Please do all the questions.questions are on the pdf fileall the necessary data files are being provided
ECON4060H Take-Home Exam, 2022 Winter ECON4060H Take‑Home Exam, 2022 Winter 19 April 2022 Trent University ECON4060H Take‑Home Exam 2022Winter DUE BY 8:00PM EASTERN TIME ON APRIL 20TH. UPLOAD YOUR ANSWERS TO BLACKBOARD. Total points: 50. Instructions 1. This exam is open‑book. References allowed include lecture slides, course handouts, personal notes, and the reading list of the course. 2. Any form of collaboration or asking help from anyone is prohibited. Answers that appear very close to each other will be inspected for possible collaboration. 3. If answers are hand written, writing must be legible. 4. You can use any programming languages. Python and/or R are recommended. 5. All computer programs must be submitted in their original formats that are used to obtain an‑ swers. Computer programs submitted in the PDF format or as images will be discarded and result in zero marks. Original formats of programs can be plain text files with suffix .py or .R, they can be also saved in Jupyter notebooks. 6. If there are multiple files to submit, please consider to compress them into one zip file for sub‑ mission. 7. Write your name and student ID number in all files you submit. 8. For full credit, show necessary steps and necessary explanation. Question 1 Consider the intertemporal decisions of an individual. The individual is born in period 0, lives for? +1 periods, and dies at the end of period ? . At the beginning of any period ?, the individual’s asset level is ??, which is used for production. The amount of production is ??? with 0 <>< 1.="" produced="" output="" is="" used="" for="" consumption="" and="" for="" asset="" in="" the="" future.="" the="" budget="" constraint="" is="" +="" +1="???" ,="" for="" =="" 0,="" 1,="" 2,="" ⋯="" ,="" .="" the="" initial="" asset="" value="" 0=""> 0 is given. When the individual dies, she/he leaves no bequest, that is, ?? +1 = 0. The utility flow from consumption in period ? is given by ?(??) = ln(??). The discount factor is ? with 0 <>< 1. 1. (4 points) formulate the individual’s dynamic optimization problem. you can use the bellman equation, but do not have to. be thorough in describing the problemmathematically. shutao cao 1 econ4060h take‑home exam, 2022 winter 19 april 2022 2. (6 points) suppose that the individual lives for 3 periods, i.e., ? = 2. solve for the optimal consumption in all periods. do not use programming, solve the problemwith algebra using the backward induction and optimal conditions. show necessary steps. 3. (5 points) suppose that the individual lives for 11 periods, i.e., ? = 10. write a program to numerically solve for the individual’s life‑cycle choices. plot the optimal consumption and the asset level over the life cycle. in doing so, you can use these values ? = 0.5, ?0 = 1, ? = 0.7, or set them to values of your own choice. hint: you can either program by extending the pattern displayed in the second part above, or solve for the value function with backward induction. question 2 this question asks you to train a classification model and make prediction, using data from the labor force survey (lfs) 2021. data files and codebook are linked in blackboard together with this exam. there are two data files, lfs2021trainingsample.csv is the training sample, and lfs2021testsample.csv is the testing sample. the training sample is used to train the models, and the testing sample is used for prediction. the dependent variable ? is the employment type. ? = 1 if an individual is a permanent (regular) worker, and? = −1 if an individual is a temporaryworkerwhoseemployment is a fixed‑termcontract. employment type in the provided data is the variable permemploywhich is coded with 0 and 1. if you use python, the package scikit-learn is sufficient, you may want to reference the user guide https://scikit‑learn.org/stable/user_guide.html. 1. (5 points) preprocess the data. this includes properly coding all variables, creating categorical variables where necessary, allowing for interactions of variables, choosing the set of indepen‑ dent variables. for the independent variables, you should use as many variables as possible provided in the data files. please explain to justify if a variable is not used as an independent variable. 2. (6 points) using the processed training sample to train a regularized logistic regression model. show the training steps and report the training results. use the trained model to make predic‑ tion in the processed testing sample. report the prediction results (mainly the mse). in report‑ ing training and prediction results, briefly write them up by either using print function in your program or writing them as comments in your program. 3. (6 points) using the processed training sample to train a support vectormachines (svm)model. show the training steps and report the training results. use the trained model to make predic‑ tion in the processed testing sample. report the prediction results (mainly the test error rate or similar). in reporting training and prediction results, briefly write them up by either using print function in your program or writing them as comments in your program. hint: you can choose just one svmmodel, for example, support vector classifier (svc), or linearsvc. in training each model, you should make a choice on the specific model you use. for example, you can choose which regularization you use for the regularized logistic regression. shutao cao 2 https://scikit-learn.org/stable/user_guide.html econ4060h take‑home exam, 2022 winter 19 april 2022 question 3 this question concerns issues in estimating empirical models of households and firms. wage equation. consider the empirical wage equation as follows: ln(???) = ?0 + ?1 ⋅ educ?? + ?2 ⋅ exper?? + ?3 ⋅ tenure?? + ?4 ⋅ ln(????) + ?5 ⋅ ??? + ???. in this equation, ln(???) is the natural logarithm of hourly labor earnings of individual ? at time ?, educ?? is educational attainment, exper?? is the number of years of working experience, tenure?? is the number of years that individual ? has worked in the current employer, and ??? includes social demographic variables such as individual’s age, gender and race. finally, ln(????) is the natural log‑ arithm of minimumwage in province ? where individual ? works. 1. (6) point out and discuss potential problems of using the ordinary least squares (ols) to esti‑ mate the wage equation above. 2. (4) if not using the ols estimation, how would you estimate the determinants of individual wages? estimating wages may be not limited to use the equation above. ? regression. consider the ? regression of investment as follows: ???/??? = ?0 + ?1e???+1 + ?2???/??? + ???. in this equation, ???/??? is the investment‑to‑capital ratio in firm ? at year ?, e???+1 is the expected average ? value of firm ? in year ?, and ???/??? is the ratio of cash flow to capital in firm ? at year ?. 1. (4 points) estimating the equation above shows that ?2 is positive and statistically significant. explain the implications of this result. 2. (4 points) suppose that firms are possibly constrained by external credit which may limit firms’ ability to invest a desirable amount. howwould you estimate firm‑level investment in the pres‑ ence of credit constraint? shutao cao 3 trent university econ4060h take-home exam 2022 winter due by 8:00pm eastern time on april 20th. upload your answers to blackboard. total points: 50. instructions question 1 question 2 question 3 variable listing position / positionlength / longueurstart / débutstart / finvariable / variablevariable name - english / nom de variable - anglaisvariable name - french / nom de variable - francaisnote 1 - englishnote 1 - frenchnote 2 - englishnote 2 - frenchcode / codelabel - english / étiquette - anglaislabel - french / étiquette - francais 171976rec_numorder of record in fileordre de l'observation dans le fichier 1-9999999 841976survyearsurvey yearannée d'enquête 1976- 1221976survmnthsurvey monthmois d'enquête 1januaryjanvier 2februaryfévrier 3marchmars 4aprilavril 5maymai 6junejuin 7julyjuillet 8augustaoût 9septemberseptembre 10octoberoctobre 11novembernovembre 12decemberdécembre 1412017 lfsstat labour force statussituation d'activité 1employed, at workpersonnes occupées, au travail 2employed, absent from workpersonnes occupées, absentes du travail 3unemployedchômeurs 4not in labour forceinactifs 1521976provprovinceprovince 10newfoundland and labradorterre-neuve-et-labrador 11prince edward islandîle-du-prince-édouard 12nova scotianouvelle-écosse 13new brunswicknouveau-brunswick 24quebecquébec 35ontarioontario 46manitobamanitoba 47saskatchewansaskatchewan 48albertaalberta 59british columbiacolombie-britannique 1712017cmanine largest cmasneuf plus grandes rmr 1québecquébec 2montréalmontréal 3ottawa–gatineau (ontario part)ottawa–gatineau (partie de l’ontario) 4torontotoronto 5hamiltonhamilton 6winnipegwinnipeg 7calgarycalgary 8edmontonedmonton 9vancouvervancouver 0other cma or non-cmaautres rmr ou non-rmr 1821976age_12five-year age group of respondentl'âge du répondant (groupes de 5 ans) 0115 to 19 years15 à 19 ans 0220 to 24 years20 à 24 ans 0325 to 29 years25 à 29 ans 0430 to 34 years30 à 34 ans 0535 to 39 years35 à 39 ans 0640 to 44 years40 à 44 ans 0745 to 49 years45 à 49 ans 0850 to 54 years50 à 54 ans 0955 to 59 years55 à 59 ans 1060 to 64 years60 à 64 ans 1165 to 69 years65 à 69 ans 1270 and over70 et plus 2011976age_6 age in 2 and 3 year groups, 15 to 29l'âge des personnes de 15 à 29 ans (groupes de 2 et 3 ans) 115 to 16 years15 à 16 ans 217 to 19 years17 à 19 ans 320 to 21 years20 à 21 ans 422 to 24 years22 à 24 ans 525 to 26 years25 à 26 ans 627 to 29 years27 à 29 ans blanknot applicableindisponible 2111976sexsex of respondentsexe du répondant 1malehommes 2femalefemmes 221nov. 1999 marstatmarital status of respondentétat matrimonial du répondant 1marriedmarié(e)s 2living in common-lawvivant en union libre 3widowedveuf(ve)s 4separatedséparé(e)s 5divorceddivorcé(e)s 6single, never marriedcélibataires, n'ont jamais marié(e)s 2411990educhighest educational attainmentplus haut niveau de scolarité atteint 00 to 8 years0 à 8 années 1some high schoolétudes secondaires partielles 2high school graduateétudes secondaires complétées 3some postsecondaryétudes postsecondaires partielles 4postsecondary certificate or diplomadiplôme ou certificat d'études postsecondaires 5bachelor's degreebaccalauréat 6above bachelor's degreediplôme ou certificat universitaire supérieur au baccalauréat 2511976mjhsingle or multiple jobholderpersonnes ayant un emploi ou plus d'un emploi 1single jobholder, including job changerspersonnes ayant un emploi, incluant ceux ayant changé d'emploi 2multiple jobholderpersonnes ayant plus d'un emploi blanknot applicableindisponible 2611976everworknot currently employed, worked in the pastne travaillent pas présentement, ont déjà travaillé 1yes, within last yearoui, au cours de la dernière année 2yes, more than 1 year agooui, il y a plus d'un an 3no, never workednon, n'ont jamais travaillé blanknot applicableindisponible 2711976ftptlastfull- or part-time status of last jobsituation du dernier emploi, temps plein ou temps partiel 1full-time (30 hours or more)temps plein (30 heures ou plius) 2part-time (1 to 29 hours)temps partiel (1à 29 heures) blanknot applicableindisponible 2811976cowmainclass of worker, main jobcatégorie de travailleur, emploi principalcurrently employed or worked within the past 12 monthsprésentement occupé ou travaillé au cours des 12 derniers mois 1public sector employeesemployés du secteur public 2private sector employeesemployés du secteur privé 3self-employed incorporated, with paid helpentreprise constituée en société, 1.="" 1.="" (4="" points)="" formulate="" the="" individual’s="" dynamic="" optimization="" problem.="" you="" can="" use="" the="" bellman="" equation,="" but="" do="" not="" have="" to.="" be="" thorough="" in="" describing="" the="" problemmathematically.="" shutao="" cao="" 1="" econ4060h="" take‑home="" exam,="" 2022="" winter="" 19="" april="" 2022="" 2.="" (6="" points)="" suppose="" that="" the="" individual="" lives="" for="" 3="" periods,="" i.e.,="" =="" 2.="" solve="" for="" the="" optimal="" consumption="" in="" all="" periods.="" do="" not="" use="" programming,="" solve="" the="" problemwith="" algebra="" using="" the="" backward="" induction="" and="" optimal="" conditions.="" show="" necessary="" steps.="" 3.="" (5="" points)="" suppose="" that="" the="" individual="" lives="" for="" 11="" periods,="" i.e.,="" =="" 10.="" write="" a="" program="" to="" numerically="" solve="" for="" the="" individual’s="" life‑cycle="" choices.="" plot="" the="" optimal="" consumption="" and="" the="" asset="" level="" over="" the="" life="" cycle.="" in="" doing="" so,="" you="" can="" use="" these="" values="" =="" 0.5,="" 0="1," =="" 0.7,="" or="" set="" them="" to="" values="" of="" your="" own="" choice.="" hint:="" you="" can="" either="" program="" by="" extending="" the="" pattern="" displayed="" in="" the="" second="" part="" above,="" or="" solve="" for="" the="" value="" function="" with="" backward="" induction.="" question="" 2="" this="" question="" asks="" you="" to="" train="" a="" classification="" model="" and="" make="" prediction,="" using="" data="" from="" the="" labor="" force="" survey="" (lfs)="" 2021.="" data="" files="" and="" codebook="" are="" linked="" in="" blackboard="" together="" with="" this="" exam.="" there="" are="" two="" data="" files,="" lfs2021trainingsample.csv="" is="" the="" training="" sample,="" and="" lfs2021testsample.csv="" is="" the="" testing="" sample.="" the="" training="" sample="" is="" used="" to="" train="" the="" models,="" and="" the="" testing="" sample="" is="" used="" for="" prediction.="" the="" dependent="" variable="" is="" the="" employment="" type.="" =="" 1="" if="" an="" individual="" is="" a="" permanent="" (regular)="" worker,="" and?="−1" if="" an="" individual="" is="" a="" temporaryworkerwhoseemployment="" is="" a="" fixed‑termcontract.="" employment="" type="" in="" the="" provided="" data="" is="" the="" variable="" permemploywhich="" is="" coded="" with="" 0="" and="" 1.="" if="" you="" use="" python,="" the="" package="" scikit-learn="" is="" sufficient,="" you="" may="" want="" to="" reference="" the="" user="" guide="" https://scikit‑learn.org/stable/user_guide.html.="" 1.="" (5="" points)="" preprocess="" the="" data.="" this="" includes="" properly="" coding="" all="" variables,="" creating="" categorical="" variables="" where="" necessary,="" allowing="" for="" interactions="" of="" variables,="" choosing="" the="" set="" of="" indepen‑="" dent="" variables.="" for="" the="" independent="" variables,="" you="" should="" use="" as="" many="" variables="" as="" possible="" provided="" in="" the="" data="" files.="" please="" explain="" to="" justify="" if="" a="" variable="" is="" not="" used="" as="" an="" independent="" variable.="" 2.="" (6="" points)="" using="" the="" processed="" training="" sample="" to="" train="" a="" regularized="" logistic="" regression="" model.="" show="" the="" training="" steps="" and="" report="" the="" training="" results.="" use="" the="" trained="" model="" to="" make="" predic‑="" tion="" in="" the="" processed="" testing="" sample.="" report="" the="" prediction="" results="" (mainly="" the="" mse).="" in="" report‑="" ing="" training="" and="" prediction="" results,="" briefly="" write="" them="" up="" by="" either="" using="" print="" function="" in="" your="" program="" or="" writing="" them="" as="" comments="" in="" your="" program.="" 3.="" (6="" points)="" using="" the="" processed="" training="" sample="" to="" train="" a="" support="" vectormachines="" (svm)model.="" show="" the="" training="" steps="" and="" report="" the="" training="" results.="" use="" the="" trained="" model="" to="" make="" predic‑="" tion="" in="" the="" processed="" testing="" sample.="" report="" the="" prediction="" results="" (mainly="" the="" test="" error="" rate="" or="" similar).="" in="" reporting="" training="" and="" prediction="" results,="" briefly="" write="" them="" up="" by="" either="" using="" print="" function="" in="" your="" program="" or="" writing="" them="" as="" comments="" in="" your="" program.="" hint:="" you="" can="" choose="" just="" one="" svmmodel,="" for="" example,="" support="" vector="" classifier="" (svc),="" or="" linearsvc.="" in="" training="" each="" model,="" you="" should="" make="" a="" choice="" on="" the="" specific="" model="" you="" use.="" for="" example,="" you="" can="" choose="" which="" regularization="" you="" use="" for="" the="" regularized="" logistic="" regression.="" shutao="" cao="" 2="" https://scikit-learn.org/stable/user_guide.html="" econ4060h="" take‑home="" exam,="" 2022="" winter="" 19="" april="" 2022="" question="" 3="" this="" question="" concerns="" issues="" in="" estimating="" empirical="" models="" of="" households="" and="" firms.="" wage="" equation.="" consider="" the="" empirical="" wage="" equation="" as="" follows:="" ln(???)="?0" +="" 1="" ⋅="" educ??="" +="" 2="" ⋅="" exper??="" +="" 3="" ⋅="" tenure??="" +="" 4="" ⋅="" ln(????)="" +="" 5="" ⋅="" +="" .="" in="" this="" equation,="" ln(???)="" is="" the="" natural="" logarithm="" of="" hourly="" labor="" earnings="" of="" individual="" at="" time="" ,="" educ??="" is="" educational="" attainment,="" exper??="" is="" the="" number="" of="" years="" of="" working="" experience,="" tenure??="" is="" the="" number="" of="" years="" that="" individual="" has="" worked="" in="" the="" current="" employer,="" and="" includes="" social="" demographic="" variables="" such="" as="" individual’s="" age,="" gender="" and="" race.="" finally,="" ln(????)="" is="" the="" natural="" log‑="" arithm="" of="" minimumwage="" in="" province="" where="" individual="" works.="" 1.="" (6)="" point="" out="" and="" discuss="" potential="" problems="" of="" using="" the="" ordinary="" least="" squares="" (ols)="" to="" esti‑="" mate="" the="" wage="" equation="" above.="" 2.="" (4)="" if="" not="" using="" the="" ols="" estimation,="" how="" would="" you="" estimate="" the="" determinants="" of="" individual="" wages?="" estimating="" wages="" may="" be="" not="" limited="" to="" use="" the="" equation="" above.="" regression.="" consider="" the="" regression="" of="" investment="" as="" follows:="" =="" 0="" +="" 1e???+1="" +="" 2???/???="" +="" .="" in="" this="" equation,="" is="" the="" investment‑to‑capital="" ratio="" in="" firm="" at="" year="" ,="" e???+1="" is="" the="" expected="" average="" value="" of="" firm="" in="" year="" ,="" and="" is="" the="" ratio="" of="" cash="" flow="" to="" capital="" in="" firm="" at="" year="" .="" 1.="" (4="" points)="" estimating="" the="" equation="" above="" shows="" that="" 2="" is="" positive="" and="" statistically="" significant.="" explain="" the="" implications="" of="" this="" result.="" 2.="" (4="" points)="" suppose="" that="" firms="" are="" possibly="" constrained="" by="" external="" credit="" which="" may="" limit="" firms’="" ability="" to="" invest="" a="" desirable="" amount.="" howwould="" you="" estimate="" firm‑level="" investment="" in="" the="" pres‑="" ence="" of="" credit="" constraint?="" shutao="" cao="" 3="" trent="" university="" econ4060h="" take-home="" exam="" 2022="" winter="" due="" by="" 8:00pm="" eastern="" time="" on="" april="" 20th.="" upload="" your="" answers="" to="" blackboard.="" total="" points:="" 50.="" instructions="" question="" 1="" question="" 2="" question="" 3="" variable="" listing="" position="" position="" length="" longueur="" start="" début="" start="" fin="" variable="" variable="" variable="" name="" -="" english="" nom="" de="" variable="" -="" anglais="" variable="" name="" -="" french="" nom="" de="" variable="" -="" francais="" note="" 1="" -="" english="" note="" 1="" -="" french="" note="" 2="" -="" english="" note="" 2="" -="" french="" code="" code="" label="" -="" english="" étiquette="" -="" anglais="" label="" -="" french="" étiquette="" -="" francais="" 1="" 7="" 1976="" rec_num="" order="" of="" record="" in="" file="" ordre="" de="" l'observation="" dans="" le="" fichier="" 1-9999999="" 8="" 4="" 1976="" survyear="" survey="" year="" année="" d'enquête="" 1976-="" 12="" 2="" 1976="" survmnth="" survey="" month="" mois="" d'enquête="" 1="" january="" janvier="" 2="" february="" février="" 3="" march="" mars="" 4="" april="" avril="" 5="" may="" mai="" 6="" june="" juin="" 7="" july="" juillet="" 8="" august="" août="" 9="" september="" septembre="" 10="" october="" octobre="" 11="" november="" novembre="" 12="" december="" décembre="" 14="" 1="" 2017="" lfsstat="" labour="" force="" status="" situation="" d'activité="" 1="" employed,="" at="" work="" personnes="" occupées,="" au="" travail="" 2="" employed,="" absent="" from="" work="" personnes="" occupées,="" absentes="" du="" travail="" 3="" unemployed="" chômeurs="" 4="" not="" in="" labour="" force="" inactifs="" 15="" 2="" 1976="" prov="" province="" province="" 10="" newfoundland="" and="" labrador="" terre-neuve-et-labrador="" 11="" prince="" edward="" island="" île-du-prince-édouard="" 12="" nova="" scotia="" nouvelle-écosse="" 13="" new="" brunswick="" nouveau-brunswick="" 24="" quebec="" québec="" 35="" ontario="" ontario="" 46="" manitoba="" manitoba="" 47="" saskatchewan="" saskatchewan="" 48="" alberta="" alberta="" 59="" british="" columbia="" colombie-britannique="" 17="" 1="" 2017="" cma="" nine="" largest="" cmas="" neuf="" plus="" grandes="" rmr="" 1="" québec="" québec="" 2="" montréal="" montréal="" 3="" ottawa–gatineau="" (ontario="" part)="" ottawa–gatineau="" (partie="" de="" l’ontario)="" 4="" toronto="" toronto="" 5="" hamilton="" hamilton="" 6="" winnipeg="" winnipeg="" 7="" calgary="" calgary="" 8="" edmonton="" edmonton="" 9="" vancouver="" vancouver="" 0="" other="" cma="" or="" non-cma="" autres="" rmr="" ou="" non-rmr="" 18="" 2="" 1976="" age_12="" five-year="" age="" group="" of="" respondent="" l'âge="" du="" répondant="" (groupes="" de="" 5="" ans)="" 01="" 15="" to="" 19="" years="" 15="" à="" 19="" ans="" 02="" 20="" to="" 24="" years="" 20="" à="" 24="" ans="" 03="" 25="" to="" 29="" years="" 25="" à="" 29="" ans="" 04="" 30="" to="" 34="" years="" 30="" à="" 34="" ans="" 05="" 35="" to="" 39="" years="" 35="" à="" 39="" ans="" 06="" 40="" to="" 44="" years="" 40="" à="" 44="" ans="" 07="" 45="" to="" 49="" years="" 45="" à="" 49="" ans="" 08="" 50="" to="" 54="" years="" 50="" à="" 54="" ans="" 09="" 55="" to="" 59="" years="" 55="" à="" 59="" ans="" 10="" 60="" to="" 64="" years="" 60="" à="" 64="" ans="" 11="" 65="" to="" 69="" years="" 65="" à="" 69="" ans="" 12="" 70="" and="" over="" 70="" et="" plus="" 20="" 1="" 1976="" age_6="" age="" in="" 2="" and="" 3="" year="" groups,="" 15="" to="" 29="" l'âge="" des="" personnes="" de="" 15="" à="" 29="" ans="" (groupes="" de="" 2="" et="" 3="" ans)="" 1="" 15="" to="" 16="" years="" 15="" à="" 16="" ans="" 2="" 17="" to="" 19="" years="" 17="" à="" 19="" ans="" 3="" 20="" to="" 21="" years="" 20="" à="" 21="" ans="" 4="" 22="" to="" 24="" years="" 22="" à="" 24="" ans="" 5="" 25="" to="" 26="" years="" 25="" à="" 26="" ans="" 6="" 27="" to="" 29="" years="" 27="" à="" 29="" ans="" blank="" not="" applicable="" indisponible="" 21="" 1="" 1976="" sex="" sex="" of="" respondent="" sexe="" du="" répondant="" 1="" male="" hommes="" 2="" female="" femmes="" 22="" 1="" nov.="" 1999="" marstat="" marital="" status="" of="" respondent="" état="" matrimonial="" du="" répondant="" 1="" married="" marié(e)s="" 2="" living="" in="" common-law="" vivant="" en="" union="" libre="" 3="" widowed="" veuf(ve)s="" 4="" separated="" séparé(e)s="" 5="" divorced="" divorcé(e)s="" 6="" single,="" never="" married="" célibataires,="" n'ont="" jamais="" marié(e)s="" 24="" 1="" 1990="" educ="" highest="" educational="" attainment="" plus="" haut="" niveau="" de="" scolarité="" atteint="" 0="" 0="" to="" 8="" years="" 0="" à="" 8="" années="" 1="" some="" high="" school="" études="" secondaires="" partielles="" 2="" high="" school="" graduate="" études="" secondaires="" complétées="" 3="" some="" postsecondary="" études="" postsecondaires="" partielles="" 4="" postsecondary="" certificate="" or="" diploma="" diplôme="" ou="" certificat="" d'études="" postsecondaires="" 5="" bachelor's="" degree="" baccalauréat="" 6="" above="" bachelor's="" degree="" diplôme="" ou="" certificat="" universitaire="" supérieur="" au="" baccalauréat="" 25="" 1="" 1976="" mjh="" single="" or="" multiple="" jobholder="" personnes="" ayant="" un="" emploi="" ou="" plus="" d'un="" emploi="" 1="" single="" jobholder,="" including="" job="" changers="" personnes="" ayant="" un="" emploi,="" incluant="" ceux="" ayant="" changé="" d'emploi="" 2="" multiple="" jobholder="" personnes="" ayant="" plus="" d'un="" emploi="" blank="" not="" applicable="" indisponible="" 26="" 1="" 1976="" everwork="" not="" currently="" employed,="" worked="" in="" the="" past="" ne="" travaillent="" pas="" présentement,="" ont="" déjà="" travaillé="" 1="" yes,="" within="" last="" year="" oui,="" au="" cours="" de="" la="" dernière="" année="" 2="" yes,="" more="" than="" 1="" year="" ago="" oui,="" il="" y="" a="" plus="" d'un="" an="" 3="" no,="" never="" worked="" non,="" n'ont="" jamais="" travaillé="" blank="" not="" applicable="" indisponible="" 27="" 1="" 1976="" ftptlast="" full-="" or="" part-time="" status="" of="" last="" job="" situation="" du="" dernier="" emploi,="" temps="" plein="" ou="" temps="" partiel="" 1="" full-time="" (30="" hours="" or="" more)="" temps="" plein="" (30="" heures="" ou="" plius)="" 2="" part-time="" (1="" to="" 29="" hours)="" temps="" partiel="" (1à="" 29="" heures)="" blank="" not="" applicable="" indisponible="" 28="" 1="" 1976="" cowmain="" class="" of="" worker,="" main="" job="" catégorie="" de="" travailleur,="" emploi="" principal="" currently="" employed="" or="" worked="" within="" the="" past="" 12="" months="" présentement="" occupé="" ou="" travaillé="" au="" cours="" des="" 12="" derniers="" mois="" 1="" public="" sector="" employees="" employés="" du="" secteur="" public="" 2="" private="" sector="" employees="" employés="" du="" secteur="" privé="" 3="" self-employed="" incorporated,="" with="" paid="" help="" entreprise="" constituée="" en="">