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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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