Determinants of Productivity among Senior Citizens in

Marikina City

Determinants of Productivity among Senior Citizens in

Marikina City

Presented by:Ines Alcantara-de Guzman

October 18, 2007

Background of the StudyBackground of the Study

Increasing number of senior citizens

Outright discrimination in the workplace

Limited access to adequate health services

Inadequate retirement plans

Economic difficulties-reduced income

Statement of the ProblemStatement of the Problem

1. What is the socio-demographic and health profile of senior citizens in Marikina City?

1.1. Age1.2. Gender1.3. Civil Status1.4. Educational attainment1.5. Occupation upon retirement1.6. Living arrangement1.7. Health status

2. Which among the variables are possible determinants of productivity among senior citizens?

2.1. Economic activities2.1.1. Employment status2.1.2. Workplace

2.2. Volunteer Work2.2.1. Membership in organization2.2.1. Involvement in community affairs

2.3. Life activities 2.3.1. Activities of daily life2.3.2. Instrumental activities2.3.3. Household activities

Statement of the ProblemStatement of the Problem

Conceptual ParadigmConceptual Paradigm

Engaged in economic activitiesMembership in civic org.Involvement in volunteer workPerformance of daily life activitiesAbility to adjust to new ways of living

•Inability to cope with retirement and reduced income•Non-participation in age-group activities•Non-involvement in social and civic obligations•Inability to adjust to new living arrangements

PRODUCTIVE NON-PRODUCTIVE

Productivity(Economic Activities, Life Activities, Volunteer Work)

SENIOR CITIZENSHealth Status

Socio-Demographic Profile

Objectives of the StudyObjectives of the Study

The study is aimed at accomplishing the following objectives:

1. to describe the socio-demographic and health profile of senior citizens in Marikina City;

2. to identify the variables that are possible determinants of productivity among senior citizens in Marikina City; and

3. to develop a productivity scorecard for senior citizens.

MethodologyMethodology

• Locale: Barangka and Concepcion/Tumana, Marikina City

• Sampling Design: 354 male and female senior citizens aged 60-79

• Data Gathering Instrument: Survey questionnaire, face-to-face interview, and Focus Group Discussion (FGD)

• Data Analysis: Frequency, Cluster Analysis and Logistic Regression

Study ResultsStudy Results

DemographicsDemographics

• Majority of the respondents belong to the 60-69 age group.

CIVIL STATUSSingle5%

Widow/Widower42%

Married51%

Separated2%

• Majority of the respondents are married.• Majority of the respondents are females.

EducationEducation

EDUCATIONAL ATTAINMENT

None3%

Elementary

49%

High School22%

VocTec/Undergrad

10%

College/Grad units

15%

Graduate Degree

1%

• Majority are elementary graduates.

• 22 percent are high school graduates.

• 10 percent are college graduates.

• 10 percent are Vocational/Technical graduate

• 1 percent with graduate degrees

EmploymentEmployment

TYPE OF EMPLOYMENT (Base = All Employed)

Self-employed

24%

Private62%

Government14%

EmploymentEmployment

EMPLOYMENT STATUS

Otherwis34% Employed

17%

Retired49%

EmploymentEmploymentEMPLOYMENT

37.6

13.0

11.6

9.0

7.3

5.9

3.4

3.1

2.0

1.7

0.3

1.1

1.1

1.1

0.6

0.3

0.3

0.3

0.3

No Employment

Laborer

Shoemaker

Professional

Service Worker

Manager/Supervisor

Clerk

Sales Worker

Machine Operator

Vendor

Unskilled Worker

Farmer

TechnicianStore

Owner/OperatorUnderwriter

CarinderiaCorporateExecutive

Fisherman

Shop Worker

Living ArrangementsLiving Arrangements

LIVING ARRANGEMENT

with Child/Chil

dren36%

with Spouse

12%

with Other

Relatives8%

Alone8%

with Spouse/Ch

ildren36%

• Majority live with spouse/children

• And with a child/children

• 12 percent live with spouse

• 8 percent with other relatives

• 8 percent live on their own.

HealthHealth

MEDICAL FACILITY

39.836.7

18.4

11.6 11.6

GovernmentHospital

HealthCenter

PrivateClinic

Home PrivateHospital

HealthHealth

HEALTH SELF-ASSESSMENT

Good to Excellent

75%

Poor to Fair 25%

HealthHealth HEALTH PROFILE

56.2

29.9

23.7

14.4

14.4

10.5

9.0

5.6

5.1

4.8

3.1

2.8

1.1

0.8

0.6

Poor Vision

Arthritis

Hypertension

Poor Hearing

CataractHeart

DiseaseDiabetes

AsthmaKidney

ProblemOsteoporosis

PulmonaryDisease

Ulcer

Tuberculosis

Emphysema

Glaucoma

Respondents ProfileRespondents Profile

SMOKING & DRINKING PATTERNS

11.6

10.5

5.4

3.7

2.3

Smoke

DrinkAlcoholicBeverages

Beer

Wine

Liquor

• A small percentage smoke and drink alcoholic beverages mostly beer.

Respondents ProfileRespondents ProfileLEISURE ACTIVITIES

84.7

63.3

15.0

6.5

4.0

2.5

2.3

1.1

0.3

WatchingTelevision

Listening toMusic

Needlework

BallroomdancingPlayingBoardgames

Fishing

Handicrafts

Painting

Collectingitems

• Majority indicated watching television as their way of spending leisure time.

IncomeIncome

Majority are receiving an income below 4,000.00Php

MONTHLY INCOME

55.8

12.0

5.8

1.2

0.5

0.0

29.7

Below P4, 000

4,001-8,000

8,001-15,000

15,001-30,000

30,001-50,000

5,0001 and above

No data

IncomeIncomeMONTHLY PENSION

63.3

21.7

6.7

1.7

1.7

1.7

3.3

Below P4, 000

4,001-8,000

8,001-15,000

15,001-30,000

30,001-50,000

50,001 and above

No Data

Majority are receiving a monthly pension below 4,000.00Php

Majority claimed that their income is not sufficient to provide for their basic needs in life.

Preparation for old agePreparation for old age

PREPARATION FOR OLD AGE

71.8

57.9

26.3

5.7

5.4

4.8

28.2

12.1

16.1

1.1

Prepared for Old Age

Sent children to school

Saved money in thebank

Acquired land

Got an insurance plan

Invested in a business

Did not prepare for oldage

Did not see it as a need

No extra income

Busy to think of saving

ADL and IADL EngagementADL and IADL Engagement

ENGAGEMENT IN IADLs

92.1

73.2

92.9

76.3

81.6

Doing lighthousehold

work

Managing ownmoney

Preparing ownmeal

Shopping forown groceriesor personal

item

Using publictransportation

ENGAGEMENT IN ADLs

96.9

96.9

97.5

98.6

Dressingup/Puttingon clothes

Eating

Taking abath

Walkingaround the

house

Respondents ProfileRespondents Profile

HOUSEHOLD ACTIVITIES

80.5

78.0

68.6

54.8

54.8

46.6

37.0

36.7

Cleaning thehouse

Cooking

Washing thedishes

Ironing clothes

Washingclothes by hand

Taking care ofinfants/children

Homedecorating

Washingclothes bywashing

• The top three household activities the respondents are involved in are:

– Cleaning the house– Cooking– Washing the dishes

Volunteer Work / Community InvolvementVolunteer Work / Community Involvement

REASONS FOR NO VOLUNTEER WORK

28.8

18.1

12.1

2.8

1.4

Healthreasons

Lack oftime

Don'tknow howand where

Lack ofnecessary

skills

Lack offunds

VOLUNTEER WORK PARTICIPATED IN

28.2

10.2

5.6

4.8

3.7

2.3

1.7

1.7

1.7

1.1

0.8

0.8

0.6

BarangayActivities

Homeowners'Association

Church choir

Lay Minister

Lector

Feedingprograms

Catechist

LivelihoodprogramsFamily

counselingMotherButler

GawadKalingaTraffic

Enforcer

Acolyte

Cluster Analysis ResultsCluster Analysis Results PRODUCTIVE VS NON-PRODUCTIVE

46%21%38%78%

100%100%99%99%98%89%99%94%94%98%96%60%89%66%50%73%93%76%71%

50%91%

62%13%38%65%94%94%95%98%86%57%86%57%68%62%59%13%19%26%23%36%43%73%56%38%81%

• Cluster Analysis showed that 183 of the respondents are “productive” and 173 are “non-productive”.

• This is based on the 25 variables aligned with the Havighurst’s paradigm.

Head of the familyEmployedIncomePrepared for old ageDressingEatingBathingWalkingLight ChoresManaging Own MoneyPreparing Own MealShopping for GroceriesRiding a Public TransportCleaning the HouseCookingHome DecoratingIroning ClothesCaring for ChildrenWashing Clothes (by machine)Washing Clothes (by hand)Washing the DishesHealthy (Perceived)Organization (Membership)VolunteerProductive (Perceived)

Development of the Productivity ScorecardDevelopment of the

Productivity Scorecard

Logistic Regression: FULL MODEL (25 variables)Logistic Regression: FULL MODEL (25 variables)

B S.E. Wald df Sig. Exp(B)V6(1) -0.07360 0.55719 0.01745 1.00000 0.89491 0.92904I7EMPSTA(1) 1.63568 0.81396 4.03823 1.00000 0.04448 5.13293I13INCME(1) 0.56825 0.60100 0.89399 1.00000 0.34440 1.76517I16PREPA(1) 1.20478 0.59663 4.07757 1.00000 0.04346 3.33604B6DRESSI(1) 19.85974 7202.85698 0.00001 1.00000 0.99780 421673546.89229B6EATING(1) 9.32784 7202.85853 0.00000 1.00000 0.99897 11246.82413B6BATHIN(1) -2.03175 41.71401 0.00237 1.00000 0.96115 0.13111B6WALKIN(1) 0.98608 41.69646 0.00056 1.00000 0.98113 2.68072B7LIGHT(1) 0.22836 1.86287 0.01503 1.00000 0.90243 1.25654B7MANAGI(1) 1.87337 0.72819 6.61851 1.00000 0.01009 6.51020B7PREPM(1) -3.21565 1.56648 4.21391 1.00000 0.04009 0.04013B7SHOPPI(1) 3.08804 0.87897 12.34281 1.00000 0.00044 21.93399B7PUBTRA(1) 1.65616 0.84654 3.82744 1.00000 0.05042 5.23916B8CLEANI(1) 0.72461 1.19682 0.36656 1.00000 0.54488 2.06392B8COOKIN(1) 3.20908 0.94830 11.45183 1.00000 0.00071 24.75642B8HOMDEC(1) 1.11888 0.64857 2.97615 1.00000 0.08450 3.06142B8IRONIN(1) 4.27764 0.73252 34.10116 1.00000 0.00000 72.06987B8CARING(1) 2.26907 0.57065 15.81079 1.00000 0.00007 9.67039B8WASHIN(1) 1.51751 0.60944 6.20012 1.00000 0.01277 4.56087B8WCLOTH(1) 1.63393 0.60584 7.27355 1.00000 0.00700 5.12397B8WDISHE(1) 1.83901 0.74201 6.14251 1.00000 0.01320 6.29030B10HEALT(1) -2.09528 0.73078 8.22071 1.00000 0.00414 0.12304C1MEMBER(1) 0.66267 0.56741 1.36396 1.00000 0.24285 1.93997C2VOLUNT 0.94411 0.58968 2.56341 1.00000 0.10936 2.57052C6PRODUC 0.11467 0.81185 0.01995 1.00000 0.88767 1.12150Constant -42.25201 10158.07219 0.00002 1.00000 0.99668 0.00000

Logistic Regression: 14 VariablesLogistic Regression: 14 Variables B Sig.

I7EMPSTA(1) 1.4243 0.0349I16PREPA(1) 0.7588 0.1532B6WALKIN(1) 4.5917 0.0132B7MANAGI(1) 1.9924 0.0031B7SHOPPI(1) 2.4659 0.0021B7PUBTRA(1) 1.7397 0.0202B8COOKIN(1) 2.4571 0.0015B8HOMDEC(1) 1.0608 0.0553B8IRONIN(1) 3.6348 0.0000B8CARING(1) 1.9034 0.0002B8WASHIN(1) 1.2632 0.0188B8WCLOTH(1) 1.5427 0.0030B8WDISHE(1) 1.9588 0.0031C2VOLUNT 0.6943 0.1696Constant -19.5457 0.0000

Logistic Regression: 11 VariablesLogistic Regression: 11 Variables B Sig.

B6WALKIN(1) 4.1218 0.0179B7MANAGI(1) 2.0202 0.0019B7SHOPPI(1) 2.2135 0.0028B7PUBTRA(1) 1.8509 0.0125B8COOKIN(1) 2.2796 0.0023B8HOMDEC(1) 1.0704 0.0469B8IRONIN(1) 3.4044 0.0000B8CARING(1) 1.9376 0.0001B8WASHIN(1) 1.1254 0.0294B8WCLOTH(1) 1.5623 0.0022B8WDISHE(1) 1.5934 0.0071Constant -16.5552 0.0000

ScorecardScorecard

Summary and ConclusionsSummary and Conclusions

1. Using the 25 variables, out of 354 respondents, 181 are “productive” and 171 are “non-

productive”.

2. Using the same 25 variables and applying Logistic Regression, 11 variables were found to be predictors of productivity among the respondents.

3. There are two Productivity Models generated:– 14-Variable Productivity Model– 11-Variable Productivity Model

Summary and ConclusionsSummary and Conclusions

4. Based on the Havighurst’s paradigm, the data shows that economic activity, membership in civic organizations, and involvement in volunteer work do not seem to discriminate between “productive” and “non-productive” senior citizens.

5. Life activities are the only specific indicators of productivity among senior citizens.

6. A scoring instrument for the 14-Variable Productivity Model and the 11-Variable Productivity Model

RecommendationsRecommendations• The findings of the study led to the

following recommendations:• Plan and conduct personality development

programs• Formulate public policy , maintenance, monitoring,

and evaluation for the productive and democratic participation of senior citizens in community and nation building

• Possibility of adjusting existing labor practices that apply to ageing workers (optional retirement at age 65)

• Inclusion of topics on the elderly or later maturity in the basic and tertiary education home economics curriculum

RecommendationsRecommendations

•A pilot test on the productivity scoring instrument

•Utilization of the Productivity-Scoring Instruments to assess the senior citizens capabilities and preparedness to undergo programs designed to provide them with sustained stimulating environment towards high levels of attentiveness and productivity

•In-depth study using a larger and more varied samples

•Further study on the 14 Predictor Productivity Model

Thank YouThank You