Assessment of District Performance in Making Progress Towards MDGs in Bangladesh

8/9/2019 Assessment of District Performance in Making Progress Towards MDGs in Bangladesh

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Assessment of district performance in making

progress towards MDGs in BangladeshCarel de Rooy

1and Siping Wang

2

OverviewProperly planned and conducted household surveys are the most reliable mechanism to assess

progress regarding achievement of Millennium Development Goals (MDGs) in countries

where the routine availability of development outcome or impact information is not readilyavailable. UNICEF has been supporting the Government of Bangladesh (GoB) since the year

2000 to undertake such surveys. Their results have been made available to GoB as well as

Development Partner institutions and organizations for planning, and prioritizing investment

and action. A sizeable share of data that has enabled the understanding of progress regarding

achievement of MDGs has come from these surveys.

Since the year 2000 three such surveys’ data have been made available (2000, 2003 and

2006)3. All these were undertaken with a household sample size of approximately 60,000 andhad a geographical resolution to the district level, allowing the comparison between the 64

districts of Bangladesh in each of these years. Approximately 20 indicators covering most

MDGs were used in each survey. Unfortunately somewhat different indicators were used

every time allowing for comparison over time of only approximately one third of the

indicators used.

This brief paper seeks to make a trend analysis over the 2000 to 2006 period using eightindicators that could be compared over time.

Data for the analysisThe Child Risk Measure (CRM) is a composite index. It comprises 8 indicators which have

data by district for the years 2000, 2003 and 2006. The 8 indicators are:

• Infant mortality rate (IMR),

• Proportion of births not attended by skilled health personnel,

• Proportion of children 6-59 months without supplementation of vitamin A,

• Proportion of households without consuming iodized salt,

• Proportion of households without access to an improved water source,

• Proportion of households without access to an adequate sanitation facility,

• Proportion of primary school age children not attending school, and;



Source of dataExcept for IMR, data for 7 indicators are from the MICS 2000, MICS 2003, and MICS 2006.

The data for IMR are from the Bangladesh Annual Vital Registration Sample Survey.

Methodology of computationThe index of each indicator for each district is calculated as a relative deviation from the

national average. A district with a negative value means that it has a comparatively lower risk

than a district with a positive value.

The CRM is the weighted average of the index of each of the 8 indicators. Each index is the

standard deviation of a given district value from the national average. The weight is

determined based on a conceptual framework (see the figure on the next page). The IMR is

given a weight of 4. The proportion of births not attended by skilled health personnel and

proportion of children 6-59 months without supplementation of vitamin A are given a weight

of 3. A weight of 2 is allocated to the following indicators: proportion of households without

consuming iodized salt; proportion of households without access to an improved water source;

proportion of households without access to an adequate sanitation facility; proportion of primary school age children not attending school. The proportion of children under 5 without

a birth registration is given a weight of 1.

CRM mapsFor the years 2000, 2003 and 2006 a color code was given to child risk related ranges for

each index. Red was allocated to represent districts where children are most at risk, here

represented by the value of a given index for districts being higher than the upper limit of thestandard deviation from the national average of that index. Blue was used to represent

districts where children are exposed to the relatively lowest risks, represented by index values

lower than the lower limit of standard deviation from the national average of that index.

Yellow represents districts where children are exposed to relatively medium risks and is

characteristic of index values between the lower limit and upper limits described above.

Maps depicting CRM trendsAdditionally, color coding has been used to show evolution or changes over time in the CRM

ranking. This was done by showing the difference between data from 2003 and 2000; 2006

and 2003, as well as 2006 and 2000. Red represents a drop in CRM ranking. Pink depicts no

progress, at high risk in CRM rank. Yellow represents no progress at medium risk in CRM

rank. Blue shows districts that sustained a low risk CRM rank. Brown identifies those

di i h h l d f hi h di i k i CRM k G h



The rapid assessment, undertaken by UNICEF field staff over a period of a three days,

revealed that for several indicators more favourable results emerged for the “Low

Performance Districts” (>10% difference):• Pupil-teacher ratio

• Population-doctor ratio

• Population-health personnel ratio

• % population affected by major natural disasters

• Frequency of turn-over in key district level posts

• Per-capita expenditure of the MoHFW

• Average NGOs per district

Other indicators did not show any relevant difference between “Low Performance Districts”

and “High Performance Districts” (<10% difference):

• Average population per district

• Frequency of turn-over in key UNO level posts

• Frequency of turn-over in key Upazila level posts

•

INGOs• Bi-lateral donors

• Multi-lateral donors

The only indicators found that might explain the difference in performance between “Low

Performance Districts” and “High Performance Districts” were poverty and geographic

isolation:

• % of Poor population (20% higher in low performance districts)4

• % of Unions not seasonally accessible (two and one half more disfavourable for the

low performance districts)

ConclusionWhen comparing data from 2000 with that of 2006, twelve districts substantially declined

while nine districts improved in their CRM ranking. Of the 9 originally classified in 2000 as

relatively high-risk districts 4 actually evolved into the medium risk category. In contrast

seven districts out of the 13 originally classified as relatively low-risk dropped into themedium-risk category. Roughly 20% of the districts in the medium-risk category moved

either into the high-risk or low-risk categories.

Out of 15 variables assessed to attempt to explain the difference in performance between the

two categories of districts only two variables emerged: poverty and geographic isolation



The current drive of the MoHFW to promote the establishment of community clinics seems

very well placed in the context of the above related findings. If these clinics are:

• Well equipped;• Provided with a regular supply of high quality medicines;

• Sustainably resourced with qualified human resources;

• Targeted upon the areas of the country that are seasonally not accessible and;

• Focused upon issues where inequity of access or outcome are greatest .......

this strategy could have an impact upon under-five, new-born and maternal mortality

reduction.

A recent study published in The Lancet5

implicitly suggested that economic determinants

have a lot of weight in explaining health outcomes in Bangladesh. This also implies that rapid

economic growth tends to shadow other determinants of health (and possibly other

development) outcomes. The above mentioned rapid assessment substantiates this finding.

In 2009 UNICEF supported the GoB through its Bangladesh Bureau of Statistics to conduct

yet another MICS. This time however 300,000 households were surveyed allowing for anenhanced geographic resolution down to the upazila (sub-district) level. The results of this

survey will be launched in November 2009. They are potentially an important baseline for the

current government, both its administration and the country’s elected officials.

Similar surveys – with geographic resolution at sub-district level - will be conducted in 2012

and 2015 allowing the government to continuously assess progress towards the achievement

of MDGs. Subsequent ranking of districts will be made possible to recognize, acknowledgeand better understand those that have made most progress. Most importantly, this approach

will be replicated for sub-districts as well. This will, with other management tools emerging

for fine-tuning of social sector investment decisions6, facilitate the prioritization of

investment decisions by the Government of Bangladesh and its development partners alike. It

will also allow for the undertaking of remedial action for the least performing upazilas and

districts so that MDGs can be achieved with equity.

Finally, it is interesting to note that the rapid assessment suggests that over one third of thepopulation in both categories of districts assessed has been affected by major natural disasters.

This finding, although likely to be an over-estimation and therefore it requires substantiation,

however calls for a much more proactive approach to address emergencies. Instead of being

reactive the government, with support of its development partners, should enhance its

investment in resilience building to minimize impact of natural disasters and allow



Conceptual Framework of the Child Risk Measure

The methodology of computation of the child risk measure (CRM), a composite index, is:

CRM = 4 * RD1 + 3 * (RD2 + RD3) + 2 * (RD4 + RD5 + RD6 + RD7) + RD8

where RDij is the relative deviation of the indicator Rij,

RDij = (Rij – ARi) / SDi,

i = 1, 2, … 8;

j = 1, 2, … 64

Rij is the indicator i for the district j;

• R1j: Infant mortality rate (IMR),

• R2j: Proportion of births not attended by skilled health personnel,

R3j P i f hild 6 59 h i h l i f i i A

Infant Mortality

Households with no

access to an

improved drinking

water source

Households with no

access to adequate

sanitation facility

Households not

using iodized

salt

School-age

children not

attending school

Births not re istered

Manifestation

Immediate factor

Underlying factors

Process factor

Births not attended by

skilled health

personnel

Vitamin A not

supplemented to

children 6-5 months



Annual Child Risk Measure (CRM)

Figure 1. CRM 2000 Figure 2. CRM 2003

Figure 3. CRM 2006

Index

-18.4--9.5

-9.4-9.3

9.4.-18.0

Index

-15.4--9.6

-9.5-9.5

9.6-24.3



Trends in Child Risk Measure (CRM)

Figure 4. Change from 2000 to 2003 Figure 5. Change from 2003 to 2006

Figure 6. Change from 2000 to 2006

Code

DeclinedinCRMranking

Noprogress,staticathighriskofCRMrank

Noprogress,staticatmediumriskofCRMrank



Distribution of Districts According to the Child Risk Measure2006

-5

0

5

10

15

20

25

10 districts, 16% 10 districts, 16%

44 Districts, 68%

Low risk

Medium riskHigh risk



Complete Data: Child Risk Measure RankingAnnual Data Trend Data

Districts 2000 2003 2006 2003-2000 2006-2003 2006-2000

Cox''s Bazar 18.0 21.1 17.7Bandarban 17.4 20.6 13.5Sherpur 16.5 2.7 22.1

Rangamati 15.4 -4.6 18.1Jamalpur 14.7 13.7 10.8Brahmanbaria 14.5 9.4 4.6Panchgarh 12.4 -0.5 -6.2Sunamganj 11.9 24.3 7.1Noakhali 10.8 6.8 5.0Sylhet 9.2 -12.5 -0.8Sirajganj 9.2 4.9 11.8Netrokona 9.0 7.8 16.3Kishoreganj 8.5 13.3 14.9Khagrachhari 8.5 1.7 7.5

Bhola 8.0 2.8 7.1Nilphamari 7.3 11.0 -1.7Rangpur 7.2 7.5 -0.7Sariatpur 6.1 10.8 6.7Joypurhat 6.0 -12.8 -1.3Kurigram 5.6 6.8 0.3Baherhat 5.4 0.6 -2.0Naogaon 5.4 -5.0 1.9Pabna 5.3 6.2 5.0Thakurgaon 4.6 12.2 7.0Habiganj 3.7 10.5 16.8Gopalganj 2.0 -6.7 -0.6Manikganj 1.9 -11.0 1.6Chittagong 0.9 -10.8 -1.8Madaripur 0.6 0.9 -0.2Moulvi Bazar 0.1 7.2 -6.0Bogra -0.1 -6.1 -3.9Narsingdi -0.2 3.2 -6.9Mymensingh -0.5 9.2 17.1Patuakali -0.6 6.7 6.5Barisal -0.7 0.2 -13.9Laxmipur -1.5 7.7 -1.4Chandpur -2.0 -15.4 -6.7

Gaibandha -2.1 3.1 6.1Nawabganj -2.3 -2.5 8.3Lalmonirhat -4.1 4.7 2.0Rajbari -4.4 -4.4 2.4Comilla -4.7 -9.9 -11.5Pirojpur -4.9 -9.6 -2.2Faridpur -5.1 -6.5 -2.7Satkhira -6.0 -2.5 5.3Dinajpur -6.0 -3.1 3.2Feni -6.2 -2.9 -16.7Chaudanga -6.9 -5.9 -13.5

Natore -7.3 -4.4 -2.9Tangail -8.5 4.6 7.4Rajshahi -9.0 -11.3 -7.3Jessore -10.0 -14.0 -9.0Kushtia -10.2 -10.5 -9.5Jhenaidah -10.2 -9.1 -13.9Narayanganj -10.3 -6.9 -6.1Meherpur -10.3 -13.0 -23.3Gazipur 10 4 1 9 3 1



AcknowledgementsAll UNICEF sections: Health & Nutrition, Water and Environmental Sanitation, Child

Protection, Education, Planning, Monitoring & Evaluation and Field Operations participated

in development of this brief paper. Field Operations were instrumental in very rapidly

collecting data for the Rapid Assessment of District Performance Indicators.

Annex 1. Questionnaire for Rapid Assessment of District

Performance Determinants

1. Basic Information:

Indicator Total

Total population of the district (2006) (source: DC/CS Office)

Number of Unions

Number of Unions seasonally not accessible by road or boat

Number of Primary Schools (include both Government and registered non-

Government schools)Total number of primary school teachers (2000/2003/2006, absolute number

corresponding to year )

Total number of primary school students (2000/2003/2006, absolute number

corresponding to year )

Number of Health Facilities (hospitals and clinics, meaning: District

Hospital, UHCs, H&FWCs, Health Sub-centres)

Total number of doctors (2000/2003/2006, absolute number corresponding toyear)

Total number of medical assistants (2000/2003/2006, absolute number

corresponding to year)

Total number of nurses (2000/2003/2006, absolute number corresponding to

year)

Number of population affected by major natural disasters from 2000 to 2006

Names of major natural disasters from 2000 to 2006 ( Cyclone or Flood year

wise )

2. Governance

2.1 Frequency of turn-over of key government officials at district level



2.2 Frequency of turn-over of key government officials at upazila level

No of post in the district Number of

persons onthe posts

from 2000 to

2006

Estimated number

of months that postsremained vacant

between 2000 to

2006

Key Upazila Posts

Total no. of

posts in the

district

Currently

occupied

Remarks

(if any)

Upazila Nirbahi Officers

Upazila Health andFamily Planning Officers

Upazila Primary

Education Officers

Sub Assistant Engineer,

DPHE

Upazila Social Service

Officers

Total

3. Financial resources

3.1 Annual allocations in thousands of Taka:

Sectors 2000 2001 2002 2003 2004 2005 2006

Health &

NutritionEducation

WATSAN

Social Welfares

Local Government

3.2 Annual expenditure in thousands of Taka

Sectors 2000 2001 2002 2003 2004 2005 2006Health &

Nutrition

Education

WATSAN

S i l W lf



12 Assessment of district performance in making progress towards MDGs in Bangladesh

Annex 2. Outcome of the Rapid Assessment of District Performance Determinants

% ofpopulation

to totalpopulation

Totalpopulation

% of

Populationlivingunder

nationalpoverty

line

% of unionsseasonally

notaccessible

Pupil-teacher

ratio

Populationdoctorratio

Population-health

personnelratio

% ofpopulationaffected by

majornatural

disasters

Frequencyof turn-

over in DCpost

Frequencyof turn-

over in thedistrict key

posts

Frequencyof turn-over

in UNOposts

Frequencyof turn-overin Upazilakey posts

Per capitaexpenditureof MoHFW LNGOs INGPs

Bilateraldonors

Multi-lateraldonors

2006 2006 20052000-2006

2000-2006 2000-2006 2000-2006 2000-2005

Cox''sBazar 1.6 2,257,809 52 63 109 24,541 12,117 1.1 6 6 5 3 83.94 9 3 0 5

Bandarban 0.2 318,616 65 41 52 7,586 3,402 1.4 6 5 4 3 294.32 17 3 2 7

Jamalpur 1.6 2,234,166 54 88 77 26,597 9,009 8.4 10 7 7 4 90.43 773 4 2 9

Habiganj 1.3 1,880,380 47 39 68 22,385 11,083 8.5 6 5 5 3 91.59 4 1 1 0

Barguna 0.7 996,986 61 21 42 24,719 8,497 0.0 9 3 4 2 115.36 7 1 1 3

Panchgarh 0.7 948,572 56 0 157 27,102 10,236 0.0 7 7 6 3 105.05 9 1 0 0

Sunamganj 1.6 2,305,939 49 56 107 37,597 18,062 8.5 5 7 5 2 77.55 8 3 1 0

Barisal 2.0 2,855,780 60 26 63 36,302 12,692 4.7 6 5 5 3 254.44 9 1 2 3

Munshiganj 1.0 1,463,010 19 0 76 14,630 6,989 7.7 7 8 8 3 109.76 0 1 0 7

Narail 0.6 792,335 45 0 46 25,559 8,225 1.9 7 6 9 5 111.35 307 2 1 8

Low performance for children

High performance for children

Averagepopulation

perdistrict

% PoorPopulation

% ofunions

seasonally

notaccessible

Pupil-

teacherratio

Population-

Doctorratio

Population-health

personnelratio

%Population

affectedby major

naturaldisasters

Frequencyof turn-

over in DCpost

Frequencyof turn-

over in the

districtkey posts

Frequencyof turn-over in

UNOposts

Frequencyof turn-over in

Upazilakey posts

Per capita

expenditureof MoHFW

AverageLNGOs

perdistrict INGPs

Bilateraldonors

Multi-

lateraldonors

1,537,591 56 55 71 22,458 9,433 32 7 30 5 3 168.91 162 2 1 5

1,673,127 46 22 79 27,339 11,139 38 6 39 6 3 131.63 67 2 1 4

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Assessment of District Performance in Making Progress Towards MDGs in Bangladesh