PublicationsYou may find these publications very useful.
Cornelis Maaskant. “Risk Sharing within Geographically Spread Extended Families: Evidence from Rural Tanzania“, June 2015. MSc Thesis, University of Oxford.
Paul Gertler; Manisha Shah; Maria Laura Alzua; Lisa Cameron; Sebastian Martinez; Sumeet Patil. “How does Health Promotion Work? Evidence from the Dirty Business of Eliminating Open Defecation.” NBER Working Paper No. 20997, March 2015. PLEASE NOTE: EDI collected the endline data and not the baseline data for this WSP project.
Link here (NBER subscription required)
Yadav, P., Cohen, J., Alphs, S., Arkedis, J., Larson, P., Massaga, J. and Sabot, O. 2012 “Trends in availability and prices of subsidized ACT over the first year of the AMFm: evidence from remote regions of Tanzania” Malaria Journal 11:299
Quin, S. and Fafchamps, M. 2012. Results of Sample Surveys of Firms”, chapter 5 in Dinh, H. and Clarke, G. (eds) Performance of Manufacturing Firms in Africa: an empirical analysis, The World Bank, Washington DC.
Dillon, Andrew & Bardasi, Elena & Beegle, Kathleen & Serneels, Pieter. 2012. Explaining Variation
in Child Labor Statistics. Journal of Development Economics, 98 (1): 136-147.
Caeyers, B., Chalmers, N. and De Weerdt, J. 2012. Improving Consumption Measurement and other Survey Data through CAPI: Evidence from a Randomized Experiment”, Journal of Development Economics 98:19-33
Beegle, K., De Weerdt, J., Friedman J. and Gibson, J. “Methods of Household Consumption Measurement through Surveys: Experimental Results from Tanzania”, Journal of Development Economics 98:3-18
De Weerdt, J. “Mobility Pays”, Rural 21, January 2011 edition (popularizing article, summarizing some of the technical work on migration listed below)
Dillon, B. 2011. Using mobile phones to collect panel data in developing countries. Journal of International Development. 24(4): 518–527
Beegle, K., De Weerdt, J. and Dercon.2011. Migration and Economic Mobility in Tanzania: Evidence from a Tracking Survey. Review of Economics and Statistics 93(3): 1010–1033
De Weerdt, J. and Fafchamps, M. 2011. Social Identity and the Formation of Health Insurance Networks. Journal of Development Studies 47(8): 1152-1177
Beegle, K., De Weerdt, J. and Dercon.2011. 2011 Patterns of Migration in Tanzania. Chapter 2 in Jennica Larrison, Edmundo Murrugarra and Marcin Sasin (eds) “Migration and Poverty: Towards Better Migration Opportunities For the Poor”,The World Bank
Mother Is ‘More Essential’ to Orphans Than Breadwinner Father, Research Suggests
Orphanhood and Human Capital Destruction: Is there Persistence into Adulthood? – Kathleen Beegle, Joachim De Weerdt and Stefan Dercon
Demography, Vol. 47(1): 163-180
Moving out of Poverty in Tanzania: Evidence from Kagera – Joachim De Weerdt
Journal of Development Studies, Vol. 46(2): 331-349
Moving Away from Home and Away from Poverty – Kathleen Beegle, Joachim De Weerdt and Stefan Dercon
Contribution to Dilip Ratha’s “People Move” Blog
Go to Blog Entry
THE INTERGENERATIONAL IMPACT OF THE AFRICAN ORPHANS CRISIS: A COHORT STUDY FROM AN HIV/AIDS AFFECTED AREA – Kathleen Beegle, Joachim De Weerdt and Stefan Dercon
International Journal of Epidemiology, Vol. 38(2):561-568
Beegle, K., De Weerdt, J. and Dercon, S. 2008. “Migration and Economic Mobility in Tanzania: Evidence from a Tracking Survey”. Policy Research Working Paper, WPS 4798, World Bank, Washington DC.
Go to Download Site
METHODOLOGICAL ISSUES IN THE STUDY OF THE SOCIOECONOMIC CONSEQUENCES OF HIV/AIDS – Kathleen Beegle and Joachim De Weerdt
ADULT MORTALITY AND ECONOMIC GROWTH IN THE AGE OF HIV/AIDS – Kathleen Beegle, Joachim De Weerdt and Stefan Dercon
Economic Development and Cultural Change, Vol. 56, No. 2: 299-326
FIELD NOTES ON ADMINISTERING SHOCK MODULES – Joachim De Weerdt
Journal of International Development, Vol. 20, pp. 398-402
MEMBERSHIP-BASED INDIGENOUS INSURANCE ASSOCIATIONS – Joachim De Weerdt, Stefan Dercon, Tessa Bold and Alula Pankhurst
in: Martha Chen, Renana Jhabvala, Ravi Kanbur, Carol Richards (eds.), “Membership Based Organisations of the Poor”, Routledge Download Brief Volume Overview
View / Buy book at Routledge
Moving out of Poverty in Tanzania’s Kagera Region
ID21 Research Highlight
GROUP-BASED FUNERAL INSURANCE IN ETHIOPIA AND TANZANIA – Stefan Dercon, Tessa Bold, Joachim De Weerdt and Alula Pankhurst, World Development, Vol 34, Issue 4, pp. 685-703
ORPHANHOOD AND THE LONG-TERM IMPACT ON CHILDREN – Kathleen Beegle, Joachim De Weerdt and Stefan Dercon, American Journal of Agricultural Economics, Vol. 88, No. 5, pp. 1266-1277.
CWIQ COMPARATIVE STUDY: COMPARISON OF 16 BASELINE SURVEYS ON POVERTY, WELFARE AND SERVICES IN SELECTED DISTRICTS IN KAGERA, SHINYANGA AND NORTHERN HIGHLANDS – Joachim De Weerdt, Tadeo Rweyemamu and James Mitchener
KONDOA CWIQ BASELINE SURVEY ON POVERTY, WELFARE AND SERVICES IN RURAL SHINYANGA DISTRICTS – Sonya Krutikov, Joachim De Weerdt, and James Mitchener
MBULU CWIQ BASELINE SURVEY ON POVERTY, WELFARE AND SERVICES IN RURAL SHINYANGA DISTRICTS – Sonya Krutikov, Joachim De Weerdt and James Mitchener
MONDULI CWIQ BASELINE SURVEY ON POVERTY, WELFARE AND SERVICES IN RURAL SHINYANGA DISTRICTS – Sonya Krutikov, Joachim De Weerdt and James Mitchener
KARATU CWIQ BASELINE SURVEY ON POVERTY, WELFARE AND SERVICES IN RURAL SHINYANGA DISTRICTS – Sonya Krutikov, Joachim De Weerdt and James Mitchener
MEASURING RISK PERCEPTIONS: WHY AND HOW – Joachim De Weerdt
Social Protection Discussion Papers Series, No. 0503, World Bank, Washington DC
MEMBERSHIP-BASED INDIGENOUS INSURANCE ASSOCIATIONS – Joachim De Weerdt, Stefan Dercon, Tessa Bold and Alula Pankhurst, Forthcoming in Kanbur, R. (ed.), “Membership Based Organisations of the Poor”, Routledge.
This edited volume grew from a conference organised by Cornell University and SEWA. Other contributions can be found on the conference website
RISK-SHARING AND ENDOGENOUS NETWORK FORMATION – Chapter 10 in “Insurance against Poverty”, ed. Stefan Dercon Oxford University Press, 2004
Other contribution to this book can be found here
RURAL INCOME DYNAMICS IN KAGERA REGION, TANZANIA – Flora Kessy
RURAL SHINYANGA CWIQ BASELINE SURVEY ON POVERTY, WELFARE AND SERVICES IN RURAL SHINYANGA DISTRICTS – Sonya Krutikov, Joachim De Weerdt, Tadeo Rweyemamu and James Mitchener
KAGERA RURAL CWIQ BASELINE SURVEY ON POVERTY, WELFARE AND SERVICES IN KAGERA RURAL DISTRICTS – Sonya Krutikov and Joachim De Weerdt
ADOPTION OF SUPERIOR BANANA VARIETIES IN THE KAGERA REGION: ACCOMPLISHMENTS AND CONSTRAINTS – Joachim De Weerdt
DISENTANGLING NETWORKS: DEFINING AND ANALYZING COHESIVE SUGROUPS – Joachim De Weerdt and Dirk Van de gaer
paper presented at the 2003 ESEM in Stockholm
COMMUNITY ORGANISATIONS IN RURAL TANZANIA: A CASE STUDY OF THE COMMUNITY OF NYAKATOKE, BUKOBA RURAL DISTRICT – Joachim De Weerdt
KHDS: An Introduction
The Kagera Health and Development Survey (KHDS) is a study into the long-run wealth dynamics of households and individuals within North West Tanzania. This study entails the resurvey of a panel of households, originally interviewed for 4 rounds from 1991 to 1994. Resurveys were then organised in 2004 and 2010. A multi-topic household questionnaire is administered to all split-off households originating from the baseline households, including those that have moved out of the baseline location.
This constitutes one of the longest-running (if not the longest) African panel data set of this nature and offers an unprecedented set of research opportunities for examining long-run (nearly 20 years) and intergenerational (as the children of the original respondents have now formed their own households) trends in and mechanisms of poverty persistence and economic growth in rural households. Interviewing people who moved out of their baseline location is important for understanding how migration and economic development interlink. Finally, of note to survey innovation, the 2010 round of the survey was conducted using electronic survey questionnaires administered on handheld computers. Caeyers et al. (2010) do a detailed, formal comparison of electronic versus paper-based data collection methods, through a randomized survey experiment.
KHDS has maintained a highly successful tracking rate. The table below shows that in 2010 88% of the original 6353 respondents had either been located and interviewed, or, if deceased, sufficient information regarding the circumstances of their death collected.
Table: Status of the 6353 original respondents
|interviewed||4430 (70%)||4336 (68%)|
|deceased||961 (15%)||1275 (20%)|
|untraced||962 (15%)||742 (12%)|
|TOTAL||6353 (100%)||6353 (100%)|
The KHDS 2010 was primarily funded by the Rockwool Foundation and the World Bank, with additional funds provided by the Hewlett Foundation through the Agence Inter-établissements de Recherche pour le Développement (AIRD). The 2004 round was funded by the Knowledge for Change Partnership Trust Fund at the World Bank and DANIDA.The baseline 1991-1994 KHDS was funded by the World Bank Research Committee.
KHDS Data and instruments
The full 13-year panel data set, the questionnaires and a basic information document for data users are now publicly available.
Data users, using the questionnaires to link up with the downloaded data sets, are advised to download the questionnaires directly from the above mentioned websites rather than from the links below (Scroll down).
At the start of KHDS details of all respondents listed on the KHDS-1 roster in 1991/94 were recorded on a Household Tracking Form. For each member that was not living in the original village or in the immediate vicinity of it an Individual Tracking Form was filled in containing detailed information on how to reach the individual. Very often the interviewers were instructed to speak to other informants. In case this informant him or herself lived far away, an Informant Tracking Form was filled to reach him or her.
The main KHDS instrument was an elaborate Household Questionnaire. Two cards were inserted in the questionnaire. Each of these cards lines up with the rows in certain sections. The Household Roster shows the name, sex, age and ID number of each current household member. When a KHDS1 respondent is currently found to be living apart from other members his/her 1991/94 household, then these people are recorded on the Network Roster. Except for these split-off household members this roster also includes children previously recorded as living elsewhere. The network roster allows for the collection of linked relational data on the extended family.
Anthropometric measurements of all current household members were taken (even if they were not KHDS1 respondents). These measurements were recorded on the Anthropometric Questionnaire. For all KHDS-1 respondents who died between 1991/94 and 2004 information on the circumstances of their death was collected in a Mortality Questionnaire.
Price data was collected at local markets and recorded in the Price Questionnaire. For each KHDS-1 cluster a Community Questionnaire was administered to a group of key informants, as well as a School Questionnaire in the primary school(s) in the community.
KHDS collects longitudinal data by revisiting respondents interviewed nearly 20 years ago. It is one of the few household surveys that has data over such a long period and that can address questions concerning long-term effects of childhood circumstances. It provides a unique opportunity to assess who stayed in poverty over this period and why; who moved out of poverty and how.
The sampling strategy in KHDS 2004 and KHDS 2010 was to re-interview all individuals who were household members in any wave of the KHDS 91-94, a total of 6,355 people. The Household Questionnaire was administered in the household in which these Previous Household Members (PHHMs) lived. For all household members alive during the last interview in 1991-1994, but found to be deceased by the time of the fieldwork in 2004 and 2010, information about the deceased would be collected in the Mortality Questionnaire. The next sections provide statistics of the KHDS 2004 and 2010 households.
Although the KHDS is a panel of individuals and the concept of a household after 10-19 years is a vague notion, it is common in panel surveys to consider re-contact rates in terms of households. Table 1 shows the rate of re-contact of the baseline households in KHDS 2004, where a re-contact is defined as having interviewed at least one person from the household.
Excluding households in which all previous members are deceased (17 households and 27 respondents), the KHDS 2004 field team managed to re-contact 93 percent of the baseline households. Not all 915 households received four interviews. Unsurprisingly, households that were in the baseline survey for all four waves had the highest probability of being re-interviewed. Of these 746 households, 96 percent were re-interviewed.
Turning to re-contact rates of the sample of 6,353 respondents, Table 2 shows the status of the respondents by age group (based on their age at first interview in the 1991-1994 waves). Re-interview rates are monotonically decreasing with age, although the reasons (deceased or not located) vary by age group. The older respondents were much more likely to be located if alive. Among the youngest respondents, over three-quarter were successfully re-interviewed. Excluding people who died, 82 percent of all respondents were re-interviewed.
Table 1: KHDS 2004 and 2010 Households
|KHDS 91-94||KHDS 2004 Re-interview Rates||KHDS 2010 Re-interview Rates|
|Number of interviews during 1991-1994||Re-interviewed||Deceased||Untraced||Re-interviewed||Deceased||Untraced||Total|
|54 %||10 %||37 %||54 %||10 %||37 %|
|83 %||4 %||13 %||78 %||4 %||17 %|
|86 %||1 %||13 %||78 %||3 %||19 %|
|94 %||2 %||4 %||93 %||2 %||5 %|
|91 %||2 %||7 %||89 %||3 %||8 %|
|Notes: “Re-interviewed” means that at least one member of the baseline household was re-interviewed. “Deceased” means that all Previous Household Members are reported to be dead. “Untraced” means that no Previous Household Member was re-interviewed.|
Table 2: KHDS 2004 and 2010 Individuals by Age
|KHDS 2004||KHDS 2010|
|Age at baseline 1991-1994||Re-interviewed||Deceased||Untraced||Re-interview rate among survivors||Re-interviewed||Deceased||Untraced||Re-interview rate among survivors|
|Notes: Sample of individuals interviewed in KHDS 91-94. Age categories are based on age at first interview. “Re-interviewed” means that the person was found and was re-interviewed. “Untraced” means that the person was not found or refused to be re-interviewed.|
The re-contact rates in the KHDS 2010 are in line with the ones achieved in KHDS 2004. Table 1 shows the KHDS 2010 re-contacting rates in terms of the baseline households. Excluding the households in which all PHHMs were deceased, 92 percent of the households were re-contacted.
As in KHDS 2004, households that were interviewed four times at the baseline were more likely found in 2010. Excluding the households in which all members had died, 95 percent of these households were re-interviewed in 2010.
The KHDS 2010 re-contact rates in terms of panel respondents are provided in Table 2. As in 2004, the older respondents, if alive, were much more likely to be re-contacted than younger respondents. In the oldest age category, 60 years and old at the baseline, the interview teams managed to re-contact almost 98 percent of all survivors. The length of the KHDS survey starts to be seen in this age category however, as almost three quarters of the respondents had passed away by 2010.
Table 3 provides the KHDS 2010 re-contact rates by location. More than 50 percent of the re-interviewed panel respondents were located in the same community as in KHDS 91-94. Nearly 14 percent of the re-contacted respondents were found from other region than Kagera. The survey team also tracked panel respondents in Uganda where one percent of the interviewed panel respondents were located.
The location of the untraced respondents is based on the tracking data. More than half of the untraced respondents are reported to be living in Kagera.
Table 3: KHDS 2010 Re-Contact Rates by Location
|Elsewhere in Kagera||24|
|Dar es Salaam||9|
|Other country (b)||8|
|Notes: Location for untraced respondents is reported by other household members from the baseline survey who were successfully located, interviewed, and able to provide location information on the respondent. In some cases, this information comes from other relatives or neighbours residing in the baseline communities.
a. KHDS 2010 tracked international migrants in Uganda only.
b. Countries to which the 58 untraced respondents had moved are: Burundi, Denmark, Kenya, Norway, Rwanda, South-Africa, Sweden, UK and USA.
Location of households in 2004
Note to the figure above: “Traced” means that there was adequate address information for at least one surviving household member during the initial field visits in October – November 2004. Subsequent field work from January – May 2004 yielded additional information, which increased the number of traced households, and this is not reflected in the statistics presented here
|Team of Core Researchers on KHDS 2004Kathleen Beegle (World Bank)
Joachim De Weerdt (EDI)
Stefan Dercon (University of Oxford)
Flora Kessy (ESRF)
Godlike Koda (UDSM)
Gideon Kwesigabo (UDSM)
Phare Mujinja (MUCHS)
Innocent Semali (MUCHS)Data Entry Programme written by :Bjorn Van Campenhout
|Team of Core Researchers on KHDS 2010Kathleen Beegle (World Bank)
Helene Bie Lilleør (Rockwool Foundation)
Joachim De Weerdt (EDI)
Stefan Dercon (University of Oxford)
Sonya Krutikov (University of Oxford)
Gideon Kwesigabo (MUHAS)
Phare Mujinja (MUHAS)
Vera Ngowi (MUHAS)Electronic Survey (CAPI) Application written by:Neil Chalmers
|Fieldwork 2004 coordinated by:
Joachim De Weerdt
Mujobu MoyoData Entry 2004:
Malicky HamiduFieldwork 2004 Supervised by:
Yvonne Swai2004 Interviewing Team:
Habibu Ismail Rutta
Hamida Issa Selemani
|Fieldwork 2010 coordinated by:
Joachim De Weerdt
Leonard KyaruziIn-field survey-technical support:
Kalle HirvonenData Processing 2010
Alexander KaturaFieldwork 2010 supervised by:
George J. Musikula
Bernard M. Matungwa
Mwenge Godlaid2010 Interviewing Team:
Adella T. Kamugisha
Pius S. Alibaliwo
Hildephonce H. Mulashani
Joseph G. Manana
Englibetus N. Alphonce
Godfrey L. Benedicto
Kimbugwe J. Francis
Penina Pius Antony
David C. Rutta
KHDS Data Downloads
More information on the surveys can be found in their Basic Information Documents:
Researchers using the data may benefit from the following additional constructed data sets:
KHDS 2010 distance to borders, border crossings, refugee camps and original location and linked HHs (from Jose Funes and Jean-Francois Maystadt)
KHDS 2010 matrix of distances between all interviewed HHs in 2010 – note this is an 80MB file (from Jose Funes and Jean-Francois Maystadt)
Price, Consumption and Assets Aggregates (1991-2004) – updated in 2004 (do not use with 2010 round)
Price and Consumption Aggregates (1991-2010) – updated in 2010 (for use with 2010 round)
GPS data 2004: distance of KHDS communities to Rwanda border (from Javier Baez)
GPS data 2004: distance of KHDS communities to Rwanda, Burundi and Uganda borders (from Monica Fisher)
GPS data 2004: distance of KHDS communities to refugee camps (from Jean-Francois Maystadt)
NASA weather data (from Kalle Hirvonen)
this is daily data spanning 1981-2010 on all baseline villages on these variables:
• Atmospheric Pressure (kPa)
• Minimum Air Temperature At 2 m Above The Surface Of The Earth (degrees C)
• Maximum Air Temperature At 2 m Above The Surface Of The Earth (degrees C)
• Humidity Ratio At 2 m Above The Surface Of The Earth (%)
• Relative Humidity (%)
• Dew/Frost Point Temperature (degrees C)
• Earth Skin Temperature (degrees C)
• Wind Speed At 10 m Above The Surface Of The Earth (m/s)
• Precipitation (mm/day)
• Air Temperature At 2 m Above The Surface Of The Earth (degrees C)
Those last two variables are also available for the migration destinations
If the data you are looking for is not in this list, then try here: http://openmicrodata.wordpress.com/
The raw data from five rounds, collected between 1991-2004, are now publicly available. This page intends to keep track of on-going and completed work using the KHDS data set. Papers based on the short-run KHDS-1 panel are listed below. In cases where copyright laws do not allow us to post published papers, please refer to the journal or e-mail the corresponding author
Research Papers Using the Long-term Panel
- De Weerdt, Joachim and Kalle Hirvonen. Risk Sharing and Internal Migration. Economic Development and Cultural Change 65(1):63-86. Download
- Hirvonen, K. 2016. Temperature changes, household consumption and internal migration: Evidence from rural Tanzania, American Journal of Agricultural Economics, 2016, vol 98(4): 1230-1249.
- Corno,L. Voena,A. 2016. “Selling daughters: age of marriage, income shocks and the bride price tradition.”IFS Working Paper W16/08
- De Weerdt, Joachim, Garance Genicot and Alice Mesnard. 2016. Asymmetry of Information within Family Networks. NBER Working Paper No. 21685
- Counts, C. J., &Skordis-Worrall, J. 2016. Recognizing the importance of chronic disease in driving healthcare expenditure in Tanzania: analysis of panel data from 1991 to 2010. Health policy and planning, 31(4), 434-443.
- Christian, P., & Dillon, B. 2016. Long-Term Consequences of Consumption Seasonality. Working Paper Series, 241, Abidjan: African Development Bank.
- Moradi, A., & Hirvonen, K. 2016. The African Enigma: The mystery of tall African adults despite low national incomes revisited. In J. Komlos& I. R. Kelly (Eds.), The Oxford Handbook of Economics and Human Biology. Oxford: Oxford University Press.
- Ruiz, I., & Vargas-Silva, C. 2016. The labour market consequences of hosting refugees. Journal of Economic Geography, 16(3), 667-694.
- Krutikova, S.,& Lilleør, H. B. 2015. Fetal Origins of Personality: Effects of early life circumstances on adult personality traits. CSAE Working Paper, WPS-2015-03.
- Alam, S.A. 2015. Parental Health Shocks, child labor and educational outcomes: Evidence from Tanzania. Journal of Health Economics.
- Cornelis Maaskant. 2015. “Risk Sharing within Geographically Spread Extended Families: Evidence from Rural Tanzania“, MSc Thesis, University of Oxford.
- Hirvonen, K and Bie Lilleor, H. 2015. Going Back Home: Internal Return Migration in Rural Tanzania. World Development, vol 70: 186-202.
- De Weerdt J, Kutka, A. Urbanisation and Youth Employment in Tanzania. ICAS-VI Improving Statistics for Food Security, Sustainable Agriculture, and Rural Development. Linking statistics with decision making, Pages 576 – 586
- Kudo, Y. 2015. Female Migration for Marriage: Implications from the Land Reform in Rural Tanzania.World Development 65, pp. 41-61
- Fujii, T. 2015. Poverty decomposition by regression: An application to Tanzania. WIDER Working Paper, 102, Helsinki: UNU-WIDER.
- Gaddis, I., &Hoogeveen, J. 2015. Primary Education in Mainland Tanzania: What Do the Data Tell Us? In A. R. Joshi & I. Gaddis (Eds.), Preparing the Next Generation in Tanzania. Washington, D.C.: The World Bank.
- Fichera, E., & Savage, D. 2015. Income and Health in Tanzania. An Instrumental Variable Approach. World Development, 66, 500-515.
- Ruiz, I. & Vargas-Silva, C. 2015. The labor market impacts of forced migration. The American Economic Review, papers and proceedings, 105(5), 581-586.
- Maystadt, JF, Duranton, G. 2014. The Development Push of Refugees: evidence from Tanzania. Economics Working Paper Series 19, University of Lancaster, Department of Economics.
- Corno, Lucia. 2014. Learning (or not) in health seeking behavior: Evidence from rural Tanzania. Economic Development and Cultural Change 63(1):27-72
- Maystadt, JF and Verwimp, P. 2014.Winners and Losers among a Refugee-Hosting Population, Econonomic Development and Cultural Change 62(4):769-809.
- Scott, L., Hanifnia, K., Shepherd, A., Muyanga, M., &Valli, E. 2014. How resilient are escapes out of poverty? London: Chronic Poverty Advisory Network, Overseas Development Institute.
- Scott, L., Hillier, D., & Underhill, H. 2014. Investigating resilience thresholds in Sub-Saharan Africa, Chronic Poverty Advisory Network and Oxfam.
- Marie CastaingGachassin. 2013. Should I Stay or Should I Go? The Role of Roads in Migration Decisions, Journal of African Economies 22(5): p. 796-826.
- Hirvonen, K. 2014. Measuring catch-up growth in malnourished populations. Annals of Human Biology, Annals of Human Biology, 41(1): 67-75.
- McKay, A. and Perge, E. 2013. How Strong is the Evidence for the Existence of Poverty Traps? A Multi-country Assessment. Journal of Development Studies 49(7):877-897.
- Bengtsson, N. 2013. Catholics versus Protestants: On the Benefit Incidence of Faith-Based Foreign Aid, Economic Development and Cultural Change61(3):479-50.
- Christiaensen, C., De Weerdt, J. and Todo, Y. 2013. Urbanization and Poverty Reduction – The Role of Rural Diversification and Secondary Towns. Agricultural Economics 44:447-459.
- Devicienti, Francesco and Mariacristina Rossi. 2012. Liquidity Constraints, Uncertain Parental Income and Human Capital Accumulation. Applied Economics Letters 20(9):826-829.
- Peterman, A. 2012. Widowhood and Asset Inheritance in Sub-Saharan Africa: Empirical Evidence from 15 CountriesDevelopment Policy Review 30 (5): 543-571
- Adhvaryu, A. and Nyshadham, A. 2012. Schooling, Child Labor, and the Returns to Healthcare in Tanzania. The Journal of Human Resources, 47(2):364-396.
- Kirchberger, K. and Mishili, F. 2011. Agricultural Productivity Growth in Kagera between 1991 and 2004, IGC Working Paper 11/0897.
- Beegle, K., De Weerdt, J. and Dercon, S. 2011. Migration and Economic Mobility in Tanzania: Evidence from a Tracking Survey.Review of Economics and Statistics, 93(3): 1010–1033
- Baez, Javier E. 2011. Civil Wars Beyond their Borders: The Human Capital and Health Consequences of Hosting Refugees. Journal of Development Economics 96(2):391-408.
- Guendel Rojas, Sebastian, Houngbonon, Georges and Tran, Viet-Anh. 2011. The importance of tracking in long-term household panel survey: evidence from the impact of orphanhood on human development in rural Tanzania”. Econometric Team Work from Paris School of Economics, Master in Public Policies and Development.Download PDF
- Opuni, M., Peterman, A. and Bishai, D. 2011. Inequality in prime-age adult deaths in a high AIDS mortality setting: Does the measure of economic status matter?” Health Economics 20 (11), 1298-1311.
- Peterman, Amber. 2011. Women’s property rights and gendered policies: Implications for women’s long-term welfare in rural Tanzania, Journal of Development Studies 47(1):1-30.
- Beegle, Kathleen, Joachim De Weerdt and Stefan Dercon. Patterns of Migration in Tanzania. Chapter 2 in Jennica Larrison, Edmundo Murrugarra and Marcin Sasin (eds) “Migration and Poverty: Towards Better Migration Opportunities For the Poor”, Washington DC: The World Bank.
- Bengtsson, N. 2010. How responsive is body weight to transitory income changes? Evidence from rural Tanzania, Journal of Development Economics 92 (1): 53-61.
- Maystadt, Jean-François. 2010. Conflict and Forced Migration. PhD Thesis. Université Catholique de Louvain. Download PDF
- Hagen, J., Omar Mahmoud, T., Trofimenko, N. 2010. Orphanhood and Critical Periods in Children’s Human Capital Formation: Long-Run Evidence from North-Western Tanzania. Kiel Working Paper, 1649, 32 pp. Download PDF
- De Weerdt, J. 2010. “Moving out of Poverty in Tanzania: Evidence from Kagera”, forthcoming Journal of Development Studies 46(2): 331-349.
- Beegle, Kathleen, Joachim De Weerdt and Stefan Dercon. Orphanhood and Human Capital Destruction: Is there Persistence into Adulthood? Demography 47(1): 163-180.
- RalitzaDimovaKunal Sen, 2010. Is household income diversification a means of survival or a means of accumulation? Panel data evidence from Tanzania “, SSRN
- Ikegami, M. 2009. Agricultural Productivity and Mortality: Evidence from Kagera, Tanzania, mimeo, download
- Peterman, A. 2009. Contraceptive use and women’s well-being: Spillover effects of family planning services in rural Tanzania. Doctoral Dissertation: University of North Carolina at Chapel Hill.
- Beegle, Kathleen, Joachim De Weerdt and Stefan Dercon. The Intergenerational Impact of the African Orphans Crisis: A Cohort Study from an HIV/AIDS Affected Area. International Journal of Epidemiology 38(2):561-568.
- Adhvaryu, A. and Beegle, K. 2009. The Long-run Impacts of Adult Deaths on Older Household Members in Tanzania. World Bank Policy Research Working Paper 5037.
- Troerup, S. and Mertz, O. 2009. Linking climate trends to coping strategies in northern Tanzania. IOP Conference Series: Earth and Environmental Science 6. doi:10.1088/1755-1307/6/1/412005. download pdf
- Berger, S. 2008. Understanding Disease Progression in the Kagera Region of Tanzania: A framework for efficient health care delivery. Thesis for Master of Public Policy at Georgetown Public Policy Institutedownload pdf
- Julie Litchfield & Thomas McGregor, 2008. Poverty in Kagera, Tanzania: Characteristics, Causes and Constraints. PRUS Working Papers 42, Poverty Research Unit at Sussex, University of Sussex
- Lilleor, H. 2008. Human Capital Diversification within the Household. Findings from Tanzania. PhD Chapter. University of Copenhagen. download pdf
- Beegle, Kathleen; Rajeev Dehejia; Roberta Gatti and SofyaKrutikova. 2008. The Consequences of Child Labor: Evidence from Longitudinal Data Rural Tanzania. World Bank Policy Research Working Paper No. 4677.
- Alderman, H., Hoogeveen, J. and Rossi, M. 2008. Preschool Nutrition and Subsequent Schooling Attainment: Longitudinal Evidence from Tanzania. Economic Development and Cultural Change57(2):239-260
- Dercon, Stefan. 2008. Fate and Fear: Risk and its Consequences in Africa. Journal of African Economies 17(2):97-127
- Lassen, D. and Lilleor, H. 2008. Informal Institutions and Intergenerational Contracts: Evidence from Schooling and Remittances in Rural Tanzania. mimeo, University of Copenhagen download pdf
- 2007. Social Learning in Health Behaviour: The Case of Mosquito Bed Nets in Tanzania. Yhesis submitted in partial fulfilment of the requirements for the Degree of Master of Philosophy in Economics, Oxford University. download pdf
- Peterman, Amber. 2007. Women’s property rights and gendered policies: Implications for women’s long-term welfare in rural Tanzania. Dissertation. University of North Carolina.
- Simonsen, M. and Skipper, L. 2007. Child Health in a Developing Country: Consequences for Short- and Medium Term Outcomes”, mimeo, University of Arhus and Institute for Local Government Studies. download pdf
- Udry, C. and H. Woo. 2007, Households and the Social Organization of Consumption in Southern Ghana. African Studies Review, Volume 50, Number 2, pp. 139-53.
- Baez, J. 2007. Do Local Children Suffer from Foreign Refugees Inflows? Evidence from Host Communities in Northwestern Tanzania. Available at SSRN.
- Beegle, K., De Weerdt, J. and Dercon, S. 2007. Adult Mortality and Economic Growth in the Age of HIV/AIDS. Economic Development and Cultural Change 56(2): 299-326
- Ksoll, C. 2007. Family Networks and Orphan Caretaking in Tanzania. Department of Economics Series 361, Oxford University.
- Beegle, K., Krutikov, S. 2007. Adult Mortality and Children’s Transition into Marriage”, World Bank Policy Research Report 4139, Washington, DC.
- Udry, Christopher and Hyungi Woo. 2007. “Households and the Social Organization of Consumption in Southern Ghana.” African Studies Review. 50(2): 139-53.
- Roberts, Peter, KC Shyam, and CordulaRastogi. 2006. Rural Access Index: A Key Development Indicator. Transport Papers No 10, Transport Sector Board, World Bank, Washington DC.download pdf
- Beegle, K., De Weerdt, J. and Dercon, S. 2006. Orphanhood and the Long-term Impact on Children”, American Journal of Agricultural Economics, Vol. 88, No. 5, pp. 1266-1277
- Beegle, K., R. Dehejia and R. Gatti. 2006. Child Labor and Agricultural Shocks. Journal of Development Economics 81(1): 80-96.
- Alderman H., H. Hoogeveen and M. Rossi. 2006. Reducing Child Malnutrition in Tanzania: Combined Effects of Income Growth and Program Interventions. Economics and Human Biology 4: 1-23
- Sahn, David E. and Stephen D. Younger. 2006. “Testing the Kuznets Curve for Countries and Households Using the Body Mass Index.” Strategies and Analysis for Growth and Access. Working Paper. September 2006.
- Krutikov, S. 2006. Impact of Child Labour on Educational Attainment in Adulthood: Evidence from Rural Tanzania”, mimeo, Oxford University. download pdf
- Seebens, H. 2006. The Contribution of Female non-farm Income to Poverty Reduction. Paper prepared for the Tanzania Gender and Growth Assessment.
- Suliman, EldawAbdalla. 2005. “Orphanhood, fostering, and child well-being in Tanzania.” Ph.D. dissertation, The Johns Hopkins University, United States
- Beegle, K.. 2005. Labor Effects of Adult Mortality in Tanzanian Households. Economic Development and Cultural Change 53:3.
- Ainsworth, M., K. Beegle, and G. Koda. 2005. The impact of adult mortality and parental deaths on primary schooling in Northwestern Tanzania. Journal of Development Studies, Vol.41, No.3, April 2005, pp.412 – 439.
- Papa Seck, 2005. Do Parents Favor their Biological Offspring over Adopted Orphans? Theory and Evidence from Tanzania. Hunter College Department of Economics Working Papers 409, Hunter College Department of Economics.
- Burke, K. and K. Beegle. 2004. Why children aren’t attending school: The case of Northwestern Tanzania. Journal of African Economies, 13(2).
- Lundberg, M., M. Over. and P. Mujinja. 2003. Do Savings Predict Death? Precautionary Savings During an Epidemic, manuscript prepared for UNAIDS, Geneva. download pdf
- Ainsworth, M. and J. Dayton. 2003. The impact of the AIDS epidemic on the health of the elderly in Tanzania. World Development 31(1): 131-148.
- Lundberg, M., M. Over. and P. Mujinja. Transfers and Household Welfare in Kagera, Tanzania. 2003. Prepared for UNAIDS. Presented to research seminar, University of East Anglia; the IAEN Economics of AIDS Symposium; the XIII International AIDS conference, Durban, South Africa, July 2000; and the International Health Economics Association Conference, July 2001, York, England
- Dayton, J. and M. Ainsworth. 2002. The elderly and AIDS: Coping strategies and health consequences in rural Tanzania. Social Science and Medicine 59: 2161-2172
- Lundberg, M., M. Over, and P. Mujinja. 2000. Sources of Financial Assistance for Households Suffering an Adult Death in Kagera, Tanzania. South African Journal of Economics, 68:5:947-984.
- Ainsworth, M. and I. Semali. 2000. “The impact of adult deaths on child health in Northwestern Tanzania”. World Bank Policy Research Working Paper 2266. Washington, D.C.: The World Bank. Presented at the Annual Meetings of the Population Association of America, New York City, March 1999, and the 2001 International Health Economics Association meetings in York, UK, July 2001.
- Ainsworth, M.and I. Semali. 1998. “Who dies from AIDS? Socioeconomic correlates of adult deaths in Kagera Region, Tanzania” in Ainsworth, Fransen and Over, eds. (1998). Confronting AIDS: Evidence from the Developing World Background Papers from the World Bank Policy Research Report, Confronting AIDS: Public Priorities in a Global Epidemic. Brussels: European Commission.
- Ainsworth, M., D. Filmer and I. Semali. 1998. “The impact of AIDS mortality on individual fertility: Evidence from Tanzania” in M. Montgomery and B. Cohen, eds. From Death to Birth: Mortality Decline and Reproductive Change. Washington, DC: National Academy Press.
- Ainsworth ,M.and M. Over. 1997. Confronting AIDS: Public Priorities in a Global Epidemic. A World Bank Policy Research Report. Washington, D.C.: Oxford University Press. 355 pages. Revised edition, 1999.
- Semali, I. and M. Ainsworth. 1995. “A profile of traditional healers in an area hard-hit by the AIDS epidemic: Kagera Region, Tanzania”. University of Dar es Salaam and The World Bank. August 17. download pdf
- Ainsworth, M., S. Ghosh and I. Semali. 1995. “The impact of adult deaths on household composition in Kagera Region, Tanzania”. Preliminary results presented at the annual meetings of the Population Association of America, San Francisco, California, April 1995, and at the IXth International Conference on AIDS and STDs in Africa, Kampala, Uganda, December 1995.
- Ainsworth ,M.and G. Koda. 1993. “The impact of adult deaths on school enrollments and attendance in Northwestern Tanzania”. Paper presented at the Annual Meetings of the Population Association of America, Cincinnati, Ohio, April 1993.
- Ainsworth, M., G. Koda, G. Lwihula, P. Mujinja, M. Over and I. Semali. 1992. Measuring the Economic Impact of Fatal Adult Illness in Sub-Saharan Africa: An Annotated Household Questionnaire. Living Standards Measurement Study Working Paper, no. 90. Washington, D.C. The World Bank. Download.
CWIQ currently constitutes one of the largest socio-economic household survey databases on Tanzania. Since 2003 EDI has interviewed roughly 20,000 households in 35 different districts. For 9 districts repeat surveys have been organised to track changes over time.
RATIONALE: Absence of district level survey data does not rhyme with the devolution of power to districts. Tanzania is undergoing a decentralisation process whereby each of its roughly 128 districts is becoming an increasingly important policy actor. A district taking on this challenge needs accurate information to monitor and develop its own policies. Much relevant information is currently not available as national statistics are not representative at district level and many of the routine data collection mechanisms are still under development. CWIQ then provides an attractive, one-stop survey-based method to collect basic development indicators. Furthermore, the survey results can be disseminated – through Swahili briefs and posters – to a district’s population; thus increasing the extent to which people are able to hold their local governments accountable. Exciting new ground is being broken on such population-wide dissemination by the Prime Minister’s Office.
METHODOLOGY: The data are collected through a small 10-page questionnaire (downloadable below), called the Core Welfare Indicators Questionnaire (CWIQ). The questionnaire and data software constitute an off-the-shelf survey package developed by the World Bank to produce standardised monitoring indicators of welfare. The questionnaire is purposively concise and is designed to collect information on household demographics, employment, education, health and nutrition as well as utilisation and satisfaction with social services. Questionnaires are scannable, with interviewers shading bubbles and writing numbers later recognised by the scanning software. The data system is fully automated allowing the results to roll out within weeks of the fieldwork.
FUNDING: projects are typically funded by organisations that care about making decentralisation work in Tanzania. CWIQ is a method to promote evidence-based policy formulation and debate in the district and a tool for the population to hold their local governments accountable. With funding from the RNE (Royal Netherlands Embassy) and SNV (Stichting Nederlands Vrijwilligers), CWIQ surveys were implemented between 2003-2005 in 16 districts. In 2006/07 PMO-RALG (Prime Minister’s Office – Regional Administration and Local Government) commissioned EDI to cover a further 28 districts. In 9 of these districts this constituted a repeat survey and thus a unique opportunity arises to monitor changes that occurred in the district over this time period.
DISSEMINATION: EDI disseminated the results of CWIQ on posters and briefs to district level stakeholders (councillors, district officials, NGOs, CBOs, Advocacy Groups, MPs, ‘interested citizens’, etc.), with the aim at district level, to: (i) promote evidence-based policy debate, (ii) promote evidence-based policy formulation, (iii) provide tools for district level M&E and (iv) increase accountability of LGA to citizens.
PUBLIC DOMAIN: Currently in the public domain are (i) all CWIQ reports – note that Shinyanga 2004 and Kagera 2003 reports are organised into one region-wide report (ii) Swahili and English briefs for 5 pilot dissemination districts funded by the Prime Minister’s Office – and (iii) raw data for all CWIQs conducted between 2003 and 2007.
RAW DATA IN PUBLIC DOMAIN?
|Bariadi DC||Shinyanga||2004 & 2006||YES||RNE & PMO-RALG|
|Bukoba DC||Kagera||2003 & 2006||YES, BOTH YEARS||RNE & PMO-RALG|
|Bukombe DC||Shinyanga||2004 & 2006||YES, BOTH YEARS||RNE & PMO-RALG|
|Kahama DC||Shinyanga||2004 & 2006||YES, BOTH YEARS||RNE & PMO-RALG|
|Karagwe DC||Kagera||2003 & 2006||YES, BOTH YEARS||RNE & PMO-RALG|
|Kishapu DC||Shinyanga||2004 & 2007||YES, BOTH YEARS||RNE & PMO-RALG|
|Maswa DC||Shinyanga||2004 & 2007||YES, BOTH YEARS||RNE & PMO-RALG|
|Meatu DC||Shinyanga||2004 & 2007||YES, BOTH YEARS||RNE & PMO-RALG|
|Ngara DC||Kagera||2003 & 2006||YES, BOTH YEARS||RNE & PMO-RALG|
DC: District Council (i.e. rural areas)
MC: Municipal Council (i.e. urban areas)
PMO-RALG: Prime Minister’s Office – Regional Administration and Local Government
RNE: Royal Netherlands Embassy
SNV: Stichting Nederlands Vrijwilligers
Download Comparative Report
A Comparative Report comparing results of 16 CWIQ surveys in Shinyanga, Kagera and the Northern Highlands
Download Raw Data Sets
For data documentation please refer to the manuals.
Please drop me an e-mail at firstname.lastname@example.org to say for what purpose you are using these data and keep me informed of analysis based on them.
SHWALITA – probably the most exotic name that has ever been conjured up for an EDI research project – is short for ‘Survey of Household Welfare and Labour in Tanzania’. It is a unique experiment in survey design that Joachim De Weerdt and the team at EDI conducted on behalf of the University of Dar es Salaam and the World Bank. The project was developed by the Living Standards Measurement Study (LSMS) Team in the World Bank in collaboration with the University of Dar es Salaam and EDI. The survey experiment is an important component of the LSMS’ multi-year research agenda in survey methodology (LSMS Phase IV).
The consumption experiments in the survey benefited from substantial inputs from John Gibson at the Waikato Management School, Adolf Mkenda at the University of Dar es Salaam, Jed Friedman and Peter Lanjouw from the World Bank; the labor experiments from the inputs of Elena Bardasi from the World Bank and Andrew Dillon from IFPRI; and the subjective welfare experiment from inputs by and Adelbertus Kamanzi from Uganda Martyrs’ University. Kathleen Beegle of the LSMS team is task manager of the project. Other members of the LSMS team include Kinnon Scott, Calogero Carletto, Diane Steele and Kristen Himelein.
This 4,000 household survey randomly assigns 8 slightly different survey modules to its respondents. The survey modules reproduce 8 different ways in which research projects across the globe have aimed at measuring households’ welfare. In addition, the instruments aim at validating the way labour allocation and subjective welfare can be measured. By randomly assigning households to certain modules, the goal is to highlight differences in outcomes that are purely related to the research design, but do not reflect ‘real’ differences.
This survey consists of 3 separate experiments, carefully bundled into one survey: (i) consumption experiments (ii) labour module experiments (iii) subjective welfare experiments, conducted electronically on CWEST, which was EDI’s precursor software to Surveybe.
On this page you will first find publications made on this dataset and then some more detailed explanations of each of SHWALITA’s components, its sample and cluster locations.
The rationale behind the CONSUMPTION EXPERIMENTS comes from the observation that there are large and growing gaps between micro and macro estimates of household consumption. These discrepancies have profound implications for measuring global progress in poverty reduction and the effect of economic growth on that process. Currently it is difficult to reconcile these differences due to the wide variation in methods used to measure household consumption. While macro measures are broadly consistent around the world, under the SNA framework, micro measures of household consumption have no such standardization. Household expenditure surveys vary widely across many dimensions, including: the method of data capture (diary versus recall), the level of respondent (individual versus household), the length of the reference period for which consumption is reported (varying from 3 days, to one week, to one year) and the degree of commodity detail in recall surveys (varying from less than 20 to over 400 items). These variations occur both across countries and also over time as statistical offices alter survey design, with little understanding of the implications of such changes for spatially and temporally consistent measurement of household consumption and poverty. This variation hampers both cross-country studies of poverty and well-being measures as well as measuring poverty trends within country. This experiment implements alternative methods to measure household consumption.
The researchers developed eight alternative consumption questionnaires which were randomly distributed across 4,000 households. These eight designs vary by method (3 diaries and 5 recall modules), length of reference period in recall modules, and the number of items in the recall modules. In addition to assessing how the alternative methods affect consumption calculations and household rankings, the evaluation will include a comparison of costs across numerous dimensions: length of field work (in part based on length of interview which will be recorded), coding and data entry inter alia. The study also assesses the sensitivity (robustness) of poverty line calculations where the food poverty is based on calorie assignment of food groups in turn affected by level of disaggregation of food items.
The LABOUR EXPERIMENTS assess the effect of different ways of collecting labour statistics. It uses two different modules, a long module and a short module, and administers each to either the person him/herself or to someone else in the household answering on their behalf (a proxy respondent). Both proxy respondents and self-reporting respondents are sampled randomly from the roster of household members.
The SUBJECTIVE WELFARE EXPERIMENTS use an innovative approach to enhance comparability of subjective welfare questions. The technique, developed in political sciences by Gary King, involves the respondent to provide scaled answers on qualitative questions (on a scale of 1 to 5, how do you feel about….). In order to ‘anchor’ the response the respondent is given a ‘vignette’ a short, but powerful story about a fictitious person and is then asked to place this person on the same scale. The placing of the vignette on the same scale allows answers to become more comparable across households, communities and countries. Data were captured electronically through CWEST.
Sampling & Module Assignment
The 7 districts covered in this project were previously surveyed through EDI’s CWIQ project (see tab at the top of this page for more detail), in which a sample of households was drawn to be representative at district level. Data from the 2002 Census was used to put together a list of all villages in the district. In the first stage of the sampling process villages were chosen proportional to their population size. In a second stage the sub-village (kitongoji) was chosen within the village through simple random sampling. In the selected sub-village, or cluster all households were listed. Shwalita makes use of CWIQ’s sampling frame to randomly select 24 clusters out of the 30 CWIQ clusters and draw its random sample of households from the CWIQ listing forms. The following table shows the selected districts and is sorted in the order in which they will be visited.
|District||region||urban/rural||adult literacy rate according to CWIQ||Available CWIQ documents|
|report – brief ENG – brief SWA|
|report – brief ENG – brief SWA|
|report – brief ENG – brief SWA|
|report – brief ENG – brief SWA|
|Temeke||Dar es Salaam||
|report – brief ENG – brief SWA|
The following 8 modules are randomly assigned to 3 households within each cluster:
Consumption Recall and Labour Modules:
|module No.||type of labour module||recall length in consumption module||type of item list in consumption module||total sample size(24 clusters in each of 7 districts)||downloads questionnaires|
|1||short labour module with reporting by proxy respondent||
|long item list||504 obs.(1/3 without labour module)||ENGLISH – SWAHILI|
|2||short labour module with members self-reporting||
|long item list||504 obs.(1/3 without labour module)||ENGLISH – SWAHILI|
|3||long labour module with reporting by proxy respondent||
|short subset of long list||504 obs.(1/3 without labour module)||ENGLISH – SWAHILI|
|4||long labour module with members self-reporting||7 days||short collapsed list (aggregation of items from long list)||504 obs.(1/3 without labour module)||ENGLISH – SWAHILI|
|Module No.||level at which administered||diary period||frequency of visits by interviewer||frequency of visits by locally recruited assistant||total sample size(24 clusters in each of 7 districts)||downloads|
|6||individual||14 days||frequent visits:all individuals on days 1-3-5-8-10-12-15||every day||504||ENGLISH – SWAHILI|
|7||household||14 days||frequent visits:all households on days 1-3-5-8-10-12-15||every day||504||ENGLISH – SWAHILI|
|8||household||14 days||infrequent visits:Literate households: days 1-8-15.Illiterate households days 1-3-5-8-10-12-15||no visits||504||ENGLISH – SWAHILI|
Finally, the subjective welfare questionnaire will be administered to 576 households (4 households in each of 24 clusters in each of 6 districts) and will be downloadable from this site soon.
The survey teams will visit 168 communities. In each community the nearby shops, stalls and markets will be visited to collect local price data (download price questionnaire). Additionally, a structured community questionnaire will be administered to key informants in each community (download English – download Swahili). The community questionnaire contains a price opinion section as an alternative way to collect prices. For a good discussion on various price collection mechanisms in surveys see Gibson and Rozelle’s WBER article.
EDI began piloting questionnaires and training interviewers from June 2007 onwards. Fieldwork started beginning of September 2007 and is expected to last till end of June 2008. In order to keep tight control implementation, the fieldwork is conducted by a relatively small number of 12 interviewers and spread over a longer time period. Such a set-up avoids the typical co-ordination problems faced by larger-scale fast-moving set-ups and allows for maximum control from the project management and co-ordination team. Ultimately it seems like a necessary condition to achieve an acceptable level of non-sampling error.
This assignment is being executed by the following members of staff at EDI:
Project Direction: Joachim De Weerdt
Management and Co-ordination: Respichius Mitti and Abida Nungu
Field Supervision: George Musikula, Davis Matovu, Josephine Rugomora and Pius Sosthenes
Enumeration: Abbanove Gabba, Aissa Issa, Faustine Misinde, Felix Kapinga, Geofrey Bakari, Honoratha Wyclife, Jamary Idrisa, Jesca Nkonjelwa, Kamugisha Robert, Makarius Kiyonga, Resty Simon, Hildephonce Muhashani
Data Entry Co-ordination: Thadeus Rweyemamu
Data Entry Operation: George Gabriel, Justina Katoke, Amina Suedi, Frida George
Ravallion, Martin, Kristen Himelein, and Kathleen Beegle. Forthcoming. “Can Subjective Questions on Economic Welfare Be Trusted? Evidence for Three Developing Countries.” Economic Development and Cultural Change. World Bank Policy Research Working Paper 6726.
Friedman, Jed, Kathleen Beegle, Joachim De Weerdt and John Gibson. 2015. Decomposing Response Error in Food Consumption Measurement: implications for survey design from a randomized survey experiment in Tanzania”. Mimeo.
De Weerdt, Joachim, Kathleen Beegle, Jed Friedman and John Gibson. 2015. The Challenge of Measuring Hunger through Survey. Economic Development and Cultural Change, forthcoming. download pdf
Gibson, John, Kathleen Beegle, Joachim De Weerdt and Jed Friedman. 2015. What Does Variation in Household Survey Methods Reveal About the Nature of Measurement Errors in Consumption Estimates? Oxford Bulletin of Economics and Statistics 77(3): 466-474.
Beegle, Kathleen, Joachim De Weerdt, Jed Friedman and John Gibson. 2012. Methods of Household Consumption Measurement through Surveys: Experimental Results from Tanzania. Journal of Development Economics 98:3-18.
Bardasi, Elena, Kathleen Beegle, Andrew Dillon and Pieter Serneels. 2011. Do Labor Statistics Depend on How and to Whom the Questions are Asked? Results from a Survey Experiment in Tanzania. World Bank Economic Review 25(3): 418 – 447
Dillon, Andrew, Elena Bardasi, Kathleen Beegle and Pieter Serneels. 2012. Explaining Variation in Child Labor Statistics. Journal of Development Economics, 98 (1): 136-147.
Our researchers are encouraged to provide input on a variety of research projects. Below are some of the resulting papers:
PAPERS BY Dr. JOACHIM De WEERDT:
JOURNAL OR VOLUME
(impact factor if bio-medical journal)
|What Does Variation in Household Survey Methods Reveal About the Nature of Measurement Errors in Consumption Estimates? download working paper||
|Oxford Bulletin of Economics and Statistics Forthcoming Also Policy Research Working Paper Series, WPS6372, World Bank||2014|
|Urbanization and Poverty Reduction – The Role of Rural Diversification and Secondary Towns download pdf||Luc Christiaensen Yasuyuki Todo||Agricultural Economics Vol. 44: 447-459||2013|
|Improving Consumption Measurement and other Survey Data through CAPI: Evidence from a Randomized Experiment download pdf|
Bet Caeyers Neil Chalmers Journal of Development Economics Vol. 98: 19–332012Methods of Household Consumption Measurement through Surveys: Experimental Results from Tanzania
Kathleen Beegle Jed Friedman John Gibson Journal of Development Economics Vol. 98: 3-18 Also Policy Research Working Paper Series, WPS5501, World Bank2012
Migration and Economic Mobility in Tanzania: Evidence from a Tracking Survey
PAPERS BY Dr. Johanna Choumert Nkolo:
Kere, E.N., Choumert, J., Combes Motel, P., Combes, J.L., Santoni, O., Schwartz, S., 2017. Addressing Contextual and Location Biases in the Assessment of Protected Areas Effectiveness on Deforestation in the Brazilian Amazônia. Ecological Economics 136, 148–158. Available here
Choumert, J., Phélinas, P., 2016. Farmland Rental Prices in GM Soybean Areas of Argentina: Do Contractual Arrangements Matter? The Journal of Development Studies 1–17. doi:10.1080/00220388.2016.1241388. Available here
Choumert, J., Laré, A.L., Kéré N.E., 2016. A Multi-Level Housing Hedonic Analysis of Water and Sanitation Access. Economics Bulletin 36, 1010–1037. Available here
Choumert, J., Motel, P.C., Millock, K., 2015. Climate change mitigation and adaptation in developing and transition countries: introduction to the special issue. Environment and Development Economics 20, 425–433.
Brunette, M., Choumert, J., Couture, S., Montagne-Huck, C., 2015. A Meta-analysis of the Risk Aversion Coefficients of Natural Resource Managers Evaluated by Stated Preference Methods. Working Paper n° 2015-13, Laboratoire d’Economie Forestière.
TO LINK TO JOHANNA’S FORMER PUBLICATIONS CLICK HERE
Review of Economics and Statistics
Vol. 93(3): 1010–1033
2011Social Identity and the Formation of Health Insurance Networks
Marcel FafchampsJournal of Development Studies Vol. 47(8): 1152–11772011Patterns of Migration in Tanzania
Kathleen Beegle Stefan DerconThe World Bank Chapter 2 in Jennica Larrison, Edmundo Murrugarra and Marcin Sasin (eds) “Migration and Poverty: Towards Better Migration Opportunities For the Poor”, pages 13-342011Orphanhood and Human Capital Destruction: Is there Persistence into Adulthood?
See also: article in The Guardian
Vol. 47(1): 163-180
2010Moving out of Poverty in Tanzania: Evidence from Kagera
See also: ID21 summary
noneJournal of Development Studies Vol. 46(2): 331-3492010The Intergenerational Impact of the African Orphans Crisis: A Cohort Study from an HIV/AIDS Affected Area download pdf
International Journal of Epidemiology (impact factor = 6.41)
AIDS (impact factor = 6.25)
Vol. 22, Suppl 1: S89-94
Adult Mortality and Economic Growth in the Age of HIV/AIDS
Economic Development and Cultural Change
Vol. 56, No. 2: 299-326
Field Notes on Administering Shock Modules
Journal of International Development
Vol. 20: 398-402
2008Membership-based Indigenous Insurance Associations
Chapter 9 in Martha Chen, Renana Jhabvala, Ravi Kanbur, Carol Richards (eds.), “Membership Based Organisations of the Poor”, pages 157-176.
Risk-sharing Networks and Insurance Against Illness
Journal of Development Economics
Vol. 81, No. 2: 337-356
American Journal of Agricultural Economics
Vol. 88, No. 5: 1266-1277
Vol. 34, No. 4: 685-703
Risk-sharing and Endogenous Network Formation
|Mobility Pays download pdf||none||Rural 21||2011|
Measuring Risk Perceptions: why and how
Social Protection Discussion Papers Series
OECD Development Centre Working Paper Series
(for dozens of Tanzanian districts – for a complete list of reports click here)
Adoption of Superior Banana Varieties in the Kagera Region: accomplishments and constraints
Belgian Government (BTC)2003Community Organisations in Rural Tanzania: a Case Study of the Community of Nyakatoke
University of Leuven & EDI2002Poverty in Tanzanianone
Policy Preparatory Report, BVO/97.2, DGIS
Currently working on :
|Risk Sharing and Internal MigrationLatest version|
University of Sussex
The Challenge of Measuring Hunger
World Bank and University of WaikatoInter-household variation in prices: who pays more and why?
Georgetown and City University L ondon
Insurance and re-insurance markets in rural Tanzania
The World Bank
Disentangling Networks: Defining and Analysing Cohesive Subgroups
Dirk Van de gaer
University of Ghent