Maasai Household and Village Socioeconomic Status and Decisions in Ngorongoro - Maasai women and men survey, and village-level information

SND-ID: 2022-170-1. Version: 1. DOI: https://doi.org/10.5878/1vww-vc77

Is part of collection at SND: Environment for Development

Citation

Creator/Principal investigator(s)

Heidi J Albers - University of Wyoming, School of Business, Economics department orcid

Research principal

University of Gothenburg - Environment for Development, School of Business Economics and Law rorId

Principal's reference number

MS-394

Description

The UNESCO World Heritage Site Ngorongoro Conservation Area (NCA) in Tanzania is a well-known example of the challenges of managing a conservation area for multiple goals including meeting the needs of residents within the conservation area. NCA seeks to achieve multiple goals including protecting biodiversity, providing tourism opportunities, improving resident Maasai livelihoods, and conserving Maasai culture. Within and beyond the NCA, most analysis and projects focus on Maasai men, who are cattle herders and heads of multi-household families. This dataset describe livelihoods and well-being, as affected by the protected area, from the perspective of the Maasai women. Recognizing that well-being (and poverty) is multi-dimensional, the original study examines how different factors correlate with self-reported life satisfaction and we apply the framework of the UN Sustainable Development Goals (SDGs). For each of the SDGs, we reported the available evidence from documentation and from surveys of village leaders, female heads of household, and a small supplementary sample of male heads of p

... Show more..
The UNESCO World Heritage Site Ngorongoro Conservation Area (NCA) in Tanzania is a well-known example of the challenges of managing a conservation area for multiple goals including meeting the needs of residents within the conservation area. NCA seeks to achieve multiple goals including protecting biodiversity, providing tourism opportunities, improving resident Maasai livelihoods, and conserving Maasai culture. Within and beyond the NCA, most analysis and projects focus on Maasai men, who are cattle herders and heads of multi-household families. This dataset describe livelihoods and well-being, as affected by the protected area, from the perspective of the Maasai women. Recognizing that well-being (and poverty) is multi-dimensional, the original study examines how different factors correlate with self-reported life satisfaction and we apply the framework of the UN Sustainable Development Goals (SDGs). For each of the SDGs, we reported the available evidence from documentation and from surveys of village leaders, female heads of household, and a small supplementary sample of male heads of polygamous families. We administered the surveys in all 23 Maasai villages in the NCA. The survey results confirm that poverty is widespread, but with substantial variation in the depth of poverty and in access to essentials including water, food, and fuel. Reported life satisfaction of Maasai women is correlated with food security, clothing quality, and access to market and social services, but not with family ownership of cattle, which is the most commonly used metric of Maasai wealth. Our findings suggest potential improvement in NCA programs and provide a baseline to analyze the effects of any such changes in those programs, from the perspective of Maasai women.

NCA_TZ_WomenVillage_Dataset: Contains the data collected through the survey applied to women and also the information obtained with the village assessment.
NCA_TZ_MenSurvey_Dataset: Contains the data collected through the survey applied to men.

*Men and women faced different questionnaires therefore it does not make sense to full merge both databases. Show less..

Data contains personal data

Yes

Type of personal data

Indirect identifiers: municipality, ward, income, number of family members, amount of cattle

Method and outcome

Unit of analysis

Population

Maasai households living in the Ngorongoro Conservation Area

Time Method

Sampling procedure

Mixed probability and non-probability

Village Assessment. A portion of the research team, the “village assessment” team, conducted semi-structured interviews and data collection in a specific village on the first of a two-day survey approach. This team met with the village head, members of the village council as available, a woman, a Maasai youth when available, and representatives from the health and school facilities to gain village-specific information. The village assessment included obtaining the list of households eligible for subsidized grain. The team then used that list as a sampling frame to generate a systematic random sample stratified by sub-village to select survey respondents. With a target of 20 interviews per village, the number of households surveyed per sub-village was determined based on the proportion of village households in each sub-village based on the number of households on the grain distribution list. To select households from the grain list, we divided the total number of subvillage households by the number of households to be surveyed and rounded down to get a new number, X. Each village leader was ask

... Show more..
Village Assessment. A portion of the research team, the “village assessment” team, conducted semi-structured interviews and data collection in a specific village on the first of a two-day survey approach. This team met with the village head, members of the village council as available, a woman, a Maasai youth when available, and representatives from the health and school facilities to gain village-specific information. The village assessment included obtaining the list of households eligible for subsidized grain. The team then used that list as a sampling frame to generate a systematic random sample stratified by sub-village to select survey respondents. With a target of 20 interviews per village, the number of households surveyed per sub-village was determined based on the proportion of village households in each sub-village based on the number of households on the grain distribution list. To select households from the grain list, we divided the total number of subvillage households by the number of households to be surveyed and rounded down to get a new number, X. Each village leader was asked to select a random number, Y, between 1 and X. The team then selected the Yth household as a starting point on the list and sampled every Xth household from there until the end of the list was reached.
In the above case, the village leader was asked to pick a number between 1 and 43 for Sub-village A and between 1 and 45 for Sub-village B.

After the selection of households, we consulted with the village leader whether each woman on the list was expected to be home on the following day. If the woman was known to be traveling (e.g. at a regional market), we then asked about the ID-1 (ID number minus 1) household. If that person was not home, we then asked about the ID+1 household, then ID-2, and then ID+2 households. Both ID numbers and names were recorded for every person to be interviewed. Additionally, of all the women sampled in a village, a subset of 3-4 husbands of these women was then chosen to answer a men’s survey. The aim of the men’s survey was to serve as a comparison to the women’s survey. The village assessment team provided the enumeration team with the households to be surveyed and the information collected in the village assessment in the evening following the assessment and prior to the survey administration.

Survey administration. On the second day of data collection in each particular village, the team of enumerators divided the list of survey respondents among the enumerators. The protocol stated that if the enumerator arrived at someone's house and found no one home, they went to the nearest neighbor and interviewed them instead. If this person happened to be a co-wife of the original person that was supposed to be interviewed, that information was recorded in the survey. Show less..

Time period(s) investigated

2018-07-20 – 2018-07-28

Variables

2036

Number of individuals/objects

458

Data format / data structure

Data collection
  • Mode of collection: Face-to-face interview
  • Description of the mode of collection: The data were collected through ODK Collect on Samsung tablets. The data were downloaded from the server, de-identified as per our IRB approved protocol, and shared on a restricted-access Google Team Drive.
  • Time period(s) for data collection: 2018-07-20 – 2018-07-28
  • Instrument: Tablet (Technical instrument(s)) - Samsung tablets for data collection using ODK Collect software
  • Number of responses: 458
  • Source of the data: Registers/Records/Accounts: Economic/Financial, Registers/Records/Accounts: Personal, Population group, Registers/Records/Accounts
Geographic coverage

Geographic spread

Geographic location: Tanzania

Geographic description:
The study was conducted within the population that lives in the Ngorongoro Conservation Area (NCA). The NCA is a Protected Area and a Mixed World Heritage Site in Northern Tanzania, covering 8,292 square kilometers.
It is located 180km West of Arusha in the Crater Highlands area of Tanzania, extending from the plains of Serengeti National Park and Maswa Game Reserve in the West, to the Eastern arm of the Great Rift Valley.

Lowest geographic unit

Parish

Highest geographic unit

Municipality

Administrative information

Responsible department/unit

Environment for Development, School of Business Economics and Law

Funding 1

  • Funding agency: Environment for Development Initiative

Funding 2

  • Funding agency: Sida (The Swedish International Development Cooperation Agency)
  • Funding agency's reference number: MS-394
Topic and keywords

Research area

Environment and conservation (CESSDA Topic Classification)

Community, urban and rural life (CESSDA Topic Classification)

Economics (Standard för svensk indelning av forskningsämnen 2011)

Gender studies (Standard för svensk indelning av forskningsämnen 2011)

Economic systems and development (CESSDA Topic Classification)

Income, property and investment/saving (CESSDA Topic Classification)

Publications
Published: 2023-09-08