)persongandrangedfiomonetofouneenadults Eachaduhmemberofthehouseholdwascategofizedmtooneoffivemajmgroupsbasedm their individual migration behavior. Specifically, these groups included out-migrants, in—migrants, retmnmigrantspotentialmigrantsandnon—migrants. Forexample,out-migrantsaredefinedas famahouseholdmembemwhohawreddedmmahabafionfaaperiodofmmethanthree montln(Bilsborrow,Oberai,sndStanding1984). Inomigrsntssredefinedashouseholdmemberswho havelivedinthepresentlocationforstlesstthreemonths. Returnmigrantsaredefmedaspersons whohwruurnedmthehousehoflsndhwnmamedfmmuethanmreemmthsfimesfiahfing previouslylivedinsnotherlocationforsperiodofstleastthreemonths. Potentialmigrantsare 109 homeholdmemberswhohavenotmovedinthelasttenyearsbutanticipateamoveinthenearfuture. Theflayergroup(nm-migmm)repruenmmdividmkwhodonamfidpaeammefiomtheu presentloeation. 1hein-migraflandretmnmigraNyoupswere,additionafly,subdividedmto 'potentialmovei’or'stayer'subgroups. Sortingbytheseclassificationsyieldedthefollowing:433adult out-migrants; 116 in-migrants; 83 return migrants; 421 potential migrants and 1709 non-migrants. A meanoffourandone-half(4.45)adultsperhouseholdexpressedeitheranexperienceorinterestin migrafimmigrafimrangedhomnomemberuptofouneenmembemofthehousehdd Itisclear menmumigrafimkavhdaspeaofcdbcfiwbehavbrmthePhnSienaregiondmeCmdflkn Central. Ont-migrants Theruulmdpofifiulseaionkvdsnflysisfameyeammmrwghlmlmdiuwmuom- migrationisthedominantpattern. Fortunately,asaresultofthe'multiplicitystrategy’employedinthe qmfimmhededmmfmmafimdetaifinghiambsdom-mingmthehmhomhddWmm obtained. Ofthe4330m-migrmtsfemabswemdommamatsomewhuhigherthmfifiy4ixandme- halfpercent (56.6%). Ages ranged from twelveto seventy-nine years, with twenty-three (23.07) years beingthemeanageatthetimeofout-migation. Themajorityofthesepeopleweresingleatthetime ofinigration(52.4%),withonethird (33.3%)indicatingtheyweremarried,andalittlemorethan eleven (11.1%) percent revealingtheir'free union'status,andfinally,onlyabouttwopercent (1.8%) admitted their separatedmaritalstatus. Out-migrantsreceivedameanofsevenandone-third (7.32) yearsofeducafiomwhichrangedfromnoschoolingtofourteenycars. Approximatelysinypercent (58.5%) of the out-migrants indicated their occupation as “student, housewife or domestic“ at the time ofdeparture. Asecondmajoroccupationgroupwasagriculture (26.6%),followedbytheminor classificationsof'skilledtrades'atjustbelowfivepercent(4.7%)and'retiredorinactive'peopleatjust sbovetwopercent(2.1%).Allotheroccupationgroupssccountedforlessthanonepercentoftheout- migrants. Fudandsewndbandaughtenuethehrgeatypesofom-migrmfiomthehomehdd, accountingforslightlyabove(10.2%)andbelowten(9.5%) percenhrespectively. Firstandsecond 110 bornsonsplacethirdandfourthatjustundernine(8.8%)percentandjustaboveeight(8.1%)percent. Thenestfourranksbelongtofemalesiblings,amountingtoafradionaboveeightpercenttoabout four and one-half percent (8.1%, 55%, 5.5%, 4.6%), in descending order. The ninth and tenth place classifications belong to the third (4.4%) and fourth (4.2%) born males. Heads of household (0.5%) andtheirspouses(1.6%)appeartoplayonlyaminorroleintheout-migrationprowssfromviable households. However, it should be noted that the multiplicity survey design does not account for whole hounboflsthflkaw,mdmnsequendymder-mpresen&themigrafimpanemdheadsof householdsandtheirspouses. RegardlessOberainotes, One advantage of this method (relating children to their parents, wives to their husbands, etc.) is that it facilitates identification of family type. This is important since several studies have indicated that family type, such as extended vs nuclear, can influence fertility and migration. Another advantage is that it enables the researcher to relate characteristics of one individual toanother. Fmexample,intheanalysisofferfiflty,itisimportamtoidenfifyhusbandsto examine the effect of their socioeconomic background on fertility (Oberai 1984, 141). Itkdemfiomtheabowfiendsfifltheom-migrafimprowsskprimarflymflngphmfithmthe nuclear family of mature farm households. ThecustomarypatterninlatinAmcricawherethereisagreaterpropensityforfemalesto leaveruralhouseholdsisapparent. Thefactthatlargenumbersofthirdandfourthbornsonsarealso leaving the household may indicate the pressures of farm size fragmentation, vis-a-vis the traditional landtenuresystem,whereinthepastfarmswereequallydividedamongsurvivingsiblingsand occasionallybyonlythesons. Bythelatc1980sthereisevidenceofchangesinlandtenuretoward primogeniture, which would tend to equalize the propensity to migrate among males and females. Many farmers expressed a desire to restrict the further division offamily farm holdings by designating joint sibling management or a sole inheritor-inheritress. MmhgeedmtbnmdcmphymeMamfiequenflydtedasthepfimarynasomfukaving thehousebolda‘able62). Thesereasonsaregenerallyfollowed,inimportance,byanumberof hmflhlexplmafiomfamemmwhkhnrbuflymdude:acwmpanymehmfly,nmthehmfly, andsocialorfamilyproblemssuchasarecentdeath. Allotherexplanationsgenerallysccountforless thanonepercentoftheout-migrants. Thesignificanceoftheassodationbetweenthreeofthesc 111 :63 .3 v «.8 «.o a ado «d u v.8 n6 « «.8 «.c a «.8 ed o «.8 ad a QR w.« «u «.3 Wm 3 5a m.» an «.8 and «3 3% nd « Sh hm «v «.5 vs «m 9.9. «.8 8 «fig ed v «.3 S. n v.5 fin «« n.«« w.« «u ha ha «v .520.— .EU Ecocom Econ—co...“— oataon 8.. :83: 55.58.30 «.9 03:. EB .85. .oz .5 .8 :55 283.3 .3 €23..— Bgsi: Q 8:89 8205 .2 £8 .8.— .3 8338 23 .2 235 38m 25 .3 mac—poi use—am .Q £5.— 05 32 .fi mac—am mean—80¢. .5 ages: .c 56.20 283m . 29.2.0 28st son .23 8.35 :3 3:8 was... . 23 sagas 8:33:35 a: Eon—33823:: . ionic—mam oz .« névixoh'céos Sago—mam no 0955 A 22895 .8 e033— 112 mfiablesempbymmgedmfimandage(amefimedom-migadm)mmedassofdesfinafim willbedeterminedinHypotheses (5A),(SB)and(5C). Ahhoughrougflymnaypuwmoftheom-migraanerebmnmmdpohfimlwdimsofthe Sierrmthegreatermajmityprefenedhrgedfiesastheirdesfimfimflable63). Theselargccitiesare locatedbothwithinandoutsidetheDominicanRepubhewhereroughlyone-thirdoftheemigrantsleft thecountry. Memaldestinafionsalsoindudesmandfies(n5%)andrmalueas,piimafilyrmal pohfialwaims(128%)whhaveryfewmigmmdiafingwmmudflphnmfiom(02%)utheh goal. Thefignifimdmewpreferenwswhhthekeynfiabhsageedumfimmdemploymemtype willbefurtherassessedinthefifthsetofhypotheses. NewYkaityhashadifimaflybcenthemoflimpmtamenemddesfinafimmeominkans; thisisratherwelldocumented(Gonzalezl970;Grasmuck1984;Hendricksl974;MorrisonandSinkin 1983;Uga|&,BeanandCardenas1979). However,themagnitudeofmigrationtothe'prmisedland' kmmwdmmmmmmmmmomimmmsm 1988;Mitchel11988;andWarrenl%8). Regardlessitmaybearguedthatamajorityoftheemigrants, whowere listedasleavingthecountryfortheUnited States (32.9%),wereboundfor New York City. Somesevenandsix-tenths(7.6%)percentoftheemigrantsspecificallycitedthiscityastheirfinal destination. ThedtyofSanfiagodelosCabaflerosnowappeamtobethepfimaryintemalurban destinationforout-migrantsfromtheSierraflable63). Infact,Santiagowaslistedasthemost hnportantdestination,evenmoreimportantthanNewYorkCity,byalmostthirtypercentoftheout- migrantsample. OnthedherhandSamoDommmmprefenedbyonlyamaflpercentageofthe migrantsmdicafingthmmemfiondCaphdhubumuchdhsfmmaammfmmternd migrants(Duarte1980,l986). Smafldtbsmakoimpmtamdeumafiomfathemdom-miyanudthefierraflabbés). Impatambafiommdudethemgimfldfiuofl‘nichanJmedehsMathegaJuabam andCotui. ThemdpukhodemdflqhtheSecfimoflasMesetuneutheciyofSabamlglefig mhneduthefinddesfimfionbyalktbmaethanompermdthenmpk.Ahhoughhrgely 113 memhadowedbytheflowsmmbanueaamdtormalmigrafimintheDomim“nRepubfic accounted for almost thirteen (12.8%) percent of the out-migrants from the Sierra household sample. Hypotheses(33)md(4)wiflenmmethesuengthmdwrifythediredionofthefimrehtionship beNeenthekeyvafiabksdfambwlhnddegradafimandpopuhfimpresmemtheprmd out-migration. lI-mlgi-ants Theresmofpohdealseaimkvdmlysisfmtheyeamlmmroughlwmdimtethu imWnodes(destinations)ofin-migrafionalsoarefoundinPlanSiena Consequently,aseparate portion (submodule) of the household questionnaire recorded detailed responses for the 116 in- migrants to the Sierra sample study area. In contrast to the out-migrants, iii-migrants as a group demonstrateaslightmaledominanceoffifty-one(51%)percent. Agesrangedfromthirteentoninety- twoyeargwithjustundertwenty—eight(27.95)yearsthemeanageatthetimeofin-miyation. The majorityofthesepeople,also,weresingle(56.2%)atthetimeofmigrationwithalmostseventeen (16.9%) percent indicating theyweremarried, andasurprisingtwenty(20.2%) percent-and four and one-half (4.5%) percent indicating their free union and separated marital status, respectively. In- migrantsreceivedjustunderfive(4.96)yearsofeducation,whichrangedfromnoschoolingtofifteen years. Likewise, fifty-one percent ofthe in-migrantslistedtheir occupationas'student, housewife,or domesticmaid'atthetimeofmigration. ’l‘hesecondmajoroccupationgroupwasagriculture(24.0%), followedbyanimpressivesix(6.0%)percentforindividualsskilledas'officepersonnelandteachers.‘ All other occupation groups accounted for only small percentages of the in-migrants. Theresultsmdicatethatmdeheadsofhouseholds(B%)mdtheuspouses(m%)phya moreactiveroleintheprowssofhouseholdin—migmtion. Unrelatedandothermalerelatives,suchas brothersandcousinsoftheheadofhousehold,accountforasurprisingseven(7%)percentandfive (5%) percent ofthe in-migrant sample. Firstbornmalesonsandunrelatedfemale domestics representfour(4%)percenteachofthein-migranttypes. 'I'hisassemblageissomewhattypicalofthe dimshyofhdifidudsthflmyhefoundwifihayoumfidmmendedmhddmure,udmy veryweflindicatethatthemajofityofthesehomeholdshaverecenflyreloated 114 Approximately forty-five (45.2%) percent of the in-migrants to Plan Sierra originate in nearby ruralparisheamanyofwhkhuebcatedinmherpadmsoftheamomdingSiemCTabkéA). For example,aboutsix(5.9%)percentofthein-migrantsindicatetheyleftfi'omtheparishofDamajagua, intheSeaionCagueyes;almostfivcpercent(4.7%)listedastheirsourceareatheparishesofLaBoca deAlbanhamtheSeaimlaDiferendamdthepafishofDmenmtheSecfimlosttmes Abajo. ThesemowstypifydoseprofimhymdtormdmigrafimwiminthePhnSienauseHand mayrevealareasexperiencingelevatedratesoflanddegradation. Alargenumberofin—migrants(27.4%)alsoareleavingthesmallcitiestoreturntothe countryside (Table 6.4). Approximately twenty-five (23.5%) percent of thistypeofmigrantoriginates intheregionalcityofSanJosédelasMatas. Thebalanceofthein-migrantscomefromthelargest urbancentersinthecountry,wherealmostthirteenpercent(12.9%)giveSantiagoastheirsourcearea anddighdybebwsk(5.9%)percemhathe'Capitaruthebudonoffomureddence. Unlikethe Wmigrafimsfiumsthepromdurbantormdandrmdtomdmigrafimmthe Dominican Republic are both poorly understood and largely under-reported in the professional literature. Consequently, Hypothesis (3A) attempts to determine the significance of the causal whtionshipbaweendefmeaafimandpopuhfimredkuibufionspedfiaflymthefamdm- migration. Thiswiflwnuibmetoomunderstandingofthemagnitudeofresomce'pidrfadm Similartothesituationwiththeout-migrantgroup, marriageandfamilyrelatedreasonswere fiequenflyregisteredastheprimaryexplanationsformovingtothcpresenthouseholdCI'ableGJ). In maimpommmdmmmmymamnyamommaw problems. Employmemandedncafionfoflowedasreasonsformigrafingtothenewlocafion. Other esplanationsaccountedforonlysmallpercentagesofthein-migrants. Math-migramsadmhtohavingwcesstomfmmafimabommedesfimfimpriormtheir move(Table6.6). Themajorityindiatethuknowledgeabomjobsandagricuhmaloppmmniduwu avfihbkfimhmflyandfikntAnimprufiwmhtyshpuwdthem-migmnvkhedthem priormmakingthemme.1hkpriorknowkdgedthedesfinafimmaybereflededbythe wmpanfiwlyhrgenmberdmmigms(82.6%)whmdicatemethmhnenfimdmigafingh 115 3:: 3. «.3 ed «.na «.9. cdn EN e.«« 03 S. 2. 8.8.0.— 850 88.8.— 82< 888 8898-..— .o 8:820 v6 035—. :8“ adn HG «.c ode a.” «.3 flu how can ad .3 .8335 .85 .883.— 80232 8838-25 .o 83.85350 «6 030% eaggov mono—50....— a g an R «3 mu 0:3ch .388 2.. a3 .9 25:28:.— 320888 .n A852 2583 .23. ... 3:6 ions. .13. .n 888» a 835V 8338 25m .8 32 858 .8238...— baaoo 2: c3 .1. 9533-3: amp—05:30 .m 8.85% 2588 as”. a. 8:6 :89: :25 .m ...?sz .m :33qu 3388 35m .H eons—:80 2898.36 116 QR: ado Q8 Q8 «.8 Q3 3 was. fit. nsm 6.8 9.8 a 35 W3 Wu“ w.» Qw ms .320.— .890 a2< 8.50m 33.— Bu .833— 8558-5 Ga :6 ad ad :6 MA «fig 2. Q3 2N ad 3. ad QM ad ad :6 n4 ms. eacnmmuecfizoguccovH 388.— 59:33.5 We 033—. .33 .85. 32 on .9 his... 35...: .s 8:89 8205 .2 a8 .8; .2 8,3253 as: .n 2236 38m 3. .3 2852.. £5". .9 3.5.. 2: so. .2 32am human—83‘ .2 gusts: .c 9:230 3353 H.830 28:3.— aop. 850m 33:0 n2. 3:2— ...—.5"— v3 355 8:83.935 90—. anon—ao—naouo—vaD fiéfidfioo'm ionic—mam 02 .N Eon—>038...“ ..0 $530 .H 23330 B.— :83: 117 thenearfuture. Forexample,reasonsforstayingvariouslyincludefamilyties and'havelandhere.' Leasimportantreasonsforstayinginthehouseholdwerelackoffinaneialresourcesandlackof knowledgeofbetteropportunitieselsewhere. Rear-Migrants Dataforreturnmigrantsareavailableonlyatthefarmhouseholdscale. Becausethisgroupof people represents frequent movers, they often provide current information on aspects of a migration process known as circulation. As previously indicated, a separate portion (submodule) ofthe quesfimnairewwrdedrespmmfmmeeighUMnmmmiymtothemdpofifiulwcfimsd the Sierra. Similar to the out-migrants as a group, return migrants share a slight female dominance, fifty-three(53.4%) percentoftheparticipants. Agesrangedfromthirteenyearstoeighty-sevenyears, withtwenty-nineandsixienths(29.6)yeanthemeanageatthefimeofretum 'I'hemajorityofthese people,likewise,weresingle(39.7%)atthetimeofretummigratiomwhileasubstantialnumber (37.9%) indicated they were married, with nineteen percent and about three and one-half (3.4%) percemidenfifyingtheumaritalstamsas'fieeumm'andsepuatedrespeaivdy. Mostreturn migrantshaveslightlylessthanfiveandone-half(5.4l)yearsofeducation;therangewasfromno schoolingtofourteenyears. haddifiomthemajofityofreturnmigrants(59.6%)listtheirocwpation as“student,housewife,ordomestichelp'atthetimetheyreturned. Itisimportant tonotethatthesecondmostfrequentclassofemploymentamongremrn migrantswasnotagriculture (10.5%) but "skilled trades“ (12.3%), suchasanartisan,mechanic,tailor or carpenter. An even smaller portion of this group (3.5%) were in some form of commercial activity. Thueremmmiymtgmoafikelyhdsucwsdmexperknwsmhrgembanwmmmwngvfluabk workskillstoruralareas. lnthismanner,conmtermigrafionstreamsfimctiontoinaeasestandardsof livinginruralvillages. However,aslightlylarger(5.3%)numberindicatedtheywereunskilledand couldonlysemreunskilledemployment. Perhapsthesepeoplerepresentgenerallyunsuccessful experienwsinthelargercitiesoftherepuhlic. Theirlikelycontributionstoimprovedruralliving conditionsalsoareratherlimited. Allotheroccupationgroupsaccountedforonlyminorpercentages ofthereturnmigrants. 118 Similartotheh-migraMgroutherestdmmdkatethatmdeheadsofhousehofls(l72%) andtheuspouses(155%)uemmeobfiomacfiwmrfidpmmtherammigrafimprowss However, like the out-migrant group, immediate family members play a larger role in overall return migafiomwimmemhdeueadaughmmwmpddngmereightandme-Wpermdmehomhdd mdmefirnmsewnddaughtergfounhsmsanddaughtmachwmpfisingjuubdowmn percentofthereturnmigranthouseholdstructure. UVinngmfiagqonewasawareoftheveryhigh levelsofmobilityamongyoungwomenfromruralareas. Manyurbanhouseholdsemployanumberof youngwomentoworkasdomesficsmasemi—permanengseasonalandevenmonthlybasis. Asa result,thereisaconstantstreamandcounterstreamofyoungwomenfromnearbyruralareas. Males, ontheotherhand,havefeweremploymentoppommitiesonashontembasisandthereforeappear somewhatlessmobile. Consequendy,theremrnmigrationprocessappearstotakeplaceprhnarily withinthenuclearfamilyofyouthfultomediumagedfarmhouseholds. Forty-fivepercentoftherenunmigrantstoPlanSienaoriginateinhrgeurbancemers, includingthenational capital (Table 6.7). haddifionsignificant flows (17.5%) emanateinthenall cities. Forexamplezthirty-onepercentcamefromtheUnitedStates; overtwentypercent from Sandago;dmodnheperwm&ommeregimddtydcaukjustmdersevenperwmfiom8amo Domingo;mdmmethanfivepercem&omtheregionalcemerof8anlos€dehsMatas However, largenumbers(35%)ofthereturnmigrantsalsooriginateinthenumerousruralparishesinthe DominicanRepublie. When questioned why they left the household, the majority (27.6%) responded that they departed: to “accompany the family,” followed by; change of employment; obtain an education for their children;familyorsocialproblems;andfinally,either'nowork'or'findabetterjob'respectively (Table 6.8). Prior to out-migration, most individuals (60.3%) listed their occupation in the 'student, hounwifedomeak'ywp,whikdmoummy-fiwmmmdiutedtheuwdesfimuhmmuh the 'skilled trades“ (8.6%). 38:85..— 3808600 .n :8“ 2 e n8 :2 z A852 23.3 as... .v n8 n: a. 8:6 ions: :28 .... :9. a a 3.3.3 a a a n cease 35.55 25.9. A 383m .850 .320.— Feoeaofi 33. 858 2.838-3— 9 n 52858 agate... 828536 S 03:. . :8“ 3 e 838 350 .n 98 Sn a .25 BE ... as a... o E .23: ..aamaoz .n as n: e 68E :5 as. 3... 8 82.20: a £5... a 388.— ...50 2.8.3.— aueoavoem eczague— ..e 330m sous—Eon..— .e 8.58 8538.:— 3. 0.3. Reason for Departure 1. Change of Employment 2. No Employment 3. Underemployment 4. Job Dissatisfaction 5. Purchased Land 6. Found Better Job 7. Offered Better Job 8. Educate Oneself 9. Educate Children 10. Marriage 11. Accompany Family 12. Join the Family 13. Family Problems 14. Bad Social Climate 15. Land Degradation 16. Poor Soils 17. Do Not Know Why m.nmmmmmr Reasons for Staying 1. Have a Good Job 2. Family Commitments Lack of Education Unaware of Better Jobs Have Land (Ownership) Too Old to Migrate Family too Large Have Enough Money .Currently m School 10. Physical Handicap 11. Soils are Good Here 12. Lack Money to Move 13. No Opportunity 14. Like it Here 15. Have Animals Here 16. Fear Social Rejection 17. Missionary Work 18. Do Not Like the City 19. Do Not Know Why 9999999 120 Table 6.8 Return Migrants Reason for Departure Frequency Percent 15.5 6.9 3.4 3.4 0.0 6.9 5.2 1.7 10.3 5.2 27.6 1.7 8.6 0.0 0.0 0.0 0.0 3.4 Macceuuguapwaouwrse Table 6.9 Non-migrants Reason for Staying Frequency Percent a 4.5 8§ § 1.5 5.2 10.4 7.0 0.3 9.5 1.6 0.2 9.5 0.0 4.4 1.4 0.1 0.1 0.1 3.2 fiuuugflcgufigufi§ga Cum. Percent 15.5 22.4 25.9 29.3 29.3 36.2 41.4 43.1 53.4 58.6 86.2 87.9 96.6 96.9 96.9 96.9 96.0 1001) Cum. Freq. 4.5 43.1 44.7 49.9 60.2 67.3 69.6 70.0 79. 4 81.0 81.2 90.8 ”.8 95.2 96.7 96.8 100.0 121 PotenflalMlgl-ants Pmenfialmigrmtsuerepresemdbythosehomeholdmemberswhohavemmigrafim experienceasanadultbutwhoplantomigrateinthenearfumre. Asagroup,theymaydiflerin impoeraysfromaduhhouseholdmembenwithrecentmigrafionhistories. Potentialmigrants makeupamafl(152%)perwfloftheaduhhomehddhdividuakwhohwndpufidpuedmme migrationprowsssincel977. Migrationhistoriesextendingbeyondtenyearsfromthetimeoffield my(1987)wemnmcmdderedmthkandysu(Bflsbmrw,OberaLandSMnding1984). Ofme276pmenfialmigrmtgmabsdcmhuethemupufifiy-skandmehaflperwncthk isjusttheoppositeoftheout-migrantgroup. Althoughyoungmalesactivelyexpressagreater propendtytomiyuemefiutothehmhouwhoflmsumgermmfmfemahaukrefleaedm them-migrantsroup- Rmflmmmmmmmmmrmmmm five(25.2)yearsbeingthemeanageforthegroup. Asexpedeithemjorityofthesepeoplewere fingle(68.4%),dmoafifiwnperwmmdiatedmeyweremufie¢abomfmpawmfivcdma 'fieeufim'andonlyoneandone-halfpercentwereseparated. Potentialmigrants’educationlevel rangedfromnoschoolingtosixteenyearswithameanofsixyears. Overfifty-two(52.2%)percentof mepmenfidmiyummdiwedmehocwpafimu'fludenghomewifemdmesfiduthefimedme survey. Thesewndmjuocwpnfimgroupmsagricuhm(3l5%),fonowdbypropafimsbsthm fivepercentineachoftheremainingoccupationgroups. Non-migrants Nm-minghouseholdmembemwhohawndparfidpatedmthemigrafimprocess sincel977andindicatethattheydonotplantoleaveinthenearfumre. Theyreprcsentalarge (n - 1526)coregroupofindividuals(stayers)thatclaim,forallpracticalpurposes,tobesatisfiedwith the'placeutility'oftheirlandholdingsandsituationinlife. Nm-migrantauagroupfiemomtratea unallmaledominanceofslightlyoverfifty-m(52.5%)percent. Amrangedfi'cmtwelvetolos yeargwithjustbelowthirty-five(34.92)yearsbeingthemeanageatthetimeofthefieldsurvey. The majaitydthewpeopkwredngle(493%),a&hdwemuriedwifiuaddifimflekmpawm consideredtobe'freeunion.’ 'I'hisgroupalsocontainsanotableproportionofwidowsand 122 ccmparativelyfewpeoplethataredivorcedorseparated. Mostnonomigrantsreceivedalittlemore thmfomudom-hdf(464)yemofeduafim,whkhranged&ommschmfingtodneenyears As Wepprcfimmelyfifiy(493%)perwmdmenm-migamflnedmehocwpafimu'sudeng housewifeordomestichelper'. Clearlyathird(34.5%)ofthe'stayers'declaretheiroccupationas farmers,followedbyacomparativelyhighnumber(3.9%)ofretirecsor'unabletowork'category. All ahaocwpadonyoupsacwamonlymaflpucenmmsofthenm-migraMgroup. Makheadsofhouseholdandtheirspomesmckulythemostvkibkmembersofnm- migranthouseholds. Femaleheadsofhouseholdalsoaremeaninghfllyexpressedwithinthenon— migrant household group at just over three percent. The non-migrant household is quite diversely npruemedbyaflmdifidudqpesbaedmmdrrehfimtomehaddmehouwhoflreprmnfing wmpomnmdbmhmemaMenuckuandenendedhmilflBflsbmw,OberaiaMSmn&ng1984, 185). Amietyofrespmsesmgivenfmmeprefuenwtommainmthepresemhomehddaabh 6.9), the most important being family commitments (38.6%) and land ownership (10.4%). These are fdbwedwifikswrfiequenq,bymemdardresponseszamendymxhookhckthefimndfl rcsourcestoleave;age,toooldtokave;lackknowledgeofbeneroppmtunities;havesafisfaaory employment;noopportunitytoleave;andfinally,aboutthreepercentarenotsureaboutwhytheywant to stay. Regardless, the non-migrant group may very likely serve as the benchmark for comparison mmgthewfiommigrafimdmAsmfidpfledthe'flayem’mtheddesfiwflhammageof flmoathhty-fiwyeusandthekastedumtedwithapprofimaelyfommdom-haflyeand Medmwvflafimmobifiwwbwom ResoluflonofflypotheaesSA-6B Thefirupanofthethirdhypahcdsset(3A)euabflshesawnceptudfinkagebuwen population-migrationandpeople-environmenttheay. Resultsofthisdissertationccnfirma level. Therdaqwithevidencefathkamdrdafimshigarduedrmchqmfimpondersthe 173 Unfingbdhpopflafim-migrafimmdpwpkenfimmemtheuy,memugmmm-miyamwm bedrawntofmefledueuuadesfinafimfichreprmnmrmpuflfadmsmrespmsem pemsiwpopuhfimpressmemthemceareasmmdertomafimiuthe'phmufifity'ofthe household(Wolpert1965;BrownandMocre1970;Fuller1978). Consequently,anewouldlogically expedtofindaposifivefinearrehfionshipbetweendeforestafimmdm-migrafion However,thenull hypothesis'mofthefollowingform: Hqthereismfineuwhfionshipbetweenthevafiafimincorrespondingvaluesof deforestationandin-migration. Hence,theregressioncoefficient(b - 0)isequaltozero. thealternativehypothesiscounters: H1, thereisaposifivefineunhtimshipbetweenthevafiafiminwncspondingvduesof deforestation and in-migration. Hence, in-migration depends directly upon deforestation. therefore the regression coemcient (b > 0.0) is greater than zero. Thefignificmwmnjedimkwlfmthepahdhypahesuwupredaemhedumnety-fiw(95%) percent. Onthepoliticalsecfionlevelashmtseriesofdata(1960tol980-81)wereccmpiledtospecify themMedfiefineurehfiomfipbemdefmcaafimaMpopdafimredimibufiomspedfimflym theformofin-migration. Deforestationismeasmedastheannualforestconversionratefortheyears 1960 to 1980 (FCR1980 - average annualpercentchangeinforest cover). likewise,population redisuibmbnkmeumedumeamageperwntagedm-miymmpupdifimlwdbnfameflme frame 1960t01981(YPlN6081). Resultsofsimplebivariateregressionanalysisrevealameaningful posifiwrelafionship,wheretheregressimcoefidentispeaterthanrero(b-1.103)withat-test value(t - m)significantatthe(.0173)level. Thecorrelationcoefiicient(r- .364)indicatcsa moderfldywukpodfiwmbfimshipbewunmekeynfiabkgwhereapprofimadyekmpuwm (rsquare - .lmdmespafiflnfiafimhm-migr'afimkexphinedbythefmmnionproceu. Amapofthenandardindreddmkfiomtheregesdmequafimmwewfiflustruusome meaninwllpatternsofin-migration. ThelargeSectionofDiferencia,inthewesternportionofthe studyarenkover-predictedbymorethantwostandarderrors. lnthepast,Diferendawasrather isolated.however,duringthe1970t01981perioditfunaionedasanimpcnantfrontierdestinationfor 9N- v 0d- 8 97 m.—- 8 0.7 00)isgreaterthanzero. Tbedgnifiunwmnjecfimhvelfmthepairdhypdhesumspreduemmedammtyfiw(95%) percent. Thismdysisiswndudeduthehousehoflmhwhenstandardizedvduesfahrmbvd landdegradation(PTCHNG = annualpercenttareasdegraded)arecorrelatedwithstandardized values measuring out-migration or potential migration. Values are calculated as percentages of the household memberswhoaauallyparticipatedintheom-migration prowss (PADULTE)orwho «pressed an interest to leave (PADULTPO). Results of simple bivariate regression analysis for the mfirehmehddMsfluepmraflyhmdufivehMmmunmgfideqmfiommwoduwdfm eithertheom-migrmumretummigrmtsgrouputhedesignatcdconfidencelevel Incontrast,with thepaenfidmigrmmyoup,there'mevidencefmaveqweakposifiwnhfimship(r- .(B3)between landdegradationandpotentialout-migraticnflADULTPO - percenthouseholdmembersthatare potentialmigrants). Inthiscase,theregressioncoefficientisgreaterthanzero(b- .07)withat-test value(t = 1.714)significantatthe(.0437)level. However,thesquareofthecorrelationcoeflicient (rsquare- m46)mdicatesthatlessthanmepacemOfthenrhfimmtheauaionmfiabbkbehg accounted forbytheregressionequation (HYP.3B Eq. 1,Table 6.10). SmrumdegamywakpmuiwfimunhfimsMpbemnhMW andpotenfialout-migrafimweregeneratedfiomthefomfarmsizesub-samples Forexampleonthe mdleafarmsthosermgingmdufiomtwomupmfmtymeasmeqmfimthatcmrehted potential migrants (PADULTPO)withthepredictorvariable(FPCHNG)producedacorrelation coefficientof(r- .101)atthe(.0540)level(HYP3BEq.#2). Ontheotherhand.nomeaningful relafionshipsueevidencedfromtheremainingmiddlelevelfamsizegroups. Theresultsforthe SierrafamumpbWethflexphnflayhammhathnhnddeyadafimuedirealyrdued withtheprowssofout-migration. Basedmtheaboveresuhathenullhypothesisalo)of'no relationship'shouldbeacceptedatthe(.05)levelofsignificance. Thisdeu'sionalsoavoidsthe likelihoodofa'l‘ypellerror. 128 mm mm and MN? mafia hNNd «Ed :86 RNA mead a $6. 3.9 8.: ad end. «96. «95. 53. n58. .8... N95. «no. a 32:0 Q 520: «Sew—U a. 9800; 6230: d Capt—Dam 0230: d Chum—Dam Dun—U: d Fry—Dam 33.30am Q 887:: .a> as a. .89 a. e5 .3 4n 88595: coached 633%— Le 838.: 38556 3.0 03:. an. ...m E. ...m e E: N... .5 E. em on E: N... ...m 2. ...m 0.0) rs greater than zero. The significance or rejection level for the pair of hypotheses was predetermined at ninety-five (95%) percent. Resultsofsimpleregressionanalysisfortheentirehousehold(l987)datasetare,ingeneral, inconclusive. hmnflastmthepofificalseaimlevelregressimanflysisnveakahighlydgnifimfl relafionshipbetvmenthekeymiabksforeachofthepairedyeaminquestion,1960tol970and1970 to 1981. The criterion variables (YPO6070) and (YPO7081) measure standardized annual rates of out- migrationforthepairedyears. Forexample,(YPO6070)definestheaverageannualpercentageofout- migrantspersectionbetweentheyears1960and1970. likewise,thevariables(CHGU70)and (CH7081) measure standardized yearly chanps in population pressure, for example, (Cl-16070) meannufiemmgemudpacenhgechangehpopflafimpressmebeflemtheyemlfiom 1970. Stufingfiththefirsttenyearpah(l960tol970),awrrelafioncoefidentof(r-.932)firmly enabflshuaverysumgpmifiwnhfiomhipbetweentheaituionandapredidmmhbb. The 131 regusioncoeficientisgreaterthanzero(b-.227)withat—testvalue(t-14.66)significantatthe (Mlflevel. 1hesquareoftheconehfionmefidefl(rsqwe=874)signifiesthateighty-seven perwmdmespafidvafiafionmvdwsofom-migafimmexphmedbyamemmofpopmfion pressureonhumancarryingcapacity. Theequationmaybeinterpretedasfollows, foreachonepercent increaseinpopulation pressure we would expect to see a one-quarter-of-one (.23) percent increase in annual out-migration overthetenyearperiod1960tol970. Theinterceptvalueof(-3.67)indicatestheannualpercentageof in-migrantswewouldexpecttoseeiftherewasno(zero)populationpressureoverthetenyears. A mapoftheuandmdindreddudsfiomthereyesdmeqmfimrefleasapanemdmflutothmfound for net migration during the period 1960 to 1970 (Figure 6.4). The regression equation accurately predictsthattheoveralltrendistowardout-migration. However,out-migration'mover-predictedin thefarwestemsectionofPalmarejoandthenorthemsectionofCuestaAbajo. Thesesectionsactually attraaedmiyamsdufingmisfimeperiodtherdmefewerpeopkkfimanwuprediaei Incontrast, om-miyafionkmder-predidwmmewcfionsdmerendaandlmalhoAbajomdkafingM yeaternumbemofpeopleleftthanwasprediaedbytheregressionequafion. Diferenciahadbecome pandaprmeaedfaeuresemmdlmmfitoAbajqamdifiondwfieeyowhgmkknownm havebeenamajorsourceareaforout-migrationduringthistimeperiod(Antoniniandothers1975). Similarresultsueachievedfiomtheamlysisofthesewndpairofyeam(l970t01981). Here, awrnhfimweffidemof(r=.742)reafimsaflrmgpodfiwrehfimshipbetweenmaeasing pressure on population supporting capacity and out-migration. Once again, the regression coefficient is greaterthanzero(b-.129)withat-testvalue(t=6.44)highlysignificantatthe(.0m1)level. The squareofthecorrelationcoemcienqrsquares.551)supportsthat,fortheperiod1970tol981, slightlymorethmfifly-fivepercemofthemhfimmtheaiterionvariablemeuufingom-migrafimis exphmedbytheprediaanfiabkmeanuingpressmemmaximumhummwryingapadty. Thisequafionmybeinterpretedufdlowgfaeadlmeperceminaeasempopulafim memwmfleqedmfinddighdybssmmdmepflwmaemmomm Theinterceptvalueof(-2.59)indicatestheannualperwntageofin-migrantswewouldelpecttosee,“ 132 Figure 6.4 Residuals from Re ession, Out-migration 1960 to 1970 (Y) with Population essure (X), Hypothesis 4, Equation 1 Residuals From Regression > +2.0 +1.5 to 2.0 +1.0 to 1.5 +1.0 to -1.0 -1.0 to -1.5 -1.5 to —2.0 < -2.0 Figure 6.5 Residuals from Regression, Out-migration_1970 to 1981 Y) with Population ressure (X), Hypothesrs 4, Equatlon Residuals From Regression > +2.0 +1.5 to 2.0 +1.0 to 1.5 +1.0 to -1.0 -1.0 b -1.5 —1.5 to -2.0 < -2.0 133 therewereno(zero)meanueofpopulationpressmeoverthetenyeufime&ame. Amapofthe nandudizedredduakfiomtheregessimequfimakofllmapanemdmflumthufmmt migrationduringtheperiodl970tol981(F’rgure6.5). Iargenumbersofruralmigrantsaredrawnto thewesternSedionsofDiferendaandRodeo(asnewfionfierareasfaseulemem)aswellastothe maeeflabfishedagfiwhmflththewflheaflSedionsdJaneyandJuncafitoAbajo. Out- migaflsfiomfieumtheaflemfledawcfimsappeutobedramtofiefladifimflflagingmm theSectionofSabaualglesia. Consequently,bascdupontheaboveresults,thenullhypothesis(Ho)of 'mnhfimsfip'shofldberejeaedandmedmmfiwhypmheskafl)acwmednth(05)kvdd significance (HYP. 4Eq. 1,Table 6.10). Thebahnwdmereseuchhypammmkchaptamobespedficaspedsofthepopflafion Mstibufimwomandmerebyhrgdycmuihuetopopmafim-miyafimmemymcmvmfiond andnewdistinctways. Becausethelogicalcausalityislesscertainandthedataformeasuring ocmpafiommiyafimdesfimfionandrokdthemvhmmentmthededdmmmigfleuemdmd ratherthmmtervdmrafiqdmphmrrehfimmdysiswasemployedmdetermimdguifiam assodationsbetweenthesekeyvariables. Correlationsbetweenoccupationedueafionageandthe propenfitytowuddtywardmigafionmelplacdmthefirflhypahedssa(5ASB,5C). According mpopflafion-migafimmemy,meyomgbeuaeduate¢andmmehighlysfilbdmdividmk generallywillleaveruralgeneratingareasforthelargerdtiesflee1966;ThomasandHunter1980). Olderom-migrmmwithfimhedtechmcdskflkandflukfamdeducationmyprdamweka livelihoodinruralareas. Therefmqthenuflhypothesisisofthefollowingform: Hqthereismcmrehfimbetweenthenfiafimmcmrespondingvaluesofocmpafimal preparedness and cityward migration. Hence, the Spearman’s rank correlation coefficient is (r - 0) equal to zero. Thealternativehypothesiscounters: HLthenisaposifiveccrrehfimbetweenthemiationincmrespondingvfluesof occupational preparednessandcitywardmigration. Hence,theSpearman’s rankcorrelation coefficientis(r >0.0)greaterthanzero. Thefignifimmmmjedhnkvdfmthkpaudhypahmwaspredaumheduninuy-fiw(95%) percent. 134 MwfiesOfanalysisiswnduaeduthefarmhouseholdscakempbyhgsewrdofthc sepmatemigr'afimdausetgom-migr’amreturnmigrmtsandpotenfidmigrmts Inthefirst hypothesis (5A), the variables JOB (occupation at the time of the interview) or JOBAWAY (acupuionmmewmwm)mmmeouupafimdpreparednessbasedonmeMemfiondCemm Classification System. By definition, this classification system measures a continuum of increasing occupational skills (ranging from 1 asthehighestprofessional level to9as the lowest), however, numerical values are converted to grade from low to high in order to test for a positive correlation, as predictedintheresearchhypothesis. OntheotherhandthemriableMIGDESTprovidesanordinal measure of internal migration destinations (grading from rural to urban), based on settlement size classifications according to the Dominican Census Bureau (Larson 1987). Resultsofsimphwnehfimanalyfisrewdaweakposifiwaswdafimbetweenmpafimal preparednessandcitywardmigrationfromthePlanSierrastudyarea. Forexample,fromtheout- migrantsgroup,aweakpositive association'nestablishedbetween occupation (JOB)priortomigration andthepopulationdensityMGDESDofthefinaldestination. Inthisinstance,theSpearman’srauk correlation coefficient (r = .181) definesaweakpositiveassociation, significantatthe(m12) level (Table 6.11). Perhaps theseweakresults reflectthefactthatstudentsareclassedinthelowestcategory ofthe International Census Code, along with those people who are unemployed or working as household domestics. It is well established in the Latin American migration literature that students, as wellashighlyskifledindividualgareattractedtomajorurbancentersfortheirprofessionaltraining (Thomas and Hunter 1980). In contrast, the International Census Code places principal farmers, as rurallandowners and managers (decision-makers ,ratherhighontheoccupational continuum. These findings indicate that factors other than occupational preparedness may be more strongly correlated withcitywardmigration. Despitetheweaknatureoftheseresultsthenullhypothesisof'no assodafim'shofldberejeaedmdtheahuufiwhypahedsml)mptedume(.05)bvdd Itmaybeuguedthatthenumberofyeushschodismmesflmglyasmdfledwhhintunfl m’gration. Therefquypotheds(SB)probestheassodafionbetweenlevdofedueatkmand Hyp. 5A Eq. #1 Hyp. 5B Eq. #1 Eq. #2 Hyp. 5C Eq. #1 Eq. #2 Hyp. 6A Eq. #1 Eq. #2 Eq. #3 Hyp. 6B Eq. #1 Eq. #2 135 Table 6.11 Significant Results of Bivariate Correlation Analysis (Hypotheses 5A, SB, 5C, 6A, 68) Associated Variables (Spearman’s) r MIGDEST & JOB (Out-migrants) .181 MIGDEST & SCHOOL (Out-migrants) .082 MIGDEST & EDURET (Re-migrants .460 MIGDEST & AGE (Out-migrants) ms MIGDEST & AGELEFI‘ (Re-migrants) -.126 INFLU & JOBLEFI‘ (Re-migrants) .606 ENVIRON & JOBAWAY (Re-migrants) .338 INFLU & JOB (Out-migrants) .437 INFLU & AGE (Out-migrants) .141 INFLU & AGE (Re-migrants 299 " Spearman’s Rank Correlation Coefficients Prob. .0012 .(IXM .4430 .(XIll .0012 .0120 274 39 57 52 424 414 57 136 classifieationoffinaldestinationamongthevariousmigrantgroups. Accordingtopopulation- miyafimmemy,mmuflexpeammemdividudswimmehigheanmberdyeammschodtobe diredlydrmtomemfim’shrgeumbmwnmfmehhaaddifimdmofesdmflufimngmdhed employment. ThoseindividuakwithhflhifanyJormalschodingmaybeexpeaedmseekmral destinations,whereindigenousknowledgeisofhighervalue. However,thenullhypothesisisofthe followingform: Hothereismcurehfimbetweenthevariafimmwrrespmdingvaluesdedumfimmm ofschooling)andcitywardmigration. HencqtheSpeaman’srankcorrelationcoefidentis equal(r - 0)tozero. Thealternativehypothesisofiers: HLthenisaposifiwwnehfimbaweenthemiafionmwrrespondingvaluesofedumfim (yearsofschooling)andcitywardmigration. Hence,theSpearman’srankcorrelation coefficientis(r > 0..0)greaterthanzero Thedgnifiunwmnjecfionkmlfmfiepairofhypofiuesmsmedflemhedunmeq-fiw(95%) percent. Resulmofsimpbwrrehfionanflysiswnfirmapmhiveassodafimbemeenyemoffmmd educationandcitywardmigration. Onceagain,averyweakpositiveassodationisestablishedforthe out-migrants group, with a Spearman’s rank correlation coefficient of (r s .082) sipificant at the (.0831) level. Astheresponses fortheout-migrants group are second-hand (providedbytheheadof the household and other household members) the more direct responses of the return migrants group shouldbeweighedstronger. Evenstrongerresultsarederivedfromthereturnmigrantsgroup,where aSpearman’srankcorrelationcoeflicientof(r - .460)establishesamoderatelystrongpositive umdafimbdweenthenumberofyemdfmmdeduufimprbrtotherflmntothehousehold (EDURET)andcitywardmigration(MIGDEST). Returnmigrants,asagroup,generallyhaveless formal educationthaneither out-migrantsorpotential migrants. Theirreturntotherural household, moddedwdthbwerkvekoffmmdeduafionmyindicatemdoccmomicdifiwhiesumme demdypopulatedmbanwmwueuandktherdmewnsisteflwiththemchhypmheds Coueqmfly,mdmtheseremhsmenuflhypmhedsd'masodafim'¢omdbemkaedandme ahematehypothedsflll)acceptedatthenmetyofiveperwntccnfidencelevel 137 Hypothesis(5C)correlatesdassofdesfinafimfithageatthefimeofmigrafion. Inaddition tooccupationalpreparednessandeducation,ageisanobviousfactorwhichmaybeassociatedwiththe choiceoffinaldestination. Accordingtomigrationtheory,youngerindividualsarepartiuilarly amaaedbytu“pufl“fiamdhrgeurbaanemfmedmfiomjobsandmhtedoppMum&sbah realandperceived. Ontheotherhand,asanindividualagesandacquiresadditionalindigenous knowledge,baseduponfifeexpedenceinamralsetfingmbanpuflfaaorsmaybeseentobecomeless important while local resource factors become more important. Regardless, the null hypothesis is of theform: Hqthewisnoassodafimbemeenthevafiafionmwflespmdingvduesofageanddtywd migration. Hence, the Spearman’s rank correlation coefficient (r -= 0) is equal to zero. The alternative hypothesis counters: H1, thereisaninverse associationbetweenthevariationincorrespondingvaluesofage and cityward migration. Hence, the Spearman’s rank correlation coemcient (r < 0.0) is less than zero. The significance or rejection level for the pair of hypotheses was predetermined at ninety-five (95%) percent. Results of simple correlation analysis indicate that no meaningful association can be established between age and cityward migration within the farm household survey conducted in the Plan Sierra region of the Cordillera Central. No significant Spearman’s rank correlations were produced from any of the various migration groups at the required confidence level. Consequently, based upon the above results, the null hypothesis of “no association“ should be accepted at the ninety- five percent confidence level. The hckofevidencefmaninverseaswdafionbetweeuagemdmigrafiondesfimfimmaybe explainedbytheposifionoftheDommicanRepubhcwithinthecomwofits“mobifitytransifiom“ According to Zelinsky (1971), societies experience five distinct migration phases, passing from a “premodern traditional“ and ultimately evolving into a “future superadvanced“ society. Each of the variousphasesareidentifiedbyfairlydistinctmigrationpatterns. Forexample,theDominican Republic may be classified as a “late transitional society,“ having the following characteristics: 138 1) Shekening,butsfiflmajor,movememfiomwunnysidetodty;2)Lesseningfiowof migrantstomlonizafimfionfiers;3)Emigrafionmthededineormayhaveceased mogunet;4)rmmuinaeneetindmhsonwimgowingmnmpienty(2eunsky 1971mm). mmmmmemammwmemmm frontierward (domestically), rural-urban, mm and intraurban, drculation, potential migration wwsmmmmmmmwmmm(mu Omthepnntmmyenemenomianaepnbsemurgeiypmedmwghbommdomeese ficntierandnualtourbanmigrationlevels. Tnedominmmigtnsonmemintnenominiennaepnbiienubeenmntombnnrum mommmmuengnemmnpnmnpmnepensubomennndpmeiwdoppomnisa mMODommgomdahermbmwntenrapidlydecflne.AssummgtheDommianRepuHick mamgthefomthoffiwphammthisprowssmigrafimmywrrwflybevbwedumhnponam panorwneesveoenammntneapeaedmnndnpimmydsvenoymemmomenmmd themasses(Petersenl958). Consequendy,discriminatingfactorssuchasagc,educafion,occupafion, meexmhngunnvethemeexpmmypommnmapedeneeddnsngwuetpnmdme misrationpromtwifiedbyimmfinsm Wmmmmtobflhc majafimifinghaamom-migrafimmdsfiflhrgdyafleadomuficmdmmbanmigaficnwhich wasfiequentlyobservedintliefieldbytheauthor. Thehflsadhypaheses(6A)and(6B)mnherexpandthefinkagesbeNempopulflion- mignsmmdpeopieenvnmmemmeuybydesningtnemneortnemdnsombemm mkbhsagemcmpafimdprepuedmssmdtherdedhnddegadafimmmededemiym. Drawingfrom behavioral-migration theory, Wolpert (1965) proposesthataccmbinationofboth memmmmhmmmmmmw dressesthatuiggerbehaviordresponsesspedfiuflthededsiontomigrate Theseassociated Wmmmmummmammmnmmnm literature. amusedeiontoniguekmtheuesunymademmnmiume't-m W.”MFWWWW(M.MWWWWBWNMM(WDind mnu(19n).Expnndingmtheeetheaesaimsmntneptopaedwneeptnnmooainmn 139 disseflafimarguesthfladeyadingmvhmmeflfaagiaflfiuedmfimdiomaasfimhnpflm sfimuhfingbehavioralresponsesespeciallythededsicntomigrate. Itisfurtherarguedthatone’sage andkvdofocmpafimflprepuednesspdmmom-migafimmamdmedwithmeperwpfimof degadingconditionsinthelocalfarmingeconcmy. Hence,Hypmheds(6A)definesthenumeofmeassodafimbuweenkvekofoxupafiond skillsandtheperceptionof(landdegradation)environmentalstresses. Buildingontheory,itmaybe argrwdthfltheadwndnglevdofmhdividud’socumafimflpreparafimmymuaemembe parfiadulysensifiwtorewmcesfiessesaanddegradafim)mthemdenvirmment Thisis especiallytr'ue,ifthechosenprofessionisagriculture. Regardlessthenullhypothesisisofthe followingform: Hothemkmassodafimbetweenthemfiafimmccrrespondingvaluesofocwpnfimal preparednessandtheroleoflanddegradationinthedecisiontomigrate. Hence,the Spearman’srankccrrelation coeficient (r - 0)isequaltozero. T'healternativehypothes'mofi'ers: HLthereisaposifiveassodafimbetweenthevariafionincmrespondingvduesof ocwpafionflpreparednessandtherokofhnddegradafimmthededsimmmigrae. Hence, theSpearman’s rank coefficient (r > 0.0)isgreaterthanzero. Thesignificanceorrejectionlevelforthepairofhypotheseswaspredeterminedatninety-five(95%) percent. Onceagainthemoumeamngfiumdafimsuerafizedfiomtheretmmiyamsgroup. Theperwpfimddeyafingcmdifimsagyanfingmededdmtomiymekqmfifiedbythemfinfl miabk(lNFLID,whkhkaIickatsafingmusmrangingfimnabsdmdy“mimpouana“to “muchimportance,“inafivestepgradation. Occupationalpreparednessismeasured,udefinedabove, bythevariables(JOB) and(JOBAWAY),and(JOBI.EFl'),whichdefinesaretiu'nmigrant’s occupationpriortoleavingthehouseholdforthefirsttime. ASpearman’srankcorrelationcoeficient of(r- 606),dgnifiumuthe(.anl),enabfishesamoderadymmgpodfiwasodafionbemme keymhbhswhenhighvdmfmocmpafimflpreparednessmmkmOOBIEFDuedirealy mrrehtedwithhighervdues(someandmuchimpmtance)ddwvarhbk“hfliwncef Asimilar usodafimkeuabfishedfatheremmmmetothemflhmnehdiwhereaSpearmm’srank 140 wrrehfimwefficiemd(r-338)fignifiamuthe(m72)kevidenwdbemnthenfiabbs JOBAWAY and ENVIRON (influence of the environment in migration decision to return to the household). F‘mally, somewhat stronger results are produced for the out-migrants group, where a Spearman’s rank correlation coefficient of (r . .437), significant at the (.0012) levelreafirmsa moderately strong positive association between the variables. Consequently, the null hypothesis (HO) of“noassociation“shouldberejectedandthealternativehypothesis(H1)acceptedattheninety-five percentlevel. TheseresiflmtendtownfirmthatpfindpdfamersmthePhnSiemregimgeneraflyue sensitivetolanddegradafiomhowever,theyalsoappeartobeoptingformigrationratherthanlong term occupation and intensification of the land in their attempts to maximize “place utility.“ Th'ui preferenceisfikelyduetoanumberoffaaorsinduding:alonghistoryofdifficultiesinbothaccessto credit and information for land resource investment and management; low prices paid for both rural labor and excess farm production and therefore little capital for investment; customary land tenure practicesthattendtodisaggregateewnomiesofsaleinthefummgsystemundfinaflynodalcoaa thelowlevelsofbothpersonalesteemand opportunityassociatedwiththetraditionalagricultural sector. quy,Hypothesis(6B)enminesthemmreoftheasmdafimbaweenmiafimmage,at thetimeofdeparturefromaruralsourceareanndtherolelanddegradationinthedecisionto migrate. According to theory, the age of an individual at the time of out-migration is critical, for one’s perceptions of the world and local physical environment change with time (Thomas and Hunter 1980; WilkieandWilkie 1980). However,thenull hypothesisisofthefollowing form: Hqthenisnoassodafimbetweenthenfiafimmwrrespondingvduesofagemdtherok of land degradation in the decision to migrate. Hence, the Spearman’ s rank correlation coefficient (r - 0) is equal to zero. Thealternativehypothesisoflers: HLtherekaposifiwassodafionbeWeenthemhfimmcmrespmdingvfluudageandthe role of land degradation in the decision to migrate. Hence, the Spearman’s rank correlation coefficient (r > 0.0) is greater than zero. 141 Thedguifiamamjeaimhvdfmthepahofhypahesampredaamhedunmay-fiw(95%) percent. Theruulmofsimpbccrrehfimmlysiswnfirmaweakpodfiveassodafimbetweenthe nfiablesageandmmameumedtherdedhnddegradafimmthededdmmmigate. For enmplqmequafimfiomtheom-miyanuyoupproduwsamkpodfiwusodafimwhha Spearman’srankcorrelationcoeficientoflr - .141)significantatthe(.(l)fl))level. Moreover, mommuhsmdefiwdfimmerammigamehereaSpeumm’srmkwrrehfim coefia'entoflr- .299),onceagain,confirmsaweakposifiveusodafion,significamuthe(0m) level. Thesefindmygeneraflywnfirmtheamhorsfieflimpressiomandmtewiewswhenofler farmersappearedtoexpressthemostconcernforlanddegradation. Consequently,basedcntheabove mhsmenuflhypmhedsd“musodafim“shouflberejeaedandmeahmhypuheskml) acceptedatthe(.05)levelofsignificance. Thereboftheenfiromemaanddegadafim)mthededdontomigrateappeusmphya mincrpartformostout-migrants. Ouesticnnaireresponsesindicatethatthemajorityofout-mig’ants auributefiuleornoimponancetotheenvironmemintheirmigrafiondedsicn. Ontheotherhand, ahnodNeflyperwmhwmigied“muchimpmm“mthecmdifiondthebalmvhmmmLand maddifionaltwelvepercemanached“someimportance“aspanoftheirdedsion F'mally,the remammgebwnperwmmdicatedthehdeckimwumuudregardmgthevuafitydthebal agriculturallandscape. Theroleoftheenvhmmemmthededsiontomigrateakoappemmhavephyedonlya mincrconsiderationformostin-migrants. likewisemdividualresponsesindicatethatthemajorityof incmigrantsassignedlittle(13.8%)ornoimportance(69.6%)tolanddegradationintheirpersonal migraticndecision. Onlydightlymorethansix(63%)perccntofthem-migramsdaimedtheuateof hnddegradafimhad“muchimpatance“mthehdedsimmmigruemthemrrembcafim. The balanceoftheindividuals(13.9%)assignedaneutralstatustotheroleoftheenvironmeruasafactorin thedecisiontomigrate. Theseresponsescontradidpersonalfieldobservafiongwhereamajorityof farmerscapressedgreatccncernaboutlanddegradation. Moreover,manymigrantsjustifiedtheir 142 modeowmthebafisthuthehocwpafim(ag-iaihme)uthehfmmaphwdreddwcewu insufidenttosupportthefamilymaceutility). Perhapsindividualmigrantseitherfailtoenunciateor ffllyrewgnbemehnmdhnddeyadafimmmehdeckim-mafingprmtomowammy (Fuller1978). Whenukedifhnddegradafimhflinncedmehdedsimmbavethehomehddmiginafly, onceagainthemajmity(57.9%)ofretmnmigrmts,assigned“mimponance.“Anadditionaltenand WmWMkahd‘finkhm‘hMrdedfionandthruand one-halfpercentwereneutral. Ontheotherhand,anequalfourteenpercentcreditedlanddegradation ashaving“some“and“muchimportance“asabasisforchanginglocafionthefirsttime. Generally,first mmfimmdhousehokkmmadebyyomgermembenandueequdlyflmhrgelymofiNedby thesocioeconomicpullfactorsoflargeurbancenters. However,therearereasonstobelievethese factorschangeovertime. Thereforeitisimportanttodeterminetheexplanatiomforsubsequent movestoandfiommehousehddmmdertodetermmeiftherdeofhnddegadafimmthe Esphmfimsfortheretmnmthehouseholddifiermmanyrespedsfiomothermigram groups(Table6.8). Themajorityofthereturnmigrants(15.5%)claimtodosofor“restand rdanfion,“whichamountstom&endedvaafionhsfingformorethanthreemonths Moreover, wwnperwmmmeyhaddmysplannedtonmmthehomehddafiuumhgaafisfaauy amountofmoneyoraprofessionaldegree. Oneisgiventheimpressiombasedupontheimportanceof thewrespmsegMperhpsafiirdsdpanage“(draflafim)wmpommkmbodbdbymnyram migrants. MexpressionwufiruappfledemopeanhnmiyanUwhomiyuedmtheUnhedSutu fashatprbdsoffime—umflmefiwyean-fatheeapresedpurpmedumhgdoflan Upwrdwfinghomqfiuefihdsdpmage’fieqmndypurchuedproputyandfiwdvuymflmthe moneyearnedoverseas. Ocasionally,theprowsswasrepeatedasthemoneyranlow. Finally,an fimthefieflhdkflefiflchamhhdmeappeumbedrapidlymaeadnghnputamand 143 akhoufieflensiwlyreseuchedmLafinAmaicatheisnwofhndtenmekmthyoffiume investigationinthePlanSierraregion. F'mally,thebalanceofenplanationsdtethefrequent disappointmenmmdfamflyproblemsmhtedmmsucwssfidmigrafimexpaiem Regardlessmost returnmigrants(70%)plantoremaininthehouseholdforanunspecifiedamountoftime,however, manyareuncertainaboutthespecificreasonflablean). Alargenumberdoexpressfamilialreasons forstayingincludinglandownershipandsatisfactionwithemployment Onlysmallpercentagesof thesepeopkofiadupfirmgmasmsfmsuyhgmthehmnehddfmmmphhckoffinandfl resourwsorlackofspedficknowledgeregardingbetteropportunities. Regardingthededsimtonmrntothehouseholdenfirommtdwnsidemfimsdmappeu tohavehadlittleaffectonthedecisionmakingprowssformostmigrants. Forexample,themajority (78.8%) responded “no importance“ with an additional four percent keying “little importance,“ two perceflwenneufidngardingthemkofhnddegradafiminthededsimtomigrate. Conversely, shghdylessthantenpercentindicatedtheenvironmentplayedamajorroleasadecidingfadorand onlyaboutskpercemOfthemnunmigramsfehtheenvhmmentheld“someimponance“mtheh decision. Onceagaimtheserespmsumsomewhflhcmsisteflfifigenuflfieldimpressiomand penmfldkcusfimsfififumergwhownfiflwdyexpreswdgenfinemmfmhnddegadafim andtheircontinuedabilitytoprocurealivingfrorntheland. Finally, the environment (land degradation) appears to be of greater importance to potential migrantsthananyothergroupsampleda‘ablean). Almosthalfofthepotentialmigrantsclaimthe conditionofthephysicalenvironmentdoesfactorintotheirdecisiontoleave;overone-quartersaidthe environmentwasof“muchimportance“inthededsiomafewlessofi'eredthattheenvironmentwasof “someimportance'intheirdecision. Anevensmallergroup(8.8%)wasneutral. Ontheotherhand, mermhtwnperwmsfidhnddegradafimhadfinkmflumcemmehpmsibkbafingandfimfly,a finkbsthmthirtypercemprdessedmeenfirmmemwuof“mimpa1ma“mthehdedsion hsummary,afierabriefdkaissimofthecmvenfimalmethodsfadeterminingnet migrafionmkchaptabepnwhhadewipfimdmemevaflmgpanemdpopflnhnndkuibufion inthePlanSierraregiononthepoliticalsectionlevelfrom1960tol981. Adetaileddescriptionof 144 Table 6.12 Return Migrants Reason for Staying Reasons for Staying Frequency. Percent Cum. Frequency. 1. Have a Good Job 3 5.2 5.2 2. Family Commitments 19 32.8 37.9 3. Lack of Education 2 3.4 41.4 4. Unaware of Better Jobs 3 5.2 46.6 5. Have Land (Ownership) 1 1.7 48.3 6. Too Old to Migrate 1 1.7 50.0 7. Family too Large 0 0.0 50.0 8. Have Enough Money 0 0.0 50.0 9. Currently in School 0 0.0 50.0 10. Physical Handicap 1 1.7 51.7 11. Soils are Good Here 0 0.0 51.7 12. Lack Money to Move 2 3.4 55.2 13. No Opportunity 1 1.7 56.9 14. Like it Here 1 1.7 58.6 15. Have Animals Here 1 1.7 60.3 16. Do Not Know 23 39.7 100.0 Table 6.13 Potential Migrants Reason for Leaving Reason for Departure Frequency Percent Cum. Percent 1. Change of Employment 28 10.1 10.1 2. No Employment 16 5.8 15.9 3. Underemployment 33 12.0 27.9 4. Job Dissatisfaction 11 4.0 31.9 5. Purchased Land 0 0.0 31.9 6. Find a Better Job 45 16.3 48.2 7. Offered Better Job 14 5.1 53.3 8. Educate Oneself 40 14.5 67.8 9. Educate Children 17 6.2 73.9 10. Marriage 3 1.1 75.0 11. Accompany Family 21 7.6 82.6 12. Join the Family 9 3.3 85.9 13. Family Problems 2 0.7 86.6 14. Bad Social Climate 2 0.7 87.3 15. Land Degradation 3 1.1 88.4 16. Poor Soils 7 1.4 89.9 17. Do Not Own Land 0 0.0 89.9 18. Look for a Better life 11 4.0 92.4 19. Do Not Know Why 10 3.6 100.0 145 fimbvdfiendsfoflomdbasedmmlwkfimsfiomthehousehddquesfimmheadmhkteredm demogaphkawsodwcmmkchuwerkfiafmeachdmemiyamsub-goupsmecmsidered andcompared. Thechaptercondudedwiththepresentafionandeaplanafionoftheresultsof statisticalanalysisforthefourfinalgroupsofhypotheses. ChapterSevenprovidesabriefsummaryof themflfimdcmdufimsofthisdimeflafimmdhfioduwsanmberdrecommmdflhnsfmfiume researchinthePlanSienaregionoftheCordflleraCenflaLDommicanRepublie Chapter Seven Summary of Results, Conclusions, and Recommendations for Further Research Introduction uremoetdeveiopmgwnnuienmenommimaepnbnehnsexpetienwdmpidpopmnsm growthduringthepostWorldWarIlerawhichmnunhasledtopervasivepopulationpressurein areasoftraditionalhillslopeagriculture. Itisalsoclearthatthevasttimberreservesofthe19205have beenlargelydepleted. Themajorshareofthe forest removal occurredasaresult ofcommercial lumbedngbeginninginearnestdmingWorlquHandenendingthroughtheyear1967whenthe Dominicangovernmentbannedthecuttingoflivetrees. Despitesomeinitialsuccessesinforest management, primarily by means of the military’s enforcement of this legislation, deforestation has continuedbutatasomewhatslowerpace. Simultaneously,mountainousareasofthecountryalso experienced accelerated levels of environmental degradation and unprecedented population redistribution, particularly in the form of out-migration. hspheoftheobfiomimpoflmaoftheseevenmmtheDommicanRepublkmdekewherem theThirdWorld,fewnaturalorsocialscientistshaveattemptedtoexplaintheessenceofthe interrelationships between two or more of these processes. The objective of this dissertation therefore bemmethedesignofawnceptudmodelmdacmmpmyingreseuchhypahesesmmderm accomplishthisimportanttask. Thus,thisdissertationrepresentsthefirstcasestudytocritically evaluatethenfimeofmerehfionshipsandassodafiommongmueprowssesmadnglewnceptud framework, spafianyattwodifieremsmbsofmalyskmdtempmaflymerapprofimuelyathinyyeu timeframe. Resulmofthkdissenafionprovidewbstmfidefidencethatthkcmceptudmodelka useful framework of analysis to characterize the disposition of these interrelated physical-human forces inthePlanSierraregionoftheCordilleraCentral. Finally,thefindingsfromthisresearchcontribute toefisfingbodbsdmpuhfionmhbehvbrd-miyafimandpeopbenfirmmemmeaymme followingways. 146 147 SummarhedResultswlthConduslonsaadI-pllcatlons Governmemoffidahaswenusdentismmboththemtmslmdwdalsdemwmmonly daimthatfmeuwnwrsimkthepfimarysoddrespmsetoinaeuingpopuhfimprusm. Inreality, meaaudmtmeofmerehfiMHpbemeenpopuhfimpressmmddeforeaafimmmofldewbphg countriesgenerallyisunknownorlargelymisunderstood. Infact,competingtheoriesrevealthata numberofsoddresponsestopopuhfimprusureefimhrespeaiwdfmestcmverdon Resultsfrom mmhypahedumxmfiewedmmefoflofingfomwapaphswnuibmetommdemwngd mmmmmnpmmmwmnumflymmwym relationshipchangesovertime. Simpkbivafiflerepessimandyskcmfimsamodemelymkpodfiwmhfimshipbetween populafionpressmeandratesofdeforestafiononthepoflficalsectionscale. Thisdirectlinear relationshipisestablishedforthetwentyyearfimefiamefi'om1960tol980. Prevailingthoughtonthe prowuofhnddepadafimmdewbpmguessupwsthasoddfmwssuchaspopuhfimprm muabeenmmedmthemdifidudfsrmsteadscdemmdertoprofideafisfaawyexphmfion (BrookfieldandBlaikie1987). 'I'herefore,anewmeasureofpopnlationpressurewasdevisedbasedon mahodobgydevdopedbyBemardandThommIQBIfmhrgersizedpditicsldisuiaa Resultsof dmplebivuhterepesdmmlyfisudngmkmwfam-kvdpopdafimpessurewrhbkprmide highlydgnifimMefidenwtowppoflasfimgpodfiwnhfimshipbflweenameuwedpopuhfim pressureandannualrstesofdeforestation. Iargefarmsbytheirverynature,willtendtohavepeater fmeawvermdlowerpopuhfimpressmqwhflesmaflfamwfllhavelessueareserwdforforest (fallow) and pester population pressure. To control for farm size, the relationship between defmeflflionmdpopuhtbnprasmewureasessedfufirwmbpmpsdfirmsmafifiedbyhm size(small,medium,andlarge). Inthiscase,therelationshipremainsstrongonlyforthesubpoupof smallfarms. Thismaybeexplainedinanumberoffashions. Forone,farmerswithlargeholdingsmay bemahhieraMhawakemfiwmumdobtahthcweds)mcomewhenthehdepcndenu becomelargeinnumber. Regardless,thereisconvincingevidenceforareasonablysu'ongpositive relationshipbetweenpopulationpressureandratesofdeforestation. 148 Adefmeuafim-faflowrafiowuaeatedandteuedmexaminetheshuukaneousefiedof populationpressureonforestconversionandlcngtermfallowlengths. Withinthecontextofthe nadifimdhflldopehrmmgsyuemmkarpwdmardafivdymanmounmdprimaquhndm ckaredandhrgeueuofhndnmainmbngwmfanowdmmgsoddwndifiomdbwmpflation pressure. Aspopidafionpressmemaeasesfueawnversimaccebratesandlongtumfdbwhnd declines. Restflfiofsimpkbivafiatempessimbetwenthenewdefmestafion-faflownfioand popflafimpusmeedabhshamoderflelysfimgpodfiwrdafimshipbflweenthesemhbbs. mequenfly,thisdksemfiondmprmideswidencmmppatthemfimthamaessumhmkvd popuhfimpressmehawthewmbinedefiedofdmuhamouflydeaeafingbulfmmrand reducingfarmfallowarea. F'mafly,becausethedeforestation-faflowrafioisnotinfiuenwdbyvariation infarmsize,conceptuallyitisanimpcrtantmeasure. AddifimalmsighupenainingmthenatmeofthefinearrdafimshipbeMenpopuhfim pressureanddefmestafionarereaflzedmthepdificalsedicnlevelofanalysis Althoughthe cardafimbemeenchangesmmekeymhbbswopflafimmmandntuddefmeaafim)owr thetwenty-yeartimeframe(1960tol980)producesaweakpositiverelationship,muchstronger carehfimsmachiewdwhenamaflseriesdflatkfaeflwmmeumesmemployedumogate measuresofdeforestation. Fora..ple,anequaticnthatrepessespopulaticnpressurewithforest cwerfmtheyeulmremkmupolkicdwdimsexperkndnghighkwkdpopuhfimmm areindirectlyrelatedwithlowpercentagesofforestcover. Similarresultsarerealizedfortheother pairings. Thedeaessehthesfienphoffienhtionshipoverfimgkmpanexphmedbythededim inavailableforestcoveritself. Thatistossy,asforestcoverrecedes,thereis(1)lessforestavailableto cut,(2)forestreservesmayina'easeinvalue,and(3)remainingstandsareincreasinglylikelytobe locatedinhighlyinaceessiblecrunfarmableareas. Thisdiueflafiontherefaeoffmuelplamfimfathechangeinthemenphofthe rehfimhipbeflmmpopflafionmmeandmuddefaeaafimowrfimeandmcuequendy, prwidessomeunpbkefidencemsuppmtdBflsbaroVsummdmevdufimaqwqmnce ofaocialresponsestopervasivepopulationpressureindevelopingareas. Forestecnversionin 149 respometomaeafingpopdafimpremuewiflfikdybethepfimarychdwofdedfimmkemm mmmmmmmmdmmmmmyme limitingnecessitatingachangeinsocialresponse. Onceagain,accordingtotheconceptualframework popmedhysnshonow(198n,aeahondheumnedmmhetmie-demomphkmpuhape demopaphkrupmsutopopflafimprusm(mfingdwl990s)uthemenphofmerehfimship withdeforestationfurtherdeclines. hthkvein,researchhypotheds(lB)aificaflyevfluatesanumberoftheaherwdd meopidafimprusme.1hesechdwsmbudywmemderthembrkofapicukmd intennsutionandbydesnismindnaethemmntyomndnaemdhbmmpmainmsmh landesquecapitaLandtechnologicalimprovementsinfarmingskills. WithinthePlanSierracasestudy repomhomm,dmpkbimhterepesshnmlywspoduwsmuhsmn(tomeenem)wnfiadid conventionalpopulation-economictheory. Datafroma19838piculturalsurvey(cnthepol'uical aeaimevelmvenmmintenntyintheseqneneyorhnpmedraminganmmdinmsw nnpemennnthehndmomeehmuemmeukelytooennmdemndimomwpopnusm pressure,thereverseoftheBoserupthesis. Ontheotherhand,theexpectedpositivelinear rauiomhiphawenpopnhsmptmannundnaemtmaynwnsmedmmepamm level. ltappunthutaamahetmmpopnusonpmnneuedhealytemdmmeintennqor adaptationofinnovationsinagriculture. unlikelymamnasonintmeiaemtainpartimtheinvemnusmhipoheenedm thepolitiealseetionlevet Fromanumberofsomwsmdudingobservationsmadeinthefieldfitis evidemMpopuhfimpreumekhssmmthehrgerfarmsandhkgenflaflymthehrger hrmwhenthemoflimpmtamefimmmtmdfymefammgsyuemueukingplmumemed bylandimprovementsanduseofmoderntechnology. Itmaybearguedthisisessentiallyafimctionof wealthandaccesstocreditandinformation. Thelargerlandholdersareinbothabettersocial mmmmmmmmammmmmm On theuhuhanspopnasmnemayngenennyhigheemthemnunmawhenoneammuym aeemodernintensivefarmingtechniquesbeingemployed. Customarily,thesmallfarmapiailtiualists 150 mbahcmsideredabadaedhfiskandunaflymhumtomfamafimmmingtomodem foodproductiontechniques. mstorically,ratherthanintensifytheirhborinputstowardsubsistence aopomputhrmaswithmaflhoflmphawoptedmmwrpmmmukaW“ash“aopsmto thetadiimdhmhgqflemneqmrmgapadudmtenfifimfimdthehhbormpmsandsomemma improvementsincultivationmethods. Neverthelessthereturnshavenotallowedformajor envirmmentalimprovemenuresulfingfiomreinvesunemmthehndresomcebase. Mernafivdy,mMngfldrauksprwideevidencemsuppmdapodfiwhmurehfimship betweenfarm-levelpopnlationpressureandapicultmalintensity. Astrongpcsitiverelationshipis mnfirmedbeWeenbahpopuhfionpemmeandhnd-usemtensiwfiefimdumepermd farmlandcropped(r - .764),andland-uselaborintemity,definedasthefiequencyofailtivation (r- .537),respectively. haddifiomwhenpopulafionpressureiscorrelatedwithvariafioninaverage (ompm)foodproduaimperurea,amoderateposifiverehfimshipdsokresfize¢ Productionper tareamostlikelyresultsfromthecumulativeefiectoflaborintensity,improvedfsrmingskills,and investmentsinlandesquecspital. F'mally,weakpositiverelationshipsbetweenpopulationpressureand themtensitydmnoufionsmapimkmeuecmfirmedfmmim-farmsmaflfimsandhrge-hm dam Theseresuhsfurmerommdemandingdthewdflrespmsesmpopuhfimpressuremm thewdeflofafiadifimdfamingsodetyandtherebymakemimpummcmuibufiontobmh respmsestopopulafimpreumcmmpbyedbyfiadifimdhmsbpefsrmmmtheDomMm Republic,theseresultsfurthersupporttheprevailingtemporalthesisproposedbyBilsborrow(l987). FatheHmSiemmuudyrepommembmecmomicrespmsesmpervadwmpflafimprme listedfiommosttoleastccmmonlyemployed,appearasfollows: l)deforestation,toplacenewland mtofoodpoduaim;2)hrmhbormtemifiaficmmifenedbyreduabndhflwbnphuweflu hnowueuandtoafimhedenwtheremiaimduflhimflpafingmmedwhhm maoppedhnd;3)meruherfimiwdmtmdfimfimdapicumudsfiflsmanifeuedbyaomemed impowdwedgferfilhercandhnpomdwlfivafimpaahesandfl)mfimhedhmmand 151 mnmafimsmhndesqueapimLmnifeaedbythemeofufigafiOQIthhnfingOeresnwfl anchornkvefingoffieldsandthebuildingofterrawsonsteepslopes. Themajorityofsmallfarmers maflychomemefimmoopfionswhereumethhdandfounhahemfivesappwmbeenaued toalimiteddepeebythelargerfarmoperators. Inthenearfuturewhendeforestationisnolongera fiabbopfimfmfiadifiondhifldopefumersbpunyweshouldanficipaemaeasedefiomm improwsuadnsbkfoodproducfionsfiflsandpemammmwsmmmhndesqmupdesnaed earlier,puhapsadduiondfieustudiesuemmdegafiw-yeumtemkfiomthefimmvesfigafion andshouldbeplannedfor1992and1997. Thenensetofresearchquesfions(2A&2B)definethehnkagesbetweenthepdmry economic responses to population pressure and environmental (land) depadation. According to cmfiadiamyrewuchmdefmeaafimmdewbpingwmuiesmkmmagedfmestwnwrfimappem tobedhecflyrehtedtoequaflyunwnUOHethdepadafimespedfllysoflermhnaMmm wasting. Ontheotherhandbothprofessimdfmeaenmdmmpetmgsoddtheuyuguethuthese forwsarenotnecessarilyrelatedinacauseandefiectfashion. Followingthelessoptimisticor “disaster“drwhofthewnceptudmodelmuoduwdmmisdissemfionhispropmedthnmpidfmest conversion (deforestation) is positively related with land depadation. Hypahesis(2A)doesmfaa,estabhshapmifiwhneurehfimshipbetweenthesekey variables. Thismalysiswaswnduaeduthefamhouseholdsmle,whereuandudizedvduesfa muddefmeaafionratesmsignifiandycmrehtedwithamudruesofhnddepadafim. Inthis case,farmlevellanddepadafionisdefinedastheaverageyearlyamouflofuablehndloufiomfood production. Results of simple bivariate repession analysis confirm a positive, moderately strong relationship between the criterion and predictor variable, where approximately one-third of farm level landdepadationisexplainedbyprevailingforestconversionpractices. WuhhtheuadifimdhiflslopefammgsystemfomdehnSiemaveragemudfamlewl foresteonversionranpsfromnonewlandsclearedtoahighoflSOtareas. Meanfarm-level defmeaafimrepamdighuyabowom-hflhedueuabomdngeuamuaflyJepruenfinga mficeabbdeaeasefiomesfimuedmesddefmeflafimmthewlylmofabommmymeu 152 (Werge 1976). Moreover,theaveragefamerdaimsmbehsingappronimatelytwotareuofhnd fiomproduaimeachyeu,asacmsequencederodmanddetmiorafingsoilcmdifions These menfifledwifimchfindinpmthewlylmmmmmwm),and inthemiddletolatter198(kbymvesfigatorsattheCenterforUrbanandRegionalSurdies(CEUR)of theCatholicUniversityinSantiago. Consequently,itislittlewonderthatovereighty-fivepercentof theprmdpdhrmemmmfieweddaimthucmdifimsfaapiwhmehawdeterioruedmwme depcesince19‘77. Mmy(withwhomlspoke)singleomdeforestafionasconnibufingwtheirerosion andsoillomproblems. AccmdingtownvenfimalBosempimpopulafimeccnomicthemy,memayargueforan mmrehfiomhipbuwenadnndngkwkdmnonfiwfirmingwchndogyandhnddepadafim, whichtheoreficallyconoibmestoimprovedmamgememofthehndresomcebase. Themore opfimkficmflevebpmenfdrwhdmkdimfim’smpmflmoddpropmthnwdwcmomk rupmsesmpopmafimprmemempfingmmtendfythefammgsyuemwiflhkdyimprmeme apoecosystem. Readtsofsimplebivariaterepessimanalysisgeneraflyconfimthatthehnear rehfimshipbetweenthehtemityofhmfimhapiadhueandhnddepadafimmmwrsem direction. Theresultahowewr,mweakinsfienphandnemlyeflabfishedmhrgerfmmsfithin thePlanSierrasample. Thestratifiedfarmlevelresultsindicatethatthestrengthoftheinverse rehfimshipbuweenmtensiqdmmvafiwhnprmmenmmapiadtmmdhnddepadafionmy increasewithfarmsize. Regardbsatheratherweaknatmeofthesuengthoftherelafionshipsumsts thatvuiabbsdherthmfieflenshyofapicukmdmmfimuemnrselyrehtedwhhhnd depadationtoalargerdepee. Asnotedearlier,smallfarmersintheSierrahaveconsistentlyadapted“cash“cropstothe nadifimdhmsbpehrmhgsyuemwhichmmwumaatoreduwmaomuyfanowcydesw Mthnabeenwcompaniedbythemaqsdlmvafimandhnpmemsmthehnd resourcebaseoftenleadingtosoilexhaustionorercsion. Moreover,bsseduponobservationsand WhthefieuahhoughhwbdgedthemodunwchndoygemaflykmflabbbaflyJew hrmmanafiadmnkeadmageofsdlmvafimndhndhnprovememmuepesthawoufl 153 tendtominimizelanddepadation. Theyclaimacaesstocreditisamajorproblem. Onlyinthefew caudthehrgerhmpoupswhkhhwempbyedhdgafimwchniquesandmherhnprowmems mhutenadnganderosidncmfiohisminmsemhfimshipeaabflshedbetweenthese obsernfionsthuwmparafivdyfewhnwafiwefimtsmbeenempbyedmmdatoimprowme fiaditionalhillslopefoodproduaionsystem. Insummary,itisevidentthathighratesofforest mammmmummmummmhmmmmaymm increasingratesoflanddepadation. Themhdhypmhesissa(3A&3B)pumuesthehnhgesbememthempmof“phceufilhr fiomwnwnfimdbehavhrfl-mipafimthemyandwmdruomwspmhandpuflhamadapted fromlee’sclassicconceptualfiameworkformipation. Inhypothesis(3A),itisarguedthatin- mipamsbpaflywmbedrawntofmestedfimfiermwhkhfikdymperceivedpodfiwlyu hafingamongnumdruomceamacfiomespedaflymrespmsempuvafiwwpdafimprmeh thegeneratingruralsourceareas. 'I'hisactionmaximizesthe“placeutility“ofthetraditionalfarming homeholiandmmkm,meamaaheneudthefaeuedfimfierkperwiwdmuchmmgerthan thatofotheravailablechoioes. Resuhsofsimpbbivafiflenpessionanflysismthepdifimlsecfimkvelmppataweak podfiwrehfionsfigcxplainingalhtkmuemanwnpawmdthenrhticnhm—mipafim. Similar significammmdefiwdfimmflyskdmemfmsbepougwmchmppmtawmger positiverelationshipbetweenfarmleveldeforestationandin-mipation. Theseweakpcsitive cmrehtionsmqhkelyrefieamehathatmenmfewremainmgfmeuedfiomhrueumamaa mipaMshfiePhnSierrarepondlwtoanmberdhmifingmvhmmemdandpdifialfadm Thesefindinpdoindicuethuuleastaomein-mipannandirealyrelued(uleast moderudyw)whhhrgerhmopaaficmthnueaperiendnghiprerrmdfaeuwnm Rmdmigramsuemmehkdymmowficmrepomdhighpopuhfimmespedaflyfiom gemafingmwrepmsmfiumgwmdmcepmhhdmtorepmsexpaiendngbwukvds 154 ofpopulationpressuresuchasinareaswithlargerfarms. Consequently,anunderstandingofthe deddmmafingpmofprmdpdfirmmcmuibmuwflecfiwlymmrdbodiesoftheoq,whem wefindMubngufmestedfimfierueugewaflynmdnaccufibhheadstadhionflfammg homeboflsfieqmnflyfiomemipafionhmdertomafimiufiamflflitfiramermanmakeme nemymnmafionsmthefarmmgsyuemandimprovemenumthehndresourcebase. Hypaheds(3B)fimhermmesthehnkapesbeMenmewnwptsof“phceufifitrandnumd resomwspushfaaorawhichulfimatelymaytriggeradecisiontomipate. BrookfieldandBlaikie (mmnhnddepadafimuamddphenomemwhenudhhndmaflmkfarmmmke resomcemanagememdedsimsthateitherminimizemexacerbatenatmalfmces. Thelattercasehas theefieadmaeasingpopuhfimpmmgumaeadngmoumsofuabkhndmmudlybecome non-productive. Subsequenfly,as“placeufility“ontheindividualfarmsteadlevelisfiu1her compromised,thedepadedhndbcsewifltendtofimcfimua“resomcepush'factm,whichkadstoa decisionto mipate. AsthePlan Sierra region ofthe Cordillera Centralisfrequentlycitedforboth accekmwruesofom-mipafionmdhighkvekofenvhmmemddepadafionapodfiwfinw relationshipwaspredicted. Resulfiofdmpkbiwriaterepessimmlyskuehrgelymwndudvehowevamndreved thatonlyweakcorrelationscanbeestablishedonthefarmhouseholdscale. Infact,nosignificant rehfionshipscanbewnfimedatthedesignatedninety-fiwpeernfidencekveL Oneequation that correlates potential mipants with the predictor variable (land depadation) produces evidence for averyweakpositiverelationship. haddifiomsimihrfindingsareencormteredwhenthesekey variablesareexaminedundertheconstraintsofvariousfarmsizes. hmdertoawomtfortheapparemabsenceofameaningflurdafimshipbetweenhnd depadationandom-mipafimmthefamleveLmimpuumobservafimmustbereherawd Inspite dthememodobpmladmupestobegamedfimahmfipfidtrqmfimmueduignw-mipms mstfllhrgelymdermpledperhapsbymmethantenpacemmmePhnSiemaseuudy. Thisis pdmarflyafundimofwfinhrminghousehddsthflnbmtemdmerefueuemacwumedfum 155 thesample. hfumnfieldrueuchthissituafimwuldbewrreaedifamemhouseholderswem askedtocommentontheirimmediateneighbor’smipationbehavior. any,dthoughthemagnitudeofthkpanemismhowmthereisevidewefiomthePhn Sienarepmmmfarmabmdmmemkukmgphmwichmemlymyamaamemipama hthkdtmfionabmdmedmuerathuqrficklyrwcwfiedhymwfimhghomhddswifingm trytheirluck. Unfonmately,theifineramfarmaofienisunwfllingmmabletorevemeanongoing mddenwofawekrsfingwflmnarmm'phwufihtrknahkdytobemmedand momcepushfaamsuiggeryetanaherhouehddmmuamequenwofpemsiwhmlevd populationpressure. Thisscenariowasobservedinthefield,hence,therelafionshipbetweenland dcpadafimmdm-mipafimisnabpaflymwnsktembmmdicuespwawddcompledtymthe maflhnddepadafimprocessuswriesdfarmhomehddsmaesdwlyfifltomhwlm ntility’inalong-termcycleofresourcedeterioration. Thispatternmayalsoaccountforsomeofthe cmsiderabbmwmdsqmuhgthukmkingplacemtheSienuanmbaofthaethemuwere recentlydisaissedbyRodriquezinCampesinossinTrerra(1987). Finthermaethisfindingprovides addifimdefidenwmaBrookfieMaMBhikhuQBflmcmreahMMhnddepadafimk asoddphenomenomomthuisdirealyfiedtommmcepmhandpuflfadmaswdatedwhh Arehtedgoslofth'sdissenafionismofieruexphmfionofhowvariafimmpopulafim mmmmmwmwdmmmmm isdirealyrelatedwithpopulationredistribution. Accordingtotheconceptualmodehthenatural nproducfiwpomhprowmmhummpopuhfimspadmflyinaeasesthedemandmthefood Prodndionwppoflm- meqmflyipowhfimmdthflmmwdflmm modifythefamhgsystemhpodfiwandhmfiwmysmefidfimbivabmwddmponsuthn oftenleadtonegativeecologicalccnsequencec. Ineithercase,anewpopulation«recom'cebclanceis achievedwhichallowsforpopnlationredistribntion. Customarfly,ruralheadsofhmuehddficquenflyfaceanumberofchoicesmmderm man'mize“placeutility'whenchallengedbypervasivepopnlafionpressme Accordingtocompeting 156 theuiesfiomthepopulnhnewnomklhmoneemnomicopfimmvdvesfuwmdmm plweuwhfimtofoodpoducfionhowevufiwcmdemnmkopfimrequhuthemtendfiafimof apkuhmebymeamdhnprovememsmhmmgskiflsandmmvafimsmhndesqumpualwhkham hkdytomhimiaemMflruomwpushhdusandtherebymshhmevwmafimhe'phmutflhy.‘ Onthedherhandfimhomehofldedfimmakemmypdameprimarydemopaphicopfionom- mmmwm'mwmmmuhsmwmwm remmcepufldmefmeuedapkukmdfimfiermbytheemploymemandeduafimoppmnmifiud thecity. Regardlessbemmedthehighkvekofpopdafimprusmeandmumaflyhighratuofou- migrafimdomeedfmmePhnSiarareponapodfiwfimrnhfimshipkprediaedbem thesekeyvariables. Theresultsofsimpkbimfiamrepessionmlydsmthepdifialwaimkvdrevedawtd highlysignificantcorrelationsforasmallseriesofdata. Startingwiththefirstpcirofcensusyears (1%0m1970),awryhighcurehfionwemdemfimlyeaaflkhesefidewefaammmgpmifiw linearrelationshipbetweenthecriterionandpredictorvariables. Inaddition,similarresultsare achievedfiomtheanalysisoftheseccndpairofcensusyean(1970tol981). Ontheotherhand, remksofdmpkbivadflerepushnanflyskfmfiemfirehouwhddflflnmpkuegemrafly inconclusive. Despuetheutflitydamulfiplidtyquesfionmhededmtheamoumfiinformafim penamingtotheom-mipafimprowssmthefarmhomeholdsakksfiflsomewhufimhed Reprdlemresuhsfiommkdisemfimmthepdifialwaimbvdabnepmflesufidemefidem marmpympponthedemopaphicopfimofmn-mipafiminmpmsetopemsivepopulafim pressure. Thebalanceofmehypahuespenammthepopuhfimwdkuibufimphasedthewnceptud modeldesignedforthisdissertation. Consequently,conceptsfrombothbehavicralandconventional mipafionthemyuefinkedinudertoemhmwmemporarymipafimstremsandaueuthe influenceoflanddepadationinthemipationdecisionmakingprocess. Forc-mple,thecorrelations beWnocmpflhmeduafiomageandthepopemigtmdcfiywdmipafimmesphredhthe firsthypothes'nset(5A,SB,and5C). Accordingtotheccnceptualfiameworkformipationectablished 157 bylee(1%0,themgbeflereduatedaflmaehighlysfifledmdivfiuakkawmdgewafing centersforthelargercities. Inccnu'ast,olderout-mipantswithfewmarketableurbanskilkandlittle famdeduafimfeelmaemehmapicukmdwtfingrufizingthedifimhiestheyfacemm urbcnenvironmentCl‘homasandHunter1980). Resuhsofdmpkcmrehfimandysiscmduaedatthefamhonseholdsmlewifirmaweak positiveassociationbetweenoccupationandcitywardmipation. Thesewsultsmwhmthefact thauudenuuedamedmthebweacuegmydtheWCmCodqahngwhhthue peoplewhoareunemployedorworkingashouseholddcmestics. It'uwelldocumentedinthe mipafimfituamrethatsmdenmdwmamaaedtomajmurbsnwmemfmthehprdesdmd fiaininginaddifiontoindividualswithhighlyvaluableprofessionalskifls Regardingtherelationship bummaeadngkwkofeduafimanddtywardmipafion(58),thaemsomewhnnrmger results. hfikusqmequafimfiomtherdmmipanfipoupwnfirmsamoderatelyweakpodfive correlation. However,mmeaningfiflruuhsuederivedfiomthemu-mipamsapotenfidmipam poupatthedesiredconfidencelevel. ThehsthypothesishthiswnvenfimalmipafimtheayfiiadfiQmarelatesdasof destinaticnwithageatthetimeofout-mipation. Logically,inadditiontoone’soccupaticnal prepsrednessmdeduafiomagekuobviomhdmhkdymmfluenwthechoiwdfindmipafim destination. Accmdingtotheay,youngermdividuakamamadedbythepuflfaausofhrgembcn cemmfajobsandrehtedopparmifiesmdudingeduufiommardageandmremhehmfly members. bemoastummdividualfivmgmanm-urbanseningmauuesandacquhesknowbdgeof thefiadifimdhrmingsystemmdnhtedmdfifewaysmbupuflhdmmyappeubsshnmm andlocclnaturalresourcefactorsmoreimportant. Therefore,aninverserelaticnshipispredicted, wherguagedtheom-mipammaeamthepropemityfadtywardmipafimdeaeues Resultsof shnpbcarehfimmflydshowevu,mdiatethummunhgfiduwdafimanbemnfirmedbuweu ageandcitywardmipation. Neverthelesstheredouappearmbemuwdafimbuweeneducaficmageandpopuhfion redistributicninthesamplestudyarea. Oil-mipantsfromthePlanSierrasourceareagenerallyare 158 younger,withameanageoftwenty—three(Zl.0Dyearsandbettereducated,Withsmesnofoverseven (732)yeminschool.thanallothermismtsroup& haddifionipotenfialmismtsmasroup)m odydighdyoflerawbueduatedmanom-mipmamapngmemrfiw(252)yemdagewhha meanofsixyearsofschool. Incontrast,returnmipantstoruralhouseholdsaretheoldestpoup, almostthirtyyearsatthetimeofretrun,andhavealower(5.4l)meannumberofyearsinschool. First fimein-mipantstotheSierraslsoareolder,averagingalmosttwenty-eight(27.95)yearsatthetimeof mipation,anduetheksstedumtedofthemmmipafionpoupswithshghflylusthanfiw(4.96) yearsofformaleducation. Fmafly,thenon-mipantstayers,thoseindividuakwhohavenotparticipated inthemipationprocesssincebefore1977,aretheoldestandleasteducstedpoupinthestudy,witha meanofalmostthirty-five(34.92)yearsofageatthetimeofinterviewandslightlymorethanfourand one-half (4.64) years of formal education. Regardingfiadifiondmipafimsfieamshisckarmmemajmityoffimfimeom-mipmu mdrawnmhrgermbanizedueasbahmternalandenerndtotheDommicanRepubfic Forthe period 1977 to 1987, one-third (33.8%) of the out-mipants from the Plan Sierra region left for the largest urban centers in the republic. An additional one-third (32.9%) left the country altogether, presumablyfortheUnitedStates. Thebalanceofinternalout-migrationwasequallydirectedtoward smallcities(13.5%)andruralpoliticalsectionsof the republic (12.8%). Therefore,themipation streamtofienmainhgapiaflnudfiomierkquhehmhedmmagnhudeandthemipafionsfieamto mmerddphmafimuessispraakfllymnefiaentamaamglessthmme-haflofone(02%) percent of the out-mipation flowsby1987. The preference for large urban centers is even more apparent among the potential migrants poup,whereovereighty—fivepercentofthoseintewiewedspedfiedadesiretomovetothe“bigcity.“ Almostfortyofive(443%)percemofthisyouthfulpoupakophnwbavethecounUyahogether,whfle mmme(28.4%)mgetthmternddesfinubndSmfiagokbsCaMfleroawhhafw individuak(3.1%)signafinganinterestinthenafimalcapitaLSamoDomingo. Onlyten(10.4%) perwmdthepaenfidmipamsmdiateamipuionprderencefmmafldtiegwhihodyfom percent (4.1%) expressed any interest in a move to the forested agricultural frontier. The trend toward 159 thepcnanofdheamdwmbanmipafimtothehrgeaurbanmahokapparemfiomthe resultsofthisdissertation. Neveflhelessthkrueuchprwidesevidencefmdraflmuretmnmipafimmeamsmmd areasoftheSierra. Approximatelyfmty-fivepercemoftheindividmkretumingtoPhnSierraccme ficmhrgembsnceflemofthempubficwhhmuflyeighteen(l75%)perwflmmecommgfiom intermediateandsmallcities. F’mally,overone-thirdofthereturnmipantsleftotherruralparishes andvillagesofthecountryside. Furthermore,approximatelyforty-fivepercentofthefirsttime mipanfiamefiomothermdamaswhhmadduimflthhty(27.4%)paceNmipafingficmmafl citiesandvillages. Fmally,dmosttwentyperwmofthein-mipmutothePhnSierrarepomfikefise, nmefiommehrgeurbmcemmofthenpublhmdudingmhtwnperwmfiommfiagoandsh percentfiomSantoDomingo. 'l'hedynamicsofthespatialprowssofruraltoruralandurbantoniral mipafimhtheDomhicanRepubhcmgeneraflyundoamemedumhowmbmhmpruemfufik territoryforfutureresearch mumofhnkamofmkdiuenafim’smncepunlmoddprobethecmrehfimsbetwen mmhbhmocwpafimflpeparedneuandthemfiuenwdhnddepadafimmthededdmto mipate. According to behavioral-mipation theory, Wolpert (1965) proposes that both depading phyficdandwddwndifimswmbimmwmmmbsnmvhmmemprodudngpsydiobpalm whichoftenmaytriggeradecisiontomipate. Asnotedearlier,theseassodatedstressesgenerally dgnfltheiflefieasofpopuhfimpressmerefenedtomthepeopleenvirmmemfltmmre Wethededfimtomipflekmadgmemefiafly,mmdermmafimiuthe“plmufifitrd thehousehold. Buildingmthesewnceptsitfikewisemybearguedthuaccmbinafionofdepading phydcdandwddmndifiommcemmmenvhmmemsakofunaimmproducepsydidogial stressesillidtingthedecisiontomipate. Fmally,itisfurtherhypothesiaedthatone’sageandlevelof ocwpafimdpepuedneuphrtooflmipafimmdiredlywifithebalperwptbnddepdhg ccnditionsinthetraditionalfarmingeconomy. Hypahesis(6A)definesthenuureoftherdafimshipbetweeninaeasinglevekof ocmpuiondpreparednessandthemfiuencedhnddepadafimmthededsionmmipue. Advancing 160 bvekofcne’socurpafimdfiainingbothfamdandmfmmdmayausemindividudtobe particularlyaensifivetonaturalresourcestressesinruralenvircnmenta Infact,resuksofsimple wrrdafimudysiscmfimamanmgfmposifiwrehfiomhipfmbuhtheretmandom-mipams poop. haddifim,somewhatweakerposifiveremhsueproducedfiomtheom-mipamspoup. Togetha,thewremhstendmwnfirmthnphdpdhmemmmePhnSienarepmgemaflyue swdfiwtomvhmmemddepdafiomhowemfiheyakooptfmom-mipafionrubrmanbngiam ocamamymdmtendfiafimdthehndmwhsemthehanemptsmmafiminvaaufifity.“ Furthermaqhypahesk(6B)definuthenatmedtheaswdafimbuweenmhfimmage,n thefimeddepsnmefiomamdwmcemandthemfluenceofhnddepadafimmthededdmto mipate. MargumeNpropmesthutheageofmindividuduthefimeofom-mipafimisahicd fanne’spercepfimsoftheworldandbmlenvirmmemchangewiththepudngoffime Resultsof simplecmrelafionanalysis,however,confirmonlyaweakposifiveassodafion. Strongerresultsare Itwunpmtedeerthhtypercmtdtheom-mipamshdiatehnddepadafimhaaed eitherstronglyoratleastinsomewayintheirdecisiontomipate. Thiswasalsotruefor approfimdelytwentyandtwenty-fiwpercemofthem-mipamsmdrammipMsmpbd, respectively. Maeover,dmodfifiyperceflofthepotenfidmipanusaidtheconditionofthebal environmentwasimportantintheirdedsiontolesve. Inthefield,peopleltalkedwithpenuinely wemedcmcerwdabomhnddepadafimandthegmernmem’sefimtownfidsoflerodmand managetherepublic’sremainingforests. 'l'hereisacommonheldsensethattheapiculturalfrcntieris rapidlydisappearingifnotalreadygoneforallpracticclpurposes. Inthenearfuture,smallfarm dedsbnmakasmmePhnSierrarepmdtheCmdflhraCmfidwmmbngerbeabbmdmplyopt fufueflwnverdmmoN-mipafimuamumdmafimizingthefiaceutflhfdthehomehdd, theywmbefawdmnunmaherecmomk-demopaphkchdmmtheficedpavuiwmpuhfim precsure'mordertosnrvive. 161 CrlflqueoftheConceptualModel Resuhsofthisresearchindicstethatanumberofrevisionsneedtobemadeintheconceptual model developed for this dissertation (Figure 3.1). F’mdinp from simple bivariate repession analysis revedthatwriafionmfamsiuisaaificalfaammdaemmmgthesuenphofseverdkey relationships. Themehfionbetweenpopidafimpressmemddeforeaafioninaeasesinmagnimde withfarmsize. Farmshrgerthanthemmimumsubsistencethresholdofwmreasareassodatedwith therelationshipbetweendeforestationandin-mipation,andlargefarmsappeartobeassociatedwith innovativefarmingtechniquesandinvestrnentsinlandesquecapital. Consequently,aconsiderationof farmsizeshouldbeaddedtotheconceptualmodelintermediatetotheboxesindicatingthelinkages between population powthandpopulationpressureWrgure 7.1). Om-mipafionasapfimarywddrespmsetopopuhfimpressmeakoshouldbemdicswdm the“upperphase“oftherevisedconceptualmodel. Unesshouldbeaddedtothe“populationpressure“ boxindicatingboththe om-mipafionofindividualsinresponsetopenasivepopulafionpressureand thein-mipationofindividualsfromoutsideofthelocalarea,whichcontributestolocalpopulation pressure when added to natural rates of population increase. Reference to “potential mipants“ as a separatepoupofhdividuakthfiwemsampkdmthequesfimnaireshouldbehdudedwithhthe “population redistribution“ box found in the lower phase of the conceptual framework. F'mally, “return mipants“areanimponantpoupofindividualsinthisresearchthatmedtobesccountedfor. Onthe locallevel“returnmipants“shouldbelistedintheboxwith“in-mipants“foundinthelowerright-hand corner of the model. They represent “older, perhaps, less prepared individuals returning from national urban centers or depaded rural environments“ outside of the Plan Sierra region. mnvisedwnwmudmoddmuaakoanempttoaddreumeimpaasmdhtendafimships ofprocessesthatoperateatspafialscalesbeymdthebcallevelfrpuell). Returnmipantsasa poupdemmsfiatethefinkagesMefistbaween“aayem“mmrdhousehoflsaMbmhmaja domesdeanomtemsmnmbancemersmehuthemsonalmpimlormwommgomNewvmr City. Whikawayfiomthemdfamfladmipmfiuefiequenflyrespmsiblefamaflscakmpinl investmentinthefarmoperationintheformofremittanceincome. Theymayreturnfromurban 162 Figure 7.1 Revised and Expanded Model of Social Responses to Population Pressure l—l Inecnc “WW I m littlnco— r01. Ar..— lfi loco-c — Relittcucc WWW r ‘Otutu _cora1 stash “132.01. A M . MOI! mm" _ cancel. ‘Cturn “i‘rlntl L _ Remitttcncuh FOtbcn Arcch . — Ineonc _ nCILtECInn-O “...-urn . _ Hal‘KIDCI IMMWI Fran.“ A20. 163 aperienwswithmaeuedtechmmlknowbdgeapphubhtofirmmgmmwempbymemskilk altogether,aswellassavingsfronijobearnings. Retlir'nmipantsalsorepresentthelinkagesbetween mdhousehddsandaherapiadmrdueasbmhdomesficuweflumflfirmmgrepomwtdded therepublic. Seasmalmipafionfortemporaryworkintheinternafimalapicuhmalsedadraws mmymdmdifidmkfiomMiddleAmeriaaMtheCuibbeantodomuficexpMplmhfimsmdto theUnitedStatesannually. Remittanceincomederivedfromthisemployment'mincreasinglyvitalto ruralhouseholdsindevelopingcountries. Abrmder-basedwnsiderafimofspafidsmlesaflomfmtheenmmafimoftheimpaasd political power within the context of population-resource relationships (Campbell and Olson 1991). On rcpmdmdmfimdkvehthemfluenceofpdifialpowermybefehbaflythrwghpdidutha mmymohamtofmestedhndfaapiadtmddewbpmmmeprbesofapimmdomw uthemuketphce,thusredudngmpital(acmmulafim)eaminpandthepaenfidfamd reinvestment;awesstocapitdfminvesfinemmthefmmofbwinterestshmttermbansaccessto Mormafimandtechndogyfimsferviaapiaduudenmsionammvesmmmmd Wetofadfimtememukefingdmapiwkmdwoduaimmmahemafiwfm ofempbymencandacwsstogeneraleducationandhealthcare. Ontheinternationalandglobalscale memflmnwofpofifimlpomrhkefisemybefehbmflybypdkydedgncdtocmudmfimdbwh mtobwmtereabngtemaeditmphakacwsstomtemfimdmukasfiapridngandmifi Wmtohfmmafionandtechndogyflandafiamtemfionflagendesfmewnomk devebpmennacwssmmtemafimdaidmeducafimandheahhwe;mdmsstomtemfimd muketsformipamhbofswdalrespmsetodomesticproblemsofunemploymemand underemployment related to pervasive population pressure. Abrmderbssedviewdthetempmddimensimakokrequhedoftherevkedcmceptufl model. Reamevidencefiomdewbpingwmiesrevedsthatuaresukofthepudyredimdfime fimumwhichfiadifimdmdecmomieshawhdtorespmdmrapidpopuhfimpomhtherehas nabeenmsidemsmeiammsomtoukeplamwnmnsnbsnencebasednmingryuemum proposedbyBoserup. Therefore,thefimefaaormaycontribluemanexplanafionofthelowintensity 164 bvekmtheadaptafimofmnmafiwfarmmgwdimbgyfoundmthePhnSierrarepmdthe DominicanRepublicandelsewhere. 'l‘hesepeatlyreducedtimefiameshaveseriouspolitical hnpfiafimsfmbothmfimdandmtemfimalpowersmrameswherehmybeseenthnfunm wchndopalmnwafiommapiadmfiflbearfifidafly(emgenoudy)hfioduwdmddmhktfled mthebcdhvelbymemdjomtpdiqdedsionmakinguhigherspafidsabsaehandStme 1989). Thefimedimenfionwuldbeaddedtothebofiomdtherwkedwnceptuflmodelmamanna similartothe“spiral braid“originallyproposedbyCampbellandOlson (1991). Recenfly,wmkingwithinthewnceptudfiamewakofpofifialewnomy,3rookfieldud Bhikie(1987)prwmedthatmnyhflandmommhomrepomvdthindevebpingwmies hktmimnyhawbeenmupndiudbmhpdifiuflyandewnomimflyupandtheEmopemwbnhl experience. Thevariousformsofwdopditicdmarpmhnfiommaddifionmthefadthatsensifive hiflandmomumenvirmmenmdmueecdopmflymarpndfmapiadnnehasaflmdfa conditionsconducivetoacceleratedlanddepadation. Thissituationhasactedtocausetheseupland neutofimdionubackwud“mhmalrefugec“syuemficaflyisdatedfiomtheprmof modermnfionmfingplammapkuhmemthebwlymgphmsmdmdusuidimfionmmedfiesne dewlopmembiasawayfiomtheinsuhrhiflandmomtaimmueasbymeamofseasmdand permaneflmipafimfmwageswndstoobfiuempeaflydividehbmfls’ownfiadifimdfimfly fimmgefimmerebyredudngmdrabihtymbe“wflpmdsbnmg'andmaahthehmvesmwm the land resource base (Brookfield and Blaikie 1987, 117-119). Unfortunately, Brookfield and Blaikie arguethatmarginalizationhasnotimprovedsincepostWWlIindependence: hmorerecempoa-colmhlfimespdificalmdewnomicmarpnafimionconfinuedifna deepened, although it tended to take new forms. Today, as repositories of the last remnants of surviving pre-capitalist economies, they exhibit a syndrome of environmental depadation, out- mipation, demopaphic pressure, dependency and neglect (Brookfield and Blaikie 1987, 114). Thkprocessmefiemsemupachainofewnmwnuibufingtoenvhmmemddepadafimand ultimatelypopulationredistribution. InvestigatorsworkingattheCatholicUniversity‘sCenterfor UrbanandRepmdSmdies(CEUR)mSmfiagodewbpedawmpbxfiowmodel(Figure72)ofthe 165 Figure 7.2 CEUR's Model of the Dominican Small Farmer and the Environment Wicca or: "(can mm to. M708. WAVIIALIS MOI- CULY. COHCICIAL AC“- cicc VIDA“! cos-caucus l 0 ‘------------------ m ”m cam-ma. M“ M m.» “-0. ...-........--.-.-.w 0 0 I 0 -.- -..-....1 mm WWI“. “L ”IO OIL m Wmmmmcaroueamvmn manager. I I I I l I I I l I I I I r I I I I I I. ~ I r I I I I I I I I I I I I I I I I I I l I a. L----.“--~- 166 hnkapsbeweenmeuadifimflDommianhmdopehrmaandtheenvhmmemfichmopnayu 1987). Cmsequendy,mexpandedtheaefialfiamemrkshouldalwauemptmacwumfmmulfi- mmdhnkagesmmgandbemeenthekeynhfimshipsmdermnsiderafimmthisdksamfion Whichmiabhswmbimdwithpopuhfimpresmewnfiibmetomundermndmgofthefmea conversionprocess? Whatarethemultiplecausesoflanddepadationonthesmallfarmlevel? Which aodalforwsotherthanpopuladonpressmesfimuhtepopldafimredisufliufion? Finally,results mdiammnhamsothuthanpopmfionpressmecmuibmemthemwnshyofadaptafimof innovativefarmingtechnologyintraditionalhillslopearess. Thesefactorsmayincludezfarmsize; McNupRaLmthefmmofhwmtereabammremiuancemwmeundtechndogyWam theformofapiculturalextension. Methodsforfuturestatisticslanalysisincludestep-wisemultiple rcpusbnmperhpspafiandyfisshouldbeeprredfmmmewmpbteexphmfimsdmemd theserelationships. RecommendationsforFutureStudy Theresldmofthisdissenafimgemrueanumbaofsuggesfiomfmfiumemchmbah thePlanSierraregionoftheDominicsnRepublicandThdeorld,ingenenL Traditionalpatternsof mipafimmchmpngthempitdofSantoDomhgokmbngerpuwiveduafiabbaMpromismg destinationforthemajorityofrurslmipsntsfromthePlanSierraregion. Manyprefertoleavethe wmfiymogaherafia,perhpsomyahhfmym8mfiagodehs0bdkrm(6m19m; GrassmuckandPessar1991). Itislikelythatpotentialmipants’perceptionsofcspitalcities throughoutlafinAmedcaandthedevebpingworldmchangingfmsimflureasom. Primatecities, wfiefingfiombofihighrfluofmempbymeflaunderempbymwhhadequflehoufingaflpuflk services,ahandwaterpoflufimviolencesndaimemaymbngerbeperceiveduofiaingmany opportunitiesforpotentialruralmipants. Chirrentresearchindicatestheincreasingimportanceof TheprocessofmipafionakokrapidlychanpngutheDommianRepuflkpamtoamae advancedphaseofthemobilitytrmition. Consequendy,uiticalvariablessuchasage,educctionand 167 occupationmaymlongerhavethesfimgasmdafionsfounddufingwlierfiontierwudmdtyward phases. TheprowuofretummipafiombmhtotheDommimRepubhcfiseflandspedfiaflytothe PlanSierrarep'onoftheCordillersCentral, requiresadditionalstudytocomprehendthecontinuous circularfiowofmipantstoandfiomthelargerurbancentersoftherepublic Recent evidence indicates that successful emipants “Dominican-Yorks“ are buying property andbuildingvacationandnfirementhomesaswellasinvestinginfamilyfsrmsandranchesinthe mountainsoftheDominicanRepublic. ThelargefiowofUS.dollarsdirectlytotheSierramayhavea pmifiwimpadmbdhthemhudphysicslenfirmmeflmdfiadifimflhflkbpeapicuhmmthe yearstocome. Unleresemchhasbeendonemthisuemwiththeemepfimomeario’s(l987)study mtheeconomicimpsdofremifianceincomeintheareaaroundthedtyofl‘nico. Inaddition, customarypstternsoflandownershipsndtenmesppeartobechanginginPlanSiemwhichrequire additionalstudy(Meyer1989). Fmafly,uthefiendtomrdmaessingpopflafimdensityhthepofifimlwai6nsdthePhn Siemrepmwnfinmsmroughthelmtherewiflbeewnpeaerchdhnpsphwdmmafl farmemtoimensifythemdifimalhifldopefamingsystemmnewandaeafiveways. Forallpractical purpmeathaemfewnmainmgprimmyfmeastobewnwnedtoapicuhmeandtheseue protectedbythegovernment. Additionalfieldworkshouldbeconducted,atfiveyearintervals(1992 and1997),throughoutthe1990sinordertoassessthisevolvingprocess,andfurthertestthenewfam measures (population pressure and deforestation-fallow ratio) introduwd in this dissertation. Finally, wudhiomhfieleightphwewnpeflermnflrshfimmflDommkanhomhddsmmodify mehferfifitybehafim,mmenenaepmawqmdrespmsutopopuhfimprme(3flsbarw 1987). APPENDIX A APPENDIX A Variable Directories Political Sections File REL1971 = Relative Population Pressure in 1971: Relative Population Pressure is defined as the Ratio of the estimated number of households, based on an average household size of 5.95 persons, to the carrying capacity, based on the amount of land in production for the census year. For example, Relative Capacity equals 32,744 Tareas in production divided by 80 Tareas, the number of Tareas needed for a subsistence farm household. In a sense, this is a Relative measure of Physiological Densitybecauseitisbasedontherelativemeasureofcanyingcapacityforasingleyesrofproduction. Relative Capacity -- 32,744/80 = 409.3 Farms; Estimated # of Farms 8 4336/5.95 a 728.7 Farms; Relative Pepulation Pressure = 728.7/4093 - 1.78 Also REL1977, REL1983, (” Did not use in analysis) FCR1981 8 Forest Conversion Rate for Years Listed: Measures the Percent Forest Cover Change over the Designated Years. Also FCR1980(FCR711980), and FCR1971 (FCR601971). AGI971 = Agriculture Crop/Fallow Ratio for 1971: Ratio of Cropped Land to Fallow Land for the census year in question, after Boserup. Also see A61983. MAX1971 - Maximum Population Pressure in 1971: Maximum Population Pressure is defined as the Ratio of the number of estimated farm households, based on the constant of 5.95 persons per household, to the maximum population supporting (carrying capacity), based on the total amount of land available for production (physiological density). This is the more conventional measure of density and is appropriate for change over time measures. NET6070 = Net Mipation for the Census Year 1960: Percent Change in Population attributed to MipafionbeuveenthemoCemusYeusRegardumemufingOm-mipationudwnsidahbd change. PR1960 . Percent Population Pressure in 1960: This represents the MAX19?? data standardized by 100. Canalsobeviewedasameasureofcarryingcapacity,where 100% equalsatheoreticallimitof capacity. Therefore the variable is seen to measure decreasing population supporting capacity as percent population pressure increases. Increasing population pressure signifies decreasing carrying capacity. CH6071 2 Percent Change Maximum Pop. Pressure: Percentage change in the percentage measure of maximum populafionpressmeforthedesignatedfimeintemhdeterminedfiomthevadable PPOPPR19?? above.1hkdahreveakthatpopulafimpressmeismaessingoverthefimeperio¢ F0060 - PercentForestCoverl960:PercentageofForestCowredTareasfortheMgnatedyear, basedonhectarelevelforestmeasures. 168 169 CVCH6071 . PercentForestCoverChangeforYears: Percentagechangeinforest cover for the designatedtimeframe.Calculatedforthreesetsofdates. PCROP71 :- PercemFanowIandtoCroppedIMCalcuhtedthepercentageFallowhndto Croppedlandforthedesignatedyear. PCHFCP - Percent Change in Fallow Versus Cropped Land between Years 1971 to 1983: Calculates the percentage change in the ratio ofFallow to Cropped Land for the designated time period. PCHRCC -= PercentChangeinRelativeCarryingCapacityfortheYears 1971tol983. Calculatedthe percentagechmgemmeasmesofrehfiwcarryingcapadty,basedmthemoumofhndmprodudion forthegivenyears 1971 and 1983. YPC7183 s Yearly Percent Average Change in Relative Carrying Capacity for the Years 1971 to 1983. YPO7081 - Yearly Average Percent Out-Mipation for the Designated Years: Calculates the average percent yearly change in out-mipation for the designated time period. YPI7081 - Yearly Average Percent In-Mipation for the Desiplated Years: Calculates the average percent yearly change in in-mipation for the designated time period. CULT71= =Apicultural Frequency of Cultivation. Calculates the frequency ofcultivation for the designatedyear. anuencyofadfivafimisaaandudizedmeamreofapicuhmdintensitydevisedby TurnerandDoolittle(1978),basedonameasure0tolformaximumintensity. PCH7183 2 Percent Change in the Frequencyof Cultivation 1971 to 1983: Calculates the percentage changeinthefrequencyofcultivationindexbetweentheyesrslmland 196. FRTUSE83 - IntensityofFerfifizerUsein1983zPercemOffamsthatusedferfifizerinl983. INSUSE83 - IntensityofInsecticideUsein1983: Percentoffarmsthatusedinsecticideinlm. IRRUSE83 = Intensity ofIrrigation Usein1983: Percent offarmsthatusedirrigationin1983. ERCUSE83 - IntensityofErosionConfiolin19s3zPercemoffamsthatusederosionconUOl techniqueinl983. TECHS3 = hdexofTotalInnwafiveTechndogym1983:Sumtaaloffamusageofaflfmmsofthe intensitypractices listedabove,theseinclude fertiliwuse,irrigation,insecticideuse,anderosion control. SEL83 - Percent Farms that Sold Crops in 1983: Percentage of farm families that indicated crops were sold in 1983. EAT83 s Percent FarmsWhoConsumedCrops 19$:Percentageoffarm familiesthatindicatedcrops were consumed by the household members in 1983. PWOOD83 . PacemFamsudngWoodfmCooking1983.Pacemageoffarmfamfliesthatmdicated woodwastheprimarysourceofcookingfuel. PCI-IAR - PercemFarmsusingGiucodfmCookingin1983zPercemageoffamfamfliesthat indicatedcharcoalwasprimarysourceofcookingfuelin1983. 170 PGA583 = Percent Farms using Propane for Cooking in 1983: Pawnhgeoffimfamfliesmstmdicatedpropamgaswuthepdmarymwofwofingfiielm1983. TOTINT83 = Sum Total of Agricultural Intensity in 1983: Represents all measures of agricultural intensity added together foreachsection. Thisindudesthefi'equencyofcultivationaddedwithtotaltechnologyto form one index. Farm Household File PRINFARMER 2 Principal farmer: Sort by ID number for section and sample area. Numbers used are (1)11 (male head of household), (1)22 (female head of household) etc. TAREASOWNR - Tareas owned. TAREASRENT - Tareas rented. TAREASQUAT s Tareas squatted. TOTALTAREA - Total Tareas on Farm. PTAROWN -= Percent Tareas Owned of Total Tareas. PTARRENT - Percent Tareas Rented of Total Tareas. PTARSQUAT 8 Percent Tareas Squatted of Total Tareas. TAREASIOPE - Tareas Sloped. PTARSLOPE . Percent tareas sloped of total Tareas TAREASRAVI = Tareas in Ravine. PTARRAVI = Percent tareas in Ravine. TAREASFLAT s Tareas on flat land. PTARFLAT = Percent tareasonflatlandoftotalTareas. TAREASCLRD - Tareas of farmland cleared yearly. PTARCLRD - Percent of tareas cleared yearly of total farm land (TOTALAREA). CHNBAMTCLR - Changeintheamountoflandcleared. METHCLRFOR - Method for clearing the forest land. REASONCHNG =- Reason for changing land use/abandoning the land. TAREASCHAN - Tareasdepadedorchangedlandusestatus. PTARCHNG - Percent tareas changed/depaded of the total farm area. OTHERLAND - Other Farmers with depaded land. 171 CAUSEDEGRD = Causes of land Depadation. AGCONDIMPV 2 Agricultural condition improved. ADULTSWHOM = Number of Adults who expressed a mipation preference. AVEURSDUA = Average years of education of adult out-mipants. CONDFOREST .. Condition of Forest. AVEFALLOWP = Average fallow period. CHNGFOLLOW == Change in fallow period. PERCFOODPR - Percent of food purchased. TYPEIRRIGA a Type of irrigation. TYPEFERTL x Type of Fertilizer. TAREASCROP c Tareas cropped in last year. PTARCROP a Percenttareascroppedlastyearofthetotalfarmarea. PERCFARMPR s Percent farm produce sold. TAREASGRAZ a Tareas pazed. PTARGRAZ =- Percent tareas pazed of the total farm area for sections and sample area. IRRIGAT - Irrigation. LEVEL a level the land. TERRACE :- Terrace the land. ANCHOR = Useoftreesassoilanchors. SPECISEEDS - Use of special seeds. ROWS = Plant in Rows. WEEDING - Practice weeding. INSECTPEST - Use of insecticides. HOUSEHOLDS - Household size. NUMALES :- Number of household males. NUMADULTS . Number of Adults. 172 AVEAGEHOUS -= Average age of household. AGEOLDESTD - Age oldest household member. AGEYOUNGD - Age of youngest household member. AVEYRSEDUC = Average years of education/household. NUMADULTEM a Number of adult Out-mipants. NUMADULTIM - Number of adult In-mipants. NUMADULTRE = Number of adult Return-mipants. NUMADULTPO = Number ofadult Potentialmipants. PRODUCED = Kilopams produced on farms last year. TAREA = TotalNumberofFarmTareas,includingowned,rented,andsquattcd. PTOWN =- Percenttareasownedofthetotalfarmareaflarea). PTRENT = Percenttareasrentedofthetotalfarmarea. PTQUAT- Percent tareas squatted ofthetotalfarmarea. PTSIDPE - Percenttareasonslopedlandofthetotalfarmarea. PTRAVE = Percenttareasinravinesofthetotalfarmarea. P'I'F'IAT a: Percenttareasoffiatlandofthetotalfarmarea. PTCLRD 2 Percent tareas cleared (deforested) of the total farm area over the past year. PTCHNG - Percenttareaschanged(degraded)ofthetotalfarmareaoverthepastyear. PTCROP - Percenttareascroppedofthetotalfarmarea. PTGRAZ - Percenttareaspazedofthetotalfarmarea. TINPROD = Tareas in production of the total farm area, including cropped and pazed lands. PTINPROD - Percenttareasinproduaionofthetotalfarmarea. TFAIJDW - Tareasinfallowrotationofthetotalfarmarea. PTFALIDW - Percenttareasfallowofthetotalfarmarea. AGCFR87= Apiculturalcrop/fallowratiofor198’7. Ratiooffallowtoaoppedlandforthe designatedyear. PFAL87 =- Percent fallow land of the total farm area 1987. AVPRODPT - Average production per tarea of output, measured 173 in kilocalories for both plant and animal production. AGFREO - Frequency of production, after Turner and Doolittle (1978). AGINT87 2 Agricultural Intensification 1987: Measure of the use of intensification technology, such as fertilizer, irrigation, erosion control, special seeds, etc. TOTAG87 - Total Agricultural Intensification 1987: A clunulative measure of the frequency of production added with the total intensification measure noted above. PADULTEM = Percent Adult out-mipants of the total household. PADULTPO - Percent Adult potential out-mipants of the total household. PADULTIM = Percent Adult in-mipants of the total household. PADULTRE - Percent Adult re-mipants of the total household. NE'I'MIG87 - Net Household Mipation 1987: Calculated as Percent adult in-mipstion plus re- mipation minus adult out-mipation. OVERMIG87 -= Overall Mipation 1987: Calculated as percent adult in-mipation plus remipation plus out-mipation. FPPR87 =- Farm Level Percent Population Pressure 1987: Calculated as the farm level population pressure on the land resource. Determined from the following relationship: TAREA divided by HOUSEHOLD to determine the average farm level density or tareas per person; divided by the constant 13.333 that is derived from 80 tareas divided by the average 5.95 persons per household, taken from DomificanCensusdataTheratioisthenuandmdizedasapercenmgeofcapadtyu 100%. Potential Mlgrants File RELAHEAD 8 indicates the relationship of individuals with the designated Head of Household. SEX - indicates numbers of males and females. AGE 2 Age of household members. YEARSCHOOL -- number of years in school. OCUPACION = Occupation of household members. MARITAL = MaritslorCivilStatusofthehouseholdmember. WHENPLANMI - When is mipation planned. TIMEAWAY - How long is planned mipation. WHYPIANGO . Reason for Planned Mipation. ROIBOFENVI - RoleoftheEnvironmentintheDedsionforPlannedMipation—UckenScaling 174 ANTICACITV = Anticipated activities in the Destination. WHEREPLANM = Where is the Planned Destination. DESTINCLAS - Classification of the Planned Destination. THEREBEFOR = Have you lived there before? FAMILTHERE = Have you Family or Friends there? WHYCHOSDE = Reason for selecting Destination. WHERESTAY = Where do you anticipate to Stay upon arrival? Out-migrants File RELHEAD = relationship of individual out-mipants with the designated Head of the Household. SEX 8 males and females. AGE 2 Age of out-mipants at the time of departure from the household. MARITAL 2 Marital Status of out-mipant at time of departure. YEARSCHOOL = Number of years of school before departure. OCUPACION =- Occupation of out-mipants before departure. REASONLEFI‘ a Indicated reason for out-mipation. INFLU 8 Role of the environment in the decision to mipate. Lickert Scaling. DESTINO = Destination of the Out-mipant. CLASSDESTI -= Classification of the Destination for Out-mipation. BIRTHPLACE = Birthplace of the Out-mipant. AGENOW = Current age of the Out-mipant. YEARSLEFI‘ = Years since out-mipant departure. Non-migrant Slayers File. RELAHEAD c Relationship to the head of the household. SEX = numbers of males and females. AGE . Age. YEARSCHOOL - Number ofyears in school. OCUPACION - Current occupation of household member. 175 MARITAL - CivilStatusofthehouseholdmember. REASONSTAY 8 Selected reason for staying in the Household or preference for non-mipation. Return Migrants File RELAHEAD = relationship of the Return Mipant to the designated Head of the Household. SEX - Males and Females. AGE 2 Age of the Re-mipants. YEARSCHOOL = Years of School of the Return-Mipants. OCUPACION 8 Occupation ofthe Return Mipants. MARITAL - Marital/Civil Status ofthe Return Mipants. JOBBEFLEFT = Occupation Prior to Mipation. TIMEAWAY =- Time Away from Household before Return. LOCARETURN = PlacelocationpriortoReturnMipation. CLASSPLACE = Classification ofthe location prior to Return. REASONI.EFT= Reasoncitedfororiginalmipationfromthehousehold. INFENVIGO = Influence of the Environment in the original decision to mipate from the household. Lickert Scaling. AGEWHENLF = Age of the Return Mipant when first left the household. AGEWHENRET = AgeoftheReturnMipantuponreturntothehousehold. MARITALRET = Marital Status of Return Mipant upon returning to Household. EDUWHENRET = YearsofformsleducationuponReturntothe Household. JOBWHIAWAY = Occupation of Return Mipant while Away. REASONRETU = Reason for Return to the Household. INFENVRET - InfluenceoftheenvironmentintheDedsionforReturnMipation. LickertScaling. REASONSTAY = Reason indicated for decision to stay in the household, rather than preference for re-mipation at a future date. Ic-mlgrantsFlle RELAHEAD - RelationshipoftheIn-mipanttothedesignatedHesdoftheHousehold. 176 SEX - Males and Females. AGE = Age of the In-migrants. YEARSCHOOL - NumberofyearsinschoolforIn-mipants. OCUPACION = Occupation of the In-mipants. MARITAL s MaritalstatusoftheIn-mipants. BIRTHPLACE = Birthplace of the In-migrants. AGEWHENINM = Age when in-mipated to household. SOURCEAINM a Source Area of In-migration. SOURCECLAS - Classification of Mipation Source Area. REASONCOMI :- Reason given for In-mipation to Household. ROLEOFENVI - Role of the Environment in the Decision to In-mipate to the Household. Lickert Scaling. MARITARRIV = MaritalStatusofIn-mipantsuponarrivalattheHousehold. EDUBEFCAME =- Years of Education before In-mipation to Household. OCUBEFCAME = Occupation of In-mipant before coming to Household. SOURCEJOBI a Source of Information about Jobs and Economic Opportunities. SOURCEJOBL = Source of information regarding quality of life and facilities in the area of In- mipation. REASONSTAY = Reason cited for preference to stay in the Household rather than plan for future re- mipatron.’ 177 List of Political Sections by Relative Population Pressure (Grouping based on 1977 Relative PPR Data) Low Group Middle Group High Group 250304 (Franco) 250501 (Cele) 250509 (Pied) 260301 (Mamo) 250506 (Guama) 250309 (Loma) 250503 (Diff) 250514 (Y erba) 250507 (Inoa) 260111 (Toma) 250301 (Cague) 250310 (Mese) 250512 (Mata) 750513 (Pedre) 250802 (Cebl'r) 250308 (Jagua) 250508 (J icome) 250511 (Montes) 260303 (Caciq) 250128 (Sabana) 250504 (Rubio) 250120 (Lopez) 260109 (Pslmar) 250311 (Pinali) 250505 (Eugen) 250307 (Janey) 250306 (Juncal) 250502 (Cuesta) 250305 (La Gus) 250102 (Baitoa) 250303 (Dicaya) 250510 (Las Pia) 250312 (Y aque) APPENDIX B APPENDIX B INTRODUCCION DEL CUESTIONARIOI ll tcna do invcctigacién trata cobra cl tcnélcno dc la dc~ torcctacién a la luz dc la prohlcnitica cociocconénica para cacar conclucioncc dccogfiticac y gcogrfiticac con la ayuda do t6cnicac cartogriticac. In otrac palahrac, c1 autor crcc guc hay unc in- tordcpcndcncia cntrc loc procccoc dc dctorcctacién y dcgradacién dcl achicntc--cono crocién y dctcrioro dc la ticrra--acocicda con nigraciGn. Para cxaninar cctac intordcpcndcnciac cc nccccario cntrcvictar vivicndac on lac ircac ruralcc para dotinir loc patroncc nigratorioc (onigracién, incigrccién. rcnigracién, y la ligracién potancial) dc loc mic-bros do one vivicndac. Lac cntrcvictac tanbi‘n prctcrdcn dcrcctar lac cxpcricnciac dc loc canpccinoc con cl uco do loc bocgucc, ya quc cllac ha rccultado on una vicihlc dcgradacién anhicntal quc cc pcrcihc on la crocién y dccgactc dc loc cucloc do nontahc. El invccitgador principal dc cctc cctudio as Richard a. Sanhrook, un gcogr‘to dc la Onivcrcidad dcl Ectado dc Michigan, on loc lctadoc Onidoc. 8r. Sachrook cc un protocor vicitantc con hcca del progrcna Fulbright. Durante cu vicita a la chfiblica Doninicana, cl Profocor Sanhrook ccti cnccfiando un curco dc cartograffa tonitica en la Onivcrcidad Catélica Hadrc y uacctra dc Santiago (u.c.u.n.) coco nicnhro dcl Dcpartaccnto dc aictoria y Geograffa. Bctc cctudio ticnc cl rccpaldo dc la u.c.n.u. y do la Univcrcidad dcl lctado do Michigan. La ohtcncién dc conclusioncc acad‘nicac on ccec cctndio con c1 cotivo dc la invcctigacién y no hay ninguna rclaciGn eon proycctoc gubernaccntalcc. Loc nocbrcc o apcllidoc dc lac pcrconac guc ccrfin cntrcvictadac on cctc cctudio no ccrin rcvcladoc. Dc todac cancrac, tanpoco cc nccccario dar cu apcllido para participar on care cctudio. Copiac dcl rcportc final ccrin prcccntadac a1 Contro dc Bctudioc Urbanoc y Rogionalcc (C.B.0.I.) qua actual-onto cct‘ inicifindacc on la u.c.n.u. Tachi‘n, copiac dcl rcportc final ccrin dcpocieadac on la Scccién Dominicans dc la Bibliotcca dc la 0.C.fl.fl. Attontancnt 28 files Protocor R. A. Sanbrook 178 179 3-1 SUB-HODULO A AGRICULTURA lnctruccionccr Prcguntc cl jctc dcl hogcr o c ocro cicchro dc la facilia guc toca lac dceicioncc agropccuariac. Ponga la inforcaciGn cn la hoja dc rccpucctcc, Bub-nGdulo a. A cuguirccc quc 1a pcrcona tcngc una dcccripcifin dc cu rclacién con cl jctc dcl hogar, pcro cu nocbrc no dchc aparcccr on cl cucctionario porguc 6ctc cc cn6nico. to) 1b) 2) snatcd o algfin sic-bro do an tanilia poccc u ocupc alguna ticrra para cultivcr coccchcc 0 para gcnadcria? (8£--1, lo--2i ci cc 2g, ponga lc rclaciBn con cl jctc cn la Licta A, Partc 1c. Intonccc, vaya a la Prcgunta 2.) DIE! In loc filticoc cinco choc, tuctcd o clgfin sic-bro do on Icoilic poccfc u ocupcba clguna ticrrc para cultivar coccchcc o para gnnadcrfc? (8£--1, lo--2s ci cc 22, tcrninc ccec cnrrcvicra. Si cc 2g, pongc 1c rclacién con cl chc cn la Licea A, Force 1h. racbifin. lac prcguntac ciguicnccc dcbcn cc: cn cl pacado.) loci cantidcd dc ticrrc cc cuya y/o arrcndada y/o ocupada cn cctc lugcr7 (on carcacl 8011: a) Cultivcda por uctcd h) Icntada a oero c) 8c la dc a alguicn y rcpcrtc lc cocccha dl Iipotccada on an pocccién cl In cucccci6n £1 Otro lccpccitIEE:r ARRIIDADAI a) Icntada para pagarla cn placoc h) Para sonar 1c cocccha c) Suya y dc orroc d) aipotccada cn pocccisn con oero c) A ccdia fl A no quarto g) Otro (ccpccitiquc) OCUPADI (81 no II BOYA o ARRIthDAls a) Cufintc ticrra A-2 3) ecfino clacifica uctcd gcncrclncntc 1a ticrra quc uctcd trabaja? I Parcclac Cantidad on Tarcac I Cuccta/loca ' " ‘ ‘ 2 aoyo 3 Llanoc 4 Otroc 4) coufi cantidad dc ticrra forcctcda ha uctcd limpiado gcncralnente cada one para la ciguicntc actividad? (cn tarcacl 1 Agriculture 3 Lona 2 Crianza 4 Otroc ,5) In proccdio, aha cccbiado uctcd la cantidad dc ticrra que usted ha linpicdo cada afio dccdc 1977? Auncntado cuctcncialncntc Auncntado pcqucfiancntc No ccmbiG Dicninuido pcquchancntc Dicninuido cuctcncialncnte No cplicablc GMEUNd 6) Cuando uctcd limpi6 loc bocqucc para la agricultura, aqué n‘todoc utilicé para prcpcrcr la ticrra? rumba y qucca Tractor Arrado dc bucycc Dccycrhar con (chapco q hum) Otro (ccpccitiquc) IJOIHILILJIUE 7a) aha uctcd abandonado o canbiado cl uco dc cu ticrrc? [gunman-c (SI--1, No--2; cn caco dc ££J prcguntc Prcguntac 7b 9 7:. In caco do no, pacc a la Prcgunta 8.) UD nPuLA'Hiz 09 7b) ePor qu‘ la ha abandonado o canbiado? Por cucloc pobrcc (50mm SOIL“ No cirvcn ya para nada La ha canhiado dc cultivo a crianza Erocién Por cc ahicrto 1a ticrra Otro (ccpccifique) UD RPLILRILE 7c) £Qu6 cantidad dc ticrra ha tcnido quc nbandoncr o cambiar por cca rasén? (cn tareas) “02.: 3999 [IMMDP was 07 SDLML PROBLEMS 0.5 bmmcuN-a 8) {Dated cree que otroc canpccinos han pcrdido tierra dc forms similar? (S£--1, No--2, No cé--9)flb£. $022.01 P0821311 POE )E ABIEZTD Lil 71332.8. 0 PPRAUE Lil HR LAMEMDD DE LDLTIUJ 'h LZIRUD? 181 9) deufil crcc uctcd guc cc lc cauca principal dc la pérdida do ticrra productivc 0 dc la dcgrcdacién dcl anhicntc? chuic (calc tcnpcraturc) 1 Sc han trcbajcdo nucho 2 rumba dc firholcc 3 Iroci6n, ticrra dcgradada 4 Otro (ccpccitiquc) 5 BO [04.) SE 2011700! MRI-(7 '1 £70211“ [.1 10) In tircinoc gcncrclcc, alcc condicioncc para ccnhrcr o coccchar cn cctc lugar cc han ncjorado o dctcriorado dccdc 1977? Sc han ncjorado nucho Sc han ncjorado un poco No canhi6 Sc han dctcriorado un poco Sc han dctcriorado nucho “Auto-0 11)‘ In gcncral, alac condicioncc dc loc bocqucc cn cctc lugar cc han ncjorcdo o dctcriorado dccdc 1977? So han ncjorado nucho Sc han ncjorcdo un poco No canhié Sc han dctcriorcdo un poco Sc han dctcriorado cucho “bun-D 12) eCuil cc cl proncdio dcl pcrIodo dc dcccanco cn cuc canpoc? IIcnoc dc un ailo (barbccho) 1 ucnoc dc 2 choc 2 2 - 4 anon 3 5 - 7 afioc 4 8 - 12 choc 5 (Diga cl pioncdio dc dcccanco para todoc loc cacpoc juntoc.) 13) Para cuc cultivoc, scGno ha canbiado cl pcrfodo dc dcccanco dccdc 1977? Auncntado cuctancialncntc Auncntado pcqucfiamenrc No canhi6 Dicninuido pcqucfiancntc Dicninuido cuctancialncntc No aplicablc @U‘bUNd 14) iQu‘ porccntajc dc la comida que consume an familia ticne que comprarce? 0 - 10‘ 01 51 - 60‘ 06 11 - 20‘ 02 61 - 70‘ 07 21 - 30‘ 03 71 - 80‘ 08 31 - 40\ 04 81 - 90‘ 09 41 - 50‘ 05 91 - 100\ 10 15a) 15b) 16a) 16b) toctcd a Iojado cu ticrrc con agua irrigada cn loc filtinos 10 choc? (Sf--1, lo--2; ci cc Lg! haga Prcgunta 15b. Si ca 22, pace a la Prcgunta 16a.) £Qu6 t‘cnico ha utilicado? Acucducto 1 LAMA/R [a Canal 2 rubc rfa/honba ' 3 Otro (ccpccitiquc) 4 286* MEI saa uctcd aplicado algfin rcrtilicantc durantc loc filtinoc 12 ccccc? (Sf-~1, lo--2; cn caco dc 22, poco a la Prcgunta 17.) aQu6 tipo dc tortilicantc ha ucado uctcd? Pcrtilicantc qufnico 1 Ixcrcccnto cocial 2 Otro (ccpccifiquc) 3 (Lac Prcguntac 17 - 2O cctin dctrfic dc 1a hoja dc rccpucctac, Suh-n6dulo A.) Acnvuuuocmcc. ouuo and! .Ififl‘ lIIIl IU- .Uflhh cog owc> .cuncnuca onuch\oficnuoc “I03“: I” IGIU coon-h ucu i 0.0-U ccdcneouacz oaeo cvuvucm a cedar-coerce:< onoauenu o OOdflEO> UdfidUGIU o .cccnv cmauosvoun ocoquccu .cccuca. cua>nu~au UOH‘ ceuccou ccuo> . o>aen=u ...-cc «2 condone co» co ceuccoo an on cuec> a cuuooccouo cu ccuc: caucucv .uoccu won .5— h— cucsocumlic cancelincm Illiehlf in 3.5a... 3...... etc!» a .x in Season 3.... it... n r .818. cinch-Scat! cor-cocci...- c x at .x .x .x .u sacs-o IX .x .x on Solace! x .x .x . .x .x Jase m on .x .x a J. .x .x sac-ca x x a x .x .x ... .x x ... .x 1.2 a x m ... ... . x .x .... x x m x .x ... m I x .x a... x x .x .x .x on x .x :2 x m .x .x m x .x .u and x .x .x .x x .x is: x .x ..x .x .x x .aaaas x .x .x .x .x .x x ... ... car... a a r s z c z a = e z e z c z a 83 coca-c Iccccc 83:3 599 cola-U cocccch l... ‘31: :8; 2c ace-U Scam cc acct-.30 23359.3( . 39B .cauecucucu. s— cucnucuciid odocmcinom .covuuuococc. ouuo I IOI> Quads-0 04.600 l ccucuu0u\ch0nco ouuon 1 can: coca u couucoc ii i .09 cccmnnc l 0.06:9 cdh cwnuucuuc 3! OM .acucncu on .02. cccc: «w cocauua cod cc caducch ca non onacoccou .ccucncu on .02. coco: «p coluuaa cod cc ovuvcc> .ccccncu coco: «. cod cuccuoa .0 .08. cocuuna coauoooouc C8353 cuucoccco cu cuca one cow oecuuca .0 VIOdUGIU cuuccccco 02 «conucucv cocooHc one cocnn c!» — an a. cacao-unite oaaeostnsm «cocoa Np coluuub cod no cacoccc0\ccacc«cc cmuouoooua oouccu c: o occau canaccu on on ounlcai cacao o ocucau .m— .oou«u«ocmcc. couuo concon on occuu< .cc>«> announce. emu- Iouc ca uuucocu cuca ccaonuc cc cvuucuccac IGNIHNOU CU SWdDODNUIBOU caucuu cu ucacouz OUCUCOHU\OCOMOU endocmmuua cucn cooc co ocuono icon on cwauusuuccou «ocauau ac onus accuse» .ccccc up condone can co ouccuacuOnoa cum tcac once: on o: «m .cccucu cc. «concede incencc cocc con cu Incanoo ccuc no can dune» .ccccc a. con luuau cod cc ouccul tcuOncc oeucc on «a .«ttoz ..ttum. poo-ca a. cocuuam cod cc ouccuccanca caud- ccuc: ozone oz» ouccaacuOnc: on code ¢¢¢mnh N 02 n oz n ace n anaconda. oz 2 an . um — cc: u oz o cccucdz — um «cannon: cc ccucc unannouc «conququcca % «cecuoaccu cu cooalasv couuocoun coo conduduuccca cuccuov ecu-o cocoa «cauu cc cou conduccc ccuccou 2 noun: one» loop ccuco ccucuou~ vcuco cue-an» con noun: cocoa» am¢AOUH¢0< m‘UHBU‘um mmflOHfll no can Adam on cuccucumllc canvwcinsm SUB-HODULO B CKRACTERISIICN GENERALBS D! II FINILIA-iflrlflflROS QUE USUAEHENTE” VIVBN JUNTOS Y COHEN JUNTOSa Instruccioncc: Prcguntc cl jcfc del hogar o a una pcrcona apropriada ci cl jcfc no pucdc contcctar. Ponga la informacién dc cada pcrcona en la hoja dc rccpucctac, Sub-n6dulo B. Snpicce con cl jefc del hogar. HIEHBROS DE LA CRSA (SOS RBLACIONBS CON EL JEPE DEL EDGAR) 1a) 1b) 1c) 1d) NOTA: SEXO 2) EDAD 3a) 3b) Por favor, dcnc una licta dc lac rclacioncc (con cl jcrc del hogcr) dc todac lac pcrconac quc viven ucualncnte cn cctc casa, OEL'dHIE.0MMfl¢mfiDQ (Ponga lac rclacioncc con cl jcfe en la Licta B.) sac uctcd olvidado alguna pcrcona do an fanilia, pcrcona adults vicja 0 nine quc ccti vivicndo pcrnancntcncntc con noted on ccta c ca? ‘ (Alicea puma) (Si cc 3;, ponga 1a rclacién con cl jcfc en la Licta I.) aha olvidado uctcd alguna pcrcona quc 22 cc nicnbro dc cu fanilia, cono cc una don‘cticc, amigo o conocido, un habitantc que ccti vivicndo pcrnancntcncntc con uctcd cn ccta caca? (Si cc 3;, ponga la rclaeién con c1 jcfe en la Licta I.) In adicién, econ uctcd ha vivido alguna pcrcona quc ha cctado dc paco temporal-onto, dc vacacioncc o fanilicrcc dc visits, quc haya, por lo ccnoc, pacado trcc ncccc antcc dc ircc? (Si cc 2;, ponga la rclacién con cl jcfc en la Lictc I.) Repite lo ciguicntc para cada micmbro en la Licta I. can cctc pcrcona nacculina o fenenina? (Hacculina--1, Pemenina--2) eCuil as an cdad? £En qui ncc y cho nacié usted/él/ella? (Hes , Afio ) EDUCACION '4a)‘“tCuil cc cl'filtino curco dc cccucla qua 4b) £Cu£1 compl 4c) LCufil l DU new STATUS HAT S) {Cuil Nunca cc ha cacado Viudo Divorciado Separado Otro (ccpccifique) Ninguno Prinaria T‘cnico cin bachillerato Sccundaria Ticnico con bachillcrato Univcrcidad Otroc No c6 QQGUIhUN-o “ted/il/ella’eonpletfi? a nuisance. IE I'MUDMAHED 99 l nfincro dc choc dc cccucla que uated/él/ella ha ct6 cc cu ocupacién? Profcccional, ticnico Adminictrador, capatar,‘ccrvicio pfiblico Oticinicta, profocor, Dilef‘l’ Vcndcdor, conerciantc, alncccnicta Agricultor, ranchcro Chofcr . Artccano, nccinico, cactrc, carpintero, ohrero Jornalcro, ayudante, BMW lnhfibil, chiripcro Sirvientc dc caca, can do caca, ectudiantc No hacc nada Otro (ccpccitiquc) RIHONIAL cc cu cctado marital on cctc memento? Cacado Uni6n libre QOMhuN-l LUGAR DE NACIHIENTO 6a) tfla nacido uctcd/él/ella cn cctc lugar? (s£--r, uo--2) PLUM“: 6b) Si no ha nacido aqui, scuindo llcgé a este citio, pucblo, o lugar ? (Afio , Hes ) 190 ANEXO PARENTESCO (CODIGOS) RELACION DE OTRAS PERSONAS EN LA FAMILIA CABEZA ESPOSO(A) HIJO(A) HERMANO(A) PADRE/MADRE 11) (2) (3) -(4) (5) CABEZA 01 - - - - ESPOS0(A) or - - 72 82 Ire 11 21 31_ - - 2do 12 22 32 - - 3rd 13 25 33 - - (I) g 41:0 14 24 34 - - a 51:0 15 25 35 - - In s g, 5to 15 25 35 - - : = 7100 17 27 37. - - E 8vo 18 28 38 - - 3 900 19 29 39 - - :5 :8 5 he 41 51 51 - - rd 2do 42 52 52 - - 5 A 3m 43 53 63 - - u < o :5 4to 44 54 54 - - U z 83 g 51:0 45 55 55 - - .— § % 5to 45 55 55 - - E ' 71110 47 57 57 - - PADRE/MADRE . 81 83 - 84 85 arms 91 _ - _ _ RELACIONES PERSONAS 92 _ _ _ _ IRRELACIONADAS SIRVIENTE 93 - - - - 191 SUB-HODULO C resurrrrcacrou or-rn1caaurss Inctruccioncci Prcguntc cl jcfc del hogar cobrc cada cmigrantc y ponga lac rccpucctac en la hoja dc rccpuestac, Sub-m6du10 C. Pre- gunte c una pcrcona apropriada ci cl jefe no pucdc contestar. 0n cmigrantc cc un micmbro regular dc la recidcncia que cc ha ido per mic dc trcc mecca o intcnté ircc por mic de tree mecca. Si no hay emigrantcc, vaya a1 pr6ximo cub-médulo. 1a) shay alguna pcrcona qua vivia en cctc caca que cc ha ido en los filtimoc cigag shes a vivir a otro parajc, pueblo o ciudad? (oi as 12, vcya a1 préximo cub-médulo.) 1b) £Cu51 cc 0 cufilcc con cuc rclacioncc con c1 jcfc dc la familia? (Ponga una dcccripcién dc la rclacién con cl jcfc dc cada cmigrantc en la Licta C.) NOTA: chita lo ciguicnte para cada cmigrante en la Lictc C. 2) ans cctc pcrcona macculina o fcmenina? (Hacculina--1, Pcmcnina--2) 3) £Cuil cra cu cdad cuando il/ella partié dc cctc lugar? (Afioc Completes) 4) tCufinto ticmpe hacc quc il/ella partié do estc citio? (Hcccc , Ahos ) S) (Cuil crc cu cctado civil en el memento dc él/clla partir dc cctc lugar? Nunca cc habfa casade Cacado UniGn librc Viudo Divoxciado Separado Otro (ccpccifique) No of OdfiifibuN-c (Siguc) 6a) inacta qu‘ curco llcgé él/ella a complctar a1 ticmpo que parti6 dc cctc lugar? Ninguno Primaric T6cnico cin bachillcrato Sccundaria T‘cnico con bacillcrato Univcrcidad Otroc No c6 Udd‘UlbUN-c 6b) tCuSl cc cl nfimcro dc afios dc cccuela que él/ella ha complctado? 6c) eCull cra cu ecupacién antes de calir? Professional, ticnico 00 Administrador, capatar, cervicio pfiblico O1 Oficinicta, profccor 02 Vcndcdor, comcrciante, almaccnicta 03 Agricultor, ranchcro 04 Chotcr 05 Artccano, mccfinico, cactrc, carpintero, obrero 06 Jornalcro, ayudantc 07 Inhibil, chiripcro 08 Sirvicntc dc caca, ama do casa, cctudiantc 09 No hacc nada 10 Otro (ccpccifiquc) 11 7a) LCuil cc 1a vcrdadcra rarén para quc il/ella particra do cctc lugar? Cambio dc trabajo 01 No trabajo 02 El trabajo era incuficicntc para cl coportc familiar 03 Incaticfaccién natural del trabajo 04 Compra do ticrra OS Incontrar un mejor trabaje 06 Ofcrta dc mcjor trcbajo 07 Para conccguir cducacién para él/clla 08 Para conccguir cducacién para auc hijos 09 Para casarse 10 Para acompafiar a la familia 11 Para juntar la familia 12 Per problemas cociales o familiares 13 Salts do amabilidad 14 Ticrra dcgradada 1S Sucloc pobres 16 Otroc (ccpecifique) 17 No c6 99 EYJZDDB‘D uncanny I: 1'4)me PMMM m IQ 7b) 8a) 8b) 9) 10) In una cccala do 1 a 5, equfi papal jugaba la dcgrcdacién dcl ambientc (como dafio dcl cuclo o cuclos pobrcc) en la dccicién ra partir dc cctc lugar? Ninguna importancia Poca importancia His 0 mcnoc importancia Alguna importancia Hucha impertancia MfiUN-D Cuando él/clla cc fucron dc aqui, sdénde cc qucdaron a vivir per 3 ncccc o mic? (Hombre o Dcccripcién del Lugar) eQuC tipo dc lugcr era ccc? Hetrépeli/capital Ciudad Pueblo Pinca/campo Villa/plantacién Otro (ecpccifiquc) .DUT'DF LDWLNHU4 £Cu£ntoc afioc ticnc éllclla ahor JGUIwa-c ? (Idad cn Afioc Complctoc) dbénde naci6 61/clla? (Nombrc o Dcccripcién dcl Lugar) D-l SUB-HODULO D INHIGRRNTBS ‘DIE Inctruccioncc: 0n inmigrcntiScc un micmbro dc lc recidcncia que ha llcgcdo en los filtimoc cincc choc y ha vivido en la rccidcncia per mic dc trcc mccec. Prcgunte c ccdc inmigrantc do 0 - 5 choc y pongc lc informccién en la hojc do lac rccpuectcc, Sub-mGdulo D. Si no ecti en cccc, pregunte a una pcrcona cpropriada. Si no hay inni- grcntcc, vcyc cl préximo cub-médulo. NOTA: Prcguntc lc Cuectién 1 cl jefe del hegar o a una persona apropricdc. 1) Lucy clguncc pcrconcc on case quc ha llcgcdo en los filtimoc m choc y ha vivido cn 1c rccidcncic por mic dc tree mecca? (Si cc £2, vcyc cl préximo cub-médulo.) NOTA: Prcgunte lo siguiente c cadc inmigrcnte. 2) £Cuil cc cu rclaci6n con cl jete dcl hogar? (Pongc unc dcccripci6n on 1c Lictc D.) 3) zoénde ncci6 uctcd? (Hombre o Dcccripcién del Lugcr) 4) tQué tiempo hace qua cc mud6 c cctc lugcr? (Heccc ___, Ahos 5) LCufil era cu cdad cucndo uctcd cc mudé a cctc lugar? (Idad cn Ahoc Completes) NOTA: Si cctc pcrcona tonic mcnoc de doce choc, termine este sub-médulo para cctc pcrcona. 6c) éD6nde vivfc uctcd antes de mudarse a este lugar? (Nombre o Descripcién del Luger) 6b) £Cuil era e1 tipo de citio de su residencia cl tiempo que la dejé? Metrépoli/capital Ciudcd Pueblo Pincc/ccmpo Villa/plantacién Otro (ccpecifiquc) (DUT'DF‘LJDULYNV4 _JO‘U|Atkod 7c) cCufil cc la vcrdcdcrc rcrén para que uctcd praficricrc mudarcc c cctc citio? Ccmbio dcl trcbcjo 01 No trcbcjo 02 I1 trcbcjo crc incuficicnte para el coportc familiar O3 Inccticfcccifin naturcl dcl trcbcjo O4 Comprc dc la ticrrc 05 Incontrcr mcjor trcbcjo 06 Ofcrtc dc mcjor trcbcje o7 Pcrc conccguir major cducccién para uctcd 08 Pcrc conccguir mejor cducccién para cuc hijoc O9 Pcrc ccccrcc 1O Pcrc ccompchcr a la familia 11 Para juntcr la fcmilic 12 Per problcmcc cociclcc y familiarcc -I£ namr 13 Pcltc dc cmcbilidcd 14 Ticrrc dcgrcdcdc 1S Sucloc pobrcc 16 Otro (ccpccifiquc) 17 No c‘ . 99 TDD mmw TD use m I: 7b) In unc cccclc do 1 c 5, squé pcpcl jugcbc 1c dcgrcdccifin dcl cmbicntc (coco dcho dcl cuclo o cucloc pobrcc) cn lc dccicié pcrc partir dc ccc lugar? ningunc importancic Poca importancic His o mcnoc importancia Algunc importcncic Huchc importancic mature- 7c) £Qui£n tom6 lc dccicién do mudarse a ecte lugcr? Yo micmo Icpocc/ccpoco Nihoc Pcdrcc Otroc rclctivoc Implccdoc Otro (ccpccifique) \IOwwa-o (Sigue.) 8) Al ticmpo dc uctcd cclir, scuil crc cu cctcdo civil? Nuncc cc hcbfc ccccdo Ccccdo Uni6n librc Viudo Divorcicdo Scpcrcdo Otro (ccpccifiquc) QCIMbUN-I 9c) &Cu51 fun on filtimo curco dc cccucla complctado? Ninguno Primaric Ticnico cin bachillcrcto Sccundcric T‘cnico con bachillcrcto Univcrcidcd Otroc No c6 ”#01015“ch 9b) tCuSntoc choc complct6 uctcd cn lc cccucla? 9c) snail crc cu ocupcci6n cl momcnto dc uctcd ircc? Profccioncl, ticnico 00 Adminictrcdor, ccpctcz, ccrvicio pfiblico 01 Oficinictc, profccor 02 Vcndcdor, comcrcicnte, clmcccnictc 03 Agricultor, rcnchcro 04 Chofcr 05 Artcccno, mccinico, ccctrc, ccrpintcro, obroro 06 Jornclcro, cyudcntc 07 Inhibil, chiripcro 08 Sirvicntc dc cccc, amc dc cacc, cctudicntc 09 No hccc ncdc 10 Otro (ccpccifiquc) 11 10a) Antcc dc uctcd vivir cgui, étcnic uctcd alguna informccién dc trcbcjo y oportunidcd cn cctc lugcr? (s£--1, No--2; ci cc 22, vayc c lc Prcguntc 10c.) 10b) tcémo conciguié ucted cca informccién? Conoci doc I FlflllL‘l Amigos Peri6dico, radio, tclevicor Vicité cntcc c1 lugcr Otro (ccpccifique) LDD flfltutfl @U‘OUN-fi 10c) Antcc dc vivir cquf, ttcnic uctcd infornccién cccrcc dc lac condicioncc dc vidc y fccilidcdcc quc hcy on cctc lugcr? (SI-~1, No--2; c1 cc 22, no prcguntc 1c Cucction 10d.) 10¢) tCGno oonciquié uctcd ccc intorcccién? Conocidoc, FIDMLU Anigoc Pcriédico, rcdio, tclcvicor Vicito cntcc c1 lugcr Otto (ccpccitiquc) l”? HALMMO mauua 8‘ 8-1 SUB-HODULO E RB-HIGRANTES INEZ. Inctruooioncc: Pccguntc c1 jcfc dcl ogcr c1 hcy rc-cigrcntcc-- o ccc, pcrconcc quc vivfcn cn occc, cclicron pot cic dc trcc ccccc y rcgrcccron c cccc cn loc filtinoc scans-choc. Prcquntc dircctclcntc c ccdc zc-nigrcntc y pongc lcc rccpuectcc on In hojc dc rccpucctcc, Sub-modulo E. 81 un re-nigrcntc no ccti on cccc, prcguntc c1 jcfc o c unc pcrcona cpropricdc. Si no hcy rc-nigrcntcc, vcyc c1 proxino cub-modulo. NOTA: Prcguntc 1c Cuestién 1c :1 jcfe del hogar o c unc pcrcona cpropricdc. 1h) eflcy pcrconcc quc Vivian cn cccc, cclicron pot cic do trec ncccc, y rcgtcccron c cccc cn loc filtinoc ciao. cfioc? ZWEZ (81 cc no, vcyc c1 préxiuo cub-nédulo.) NOTA: Preguntc 1o ciguicnte c ccdc tc-nigrcntc. 1b) Decdc quc uctcd concncé c vivir cn cctc conunidcd, ahcbic uctcd vivido cn otro lugcr, ciudcd, pucblo, unnicipto, o pcrcjc pot trcc ncccc o lic? (Si cc £2, terninc cctc cub-n6dulo pcrc cctc pcrcona.) 1c) £Cu£1 cc cu rclccién con c1 jcfc dcl hogcr? (Pongc unc dcccripcién cn 1c Lictc E.) .2) iCuSl crc cu principcl cctividcd cucndo acted cctcbc cn cctc citio cntec? Profccioncl, tccnico 00 Aduinictrcdor. ccpctcc, ccrvicio pfiblico O1 Oficinictc. profccor 02 Vendcdox, coccrcicntc, clncccnictc O3 Agricultor, rcnchcro 04 Chofcx OS Artcccno, cccintco, ccctre, ccrpintcro, obrero 06 Jornclcro, cyudcnte 07 Inhibil, chiripcto 08 Sirvicntc dc occc, can do ccccf cctudicntc 09 No hccc ncdc 10 Otto (ccpccifiquc) 11 NOTA: Loc cigaicntec refieren c lc Gltinc vez qae vivic faerc. 3) teainto tlcnpo hccc qae rcgrecé por la filtinc vec c cctc lager? (uccec , Afioc ) NOTA: Si mic de cinco cnoc, ternlne ccte cab-codalo pcrc cctc pcrcona. 4) Antcc de regreccr, acainto tlempo tcnic acted facrc dc ca prccente citio de recidenclc? (Hecec , Afioc ) Sc) tCail crc e1 citio qac actcd acaclncntc recidic eacndo cctcbc faerc? (Hombre o Dcceripcién dcl Lager) 5b) Cacndo acted vivic facrc dc cqaf, aqaé tipo de lager erc ece citio? Hetrépoli/ccpitcl Ciadcd Paeblo rincc/ccnpo Villa/plcntutién Otro (ccpecifiqac) mmbUN-c 6c) £Cail erc lc rczén mic importantc pcrc dejcr cctc citio? Ccnbio dc trcbcjo 01 No trcbcjo 02 El trcbcjo crc incaticlcntc pcrc cl coporte tcnllicr 03 Inccticfcccién nctarcl del trcbcjo 04 Conpra de ticrrc OS Encontrcr an ncjor trcbcjo O6 Ofertc de nejor trcbcjo O7 Para concegair edacccién pcrc acted 08 Para conccguir cdacccién pcrc cac hijoc 09 Para ccccrcc 10 Porn ccompcficr c lc familic 11 Porn jantcr lc fcnilic 12 For problemac cociclec y fcmilicres 13 Faltc de cmbilidcd 14 Tierrc degrcdcdc 1S Saelos pobres 16 Otroc (ecpecifiqae) 17 No 85 99 ILL nan. mam-mm , 3 6b) 7a) 7b) 8) 9a) 9b) 10) an ana cccala de 1-5, aqaé papel jagaba la degradacién del cmbicntc (cono dafio del cuclo o caelos pobrcc) en la decicién para partlr dc cctc lage ' Nlngana inportancia Poca inportancia Mic o ncnoc inportancia Algana importancia Masha importancia U‘fiUN-fi Caando dejé acted ecte citio, Lcail cra ca edad? (Afios Conplctoc) Caando llegé acted ccte citlo otra vcz, acail era ca edcd? (Afios Completos) &Ca£l era ca cctcdo marital caando llegé aqai la Gltima vec? Nanca se ha casado Cacado ‘ Union libre Viado Divorciado Separado Otro (ccpeclfiqac) dd‘mawN-o anacta qaé carco llcgé actcd? Ningano Primaria Ticnico cin bachillerato Scoandaria Técnico con bachillerato Universidad Otros \IU‘U‘bUN-I £Caintos afios completé acted en la cccucla? LCaSl era ca principal actividad caando acted ectaba en otro citio? Profesioncl, técnico 00 Administrador, capataz, cervicio pfiblico O1 Oficinista, profccor 02 Vendedor, comerciante, almacenicta 03 Agricultor, ranchero 04 Chofer . OS Artecano, mccinico, castre, carpintero, obrero 06 Jornalero, ayadante O7 Inhibil, chiripero 08 Sirvicnte de casa, ana de casa, estadiante 09 No hace nada 1o Otro (especifiqae) 11 Z31 11a) £Ca$l cc la racon mic importante para acted volver a cctc lagar? aajoc calario 01 No pado cneontrar trabajo 02 Pin del trcbajo 03 Tonia qac ahorrcr lo qac prometf ahorrar 04 acredé algana ticrrc o propicdad 05 Concigaié trabajo aqai 06 Tcmor a pcrder ca tierra aqai 07 La familla compré mic ticrrac/negocioc aqaf 08 No le gactaba cl trcbajo O9 Rctirado 10 Entermcdad, accidente 11 Trabajo trancfcrido 12 No lc gactaba cl citio 13 Prometié qacdarcc an ticmpo limitado 14 Otro (ccpccifiqae) 15 No c6 . 99 AZEIT.IuathfndLJ £5 11b) En ana eccala dc 1 a 5, aqac papel jagaba la dcgradcelén dcl cmbicntc (como dafio dcl caelo o cacloc pobrcc) cn la dcciclén para partir dc ccc otro citio? Ningana lmportcncla Poca importancic Mic o mcnoc importancia Algana importancia Haoha importancia U'Iwa-I F-1 SUB-HODULO F HIGRASTBS POTBNCIALBS Inctraccioncc: Prcqantc dircctamcntc a cada pcrcona en la caca. Si no ccti cn caca, prcqantc al jcfc del hogar. Ponqa lac recpacctac en la hoja dc rccpacctcc, Bab-modulo P. 1a) £CaSl cc ca rclaeién con el jcfc dcl hogar? (Ponga anc dcccripcién cn la Licta P.) 1b) licta actcd intentando madarce a vivir o trabajar faera de ccta villa, pacblo, o oiadad? (SI--1, No--23 c1 cc ;£, vaya a la Prcganta 2a. 51 ec £2, pace a la Prcganta 1e.) 1e) teail cc la racén para no madarce? Ticnc an trabajo caticfactorio 01 Lacoc familiarcc 02 rclta dc cdacacién 03 No cabc ci chi hay mayor trcbajo 04 Ticnc ticrra aan OS Dcmaciado viejo 06 ramilia larga O7 Ticnc caticicntc dinero 08 Ecti cctadicndo O9 Incapaeidcd ffcica 10 La ticrra cc bacna 11 ralta dc dinero 12 Otro (ccpccifiqac) 13 No c6 02. <12. can 99 ' (flora: Dccpaic de ccta Preganta, termine ecte cab-médalo para ecta pcrcona.) 2a) LCaSndo actcd picnca calir? Dcntro dc 3 mcccc Dc 3 a 6 mccec Dc 6 meccc a 1 afio Dc 1 ano a 2 afio tn 2 afioc o mic No c6, no cctoy ccgaro 00.11am»... 2b) tPor qai ticmpo lc gactaria a acted cctar facra? 3a) aPor Hanoc dc 3 mcccc Dc 3 a 6 mcccc Dc 6 mcccc a 1 afio Dc 1 aio a 2 choc Dc 2 a 3 afioc Da 3 a 5 anoc 5 anoc o mic No ci, no actoy ccgaro 0‘10“wa qae actcd intcnta calir de acte lagar? Cambio dal trabajo No trabajo El trahajo ara incaticianta para al coporte familiar Incaticfaocion nataral del trahajo Compra dc la ticrra Bncontrar major trabajo Ofarta dc major trahajo Para conccguir major cdaoacién para acted Para conccguir major cdacacién para cac hijoc Para cacarca Para acompafiar a la familia Para jantar la familia Por problcmac cocialcc y familiarac Palta dc amabilidad Tiarra dcqradada Saeloc pobrcc Otroc (ccpacifiqac) No c6 £104Lfllfl§ IHEEE 01 02 O3 04 OS 06 O7 08 09 10 11 12 13 14 15 16 17 99 ll 3b) En ana ccoala dc 1 a 5, eqaé papal jacga la dagradacién dcl cmbicntc (eomo dano dcl caelo o cacloc pobrcc) en la decicién para (Sigae.) partir da cctc lagar? Ningana importancia Poca importancia Mic o mcnoc importancia Algana importancia nacha importancia U‘bUN-i 4) 5a) 5b) 5c) 56) 5e) Sf) tQai acted ccpcra qae ceri ca principal actividad tan pronto cc made? Vicita cocial O1 Ectadiar 02 Bntrcncmicnto 03 Agricultara 04 Abrir an negocio 05 Empleado 06 Retirado O7 Ama da ccca 08 Otro (ecpccifiqae) 09 No c6 99 an; dacidido acted hacia déndc intanta cmigrar? (S£--1, No--2; ci cc 22, terminc cctc cab-médalo para acta pcrcona.) LCail cc cl nombrc da ccc lagcr o déndc ecti ccc lager? (Hombre o Deccripcién dcl Lagar) lQai tipo dc lagar cc ccc? Metrépoli/ccpital Ciadad Pacblo Pincc/campo Villa/plantaciGn Otro (ccpccitiqae) @WhuN-o cac vivido acted an ace lagar cntcc? (SI--1, Mo--2) aricna acted amigoc o conocidoc an ace lagcr? (SI--1, Mo--2) £Cail ec la major racén para cccogcr ccc lagcr? Cambio del trcbajo o1 Mejorac trcbajoc y oportanidadcc 02 Tiene ticrra a otrac propiedcdcc chi O3 Picil para aamentar loc nagocioc 04 Mic facilidadec dc cdacacién y calad 05 El cocto da la vida ac major chi 06 Qaada cerca de la familia ‘ 07 Tiene amigos o conocidoc alli 08 Major ambientacién ficica clli 09 Tierrc no degradada 10 Otro (ecpecifiqae) 11 No cé 99 6) tbfinda acted acpera qaedarca cuando llegaa alli? Con amigoc o conocidoc Con familiarcc Renter ana caca o apartamanto Racer an bihio Bl trabcjo la proviene acomodacién Otro (ccpecifiqaa) Mo ci mac: 3 ”DUE THERE JOU‘U‘bUN-fi MUCHISIMAS GRACIAS .lll. IIIIIIIIIWF .uwocmcc .umocmnc “ouaomo. .anuwo. m m .ouaowu. .ouaowo. .ommvmo. sump pump hum. .ouauwu. .ouacwu. .ouaowu. _oc-oa .quaowo. ouuoo ooaoo 1 ‘ «maloccoo Oaccocco Occcoaca cuccuOh .u~oo«uo¢ .ldfldflwuuuuuam Jflflfldflflflfll. cumaou a oocamcco Oumcaoum cho«ocou endomocou o— m— or up a. p. o. a .umoomcc .uaoomuo .uwooaac .om«mmu. Romacwu. chm. .omuowuv .cccuch. Aomaku. ooccovccnt cocoa .ouumwuv cocaacmficu ucanacu ucunacu \Opcunacu .omaowu. occwncco cwuococumcn nouuo pcmauccu cwncm encode acumaaq occamauq cacao ooaouca o m 1 covOuwz odocaoum m m h o m . v v N .umocmcc u .uuocmcc n n n mP‘ m mp . m a N ~ w a a u U c— p p . p m m Aaccuca. .accucev acacoucm t m d ucmom Hen ouch cuccaacuccou oaoaucco ouccuz Accouca. .ccouc9. .cccuca. ac mwaocacm occamaaa caucus cmuocoaummcau anemone .mwcvccuut chum Hemaocaum cucficcuu v n N c cunaq cuouaooauucuucm czocm ll ouoamz avoweuuuomnov codename. unuoa\0mcuuo HGUDNG GU “Md oucnc wuu 050$ 000 «ca l0: nuaoazuana :omouA 0 who mauvnoo NA NM‘ .«m: ocuuwam ouuo lfluvuom o ou«E=n:oonou=< vacaucuu o oniono> vacaunao o .cccmv cwaoocvoum vacuucmo Accouca. mvu>auaau ccu< enucoou nouo> m o>qunso .ccuoa fl. aoaduau cod :0 anuccou on an cucc> a :wqoocvoum ca noun: cuuaucp .uo>cu uom an— n— cuccucumllt odouwalnom .ccvauuocmcc. ouuo QOQ> Oddmnmu Onccou acacuuow\ccaoacm ouucm can: ‘ll'll coca o IO0N0§m couch I cccwuwco cam oumdocwum IIOSHQG ouoa .ccccnco cm .02. acacx a. condo”: cod cc cadaacm ca non ovuaoccoo Aconcaco cm .ozv noun: «. uoaauao cod no ovumcc> .ccucnco cm .02. noun: a. gonna»: cod cuccuom cwaoooooum .accucav cuucocccc ca chem can ecu occuuca cc vmvducmo cuucocccu cc cucmcccu\ccuca«cc cwdooououm omuccu c: 0 ocean eunuccu co cm a «councucv coccmuc uco cocoa cm» 02 u a. or cucsucumlu¢ odovwannom uccccc «. cocauau con cmccac cuouc o mcucoe Amp .covauaocncc. couuo common on amount .cc>u> caucuucm. cwac canonuc cu cwdocuccnm none ca uaooucu cucm I‘llhhflu I” BWHOOAauuUGOU caucus cu ucdc>wz omcocou0\ccouco cauocmmuuu cucm ccoc co couoco anon on cwqooouuccou «afiwudu Ho and: ovcwoo» .cccca up coaquau cod cc ouccaicuOncl cum law: once: a: o: «m .cccucu cc. «couccaa rcuoncc cocc com on lucdnco ccuc He can Mano» .ccccfl up con nauau cod cc ouccuc ucuOnca once: as «a ouccdacuoficl cumuc .unnoz .tuuum. «cocci NF coauuam cod cc cove: 0300: cm» ouccqacuOnc: cc onus fiflflflHB m— cucomcumund odouwaunom fin no OBZHHI¢¢ODEZ Amp 210 N 02 — Hm «occ cucc cm caucc ccoucowu caucc cm ccccmac mcucs ovcc: cm» Anon Accuduaocucc. ouuo coca: cmuuflfll I luduflfl ICU—ah..— caucucuw; counasdcg onuch\c cqum ucosuc cm cmco oNflMflHl wuco ococo ooou «cc: cosy cJHcccoubcz cocOMWfll omuom ouch cuaoncu couuc cac: in. c tdwmuMMmuulmm‘ c n ammumdduu oz n camcouunc oz n ccuc> ccuuc> « oz « oz u can n cancoaamc oz p um — Hm p cco u oz 0 cccmcdz — an «caducco co caucc nauseoum «ccvuouuccm a mcocuoaccu cu coouaqcv couoovoua coo ccv«o«uoccc« cuccuoo ocuco cane» «cauu cc coo ccumuacc ccoccou ocucc ccaw Icon ccoc> caucwcou vauc: cuccacu ace ucuca cucua» . XIIIIIIEWI cccca Np coaauam cod cuccuco .m‘flOUnfiO‘ m¢UHHO¢mm ou cucsmcumnnt cuccwauncm mulch”: no Om: «can 211 .oacqlv ucmoc «co cucn do no: 2.2 Mme 36nd: who who 35. emu on: no: .33. Eggnog-n11”. 3.4% m < Hu>uo o m c m c .omdmmo. ouch ac coo ounacuz ouccuaaoczulccusq ovcucu ceaocoscu comm oxcm anon on cmmocwmz .73 m w on: an!” a .71 caaaacm an op caucuccco cccuucuucuocucuunc oaccwzubcm .ccucccmcoz co cnoz 212 punemonoo 1IflmmmuMMMMdmllacuoa.mINMflo«muocmmml emu cuaaoz emu cwHecMHuaocucn emu ceuoco«u o 23.32 .2: c c n c LoocfiL emu ouocz oecoa ecem ceemm‘ cmucm cmec :00 o— a nuco much on m— '— mp ~— .— o— a o s o n v n u — comc .lmwo .uaocmcm emu cummlklcccc: comfi‘ each «a :00 cemoflwcc m K queue oecccc eunucc .eeu rec. cecce ccuccuuaam o>aueooeu oecucu onlcaa ecmm‘ occm o cucwa anew m . v n u Ana. ccuccumaamnuo cuceecnaom .ccucccmccz ce cfioz 213 .oaudocflcu emu emu .ocuaocmcm emu emu cocc emu .ooccu emm c u c c .o cac-cum emu c c ”caau .ouuwucacc emu cmuoccuouca cmdoc coo mmuocccem oecumm‘ cmucmoco encop on anon m or . . emu .oaumocmcm emu .ouuaocmcm emu amnion; cmaomauocco once coo cwHocncm m e m m com: com: ccccm o cuanz nmuhhmp couccudmccn cmwmc cmcwo emem \mecm \mmocm‘ a cucma macs mace n v n « couccuoalcuuoa odoemluaom .ccucccmccz ce cnoz wearvvuamt~¢ao PNM'UIOFOO 214 emu cmaocuauuooncm emu .ouuwmcacu emu cQMfl emu .memm emu m m emm cmmocoquuocmcm emu D ‘ ova—500$ n 4 Maximo IO—nu‘ fl ¢ cocoucm cmaocmcoo .cocem oecucmll. ecem cccOccm niepw or Diem 0 31‘s Diem cmmocomuaocmwm emu cum-oz cmmmcumumocncm emu ouch coo cmaocacz m mi, 1 com: ccccz come ccccz oucaaum menace. ccuccumazncz odoum cccom ounco> cmaocnooo m cucaq .73 v n N Ho. cauccuoqllcztum odoeozuacm cccuccamccz ce cnoz PNM'OOBOO PNM'IflOhOO :wwmuucacuommuucmm emu emu .pwmmmamwu,mmu oumaoz omo ,c «uco uaocmcu emu m Jm a u m m .uauducmcuIWWUI ccuceco umcc>nncccoccz ucmna eceaoHuoc m uuem oucm e v A emu cmaocowuwocmmm emu emu emu c~ce cOu cmmucucz m c m t cmMoco«umocmcu emu emu cc~c.occuoc ccuccumuz uuoucocOncm o soda an oznncmccc .occucu m cacao Anon bucu up a. c— ccncaoccuom couccumazulm casemzunom cccuccanccm ce anoz FNM'MOFQO PNM'MOFOO BIBLIOGRAPHY BIBLIOGRAPHY Ackerman, Edward A. 1959. 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