Calculating the Area Weighted Median (AWM) for Grocery Stores in Block Group with FIPS 370370201031.

Correlation matrix of demographic variables and food outlets in the Durham- Chapel Hill MSA (n = 303, block groups).

Abstract

From December 2009 to March 2010 the World Bank with the help of Moroccan government conducted a study of the country's young people and their engagement in economic and social activities. Researchers from the World Bank's Sustainable Development Sector of the Middle East and North Africa region utilized a mixed-method approach to study factors that impede the economic and social inclusion of Moroccans aged 15 to 29. The Morocco Household and Youth Survey (MHYS) used two survey instruments to gather quantitative data: Household Questionnaire and Youth Questionnaire.

The study used a nationally representative sample of 2,000 households, in which 1,216 households were located in urban areas and 784 households in the rural areas. The Youth Questionnaire was administered to 2,883 young people between the ages of 15 and 29, representing about 90 percent of the youth in the surveyed households. Information was collected on topics such as economic inclusion, community participation, and use of key public services. The survey was able to examine little studied issues relating to youth such as participation in the labor force, intermediation, career choice, perceived job possibilities, use of time, use of recreational and educational activities targeting young people who have completed formal education.

The focus groups discussions supplemented MHYS.

Geographic coverage

National coverage

Analysis unit

• Households,
• Individuals between the ages of 15 and 29.

Kind of data

Sample survey data [ssd]

Sampling procedure

The sample size for the Household Questionnaire was 2,000 households with 1,216 found in urban locations and 784 in rural locations. The 2,000 households were drawn from the 2004 General Census of Population and Housing. For determining the number of households in urban and rural locations, proportionality of the possible locations was used to ensure representativeness. The proportionality was based off the disaggregation of Morocco into primary units in which there are about 600 households. In the end, 125 primary units were randomly selected, with 76 rural primary units and 49 urban primary units. From these 125 primary units, 16 households were randomly selected giving us the total sample size of 2,000 households.

For the Youth Questionnaire, the sample size was 2,883 individuals between the ages of 15 and 29. These 2,883 individuals came from the selected households in the Household Questionnaire. If there was an individual or individuals between the ages of 15 and 29 living at the selected household, the Youth Questionnaire was administrated. More details on sample design are provided in Appendix 2 in "MHYS Basic Information Document".

Mode of data collection

Face-to-face [f2f]

Research instrument

Household Questionnaire covers the following topics: Educational Characteristics, Economic Activities in last 12 months, Secondary Economic Activities in last 12 months, Economic Activities in the last 7 days, Unemployment, Health and Social Security, Housing Characteristics and Durables, Agricultural Assets, Climate Change and Shocks in Agriculture,Incidence of Shocks and Household Responses, Assistance from Social Programs, Migration of Household Members, Migration of non-residents, Migration and Climate Change, Decisions on Consumption in the Household, Expenditures on Frequently Consumed Food Items; Less Frequent Non-Food and Food Expenditures Household Consumption expenditures and food source procurement, Expenditure on less frequent non-food and food products,Infrequent Expenditures, Women in Decision Making

Youth Questionnaire includes the following sections: Employment Preferences, Education, Employment during the last 7 days, First Job, Employment History, Entrepreneurship and Independent Farming, Unemployment, Job Search, Job Services Access, Financial Behavior, Participation of Youth in Educational Institutions and in Youth Centers, Participation of Youth in Family, Access of Youth to Recreation and Social Activities, Satisfaction and Communication, and Time Use.

Cleaning operations

The MHYS contains several data files, with each file pertinent to a specific section. For the case in which there are multiple sections per data file, it is because they share similar levels of observations.

The households are identified by the variable "hid" which consists of the region, province, commune, and enumerator area in which the household is located. This allows the household members to remain anonymous yet statistically unique. This is extremely important especially when it comes to merging different datasets.

Merging data sets will depend on which files are being merged. The key to merging the MHYS data files will be to use unique variables.

For the data sets, the "hid" variable will be the unique variable used to perform the merge at household level; "memid" will be the unique variable used to perform the merge at individual level.

The variable "q5" which signifies enumeration area is scrambled to preserve anonymity of sampled households.

The weights are provided in the data file "weights" and can be merged.

Abstract

An effective policy response to the economic impacts of the COVID-19 pandemic requires an enormous range of data to inform the design and response of programs. Public health measures require data on the spread of the disease, beliefs in the population, and capacity of the health system. Relief efforts depend on an understanding of hardships being faced by various segments of the population. Food policy requires measurement of agricultural production and hunger. In such a rapidly evolving pandemic, these data must be collected at a high frequency. Given the unexpected nature of the shock and urgency with which a response was required, Indian policymakers needed to formulate policies affecting India’s 1.4 billion people, without the detailed evidence required to construct effective programs. To help overcome this evidence gap, the World Bank, IDinsight, and the Development Data Lab sought to produce rigorous and responsive data for policymakers across six states in India: Jharkhand, Rajasthan, Uttar Pradesh, Andhra Pradesh, Bihar, and Madhya Pradesh.

Geographic coverage

Andhra Pradesh, Bihar, Jharkhand, Madhya Pradesh, Rajasthan, and Uttar Pradesh

Analysis unit

Household

Kind of data

Sample survey data [ssd]

Sampling procedure

This dataset includes observations covering six states (Andhra Pradesh, Bihar, Jharkhand, Madhya Pradesh, Rajasthan, Uttar Pradesh) and three survey rounds. The survey did not have a single, unified frame from which to sample phone numbers. The final sample was assembled from several different sample frames, and the choice of frame sample frames varied across states and survey rounds.

These frames comprise four prior IDinsight projects and from an impact evaluation of the National Rural Livelihoods project conducted by the Ministry of Rural Development. Each of these surveys sought to represent distinct populations, and employed idiosyncratic sample designs and weighting schemes.

A detailed note covering key features of each sample frame is available for download.

Mode of data collection

Computer Assisted Telephone Interview [cati]

Research instrument

The survey questionnaires covered the following subjects:

1. Agriculture: COVID-19-related changes in price realisation, acreage decisions, input expenditure, access to credit, access to fertilisers, etc.

2. Income and consumption: Changes in wage rates, employment duration, consumption expenditure, prices of essential commodities, status of food security etc.

3. Migration: Rates of in-migration, migrant income and employment status, return migration plans etc.

4. Access to relief: Access to in-kind, cash and workfare relief, quantities of relief received, and constraints on the access to relief.

5. Health: Access to health facilities and rates of foregone healthcare, knowledge of COVID-19 related symptoms and protective behaviours.

While a number of indicators were consistent across all three rounds, questions were added and removed as and when necessary to account for seasonal changes (i.e: in the agricultural cycle).

Response rate

Round 1: ~55% Round 2: ~46% Round 3: ~55%