xx Africa Region Survey-Based Harmonized Indicator Program (SHIP)

1. xxAfrica Region Survey-Based Harmonized Indicator Program (SHIP) By Andrew Dabalen, SaurabhShome, and Xiao Ye Africa Region Statistical Practice Group June 6, 2013

2. Vision: Pillars of Renewal

3. Pillar 3: Provide public goods for the Region

4. Presentation outline • Why harmonizing? • SHIP in a nutshell • How we harmonize and SHIP outputs • Challenges and limitations • Examples of analysis using SHIP • Dissemination and technical assistance • SHIP – next steps • Demonstration of SHIP indicators

5. Why harmonizing? A B Ghana Ethiopia Uganda Nigeria Cameroon Mozambique Mali Côte d’Ivoire Kenya Zambia Demographic information, access to services, household consumption, employment , household productions, etc.

6. Harmonization in a nutshell from raw data to 200 harmonized variables, replicable CIV 2008 CIV 2002 CMR2001 CMR 2007 GHA 1998 GHA 2013 KEN1997 KEN 2005 MWI 2004 MWI 2010 MOZ 2003 MOZ 2009 ZMB2006 ZMB 2010 UGA2005 UGA2010 Four SHIP files for each survey: Expenditure file, Individual file, Household file, Labor file

7. Replicability of the SHIP achieved through organization and documentation _SHIP

8. 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

9. SHIPing income • Incomes 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

10. SHIPing Labor variables (where informal employment/activities prevalent) • Information from all sections of the survey is used to capture employment data • Data 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

11. SHIPing other socioeconomic variables and SHIPing indicators • Demographic 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.)

12. SHIP Outputs • One manual • Four SHIP files per survey (200 variables), so far 21 countries (approx. 70% of population) 40 surveys have been completed • Sixty SHIP Indicators organized by national quintile, rural/urban quintiles and gender (serves as a tool to check data quality) • SHIP team provides feedbacks on questionnaire designs • Training workshops on SHIPing

13. Limitations of SHIP files • Extract 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

14. Challenges faced – Initial design • Balancing 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 programs • Balancing 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

15. Challenges faced - harmonization process • Keeping 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

16. Analysis: have there been structural changes in the labor market?

17. Analysis: who benefits from fuel subsidy? Featured in Africa Region Publication “Pulse”.

18. Analysis: who has access to electricity? Source: Africa Region SHIP indicators.

19. Analysis: the garbage collection division Source: Africa Region SHIP indicators.

20. Analysis: MDG universal primary enrollment less obtainable for the poor and girls Source: Africa Region SHIP indicators.

21. Use SHIP as a capacity building and dissemination tool • Communicating with national statistical offices • Provide training upon request; and • Introduce most recent thinking on questionnaire design

22. Ghana workshop on SHIPing • Through 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

23. SHIPing Cycle

24. SHIP: next steps • Create 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

25. The Value Chain of Household Survey Data

26. Thank you!xye@worldbank.orgSSHOME@worldbank.org