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Africa Region Survey-Based Harmonized Indicator Program (SHIP)
By Andrew Dabalen, Saurabh Shome, and Xiao Ye Africa Region Statistical Practice Group
June 6, 2013
Vision: Pillars of Renewal
Strong Statistical Capacity
by African Countries
Pillar 1Robust Client
Support
Pillar 2Innovation
sPillar 3
Provision of Public
Goods to the Region
Pillar 3: Provide public goods for the Region
Data warehouse
• Surveys (all types, including facility)
• Price data• Censuses• Sector or
administrative
• Already over 200 surveys in archive.
Corporate products
• Data for WDI, WDR
• Data for Global poverty estimates (Extreme poverty and shared prosperity)
• Real time data
• Production of ADI
Comparable data for users
(SHIP)• Data for
regional flagships
• The Pulse• Other AAA• Outside
researchers
Support project teams• Benchmarki
ng data• Indicators
for results framework
Presentation outlineWhy harmonizing?SHIP in a nutshellHow we harmonize and SHIP
outputsChallenges and limitations Examples of analysis using SHIPDissemination and technical
assistanceSHIP – next stepsDemonstration of SHIP indicators
Why harmonizing?
Camero
on
Côte d’Ivoire
Zambia
Mozambique
A
Ma
li
Ethiopia
UgandaGhana
Kenya
B
Nigeria
variable name variable labels14aq1 nombre de ligness14aq1a numero de lignes14aq2 code du produits14aq3 montant total de la dépense pour le produits14aq4 digit de contrôles14aq5 mode d'acquisitions14aq6 numéro d'ordre du bénéficiaires14bq0 code sections14bq1 numero de ligne évènement exceptionnels14bq3 code évènements14bq4 nature de la dépenses14bq5 dépense/acquisition exceptionelle ou pas
s14bq6montant total de la dépense/acquisition pour évènement excep
s14bq7 digit de contrôles14bq8 mode d'acquisitions14cq0 code sections14cq1 nombre de ligne de stock alimentaires14cq2 code produit (stock alimentaire)s14cq3u quantité achetée stock alimentaire (unité)s14cq3t quantité achetée stock alimentaire (type unité)s14cq3q quantité achetée stock alimentaire (quantité)
s14cq4montant total des dépenses/acquisitions stock alimentaire
s14cq5 digit de contrôle stock dépense alimentaires14cq6 mode d'acquisition (stock alimentaire)s14cq7 nombre d'acquisition (stock alimentaire)Demographic information, access to services,
household consumption, employment , household productions, etc.
Harmonization in a nutshell from raw data to 200 harmonized variables, replicable
variable name variable labelHID household unique IDREGION geographical codeRURURB area of residenceHHSIZE total number of residents excluding household helpTOTFOOD Total food expenditureTOTALCH Total food expenditureTOTFDAL Total annual expenditure on food and alcoholTOTCLTH Total expenditure on clothing and footwareGAS Expenditure on cooking gasELEC Expenditure on electricityHSUTILITY Expenditure on electricity and gasHSKEROSENE Expenditure on keroseneHSDIESEL Expenditure on diesel for non-transportation purposesTOTHOUS Total expenditure on housingTOTFURN Total expenditure on furnishingTOTHLTH Total expenditure on healthTRFUEL Transportation fuelTRPERSON Expenditure on personal transport equipment/repairs/chauffeursTRSERVE Expenditure on public transportation/airfare etc.TOTTRSP Total expenditure on transportTOTCMNQ total expenditure on communicationsTOTRCRE total expenditure on recreationTOTEDUC Total expenditure on educationTOTHOTL Total expenditure on restaurants and hotelTOTMISC Total miscelleneous expenditureTOTNFD Total annual expenditure on non-food items
GHA 1998
GHA2013
MWI2004
MWI2010
CIV 2008
ZMB2006
CIV 2002
CMR2001
CMR2007
KEN1997
KEN2005
ZMB 2010
MOZ2003
MOZ2009
UGA2005
UGA2010
Four SHIP files for each survey: Expenditure file, Individual file, Household file, Labor file
Replicability of the SHIP achieved through organization and documentation
_SHIP
SHIPing consumption aggregation
Annualized regionally adjusted consumption aggregates (if regional price index available) deflated to 2005 PPP-USD
Rent is not included in the final consumption aggregate, but actual rent paid is available as a separate variable
Very large lumpy spending is excluded from final consumption aggregate, such as hospitalization expense and purchase of vehicles, but are available as separate variables
Only per capita food expenditures are regionally adjusted using the food price index, non-food expenditures are not adjusted
Outliers in food and non-food expenditure beyond three standard deviations are replaced by their respective median values
SHIPing incomeIncomes from wage work is captured
at the individual level for cash payments only, not annualized, but payment period is included as a separate variable
Annualized gross incomes from different sources are captured at the household level, including wages, gross incomes from household enterprises, farms, as well as transfers
SHIPing Labor variables (where informal employment/activities prevalent)Information from all sections of the survey
is used to capture employment dataData from different sections are at
different levels, including the individual / farm / enterprise / household level– SHIP output at individual level
Always merge data at the individual level – convert enterprise / farm level data into individual level
Keep an account of the number of individuals throughout the process
SHIPing other socioeconomic variables and SHIPing indicatorsDemographic information (age,
sex, relationship to the head)Access to services at individual
level (health, education, immunization, etc.)
Access to services at household level (water, sanitation, electricity, garbage collection, etc.)
SHIP OutputsI. One manualII. Four SHIP files per survey (200 variables),
so far 21 countries (approx. 70% of population) 40 surveys have been completed
III. Sixty SHIP Indicators organized by national quintile, rural/urban quintiles and gender (serves as a tool to check data quality)
IV. SHIP team provides feedbacks on questionnaire designs
V. Training workshops on SHIPing
Limitations of SHIP filesExtract most commonly available
variables, thus rich information from special in-depth modules (sporadic availability only) may not be included
Household consumption in SHIP cannot be used to calculate poverty, but rank preserving, which enables distributional analysis
Challenges faced – Initial designBalancing between regional context and the
flexibility to meet countries’ needs: creating “Lego” variables. Eg. SHIP labor variables◦ Ramifications for global harmonization of the
regional harmonization programsBalancing between most available variables
in all surveys and analytical needs on a range of research topics, while keeping the complexity and the number of SHIP variables manageable
Thorough and consultative designing process with experts of different fields, minimizing changes once SHIP files finalized
Challenges faced - harmonization processKeeping assumptions relatively
consistent across countries when compiling SHIP variables but also realistic in a given country context
Differences in questionnaire designs across countries
Changes in questionnaires design over time within the same country
Analysis: have there been structural changes in the labor market?
Analysis: who benefits from fuel subsidy?
Featured in Africa Region Publication “Pulse”.
Analysis: who has access to electricity?
Source: Africa Region SHIP indicators.
Analysis: the garbage collection division
Source: Africa Region SHIP indicators.
Analysis: MDG universal primary enrollment less obtainable for the poor and girls
Source: Africa Region SHIP indicators.
Use SHIP as a capacity building and dissemination toolCommunicating with national
statistical offices Provide training upon request;
andIntroduce most recent thinking
on questionnaire design
Ghana workshop on SHIPingThrough our video conference with NSO
on Ghana SHIP they requested training on SHIP methodology using their partially finished new survey
A four week intensive hands-on workshop for NSO staff achieved objectives (know how transfer)
Benefits were mutual, we learned about the country context, clever programing and made minor revisions to the SHIP manual based on NSO’s feedbacks
SHIPing CycleAcquire surveys
from NSOs
SHIPing
Dissemination
Capacity building and exchanges of
ideas
Scope of future
collaborations
SHIP: next stepsCreate public access Involve more NSOs using SHIP
procedures (requires resources and manpower)
Demonstrate analytical uses of SHIP data and increase local ownership
Outcomes: a wider use of household surveys in policy decision making and in monitoring of development outcomes
The Value Chain of Household Survey Data
Raw Household survey data (Hundreds and thousands of variables and many data files
Unit record SHIP files (200 variables and 4 files)
60 SHIP indicators by rural urban areas and by expenditure quintiles
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