PSLM_Report_2024-25-Social-2.pdf

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Sr.No. SDG Indicator No. Description Status 14 3.b.1 Proportion of the target population covered by all vaccines included in the national programme (P&D) 15 4.1.2 Completion rate (primary education, lower secondary education, upper secondary education (P&D) 16 4.2.2 Participation rate in organized learning (one year before the official primary entry age), by sex

(P&D) 17 4.4.1 Proportion of youth and adults with information and communications technology (ICT) skills, by type of skill. Disaggregation by gender. (P&D) 18 4.5.1 Parity indices (female/ male, rural/ urban, bottom/ top wealth quintile and others such as disability status, indigenous peoples and conflict affected, as data become available) for all education indicators on this list that can be disaggregated. Disaggregation by gender, residence, wealth, disability, conflict areas (P&D) 19 4.6.1 Percentage of population in a given age group achieving at least a fixed level of proficiency in functional (a) literacy and (b) numeracy skills, by sex. (P&D) 20 5.a.1 (a) Proportion of total agricultural population with ownership or secure rights over agricultural land, by sex; and (b) share of women among owners or rights-bearers of agricultural land, by type of tenure (D) 21 5.b.1 Proportion of individuals who own a mobile telephone, by sex (P&D) 22 5.6.1 Proportion of women aged 15-49 years who make their own informed decisions regarding sexual relations, contraceptive use and reproductive health care. Disaggregation by age, location, economic quintile, education level, marital status, disability (P) 23 6.1.1 Proportion of population using safely managed drinking water services. Disaggregation by residence, gender, disadvantaged group, sub national, SES (P&D)

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Sr.No. SDG Indicator No. Description Status 24 6.2.1 Proportion of population using safely managed sanitation services, including a hand-washing facility with soap and water. Disaggregation by residence, gender, disadvantaged group, sub national, SESs (P&D) 25 7.1.1 Proportion of population with access to electricity, Disaggregation by residence. (P&D) 26 7.1.2 Proportion of population with primary reliance on clean fuels and technology, Disaggregation by cooking, heating, lighting, residence (P&D) 27 9.1.1 Proportion of the rural population who live within 2 km of an all-season road (P&D) 28 10.1.1 Growth rates of household expenditure or income per capita among the bottom 40 per cent of the population and the total population (P) 29 10.2.1 Proportion of people living below 50 per cent of median income, by age, sex and persons with disabilities (P) 30 11.2.1 Proportion of population that has convenient access to public transport, by sex, age and persons with disabilities (P&D) 31 16.6.2 Proportion of the population satisfied with their last experience of public services (D) 32 17.8.1 Proportion of individuals using the Internet. Disaggregation by age, gender, educational level, Labour, Residence (P&D) 33 3.2.1 Under 5 five mortality rates (P) Where (P) = Provincial Level
(D) =District Level

SDG Indicators Monitored through HIES(Provincial) Survey

SDG Indicators Monitored through PSLM (District) Survey

SDG Indicators Monitored through both HIES (Provincial) and PSLM(District)Surveys

APPENDICES

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APPENDIX-I: ESTIMATION FORMULAE

ESTIMATION FORMULA FOR SAMPLE SIZE

Sample size has been estimated at provincial level with urban rural breakdown by using the following formula:

 Variables: Immunization, Net Enrolment Rate (NER) and Contraceptive Prevalence Rate (CPR), Pre-Natal Care and Post Natal Care  Design Effect (Deff): 2,
 Level of Confidence (t): 97%,  Margin of Error (d): 10%  Non-Response Factor (NRF): 3%
 Proportion of exposed population (p) and HH size has been taken from 7th Population& Housing Census 2023.
 Prevalence rate (r) has been used as in the last Round of the Survey (2019-20).  Intake is 12 and 16 Households from urban and rural domains respectively.

Calculation of Sampling Weights:

Due to disproportionate allocation of sample households across divisions and strata, different sampling fractions were applied. To ensure representativeness of survey estimates, sampling weights were calculated and used in all subsequent analyses. The major component of the sampling weight is the reciprocal of the sampling probabilities employed in selecting the number of sample households in that particular sampling stratum (h) and PSU (i):

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hi hi f W 1 

The term fhi, the sampling fraction for the ith sample PSU in the hth stratum, and defined as the product of the probabilities of selection at every stage in each sampling stratum:

hi hi hi p p f 2 1  

Where p1hi and p2hi are the probabilities of selection of the sampling unit at stage 1 and 2 for the ith sample PSU in the hth sampling stratum, for two stage stratified sampling. Based on the sample design, these probabilities were calculated as follows:

, h hi h M M n 

nh = number of sample PSUs selected in stratum h

Mhi = number of households in the frame for the ith sample PSU in stratum h

Mh = total number of households in the frame for stratum h

p2hi = hi hi M m '

mhi= intake is 12 households for urban and 16 households for rural from each PSU.

M'hi = number of listed households in the ith sample PSU in stratum h

i. ESTIMATION FORMULAE FOR TOTALS AND THEIR VARIANCES

NOTATIONS:

Nh = Total number of Primary Sampling Units (PSUs) in the hth stratum of a province.

nh = Total number of sample PSUs in the hth stratum of a province.

Mhi = Total number of Secondary Sampling Units (SSUs) in the ith sample PSU of hth stratum of a province.

mhi = Number of sample SSUs in the ith sample PSU of hth stratum of a province.

Phi = Assigned probability of selection of ith PSU of the hth stratum of a province.

p1hi =

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yhij = Value of any characteristic Y of jth SSU within ith PSU of hth stratum of a province.

xhij = Value of any characteristic X of jth SSU within ith PSU of hth stratum of a province with

whose respect proportion is required.
Totals and their variances were estimated using standard design-based estimators appropriate for two-stage stratified sampling.

h h i=1 n hi hi hi j=1 m hij Y = 1 n

1 p M m

y h hi   

   Y =
Y 1 n Y p h=1 L h h=1 L h i=1 n hi hi

h   

For X, another variable of interest, we have h h i=1 n hi hi h i=1 n hi hi hi j=1 m hij X = 1 n

X P = 1 n

1 P M m

x h h hi     

   X =
X =
1 n

X p h=1 L h h=1 L h i=1 n hi hi h   

v y n s n n Y P y P n h h ht h h hi hi hi hi i n h i n h h ( ) ( )  ( )                     1 1 1 2 2 2 2 1 1

N =
N h=1 L h 

n =
n h=1 L h 

h h i=1 n hi hi Y = 1 n

Y p h   

OR

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v Y n s n n Y P y P n h ht h h h L h L hi hi hi hi i n h i n h h ( ) ( )  ( )                         1 1 1 2 1 1 2 2 2 1 1

ii. FORMULAE FOR RATIO ESTIMATES

r = Y X  

where Y^ and X^ can be estimated by equations under (i) given above.

where

s2hb = s2ht - s2hw

ht 2 hy 2 2 hx 2 hxy s = s +r s -2r s

  hx 2 h i=1 n hi 2 hi 2 2 i=1 n hi hi h s

1 n - 1 x p

x p n h h                      

  hy 2 h i=1 n hi 2 hi 2 2 i=1 n hi hi h s

1 n -1 y p

y p n h h                      

  Rel V(r)= 1 X 1 n s

  • 1 x 1 n M p m M - m M s 2 h=1 L h hb 2 2 h=1 L h i=1 n hi 2 hi 2 hi hi hi hi hw 2 h     

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hxy h i=1 n hi hi hi hi i=1 n hi hi i=1 n hi hi h s

1 n -1 X p y p

X P y P n h h h                                   

  hw 2 h i=1 n hi 2 hi 2 hi hi hi hi hi 2 s

1 n -1 1 p M m M -m M s h

For
  hix 2 hi j=1 m hij 2 2 j=1 m hij hi s

1 m -1 x

x m hi hi                      

  hixy hi j=1 m hij hij j=1 m hij j=1 m hij hi s

1 m -1
x y

x y m hi hi hi 2                       


  hiy 2 hi j=1 m hij 2 2 j=1 m hij hi s

1 m -1 y

y m hi hi                      

hi 2 hiy 2 2 hix 2 hixy s = s +r s -2r s

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APPENDIX-II: HIES (2024 -25) QUESTIONNAIRES (MALE & FEMALE)

With Changes Highlighted in Comparison with HIES Questionnaires (2018-19)

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