FIVIMS _ppt_ - PowerPoint Presentation

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					             State Council

Ministries (NBS, MOA, MOH, CAS, LGPR)

     Provinces &Autonomous Cities


           Counties (2400+)


         Administrative Village

           (Natural Village)

China’s Statistical Information System
•Administrative Reporting System (ARS) for
collecting/aggregating data in each ministry. No established
practice of data exchange between ministries; therefore,
assembling cross-cutting data sets is going to be problematic.
•Top-down planning system where leadership (State Council)
deals with central ministries, the latter with their counterpart
provincial government agencies, the latter with the counties,
then towns/townships, administrative villages, etc. Hence,
central leadership need province-level info mainly, provinces
need county-level info, counties need townhip info, etc.
•NBS and a few ministries (e.g. MOA, MOH) have employed
survey sampling methods alongside their ARSs. Capability
and acceptability of survey sampling methods not yet well
Major Databases/Sources
•NBS: - Censuses (1996 CA, 2000 CPH)
      - County Database of 128 indicators; 20 published annually
      - Sample Surveys by RSO, USO, ESO, Population Dept.
      For poverty monitoring and FIVIMS, important surveys
      include RHS (857 sample counties, 67,000 households);
      NPS (of 592 nationally designated poor counties; results for
      OLGPR exclusively).
•MOA:- County database of 140 indicators
      - Fixed Rural Observation Villages Survey,
              a panel survey run annually since 1984 with
              300 villages and 21,000 households.
•CAS:-a state of the art electronic database system with
       500 gigabytes of information.
MOH:- also maintains a country database, and conducts
      health and nutrition surveys mostly in the Center for
      Preventive Medicine.
      Emerging Need For Small Area Statistics

PA program focused on (592) counties until now. Results
remarkable. However, questions have begun to arise:

•Designated poor counties not chosen objectively.
•Half of remaining poor are in 2412-592 counties.
•Remaining poor concentrated in “poverty traps” that are
       townships and villages, many in Western region.
•Need to focus also on human (non-income) poverty.
•Urban poverty needs to be given more attention now,

Efficiency in targeting to reach the remaining poor require
that PA programs go lower, to townships & villages.
OLGPR has plans to change to a new PA policy gradually in
next 10 years.

•Main focus is on townships & villages, with counties exercising overall
administration. County is basic unit of planning; township is not a basic unit
of planning; in terms of action plan, use village level

•Provinces will rank/identify poor townships & villages; CG and provincial
funds will be allocated directly to them. Need for consistency in provinces’
methods .

•The CG will still identify poor counties (not necessarily the same 592)
because some ministries & donors may still want to target their interventions
at some counties.
OLGPR will need help in having the needed county,
township and village data, and in developing
methodologies for identifying/ranking poor areas.

There are some initiatives in this direction from ADB,
WFP, UNDP, WB; and of course FIVIMS/FAO.
FIVIMS Information Requirements

•Crosscutting; e.g. agriculture (area, production per
capita,…),agro-climate (precipitation, soil type, …), primary
education (enrolment ratios, gender balance,…), nutrition
(energy intake, stunting, underweight, …), health (mortality
and morbidity rates, …). Importance of sharing,
networking between sources, users, and stakeholders.

•In addition to levels, trends and variabilities (time series)
for some indicators are needed for analysis and assessment
of insecurity and vulnerability.

•Disaggregated to small areas/domains. In China, uniform
geo-codes available down to counties only.
Sources and Methods of Indicators/Statistics Production

1. Direct from primary source. Eg. Tabulate ag census
   for villages, townships, counties, provinces, regions.
   Then there will be no need to reprocess every time
   someone needs info. Estimates from reporting
   systems, see e.g. NBS and MOA county indicators in
   Annexes 1 & 5. Estimates from surveys, e.g. see
   Annex 2 from RHS.

2. Combining sources to improve direct estimates, as in
   ratio- and regression-type estimators.
Suppose y is direct estimate from RHS sample, x is
corresponding estimate using data from auxiliary source (ag
census or reporting system, and X is known total from auxiliary
Ratio estimate: yr = (y/x)X
Regression estimate: yreg = y + b(X-x)
Efficiency is gained if correlation between the variables exceeds
The techniques allow combining/reconciling of survey and
reporting systems, an important problem in transition countries.

Example: cultivated land area
3. Combining sources to produce new (small area)
statistics where there are none currently available.

There are two situations: the area was not sampled (e.g
the 2412 – 857 non-sample counties in RHS), or the
questionnaire does not support computation of the
statistic (per capita kcalorie consumption from the ag

Potential solution: Small area estimation techniques.

y = bo + b1x1 + b2x2 + … + bpxp

The x’s should be available from both the sample survey
and the auxiliary source such as the Agric. Census, or
NBS, or MOA county indicators.
Example 1: Estimate for each of the 2412 counties the proportion
of the population with consumption < y = 2100 kcalories/day
(that is the proportion or number of undernourished).
•Estimate y for each of the 857 RHS sample counties.
•Choose explanatory variables present in both RHS and auxiliary
source; run regression using RHS data (both y and x’s); test for
goodness of fit and search for good fit iteratively.
•Plug the x’s from auxiliary source to estimate y for the 2412 –
857 counties.
•For sensitivity/ assessment, compare for the 857 RHS counties
direct estimates with the regression estimates using the x’s from
the auxiliary source.
Note: County in above example can be replaced by township or
Example 2. Use household models (similar to World Bank
approach in its Poverty Mapping Project).

•Suppose y is household per capita income, available from
sample households in a survey only, but not in census.

•Choose x’s available from both survey and census; the
timing of both sources should be as close as possible.

•Run a regression of y (or log y) on x’s using the household
level survey data. Test goodness of fit and run iteratively.

•Calculate predicted y’s household by household, using the
census. It is not recommended that these the used directly for
targeting households; instead,
•Count the number of households in the village whose
predicted y’s fall below a threshold. Or you can have an
estimated income dist’n. for villages, townships, counties.
ADB WPI for Villages. Uses scores for 8 indicators.

•Livelihood poverty: grain output/person/year, cash income
per person per year, % of bad quality houses

•Infrastructure poverty: % of hhs with access to potable water,
% of natural villages with reliable electricity, % of natural
villages with all-weather road to town.

•Human resource poverty: % of women with long-term health
problems, % of eligible children not in school

The higher the index, the poorer the village.

Where to get the indicators, short of collecting them directly?
Participatory poverty surveys
These are not meant to replace , but can only
supplement, statistical sample surveys like RHS. At
some point in the assessment and monitoring process
you will need “statistics” that can be subjected
scientific inference procedures. The results of surveys
like RHS possess important properties like
replicability, which pps do not in general.
The aims in inviting CAS to show and discuss their GIS were
simply to inform at this point that state-of-the-art capability
exists in China, hence no need for external professional
assistance; a tool/platform exists in which to integrate and
share databases, hence no need to reinvent elsewhere; CAS
geophysical, environmental, etc. data get integrated with the
predominantly socio-economic data from NBS, MOA, etc.
Unfortunately, this led to much of yesterday’s time being
consumed on targeting, how to do it, and what indicators are
more appropriate for it; that the indicators should be identified
first. All these are important, and OLGPR will need the results
of analyst’s researches on this matters – in the coming years.
These belong in FIVIMS’ medium-to-long-term objectives.
But these researches will need data, so we should get back this
morning to the short-term objective , which is to deliver BABY
FIVIMS. We could fuzz about the baby after it is born.
                       THE WAY AHEAD
Start small, simple.
•How? Prototype in one province, a small province not far from
Beijing, with good statistical infrastructure (Ningxia?) Why? To
maximize probability of successful “birthing”. This is a little
more ambitious than Mr. Xu’s proposal yesterday (two counties).
•How? Establish network, but keep it within key focal points
initially: NBS, MOA, OLGPR, CAS, (MOH?). Why? Maximize
probability of successful prototype.
•How? Priorities are to get agreements to share databases,
implement, and make integrated database available in a user-
friendly DBS platform. Why? To maximize probability… . So
analysts will have data to work with and pursue a research agenda
to help OLGPR among others in evolving methodologies for the
new PA policy. {Research agenda in second phase of TCP?}
•How? Don’t get bogged down by issues like what
data/indicators to include/exclude. Initially, the more the better.
Culling and expanding (e.g. derived small area estimates)
should happen incrementally and gradually from results of
comparative studies of data quality and which indicators are
analytically more appropriate, for example.
•How? Individual persons in the focal points –prime movers or
leaders – need to be identified to get FIVIMS (or any project for
that matter) off the ground and keep it going.
•How? Technical assistance during the formative years. FAO to
play lead role in soliciting/providing assistance Why? To
support pilot project in one province, a research agenda, and to
provide incentives to focal points and prime movers.

This is a Province and county GIS.
•Database: land use, crop yield, food production,
husbandry production, population, labor force and
some geographic and climatic information.
•Has township and village data in WFP/IFAD
project areas.
•Statistical capabilities: PCA, CA, ranking, z-
scores, that can be used for vulnerability mapping
and analysis –subject to data availability.

•WFP/China will be phased out in 2005.
World Bank is currently discussing with NBS re –
poverty mapping in one pilot province. Will apply
usual WB methodology based on combining sample
survey and census data using a household model to
produce poverty indicators at village level.
Ms. Wang Pingping: new data collection is not recommended; data collection
capacity is very weak below county; small area for counties and townships ok,
but difficult for villages.
•When a theoretical and field expert in data collection speaks, we should listen
and take heed. But yesterday’s discussion covered so much ground that there
may be risk of losing sight of the immediate goal(s).
•Immediate goal is to make the existing FIVIMS-related data available to more
agencies and analysts, in particular the agri census and RHS, but also other
databases in NBS, MOA, MOH, CAS, etc.
•Personal view: collect new data if and only if there is consensus that the present
level of knowledge needs augmenting or updating.
•Which leads to another important issue: How often do you do the targeting?(as
opposed to assessment and monitoring?) How often should poverty, food
insecurity and vulnerability information be updated? At what levels of
disaggregation? This is important; review and decision needed at some point, but
not here now or perhaps not even next year.
UNDP has agreed in principle to provide a technical assistance
grant to OLGPR for capacity building in poverty data
collection and analysis, monitoring and evaluation; poverty
targeting methodology; and to develop a poverty database
system initially at county level, with future plans to expand it
to townships and villages.

Highlights need/importance of coordination among donors
FIVIMS network members and focal points.
Last, but not least, a prototype FIVIMS that successfully and
convincingly demonstrates its value added to improving the
efficiency of the country’s poverty, food insecurity and
vulnerability alleviation programs is a necessary condition for the
Government to put in place the mechanisms (high level
coordinating body) and the resources required to support the
development of a national CHINA FIVIMS.

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