Impact of the Global Financial Crisis on ... · ISSN: ... Impact of the Global Financial Crisis on Households in Pekalongan City* Akhmadi and Asri ... located in Rt 3 of Rw 4 while

ISSN: 1908-6989 Vol. IX, No. 1, December 2011www.pep-net.org

Impact of the Global Financial Crisis onHouseholds in Pekalongan City*Akhmadi and Asri Yusrina1

1CBMS-Indonesia Team Leader and Researcher, respectively, of the SMERU Research Institute.*An excerpt of the paper submitted by Mr. Akhmadi and Ms. Asri Yusrina of the SMERU Research Institute.

Women working on Batik textiles. Batik is one of the small scale industries inPekalongan City affected by the Global Financial Crisis.

Source: CBMS-Indonesia

acro level data show that Indonesiaas well as Pekalongan City experienced aneconomic downturn due to the global financialcrisis (GFC). Apart from this, however, thereare also other local shocks which occurred inthe city such as the closing of a factory due tointernal mismanagement, batik’s competitionshock, and the closing of the local fish tradingplace. These crises somehow affected theeconomic sector which later affectedhouseholds that depend on them as sources ofincome. Thus, households’ experiences andresponses at any given moment not only would

indicate effects of economic downturn but alsoaccumulated effects of more localized shocks thatcompound their effects (Hossain et al, 2010).

To understand which groups of householdswere affected by the compound crisesduring the period from June 2008 to June2009, a combined analysis of the CBMS 2009census and the impact of the GFC survey isneeded. The CBMS 2009 census is a datacollection of all households in West andNorth Pekalongan City. It providesinformation about household members’

characteristics and their living condition butdoes not include an instrument aboutincome or expenditure of households. Theimpact of the GFC survey, meanwhile, is asurvey that piggybacks on the CBMS 2009census and aims to provide informationabout the impact of the crisis on thehouseholds. With a certain timeframe —June 2008–June 2009 — the survey tries tocapture the changes in households’livelihood (employment) and thesocioeconomic impact of the crisis on thehouseholds, in particular, on their foodconsumption, healthcare and educationpatterns. The survey was conducted in fivevillages in Pekalongan City that wereassumed to be affected by the GFC. Eachvillage represents a certain core industrythat has indications of having felt the GFCimpact. Based on the Industry and Trade,Cooperative and Small Medium EnterprisesAgency listing, the core industry in eachkelurahan is as follows: Medono (batik,sarong and other garments), Tirto (batik),Pasirsari (batik), Krapyak Lor (batik) andPanjang Wetan (fisheries and its products).

Table 1 shows several poverty indicatorsand their status based on the CBMS 2009census. In general, Kelurahan PasirsariM

CBMS study finds food inflation as themost common, most severe type ofhousehold shock

InsideInsideInsideInsideInside

10

CBMS-Burkina Faso Team andinternational NGO collaborate inbuilding capacities of local authority

11

CBMS-Peru presents results 12

2 CBMS Network UpdatesDecember 2011

Research Results

shows the highest indication of povertyacross five kelurahan. The PekalonganCity’s house renovation program is a wayto improve households’ standard of livingwhich means better living condition thatwould result in better health status. Localgovernment helped renovate houses inaspects related to making toiletsavailable, having protected water sourcesand installing appropriate roofing andflooring.

With lack of income or expenditure data,Table 1 could only show the difference of thepoverty situation among villages withoutbeing able to indicate household welfarestatus and to identify who the poor are. Theauthors used the Principal ComponentAnalysis (PCA) method to develop groups ofthe households’ welfare based on thevariance of their socioeconomic background

Table 2 shows the number of households fromthe merged data and after the PCA wasapplied. About 76 percent (75.55%) of totalhouseholds from the merged data (10,111households) had complete variables that canbe used for the PCA. The PCA ranks eachhousehold in each village and splits up a set ofranked data into quintiles (5 groups). It thencomes up with the households with the lowestrank (lowest 20% of total households pervillage) which were classified as the poorest,and the households with the highest rank(highest 20% of total households per village)which were in turn classified as the richest.

From these 5 groups, one can analyze thelocation of the poorest based on the village’sadministrative level.2 Table 3 shows that inKelurahan Medono, the poorest are mostlylocated in Rt 3 of Rw 4 while in Panjang Wetan,most of the poorest live in Rt 5 of Rw 13.

switching jobs, particularly getting a worse joband of households having decliningincomes. Job switching also indicates thatthere are a lot of households in the villageworking in the informal sector, therebymaking them vulnerable to economic crisis.The impact of crises also affect incomereceived by households since informalworkers working in the batik industry workon a lesser size (in square meters) of moricloth to be painted or dyed; contract labor inthe garments industry get to work less interms of number of hours; and mosthousehold members are switching to worsejobs. Based on quintiles, most of the affectedhouseholds come from the poorest group(first quintile). The proportion of theaffected households in the first quintile ishigher than the non-affected households(32.52% compared to 14.66%) as seen inTable 5.

1PCA method develops artificial index thatcould only be applied if variables neededfrom each observation are complete.

2The Rt is the lowest administer, aneighborhood unit that consists of a numberof households. And Rw consists of severalneighborhood units.

Poverty Situation

Total households (n)Household head — never attended nor finished primary schoolHigh dependency ratio*Living in house with dirt floorReceived health insurance for poorReceived Unconditional Cash Transfer 2008 (BLT2008)Received Pekalongan City’s house renovation program

Medono

3,17813.866.232.7118.6914.858.78

Tirto

2,32613.334.945.5523.9918.78.94

Pasirsari

2,06622.655.868.1338.2930.8817.52

Krapyak Lor

2,60617.277.643.6522.7618.235.37

Panjang Wetan

3,20717.765.895.5236.2333.618.61

Table 1. Poverty Situation in Sampled Villages

Source: Author’s calculation using CBMS 2009 census data*Household with higher number of members aged <15 compared to the number of household members

Table 2. Number of Households in Sampled Villages

Village/Kelurahan

MedonoTirtoPasirsariKrapyak LorPanjang WetanTotal

%

23.7417.3815.4419.4723.96100

N

2,4311,8031,5681,9312,378

10,111

%

24.0417.8315.5119.1123.52100

Before PCA After PCA

N

3,1782,3262,0662,6063,207

13,383

Source: Author’s calculation using CBMS 2009 census data

Table 3. Neighborhood Location of the Poorest

Village/Kelurahan

MedonoTirtoPasirsariKrapyak LorPanjang Wetan

R wRw 4Rw 5Rw 1Rw 1Rw 13

N96895613790

The Poorest(The Lowest10% of TotalHouseholds)

n486360313386475


Rw, RtRw 4, Rt 3Rw 5, Rt 3Rw 1, Rt 3Rw 1, Rt 5Rw 13, Rt 5

N3634212727

Group ofNeighborhoodUnit with Mostof the Poorest

NeighborhoodUnit with Most of

the Poorest

variables such as education, occupation andownership of assets.1 The variables usedalso include several poverty indicators asseen in the Table. With the PCA having beenconducted in each village, the determinationof household welfare is therefore local andspecific to each village.

Impact of Crises on HouseholdsBesides being able to locate the poorest groupwithin the village by using PCA, the mergeddata also enable one to identify householdsthat felt the compound crises (Table 4).Impacts of the crises on households are shownthrough indications of any household member

CBMS Network UpdatesDecember 2011

3

Research Results

Coping Mechanisms Adopted byHouseholds

Changes in food consumption patternsDuring the period from June 2008–June2009, 8.47 percent of the total number ofhouseholds (1,134 of 13,383 households)experienced a reduction in quantity andquality of food consumption (Table 6). Ofthe 1,134 households, 81.83 percent reducedtheir meal frequency from three times a dayto twice per day while 16.14 percent had ameal frequency reduction from twice a dayto once per day. Some households (0.88%)had smaller portions of meal consumed

Indicators

Villages

Sex of household head

Household head, neverattended nor finishedprimary schoolHead of householdworking sector

Quintiles of household

Dependency ratio ofhousehold memberaged <15Living in house withdirt floorReceived healthinsurance for the poorReceived unconditionalcash transfer 2008(BLT2008)Received houserenovation program

Description

MedonoTirtoPasirsariKrapyak LorPanjang WetanMaleFemaleYesNo

AgricultureIndustryTradeServicesReceiving transferothersmissing valuesQuintile 1Quintile 2Quintile 3Quintile 4Quintile 5missing valuesDepr<=0.5Depr>0.5 (high)

YesNoYesNoYesNo

YesNo

Number ofAffected

Households (A)

685132481272893769257

231172596161237106534729127929630

3828813718999227

55271

Number of Non-Affected

Households (B)

3,1102,2752,0342,5583,080

11,2071,8502,131

10,926

7483,6701,2585,581573415812

1,9141,9701,9731,9942,0133,193

12,265792

61712,4403,5619,4962,999

10,058

1,21011,847

% of A to TotalAffected

Households (N=326)

20.8615.649.8214.7238.9688.6511.3521.1778.83

7.0635.897.6729.454.913.6811.3532.5216.2614.428.903.6824.2390.809.20

11.6688.3442.0257.9830.3769.63

16.8783.13

% of B to Total non-Affected Households

(N=13,057)

23.8217.4215.5819.5923.5985.8314.1716.3283.68

5.7328.119.6342.744.393.186.2214.6615.0915.1115.2715.4224.4593.936.07

4.7395.2727.2772.7322.9777.03

9.2790.73

Table 4. Characteristics of Affected Households Compared with Non-affected Households


while 1.15percent of the households had toturn to food of lesser quality.

Meanwhile, Table 7 shows that from 1,134households, 115 (10.14%) are from theaffected households and 1,019 (89.86%)from the non-affected ones. The majority ofchanges in food consumption selected byhouseholds are the reduction of their mealintake frequency from 3 to 2 times, that is,77.39 percent for the affected householdsand 82.34 percent for the non-affectedhouseholds. The proportion of householdswhich reduced their frequency of mealintake from 2 times to once a day is higher in

Table 5. Affected Households, byQuintiles

Quintiles

12345Missing valueTotal

%

32.5216.2614.428.93.6824.23100

N

1,9141,9701,9731,9942,0133,193

13,057

%

14.6615.0915.1115.2715.4224.45100

AffectedHouseholds

N

1065347291279326


Non-affectedHouseholds


Research Results

the affected households compared to non-affected households.

When the survey results are combined withthe household welfare status determined

Villages

Medono

Tirto

Pasirsari

Krapyak Lor

PanjangWetanTotal%

From 3x to2x

25788.01%

14272.08%

6666.00%

23887. 82%

22582.12%

92881.83%

From 2x to1x

289.59%

4120.81%

3434.00%

3111.44%

5017.88%

18316.14%

ReducedQuality of Food

20.68%

94.57%

00%2

0.74%0

0%13

1.15%

ReducedQuantity of Food

51.71%

52.54%

00%0

0%0

0%10

0.88%

Total

292100%197

100%100

100%271

100%274

100%1,134100%

Table 6. Changes in Frequency and Quality of Meals, by villages


from the PCA, the highest number ofhouseholds that had negative changes intheir food consumption is in the poorestquintile/group (i.e., 314 households or27.69%). The number of households that hadnegative changes in food consumption dueto the impact of the shocks is shown to bedecreasing as their welfare increases.Selected changes in terms of reducing thequantity of food consumption such as from 1piece of chicken to half a piece is done by theupper quintiles (3rd and 4th quintiles) atabout 20 percent (2 of 10 households) and 40percent (4 of 10 households) of households,respectively (Table 8).

Changes in healthcare patternsBetween June 2008 and June 2009, there were647 households (4.83% of total households)that changed their healthcare patterns. Thehighest number of households that changed

their healthcare pattern is in Kelurahan Pasirsari(Table 9). When said data are combined withhousehold welfare based on the PCA, a clearpattern does not appear on whether thesechanges mean that household financial ability

Table 7. Households with Negative Changes in Food Consumption in June2008–June 2009, by Affected Groups

Changes in FoodConsumption

From 3x to 2xFrom 2x to 1xReduced Quality of FoodReduced Quantity of FoodTotal

%

77.3920.871.74

0100

N

8391591110

1,019

%

82.3415.601.080.98100

N

892420

115


Affected Households Non-affected Households

became less or not. It is possible that changes inaccessing healthcare took place because severeillness forced households to change theirtreatment to advanced healthcare. About 40

percent of 647 households across allkelurahan which consulted with midwives,private practice and government hospitalshad changed their preferences and thereuponused community health centers for theircurrent healthcare.

Moreover, 367 of 13,383 households (2.74%)experienced a change in the payment forhealthcare services while others did not.Twenty five out of 367 households (6.81%)are from the affected households while 342(93.19%) are from non-affectedhouseholds (Table 10).

The negative changes that households did tocope with the crises are focused on the changein the manner of payment, i.e., from usingtheir personal fund to using health insurancefor the poor, which exist in both affected(60%) and non-affected households (57%).The incidence of change in affectedhouseholds is slighlty higher. The change ofpayment method from using their personalfund to making loans (borrowing money) isthe second selected change chosen byaffected households (16%) and non-affectedhouseholds (12.3%).

Table 11 shows that 66 of 367 households(18%) across villages that did somechanges in payment of healthcare duringJune 2008 – June 2009 are from the poorest.These households began to reduce theirhealth expenses by using health insurancefor the poor. They also borrowed money ifthey do not have enough or received theinsurance. The proportion of householdschanging their healthcare payment fromusing personal fund to making loans is 19.6

Training of dataencoders

Source:CBMS-Indonesia


5

Research Results

Table 9. Number of Households ThatChanged Healthcare and PaymentMethod

Kelurahan

MedonoTirtoPasirsariKrapyak LorPanjangWetanTotal

%

24.119.1230.2919.32

17.16100

N

661513940

107367

%

17.984.0937.8710.9

29.16100

Changes inHealthcare

N

15659196125

111647


Changes inPaymentMethod

Table 8. Households with Negative Changes in Food Consumption in June2008–June 2009, by Household Welfare

Changes in FoodConsumption

From 3x to 2xFrom 2x to 1xReduced Qualityof FoodReduced Quantityof FoodTotal

%

27.0533.33

7.69

1027.69

N

15833

3

1195

%

17.0318.03

23.08

1017.2

N

25161

1

1314


%

13.0413.66

15.38

2013.23

N

857

3

499

%

9.163.83

23.08

408.73

N

12125

2

2150

Total

928183

13

101,134

N

584

3

065

%

6.252.19

23.08

05.73

1 2 3 4 5Quintiles

PastPersonal pocket

Health insurance

Health insurance for thepoor

Reimbursement fromcompanyLoan

CurrentHealth InsuranceHealth Insurance for the PoorReimbursement from CompanyLoanPersonal PocketHealth Insurance for the PoorLoanPersonal PocketHealth InsuranceLoanPersonal Pocket

Health Insurance for the PoorPersonal PocketTotal

Table 11. Household Changes in Healthcare Payment Method in June 2008–June 2009, by Household Welfare

%9.123.3

015.2

000

11.4100500

02518

%9.118.1

015.215.433.3

020000

025

16.1

N349070004110

0166


%15.217.618.219.623.1

00

14.3050

16.7

050

17.7

N225343107001

0046

%6.111.927.38.723.133.3

020.0

00

16.7

00

12.5

N537293005011

0265

Total

332101146133235126

14

367

N95522001002

1027

%27.32.445.54.315.4

00

2.900

33.3

1000

7.4

1 2 3 4 5Quintiles

Payment Method

N338072107000

0159

Table 10. Household Changes in Healthcare Payment Method inJune 2008–June 2009, by Affected Groups

PastPersonal pocket

Health insurance

Health insurance forthe poor

Reimbursement fromcompanyLoan


N3150400020010

025

%126001600080040

0100

N3019511421332331251

4342

%8.857.03.212.33.80.90.69.60.30.61.50.3

1.2100

CurrentHealth InsuranceHealth Insurance for the PoorReimbursement from CompanyLoanPersonal PocketHealth Insurance for the PoorLoanPersonal PocketHealth InsuranceLoanPersonal PocketHealth Insurance for the Poor

Personal PocketTotal

AffectedHouseholds

Non-affectedHouseholds

Payment Method Positive changes in healthcare payment areshown by the incidence of using healthinsurance and reimbursement facility fromcompany in the current period. However, theproportion of these changes are higher in the5th quintile, at 27.3 percent (9 of 33households) and 45.5 percent (5 of 11households), respectively.

Households selling or pawning their assetsA cross tabulation of reasons cited by thehouseholds for selling or pawning theirassets with the households’ welfare showswhich group makes use of pawning or sellingassets as a coping mechanism. Although themajority come from the first quintile or thepoorest group (22.89%), said coping

mechanism appears evenly acrossquintiles. For the poorest households, thereasons mostly given for said practice were

percent (9 of 46 households) in the 3rdquintile, the highest percentage amongquintiles. Households in the highest quintile

accessing health insurance for the poor couldalso indicate an inclusion error of the socialprotection program.


Research Results

to: fulfill daily needs, pay thechildren’s school expenses,pay debts, and pay healthexpenses. On the other hand,households in the fourth(31.15%) and fifth (22.95%)quintiles admitted that theypawned or sold assets butmore to use for businesscapital. As for the reasonsaying “to get a job”, it waspossible that the moneyearned from the sale or pawnhad to be used in applying andgetting a job position inprivate or public institutionsor in paying for overseasworkers’ licenses. However,there was no furtherexplanation given for thisparticular reason (Table 12).

Children aged below 15 whostarted workingFor a definitivealysis, themerged data of the CBMS2009 census and the impact ofthe GFC survey were also used

Table 12. Households Pawning or Selling their Assets in June 2008–June 2009, byHousehold Welfare

Reasons for Pawning orSelling Asset

daily needspay school expenses/needspay debthealth expensesfor business capitalto get a jobdaily and school needto renovate housedaily need & business capitalfor family occasionsecondary needpay debt & school needspay debt & daily needsschool needs & business capitalsalary is not enoughdid not have moneyurgent needdid not want to borrowothersdo not knowTotal

%

29.8020

23.1720.903.2833.3334.7814.29

014.29

025

33.330

7.1413.9211.63

500

21.0722.89

N

131231298724510100419110070307

%

20.4418.414.6313.4313.1129.178.7028.57

5014.29

02500

28.5724.0512.79

00

20.7719.20

N

1912519142882010110111101071366


%

16.3820

12.2013.4311.48

017.397.1410

28.572000

33.3328.5724.0517.44

033.3313.9515.95

N

62221391924211221009190537210

%

9.6717.615.8513.4331.158.3317.3914.29

1014.29

4050

33.3300

11.3922.09

041.6710.9813.13

N

105251097041121001419150447255

Total

64112582676124231410754331479862123371599

N

239891410410101116120135127

%

3.597.29.7613.4322.954.17

028.57

100200

33.3333.337.147.5913.95

08.3310.397.94

1 2 3 4 5Quintiles

as a cross-checking mechanism. The authorsalso used minimum age to limit the analysis.The minimum age recorded as child labor,according to the Statistics of Indonesia, is 10years old. Based on the survey, there are 467households with household members below15 years who started working. By applyingthe minimum age of 10 years old to thehousehold data, it shows that there are 234households that actually have householdmembers aged 10 to 14 years old who havestarted working.

In Table 13, it shows that there are 14 affectedhouseholds (5.98%) and 220 non-affectedhouseholds (94.02%) with children aged 10-14years old who started working. The proportionof households with two children who startedworking is slighlty higher in the affectedhouseholds than in the non-affectedhouseholds (21.43% versus 15.91%).

Table 14 shows a combination of the 234households with the households’ welfarebased on the PCA. Again, the majority of thepoorest households have children aged 10-14 years old who started working (68 of 234households or 29.06%). The number ofhouseholds with children who startedworking idecreases as the households’welfare increases.

Children aged 15-18 years old who startedworkingBetween June 2008 and June 2009, therewere more household members aged 15–18years old who started working than thoseaged below 15 who did. From the impact ofthe GFC survey, there are 1,166 householdswith members aged 15–18 years old whostarted working. After having it cross-checked using the CBMS census, however,only 677 households actually havehousehold members aged 15-18 years oldwho started working.

As seen in Table 15, twenty five of the 677households (3.69%) are from the affectedhouseholds which needed assistance fromyounger members to generate income orto help the parents in generating income.The proportion of households with onechild aged 15-18 years old who startedworking is slighlty higher in the affectedgroup (80%) than in the non-affectedgroup (79.29%).

Table 16 also shows that 181 of 677households (26.74%) come from the poorestgroup. On average, households in the bottom3 quintiles, middle to poor, suffer moredifficulties due to the compound crises than

the upper 2 quintiles, which force them tohave their 15-18 year-old children work.

Children aged 6-15 years old who droppedout of schoolDuring the period of crisis, June 2008–June2009, there is an indication of householdshaving their children’s educationdiscontinued as can be gleaned in Table 17.They did it to cope with the crises as theirfinancial ability had decreased. There are 121households with children aged 6-15 years oldwho dropped out of school. Ten of them(8.26%) are from the affected householdswhile the 111 households are from the non-affected ones. The proportion of householdswith drop-out children from the junior highschool level is slightly higher in the affectedhouseholds at 40 percent than in the non-affected households at 36.94 percent. Thereare 5 out of 69 households from the non-affected households with indication ofhaving more than one drop-out child fromprimary and junior high school.

The highest proportion of households withchildren dropping out from primary school is inthe first quintile, with 28 out of 75 households(37.33%). There is one household with a childaged between 6-15 years old who dropped out


7

Research Results

from senior high school (Table 18). Consideringthe age boundary, it is possible that the childwas dropped out during the first year of his/hersenior high school. There is no household in thefifth quintile with drop-out children since theyenjoy the highest level of welfare among allthe quintiles.

Policy Responses to the Impact of the GFCAs a response to the economic downturn thatoccurred in the fourth quarter of 2008resulting from the GFC’s impact, theGovernment of Indonesia (GoI) proposed forthe approval of the Fiscal Stimulus Package(FSP) to the Parliament. The FSP aims to (i)maintain people purchasing power, (ii)maintain the stability of the businessclimate, and (iii) create job opportunity andabsorb laid-off labor.

To fulfill the first objective, the GoI providedincentives such as the reduction of individualincome tax, the increase of the minimumlimit of non-income tax, and the grant ofvarious subsidies. For the second objective,the GoI gave the business sector incentiveson taxes as well as various subsidies. Thethird objective was to be accomplishedthrough the allocation of the FSP fund forlabor-intensive projects in infrastructureand the extension of the National Programfor Community Empowerment (PNPM).

The GoI allocated Rp73,3 trillion for the FSPfund, which is about 1.4 percent of the 2009gross domestic product (GDP). The FSP fundis allocated to all provinces across Indonesiabut only several districts/cities in eachprovince received the fund. The use of thisfund is determined by the GoI, e.g., whetherto be used to build new infrastructure or torestore existing infrastructure.

Based on Hastuti et al (2011), the FSP fund isnot being allocated based on the area thatwas severely affected by the GFC since dataabout the GFC’s impact across regions arenot available. Thus, the allocation was givenbased on deprived area, economic zone,political decision and other criteria.Although the project is aimed to absorb laid-off labor, there is no regulation about usinglocal labor who were laid-off.

Pekalongan City received about Rp1,933million from the National Budget through theDeconcentration and Co-administrationFund at the district/city level. However,

N

183352

220

%

15.4613.16

5015.38

Table 15. Households with Children Aged 15-18 Years Old Who StartedWorking in June 2008–June 2009, by Affected Groups

Number of ChildrenAged 15-18 Years OldWho Started Working

123

Total

%

80200

100

N

51711718652

%

79.2917.942.76100

N

205025



N

118345

157

%

17.6913.9322.2217.13

Table 16. Households with Children Aged 15-18 Years Old Who StartedWorking in June 2008–June 2009, by Household Welfare

Number ofChildren Aged 15-18 Years Old WhoStarted Working

123

Total

%

25.7031.9722.2226.74

%

21.9727.8727.7823.19

N

138394

181Source: Author’s calculation using CBMS 2009 census data

N

4412258

%

8.199.8411.118.57

N

95174

116

Total

53712218677

N

111012

%

2.050.82

01.77

1 2 3 4 5Quintiles

there is no further information about the useof the FSP fund in Pekalongan City.

Based on the GFC household survey, theobserved time frame of the GFC’s impact onhouseholds is from June 2008 – June 2009. Asfor the utilization of the FSP fund, it wasstarted in the beginning of August 2009(Hastuti et al, 2011). Therefore, the survey isunable to capture the result of the GoI’s

mitigating strategy as a response to the GFCimpact. Since the mitigating strategy wasnot given to the targeted receiver such as thehome industry of batik and fisheriesindustry, it is difficult to observe theoutcome.

Based on the impact of the GFC survey, 2,161households stated that they have receivedspecial aid programs in relation to the global

N

4510055


Numbers ofChildren Aged 10-14 Years Old WhoStarted Working

123

Total

%

28.8731.58

029.06

%

23.2026.32

023.50

N

5612068


N

223126

%

11.347.8950

11.11

N

305136

Total

194382

234

N

3003

%

1.5500

1.28

1 2 3 4 5

Quintiles

Table 13. Households with Children Aged 10-14 years Who Started Workingin June 2008–June 2009, by Affected Groups

Numbers of ChildrenAged 10-14 Years OldWho Started Working

123

Total

%

78.5721.43

0100

%

83.1815.910.91100

N

113014




Research Results

financial crisis. The data also provideinformation about the source of the aidprograms, i.e., Government, private andreligious institutions as well as mass-basedorganisations. The analysis will be focusedon aid programs given by the Government,regardless of whether it is from the NationalGovernment or from Pekalongan City.

Table 19 shows 310 households had receivedcapital loan and 598 households receivedcapital goods from the government.However, the data could not give furtherexplanation on whether the capital loan/goods received by households from thegovernment is in a specific form of PNPM ornot.

Other than as a response to the GFC, the GoIalso spent about Rp200,000 for 18.2 millionpoor households across Indonesia in the formof Unconditional Cash Transfer 2009 (BLT2009). Targeted households in 2009 are the

Table 17. Households with Drop-Out Children Aged 6-15 Years Old in June2008–June 2009, by Affected Groups

School Level of Drop-Out Children

Primary schoolJunior high schoolSenior high schoolTotal

%

60400

100

N

69411

111

%

62.1636.940.90100

N

64010



A man acting as facilitator countsvotes cast during a focus groupdiscussion.

Source: CBMS-Indonesia

same as households which receivedBLT2008 since the database used is from thePPLS08.3 According to a local newspaper,Suara Pantura (2009), there are 22,983households in Pekalongan City whichreceived the BLT 2009. The distribution ofthe BLT2009 in the city was conducted on 20-27 April 2009.

The CBMS census 2009 does not include aquestion about households receiving theBLT2009 but the impact of the GFC surveywas able to capture households that receivedthe BLT in the period June 2008 – June 2009.From ‘Others’ special aid programs (1,220households), the data reveal that householdsreceived BLT2009, Rice for the poor(Raskin), Health Insurance for the poor(Jamkesmas), scholarship/schooloperational assistance (BOS) and Houserenovation program.

N

159125

%

10.678.89

09.92


School Level ofDrop-OutChildren

Primary schoolJunior high schoolSenior high schoolTotal

%

37.3331.11

034.71

%

2020100

20.66

N

2814042


N

46010

%

5.3313.33

08.26

N

84012

Total

75451

121

N

0000

%

0000

1 2 3 4 5

Quintiles

Form of Special Aid Programs

Capital loan

Capital goods

Others

Do not know

Total

Medono

3214.10

3013.22157

69.168

3.52227100

Tirto

10927.59

8020.25191

48.3515

3.80395100

Pasirsari

198.7262

28.44135

61.932

0.92218100

Krapyak Lor

226.4933

9.73280

82.604

1.18339100

Panjang Wetan

12813.03393

40.02457

46.544

0.41982100

Table 19. Households Which Received Special Aid Programs from Government


Total

31014.35598

27.671,22056.46

331.532,161100

3Pendataan Program Perlindungan Sosial2008 (PPLS08) is a database of targetedhouseholds for the GoI social protectionprogram. The data are collected by StatisticsIndonesia through a survey.


9

Research Results

Table 20 shows 806 households admitting thatthey received BLT2009. There are 36households from the affected households(4.46%) and 770 from the non-affectedhouseholds (95.54%). The majority of affectedhouseholds which received the BLT2009 areliving in Kelurahan Panjang Wetan.

Cross-tabulating households which receivedBLT2009 with household welfare using thePCA method allows one to show a descriptionof the receiver (Table 21). The poorest grouphas the highest number of households receivingBLT2009, with 229 out of 806 households(28.41%). Households in the highest quintilealso received BLT2009, indicating an inclusionerror of the social protection program or adefect in the PCA process.

ConclusionThrough this study, indications of the GFC’simpact in Pekalongan City across macro andmicroeconomic levels were ascertained.Two main economic sectors in Pekalongan City

weakened export demand. During the period ofthe GFC, other crises existed in the local contextsuch as the closing of a garment factory due tomismanagement and family conflict, batik’scompetition shock and the closing of a localfishers trading place. However, there is noindication of GFC’s impact on the return ofoverseas workers from Pekalongan Citywhere most of them are working as domesticworkers. Furthermore, these shocks areaccumulated and difficult to be set apart inmicro level thereby leading to compound crisesexperienced by households.

This study represents the first attempt of usingmerged data of the CBMS 2009 census and theimpact of the GFC survey in 5 kelurahan inPekalongan City. It gives more informationneeded for identifying the affected groups ofhouseholds, particularly when the PCAmethod is applied to these data.

The 326 affected households were identifiedthrough the incidence of households with

Table 20. Households Which Received Unconditional Cash Transfer in June2008–June 2009, by Affected Groups

Villages

Krapyak LorMedonoPanjang WetanPasirsariTirtoTotal

%

2.788.3358.3311.1119.44100

N

131113261119146770

%

17.0114.6833.9015.4518.96100

N

13214736



N

3325461425143

%

8.3310.3413.8313.8211.1111.91

Table 21. Households Which Received Unconditional Cash Transfer in June2008–June 2009, by Household Welfare

Villages

Krapyak LorMedonoPanjang WetanPasirsariTirtoTotal

%

27.2730.1725.1826.8335.2928.41

%

25.0021.5516.3111.3816.3417.74

N

3635713354229


N

10289341

%

0.760

9.937.321.965.09

N

111239171796

Total

132116282123153806

N

010102

%

00.86

00.81

00.25

1 2 3 4 5Quintiles

members switching job, particularly to worsejobs, and of declining income in the June 2008-June 2009 period. Based on households’charactheristics, the affected households arehouseholds with heads who never attended or

finished primary school, work in the industrialsector, come from the poorest group (firstquintile), live in houses with dirt floor and hadreceived several social protection programsfrom government. Based on quintiles, itappears that the poorest group — the lowestquintile — has the highest number of affectedhouseholds.

With regards to the crises, households adoptseveral coping strategies such as changing theirfood consumption pattern and healthcarepayment method, pawning or selling assets,and driving their children to enter the laborforce and drop out from school. Once again, themajority of households carrying out thesestrategies are households in the poorest group,particularly the affected ones.

The GoI needs to have a good database oftargeted households since the socialprotection programs had helped poorhouseholds during the crises. They usedhealth insurance for the poor and BLT2009 astheir safety nets when they do not have otherfinancial assistance. An early responsesystem requires a good database that willsupport immediate action from governmentin handling any crisis. Infrastructure projectsfrom the FSP fund are less likely to reach thetargeted households since there is noregulation to prioritize the poor.Government awareness of the vulnerableeconomic sectors as well as preventiveaction like giving incentives for theindustries in the time of crisis is veryimportant. The local government shouldinitiate the provision of the database onvulnerable economic sectors and targetedhouseholds at their own cost such ashousehold data given through the CBMSproject in Pekalongan City.

majority ofhouseholds carryingout these (coping)

strategies arehouseholds in the

poorest group

”

“

were affected by the GFC impact. Textile/batikindustries were affected through weakenedexport demand and increased prices ofimported cotton. The fisheries industries,meanwhile, were also affected by the


CBMS study finds food inflation as themost common, most severe type ofhousehold shock

News Updates

reliminary results of a study onhousehold coping strategies conducted by theCBMS Team in two urban and two rural villagesin the Philippines reveal that increase in theprices of basic food commodities is the mostcommon and most severe type of shockexperienced by households. The study alsonotes that when faced with food price shocks,the most common strategy adopted byhouseholds is to shift to cheaper food items aswas actually done by more than half of theseverely affected households.

Conducted during the third quarter of 2011, thestudy likewise indicates that about 74.3 percentof the households experienced at least one typeof shock during the reference period. Fuel priceshock is the second most common type of shockin the selected sites which was experienced bymore than a quarter of surveyed households,followed by serious illness (5.4%) and naturalcalamities, particularly floods and typhoons,which affected 2.6 percent and 1.6 percent ofthe households, respectively (Table 1).

The various shocks experienced by thehouseholds have affected them in differentways. For instance, some resulted in loss of job,decrease in income, loss of assets or increase in

Type of shock

Increase in food pricesIncrease in fuel pricesSerious illnessFloodTyphoonDeathPest infestationSerious accidentDisabilityTotal No. ofHouseholds

Magnitude

1,4065621145434292097

2,108

Proportion

66.526.65.42.61.61.40.90.40.3

Table 1. Distribution of major shocksexperienced by households

Note: Some households also experienced other types of shocks whichinclude the occurrence of tsunami in Japan which affected the demand forsome of the Philippine exports, increase in power rates and increase in tollrates, among others.Source: CBMS Survey, 2011

P

expenses. Most of the severelyaffected households generallyexperienced an increase inexpenses due to the variousshocks. Almost all householdswhich were severely affected byfuel price increases reported anincrease in their total householdexpenses. Meanwhile, shocksgenerally have greater impact onincome than on assets. A majorityof the households severelyaffected by typhoon and floodsuffered a decrease in their income(Table 2).

The households were asked toidentify the top three most

severe shocks that they have experienced. Therankings, which were provided by the respondentsthemselves, are as follows: the most severe shockis ranked first, the second most severe shock isranked second and so on. Severely affectedhouseholds are those which were affected by thetop three most severe types of shocks.

Several households in the survey sitesexperienced various combinations of shocks.For instance, about 71.4 percent of the

households were affected by theincrease in both food and fuel prices while6.1 percent suffered from a seriousillness, serious accident or disability of amember. There were also severalhouseholds (3.7%) which were affectedby flood and typhoon at the same time.

Moreover, some householdsexperienced shocks of different nature.For instance, there were some who wereaffected not only by price shocks buthealth-related shocks as well. Based onthe survey data, 104 households,representing 4.9 percent of the totalhouseholds covered, reported that theyexperienced food/fuel price shock andserious illness/accident/disability during

the last 12 months prior to the date of theinterview. There are also several households (i.e.,56 households or 2.7%) which suffered from theincrease in the prices of food or fuel and at the sametime were affected negatively by flood ortyphoon.

Experiencing more shocks during a specificperiod of time would mean that impact onhouseholds will be greater. In addition, giventhe differences in the nature of these shocks,

Table 2. Impact of the most severe shock on households (% of households)

Note: This table includes estimates for households which reported each specific type of shock as the most severe shock they experienced in thepast 12 months.Source: CBMS Survey, 2011

Type of shock

Increase in foodpricesIncrease in fuelpricesSerious illnessTyphoonDeathFloodPest infestationSerious accidentDisability

Total No. of HHs“severely” affected

1,393

5451112828521887

Job loss

-

-47.534.636.09.817.737.571.4

Decrease inincome

-

-63.969.256.062.070.662.585.7

Asset loss

-

--

34.6-

30.017.725.0

-

Increase inexpenses

97.6

94.699.145.089.361.283.3100.085.7

continuation on page 11


11

T

News Updates

Among thetrainingsconducted byCBMS-BurkinaFaso Team isthe training ofenumerators.

Source: CBMS-Burkina Faso

CBMS-Burkina Faso Team andinternational NGO collaborate inbuilding capacities of local authority

he International Alliance forDevelopment and Solidarity in Africa(AIDAS), a nongovernment organization(NGO) operating in Burkina Faso, has, onceagain, requested the support of the CBMS-Burkina Faso Team in its work in projectareas covering the communes of Arbolé andGompoussoum in Passoré province. Thesupport requested included the provision oftrainings on data enumeration using CBMSsurvey instruments and on data processing.The trainings, which began in July 2011, wereaimed at increasing the awareness of the

local authorities, various NGOs, andstakeholders on the relevance of the CBMSmethodology for capacity building in thecommunes.

AIDAS has been operating in the countrysince 2005 and among its objectives are: topromote professional developmentactivities; to help protect the environmentand improve the living standards; to supportthe ongoing decentralization process in thecountry; and to promote human rights,peace, equity and justice. The AIDAS has

undertaken successful partnerships withCBMS-Burkina Faso since 2006. It has beenusing the CBMS to establish baselineinformation before they implement theirown intervention projects.

Meanwhile, the CBMS Team also reportedthat data collection in the communes ofYako, Diébougou and Koper has beencompleted early this year amid serious socialunrest due to military mutinies in thecountry. The data collection was completedmore than a month after it began in March inYako and in April in Diébougou and Koper.The data collection for all sites wascompleted in June 2011.

Apart from the data collected based on theCBMS indicators on health, nutrition, foodsecurity, housing and living conditions,income and community involvement, thisround of CBMS implementation alsoinvolved the collection of information on theGlobal Financial Crisis (GFC), climatechange, and coping mechanisms employedby households in times of crises and shocks.

The CBMS-Burkina Faso Team is headed byDr. Lassina Konate.

some households also behaved differently interms of the coping strategies. For instance, thecoping behaviour of households during priceshocks may be different from the strategies incase of a serious illness, serious accident ordisability of a member.

When households are faced with food or fuel priceshocks, the most common strategy that theyadopted is the shifting to cheaper food items whichwas actually done by about half (i.e., 46.2%) of thehouseholds severely affected by the increase inthe prices of food or fuel. Limiting the use ofelectricity and lessening the frequency of diningout are also common among severely affectedhouseholds, with 30.9 percent and 30.1 percent ofthem reporting such actions, respectively.

Meanwhile, in terms of the order of copingstrategies taken by households, the data confirmthat modifying their food consumption pattern, inparticular, by shifting to cheaper food items, is alsothe first thing that households usually do whenfaced with price shocks.

Furthermore, when households experiencedserious illness, serious accident or disability,most of them (38.9%) resorted to borrowingmoney in order to cope with these problems. Inaddition, about one-third of the affectedhouseholds received financial support fromrelatives when they encountered suchproblems. And when they experienced deathof a member, most of the households receivedfinancial assistance from relatives (53.6%).

Launched in collaboration with the UnitedNations Development Programme (UNDP)and the United Nations Children’s Fund(UNICEF), the study titled “MonitoringHousehold Coping Strategies During ComplexCrises and Recoveries” used data from twourban barangays (Barangay 192 in Pasay City,Metro Manila and Barangay Poblacion 3 in Sto.Tomas, Batangas) and two rural barangays(Barangay San Miguel in Llorente, EasternSamar and Barangay El Rio in Sibagat, Agusandel Sur).

More results from this study will be presentedduring the 9th PEP Research Network GeneralMeeting which will be held in Siem Reap,Cambodia on December 3-9, 2011.

CBMS study finds...from page 10


CBMS Network Updates is the quarterlynewsletter of the CBMS Network of the PEPProject. This work was carried out by theAngelo King Institute for Economic andBusiness Studies with financial support fromthe Government of Canada through theInternational Development Research Centre(IDRC) and the Canadian InternationalDevelopment Agency (CIDA).

The Updates may be downloaded free fromthe Project's website:

http://www.pep-net.org.

For inquiries , please write or call:PEP-CBMS Network Coordinating Office

Angelo King Institute for Economic and Business StudiesDe La Salle University - Manila

10th Flr. Angelo King International CenterEstrada cor. Arellano Sts., Malate,

Manila 1004, PhilippinesTel No: (632) 523-8888 loc. 274DL/Telefax No: (632) 526-2067E-mail: [email protected]

[email protected]

Celia M. ReyesEditor-in-Chief

Jennifer P.T. LigutonManaging Editor

Jeffrey G. AceronMarsmath A. Baris, Jr.Steffie Joi I. Calubayan

Jeremy L. De JesusNovee Lor C. Leyso

Anne Bernadette E. MandapJasminda A. QuilitisErica Paula S. SiosonAlellie B. SobreviñasResearchers/Writers

Editorial Staff Editorial Staff Editorial Staff Editorial Staff Editorial Staff

CBMS NETWORK UPDATESCBMS NETWORK UPDATESCBMS NETWORK UPDATESCBMS NETWORK UPDATESCBMS NETWORK UPDATESPEP-CBMS Network Coordinating TeamAngelo King International Center for Economic & Business StudiesDe La Salle University - Manila10th Flr. Angelo King International Center

Estrada cor. Arellano Sts., Malate, Manila 1004, Philippines

NO ONE TO RECEIVENO ONE TO RECEIVENO ONE TO RECEIVENO ONE TO RECEIVENO ONE TO RECEIVE

INSUFFICIENT ADDRESSINSUFFICIENT ADDRESSINSUFFICIENT ADDRESSINSUFFICIENT ADDRESSINSUFFICIENT ADDRESS

RESIGNEDRESIGNEDRESIGNEDRESIGNEDRESIGNED

UNLOCATED ADDRESSUNLOCATED ADDRESSUNLOCATED ADDRESSUNLOCATED ADDRESSUNLOCATED ADDRESS

MOVED OUTMOVED OUTMOVED OUTMOVED OUTMOVED OUT

UNKNOWN ADDRESSEEUNKNOWN ADDRESSEEUNKNOWN ADDRESSEEUNKNOWN ADDRESSEEUNKNOWN ADDRESSEE

REFUSED TO ACCEPTREFUSED TO ACCEPTREFUSED TO ACCEPTREFUSED TO ACCEPTREFUSED TO ACCEPT

News Updates

CBMS-Peru presentsresults

Bulk Presorted Third Class Mail UnderPermit No. BPSM-04-05 NCR, valid

unt il December 31, 2011

Having completed the processing, analysisand validation, the Community-BasedMonitoring System (CBMS) Team of Perupresented the CBMS results of the district ofVilla El Salvador on September 14, 2011 tothe Metropolitan Municipality of Lima andto the Ministry of Women and SocialDevelopment. The event was held at theHall of Mirrors, Metropolitan Municipalityof Lima.

The presentation was attended byrepresentatives from Lima, stateinstitutions, and civil society organizationsamong others. The aim of the event was tomade the public aware of the socialproblems the district is facing as well as toinform them that there are problems of

exclusion—there are poor areas that werenot covered by poverty alleviationmeasures.

CBMS-Peru is planning to present some ofthe results to other CBMS-implementingcountries, researchers and variousstakeholders during the 9th Poverty andEconomic Policy Network Meeting to beheld in Cambodia on December 3-9, 2011. Anew proposal is being drafted that focuseson the continuation of the project.

A video of the validation of results isavailable in http://www.youtube.com//watch?v=ZOqK39_paaE and in http://w w w . o b s e r v a t o r i o u r b a n o . o r g . p e /sistemas_cbms.html.

The presentation of CBMS results was attended by various stakeholders from thelocal government, academe, nongovernment organizations and civil societyorganizations.

Documents

Impact of the Global Financial Crisis on ... · ISSN: ... Impact of the Global Financial Crisis on Households in Pekalongan City* Akhmadi and Asri ... located in Rt 3 of Rw 4 while