Poverty mapping activities and lessons learned in vietnam
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Poverty Mapping Activities and Lessons Learned in Vietnam. Dr. Dang Kim Son, from ICARD, MARD Mr. Nguyen Ngoc Que, from ICARD, MARD Mr. Do Anh Kiem, from GSO Mr. Nguyen Viet Cuong, from NEU. Presentation Content. Geographic targeting methods in Vietnam Poverty mapping methodology

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Poverty Mapping Activities and Lessons Learned in Vietnam

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Poverty mapping activities and lessons learned in vietnam

Poverty Mapping Activities and Lessons Learned in Vietnam

  • Dr. Dang Kim Son, from ICARD, MARD

  • Mr. Nguyen Ngoc Que, from ICARD, MARD

  • Mr. Do Anh Kiem, from GSO

  • Mr. Nguyen Viet Cuong, from NEU


Presentation content

Presentation Content

  • Geographic targeting methods in Vietnam

  • Poverty mapping methodology

  • Impact and use

  • Lessons learned


Geographic targeting methods in vietnam

Geographic targeting methods in Vietnam

  • Poverty indicators of MOLISA, and CEMMA at commune level:

    • Data on commune-level poverty indicators are collected using local reporting systems.

    • Poor communes are defined using various criteria, however, some not consistent over time and space, some hard to determine accurately.

    • Identification of poor commune is not objective; poverty might be under or over-reported due to various incentives.


Geographic targeting methods in vietnam1

Geographic targeting methods in Vietnam

  • 1998 - Nicholas Minot (IFPRI) used 1993 VLSS and 1994 Agricultural Census to estimate district poverty rates.

  • 2000 - Nicholas Minot and Bob Baulch (IDS, University of Sussex) used 1998 VLSS and 3% sample of 1999 Census to estimate provincial poverty rates.

  • 2002/2003 - Nicholas Minot and Bob Baulch used 1998 VLSS and 33% sample of 1999 Census to estimate district poverty rates.


Poverty map in 1998

Poverty Map in 1998

  • Rural district poverty rates are estimated by Nicholas Minot.

  • Data used:

    • Vietnam living standard survey 1993: covers 4800 households, of which there are 3840 rural ones.

    • Agricultural census 1994: rural-district-level data on some variables.


Poverty map in 1998 methodology

Poverty Map in 1998: Methodology

  • Firstly, Probit model used to estimate a rural household’s probability of being poor using 1993 VLSS data:

    • Pr(Y<Z)=( X+ e)

    • Y: is per capita consumption.

    • Z: Poverty line set at the 30th percentile of per capita consumption of rural households.

    • X: variables of household characteristics that are included in both data sets.


Poverty map in 1998 methodology cont

Poverty Map in 1998: Methodology (cont.)

  • Secondly, poverty rate of districts is estimated based on this regression:

    • Pd=(^ Xid)

    • Pd: poverty rate of districts.

    • ^: estimated parameters.

    • Xid : average value of household characteristics in districts from agricultural census.

    • No standard error estimated because of no household-level data.


Poverty map in 1998 results

Poverty Map in 1998: Results

  • Poverty rates of 543 rural districts are estimated (2 district missing data).

  • It is shown that poor households are concentrated in Northern mountainous areas, the western edges of the North Central Coast, and northern part of the Central Highlands.


Poverty map in 2000

Poverty Map in 2000

  • Province poverty rates are estimated by Nicholas Minot and Bob Baulch using the method of Hentschel et al. (2000).

  • Data used:

    • Vietnam living standard survey 1998: covers 6000 households.

    • Population census 1999: 3% sample of household-level data of the census.


Poverty map in 2000 methodology

Poverty Map in 2000: Methodology

  • Firstly, a logarithm expenditure function is estimated using 1998 VLSS data:

    • Ln(Y) =  X+ e

    • Y: is per capita consumption.

    • X: variables of household characteristics that are included in both data sets.

    • e: error term with normal distribution N(0, ).


Poverty map in 2000 methodology cont

Poverty Map in 2000: Methodology (cont.)

  • Secondly, the probability that a household in population census is poor can be estimated:

    • ^, ^ : estimated parameters and standard deviation.

    • Z: poverty line of 1790 thousands VND in 1998.

    • Poverty rate of a province is the average value of households’ poverty probability.


Poverty map in 2000 results

Poverty Map in 2000: Results

  • Poverty rate of 61 provinces is estimated.

  • Standard error of poverty estimates is calculated also.

  • In addition the difference in poverty rate between provincial pairs is tested at 5% significance level.


Poverty map in 2002 2003

Poverty map in 2002/2003

  • The availability of household-level data of 33% census sample allows poverty estimates at district level.

  • Project “Poverty mapping and market access in Vietnam” funded by New Zealand Embassy with support and coordination from the World Bank has been implemented by IFPRI and IDS since 2002.


Poverty map in 2002 2003 cont

Poverty map in 2002/2003 (Cont.)

  • The project objectives:

    • Produce poverty maps at district level.

    • Analyze patterns in spatial distribution of poverty.

    • Develop capacity for poverty mapping in Vietnam.

    • Disseminate methods and results widely.

    • Promote collaboration across Ministries.

  • Strong capacity building: participants from ICARD (under MARD), GSO, MPI, MOLISA, MOF, IOE, HAU, NEU in the poverty mapping project.


Poverty map in 2002 2003 results

Poverty map in 2002/2003: Results

  • District-level poverty rates with standard error are already estimated by Nicholas Minot and Bob Baulch. The arrangement of map format and GIS data is conducted by Michael Epprecht (IFPRI).

    • Poverty mapping method allows much more disaggregated estimates than VLSS.

    • Poverty rates vary considerably within regions and provinces.

  • The difference in poverty rate between districts pairs is tested at 5% significance level.


Poverty map in 2002 2003 results1

Poverty map in 2002/2003: Results

  • The geographic targeting of poverty is more accurate if the commune-level poverty rates are estimated.

  • Commune-level poverty rates are also estimated. However large standard errors (due to small number of households) do not allow the poverty ranking of all communes.


Poverty with 95 confidence intervals for communes of khanh hoa

Poverty with 95% confidence intervals for communes of Khanh Hoa


Confidence intervals of commune level poverty rate from smallest to largest

Confidence intervals of commune-level poverty rate from smallest to largest


Poverty map in 2002 2003 results2

Poverty map in 2002/2003: Results

  • Besides the headcount index, other indices of depth and severity of poverty, and indices of inequality are estimated as well.

  • Analysis of geographic variables associated with poverty using GIS data:

    • Market access.

    • Agro-climatic variables.


Use and impact of poverty maps

Use and Impact of Poverty Maps

  • Distribution of poverty mapping results:

    • Results of poverty map in 1998 and 2000 were presented to ministries (e.g. MOLISA, MARD, MPI, GSO), international agencies (e.g. UNDP, FAO, WB, and ADB), and NGOs (e.g. Oxfam and CARE).

    • They have been published in various papers e.g. World Development, the 2001 Vietnam Development Report, Vietnam Investment Review, in a forthcomingWorld Bank’sPolicy Research Working Paper Series.


Use and impact of poverty maps cont

Use and Impact of Poverty Maps (cont.)

  • Poverty mapping in 2002/2003 involves more than 30 participants from various ministries, institutes and universities.

  • Its initial estimates of district poverty rates are presented to a workshop in May 2003 with attendants from ministries e.g. MOLISA, MARD, MPI , and international agencies e.g. WB, GTZ.

  • Reports, map posters and CD-ROM of the poverty mapping in 2002/2003 will be distributed widely from this October.


Use and impact of poverty maps cont1

Use and Impact of Poverty Maps (cont.)

  • Use and Impacts:

    • Although anti-poverty programs use MOLISA’s reports on commune poverty indicators for geographic targeting, poverty maps in 1998 and 2002 are also employed to crosscheck.

    • Some international agencies e.g. WB, FAO and NGOs e.g. CARE, and Oxfam used the maps to help their targeting programs.

    • There is increasing recognition of the advantages of the poverty maps, i.e. objective criteria, scientific and clear method, and measurable indicators.


Lessons learned

Lessons Learned

  • Involvement of various ministries’ staff in poverty mapping helps understand advantages of poverty map.

  • More disaggregated map increases its use and impact.

  • Full census household-level data should be provided to generate the poverty maps.

  • More questions on income-related characteristics of household should be added to censuses to improve the geographic targeting of maps.


References

References

  • Henninger, N. and M. Snel (2002). Where are the poor? Experiences with the development and use of poverty maps: Case Study Note for Vietnam. Published by World Resources Institute, Washington, DC Published by World Resources Institute, Washington, available at http://population.wri.org/

  • Hentschel, J., J. Lanjouw, P. Lanjouw, and J. Poggi. 2000. “Combining Census and Survey Data to Trace the Spatial Dimensions of Poverty: A Case Study of Ecuador.” The World Bank Economic Review 14(1):147-65.

  • Minot, N. 2000. “Generating Disaggregated Poverty Maps: An Application to Vietnam.” World Development 28(2):319-31.

  • Minot, N. and B. Baulch. 2002. The Spatial Distribution of Poverty in Vietnam and the Potential for Targeting. Markets and Structural Studies Division. Discussion Paper, International Food Policy Research Institute.


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