Stata Programming-Mastering Household Survey Data Processing

Download World Bank household survey data, set up folders, and save data in Stata; explore module descriptions, observations, and variables, including MP editions and the agricultural household model.

Download the Malawi fifth integrated household survey micro data from the World Bank, register and log in, unzip, and organize Stata files into modular folders with conversion factors.

Explore how to access and document Malawi's household survey data for Stata processing, including study descriptions, questionnaires, and modules. Distinguish wide and long formats and summarize observations and variables.

Order stata and explore its graphic user interface, licensing options, and editions, while using core features like the command window and do-files and learning about reshaping tools.

Apply a conceptual framework from Deaton and Zaidi linking household, enterprise, and institution dynamics to consumption aggregates via money-metric utility in survey data processing.

Set up Stata, define file paths and log files, and load and save data with a do-file for verification. Learn long-to-wide reshaping, covariates, weights, duplicates, and poverty metrics with graphing.

Create a do-file, save with a name and date, and configure global data paths and separate data folders (household, consumption, agricultural, fisheries, community) plus output, log, and graph directories.

Set up Stata 18 for household survey data by configuring memory and display options, and define a scml log path to save an smcl log for the integrated household survey.

Create and verify a log file, then save data as you define household variables for the Malawi IHS5 poverty assessment using the household questionnaire data.

Load data into Stata via the predefined data path, clear memory, inspect variables, then create case and household ids by tostring and concatenating EA and TA codes with household numbers.

Use the split command to separate interview dates into year, month, and day. Drop extraneous variables, rename, and label new date components for clear survey data analysis.

Define and label smonth as months 1–12, create lean month lmonth for December–March, label it, then derive area from reside and region and label urban and rural regions.

Create the population weight by multiplying household weight by household size, label it as population sampling weight, and save the dataset and a separate key-variables file.

Merge household data files to create covariates for analysis, using Stata. Sort by case ID and household ID; handle duplicates and many-to-many merges.

Identify and drop duplicate observations in a merged Stata file using household ID and case ID, then remove extraneous variables and save the cleaned dataset.

Learn to create covariates by merging multiple data modules in Stata, load and describe datasets from mod B, C, D, E, and V, and set up a many-to-many merge.

Create covariates related to the household head by extracting and merging head-specific variables with the variables data file for 11,434 observations, then drop extraneous variables to prepare for consumption aggregates.

Create covariates for later analyses by renaming and merging head sex, head age, and head education into the case file, preparing data for calorific requirement calculations in the next video.

Generate an age category variable describing household age range for calorific requirement. Reshape long to wide in Stata using case id and PID, keep variables and label.

Define adult equivalence and dependency variables using Deaton and Zaidi 2002 and NRC 1995; rename age and sex, create adult and child counts, then compute the adult equivalence variable.

Create child, worker, youth, and elder dependency ratios in Stata using age ranges, collapse by case ID, and replace missing values with zero while labeling results for regression analysis.

Create and label dependency ratio variables in Stata, including child, elder, and youth dependency ratios, compare them with adult equivalents, and apply 99th percentile rules when workers are zero.

Generate a new f_land_hectare variable from the f1 land roster by applying standard conversion factors for acres, meters, and yards, consolidating values.

Inspect the land hectare variable, collapse observations by household, and compute summary statistics; then graph distributions with box plots, histograms, spike plots, and kdensity, and winsorize the data.

Learn how to estimate household food consumption expenditures from survey data by analyzing food items, separating consumed, purchased, own‑production, and gifts, and building conversion factors, calories, prices, and COICOP classifications.

Estimate household food expenditure by constructing consumption aggregates from module G1 data, merging with conversion factors, and tracing item codes like GO2, GO3A, and G05.

View and label food items in Stata with tabulations, item codes, and unit labels to map Malawi's 142-item food spectrum and common staples.

View and tabulate total and purchased food item consumption in Stata; recode and validate missing values, merge region data by case ID, and save a labeled food consumption data file.

Create a total food consumption data file labeled G1A with variables (case ID, item code, price, G03A, G03B) and address missing and zero prices while reviewing unit and value labels.

Learn to create a total food consumption data file in Stata by defining units and subunits with photo aids, recoding labels, and handling missing values.

Accurately process own-produced food data (G04c) by recoding item labels, validating observations, and saving clean data files through a guided Stata do-file workflow.

Merge four food data files—including gifts and other sources—with unit conversion factors in Stata to convert quantities to weight, then address non-matches and missing factors.

Create new units and factors in the food conversion factor dta file by importing Excel data, expanding items by region, and coding regions as north, central, and south.

Create, label, and validate new unit conversion factor files in Stata, covering litre to 50 grams, and extend to regional units by importing data and mapping regions.

Append new units to the existing food conversion factor dta file in Stata, then define and label a new unit variable, and recode sources.

Recode and relabel conversion factors by updating unit codes and unit names, drop codes not in the IHS file, and prepare a clean, merge-ready dataset for household survey data processing.

Merge food conversion factors with total food consumed data by household, aligning unit codes and item codes, then clean and validate merges to produce a unified conversion factor data file.

Merge conversion factors with purchased, own-production, and gifts food data in Stata, clean missing observations, and save merged datasets for further analysis.

Learn how to compute total food consumption by household in grams using Stata, unitize prices, and collapse data to support poverty analysis.

Collapse and rename data files to compute purchased, own-production, and gifts food consumption by household and case for poverty analysis, then generate summary statistics and merge the datasets.

merge food consumption files in stata to create a single data set of total grams consumed, combining purchased, own-production, and gifts, then validate and label the consolidated variables.

Compute missing unit prices, revise seven-day grams, and calculate household food expenditure using the expenditure aggregate food0, calorific content, calorie conversion factors, and Classification of Individual Consumption according to Purpose.

Import the Excel calorie classifications based on the IHS5 item codes, save as a dta file, and merge expenditure aggregate data with calorie data by item code, then compress memory.

Generate the per-household food calorie intake by combining grams with energy content per 100 g for carbohydrates, proteins, and fats. Label new variables, drop extraneous ones, and save the file.

Collapse food calorie intake by household to sum calories and grams by case ID, relabel variables, and merge with case and variables data to create a food data file.

Compute unit prices from expenditures and food grams, then generate a Paasche price index, while merging data and handling missing prices for accurate expenditure aggregation.

Apply an iterative Stata workflow to replace missing food unit prices by generating price variants from item code, district, month, year, and area, applying a seven-observation rule, then compute price_nat.

Create a Paasche price index from unit price with log transformations and outlier limits, replace with national prices as needed, apply household and population weights, and save the indexed data.

Generate aggregated household food expenditures in Stata by computing own-production and gifts totals, creating COICOP food categories, and imputing median prices for unpriced items.

Consolidate seven-day household food expenditures in Stata by creating a total expenditure from components, handling missing values, identifying outliers, and saving the aggregated dataset.

Generate expenditure categories for food, beverages, alcohol, and vendor items from item codes, annualize expenditures by 52 weeks into yearly food consumption aggregates, then graph, inspect, and save.

Learn to estimate and aggregate nonfood expenditures, including education, health, housing, and housing rental expenditures, across recall periods, merge with food expenditures, and classify by CICP categories.

Compute household education expenditures from survey data using stata, constructing the education expenditure variable from tuition, after-school programs, and boarding costs, and compare with total expenditures to adjust estimates.

Learn to compute household education expenditures in Stata, recode miscoded items, and summarize expenditures across primary, secondary, and tertiary education while comparing boarders and day scholars.

Compute education expenditures by summing A through K, validate against the household total, and create the education expenditure consumption aggregate by case, then inspect distribution.

Compute household health expenditures in Stata by deriving monthly and annual costs from Module D, handling missing values, creating coicop categories 061–063, and aggregating by case ID.

Analyze household utility expenditures by processing rent, fuelwood, electricity, telephone, and cell phone costs using Stata; clean data, select variables, and generate aggregate and annual expense measures.

Master Stata programming to compute household electricity expenditure, create consolidated cost variables, and analyze daily, weekly, and monthly estimates for utilities and related costs.

Learn to compute and validate household utility expenditures in Stata by generating telephone and cell phone costs, drinking water expenses, annualizing monthly costs, and defining expenditure categories.

Master Stata programming for household survey data processing by computing nonfood expenditures from one-week and one-month recalls, creating expenditure categories, collapsing to per-household totals, and exploring distributions.

Compute household nonfood expenditure aggregates from a one month recall. Recoding, labeling, and creating expenditure categories by item codes, then multiplying by 12 to convert to annual totals.

Compute three months nonfood expenditure aggregates, define and label expenditure categories (clothing, shoes, utensils, lodging, etc.), recode data, adjust for quarterly recall, and visualize with box plots and histograms.

Analyze the one-year recall of household nonfood expenditures (module k) by recoding and labeling items into expenditure categories, then save the results as module k1 and proceed to k2.

Master Stata techniques to compute household nonfood expenditures from 12 months of data, recode variables, create expenditure categories, collapse by case, and visualize with box plots and histograms.

Compute housing rental expenditures from module F data by generating estimated and actual rents (F03, F03B) for households, contrasting owned and rented properties, and validating aggregates with Stata.

Compute summary statistics for rental variables, create a binary renting indicator, construct expenditure categories 040–042, impute missing values to zeros, and label and save as expenditure_aggregate.

Merge all nonfood expenditure data files into one labeled data set, combining modules C, D, F and all recall periods, then save and describe nonfood consumption aggregates.

Explore how to understand the agricultural questionnaire, identify expenses it collects, generate agriculture related covariates, and compute agriculture related expenditures across sixteen episodes.

Explore part d of the agricultural questionnaire, detailing expenditures, rainy and dry seasons, land ownership, and household production in Malawi, with data preparation steps including variable renaming and merging.

Merge agricultural covariates from module B with the dta file, label and describe garden-related variables (B06, B214), and prepare covariates for regression analysis.

Analyze household agricultural expenditures to construct a consumption aggregate from expenditures, not income, focusing on cash and cash-in-kind garden costs, rents, and output, using B2 and B208–B211 variables for comparison.

Learn to generate agricultural expenditures from land rents, create an expenditure aggregate, and build covariates for coupon use across modules B and E, with data cleaning and merging.

Learn to generate agricultural expenditure aggregates and covariates in Stata by computing transport, coupon, and bribe costs, labeling and merging datasets across module E and F.

Generate agricultural covariates and expenditures from module f inputs and costs during the rainy season, then label input types and merge with the dta file to estimate costs.

Create agricultural covariates for household seeds data by reshaping Stata data, labeling crops 1 to 48, handling missing codes, and merging with expenditure variables for regression-ready data.

Generate agricultural expenditure covariates by calculating transport and input costs from module H (H09, H10, H40), collapse by case, and merge into the expenditure file to obtain seven variables.

Apply a consistent Stata workflow to generate covariates and agricultural expenditures from household survey data, including reshaping, handling missing values, and merging covariates into the agricultural data file.

Create agricultural covariates and garden expenditures from dry and rainy season data, focusing on ownership, acquisition year, sale and rent values, and data cleaning.

Master how to create agricultural covariates and expenditures from household survey data, including dry season costs, long-to-wide reshaping, and merging for regression-ready analysis.

Learn to create agricultural covariates and expenditures from survey data using Stata, including seed purchases, cleaning crop codes, reshaping, merging, and calculating transport and input costs for the dry season.

Create agricultural covariates and expenditures by reshaping from long to wide, renaming variables, and handling missing values, then merge to yield 628 covariates across 1,954 observations, including transport costs (O10).

Create agricultural covariates and expenditures from module Q, including crop sales and transport costs. Reshape long to wide, rename and replace variables, then merge with the dta file.

Master Stata data processing for household surveys by creating agricultural covariates from livestock codes, reshaping data, and merging with the dta file to analyze livestock expenditures.

Create an expenditure variable for livestock purchases using module R1, collapse and merge with agricultural covariates, then identify wages and input costs across module R2 for 17 expenditure categories.

Create agricultural covariates and expenditures in Stata by computing input costs, generating expenditure variables from modules S and T, and merging data for robust household covariate analysis.

Explore the fisheries questionnaire by understanding its structure, generate fisheries-related covariates, and compute fisheries expenditures, following the same approach used for the agricultural questionnaire.

Analyze fisheries data from a household survey in Stata, generating covariates and expenditures across modules a–c, focusing on high and low fishing seasons for 383 households.

Create fisheries covariates from household survey data in Stata, capturing weeks, days, and hours spent on fishing and processing, then reshape, rename, and merge data with foreach and forvalues loops.

Create fisheries covariates and expenditures in Stata for Malawi households by transforming fishing gear data, calculating unit costs, and generating an expenditure category for regression analysis.

Create fisheries covariates and expenditures from module D2 data by collecting boat engines, gear costs, and other inputs during the high season; merge files to finalize six covariates for analysis.

Analyze fisheries covariates and expenditures using fish trading data, reshape and rename species variables, and generate covariates to estimate weekly high-season costs for regression.

Generate covariates and fisheries expenditures for low-season fishing households in Stata; clean, rename, and label variables, compute expenditure categories 144 and 144B, and merge with the fisheries data file.

Generate and clean fisheries expenditure covariates from the questionnaire, including boat engine, rents, gear rentals, and fish trading costs, with seasonal merging and outlier handling.

Explore the community questionnaire and learn how to create covariates related to the community. Understand the questionnaire structure and generate community-related covariates.

Aggregate and merge community covariates from the district questionnaire, combining modules CA, CD, CF1, CB, and CE for a 710-observation data file ready for regression analysis.

Create community covariates from household and agriculture data in stata, including reshaping by informant id, labeling, recoding missing values, and saving do files.

Identify and process community-related covariates from household survey data in Stata, including droughts, floods, price changes, access to services; drop missing values, remove duplicates, collapse to unique observations, save dta.

Create community-related covariates from the community questionnaire by generating group and resource IDs, reshaping data into variables of interest, and merging with the dta file for analysis.

Learn Stata programming to generate and merge community covariates from household, agriculture, and fisheries data, reshape and merge dta files, covering community resources and price modules.

Combine consumption aggregates to compute 14 real consumption aggregates for poverty analysis, generate area-specific non-food price deflators and adult equivalence scales, then winsorize and graph real consumption for regression.

Merge household, agricultural, and fisheries data to construct a consolidated real consumption aggregate using Paasche deflator and adult-equivalent scaling per Deaton–Zaidi, generating non-food COICOP-based 14 expenditure aggregates and price indices.

Label all expenditure categories, create a total consumption aggregate and expenditure shares, and save the aggregated data for regression-ready covariates.

Construct a non-food price deflator using Malawi's non-food CPI from April 2019 to April 2020, generate a non-food price index for deflating expenditures, and derive the adult-equivalents denominator.

Generate the adult equivalence denominator and factor using Deaton and Zaidi 2002, merge by area, and derive a Paasche price index deflator for real consumption.

Generate an area-based price index using the median to reflect left-skewed data. Compare it with the adult-equivalent price index and analyze price deflators for food expenditures and poverty measurement.

Merge price deflators with real consumption aggregates by using Paasche price indices, adult equivalence scales, and non-food price indices from the case dta file, then label and save the data.

Generate real consumption aggregates by deflating food and non-food expenditures with Paasche and adult-equivalence indices, create per-capita measures, label variables, and save 14 aggregates for poverty analysis.

Generate fourteen real expenditure aggregates from the expenditure categories using a Paasche price index and adult-equivalence scaling, then label and document variables for poverty analysis and data normalization in Stata.

Generate normalized and log-transformed real expenditure aggregates, apply winsorizing to outliers, enabling normality for parametric regression analyses.

Label data, then winsorize real expenditure aggregates with Winsor2, and view box plots. Generate per capita real expenditure aggregates, label them, and save as final data.

Construct poverty measurement metrics by building food poverty lines and a consolidated poverty line, compute calorie requirements and household consumption, and rebasing real consumption across surveys for age-based expenditure comparisons.

Compute average food calorie requirements and construct a food poverty line and consumption deciles using Stata, rebasing across Malawi household surveys to compare poverty trends.

Load data and variables, label sex and age, generate age category and age calories, then compute mean and median household calories by age and sex for Malawi 2019–2020.

Compute calorie consumption per household by merging the food expenditure data with the calories data, then sum carbohydrate, protein, and fat calories for weekly per-household totals.

Generate food and non-food poverty lines and consumption deciles from real expenditure aggregates and price index, adult equivalence scales, then merge data and create per capita and calorie expenditure variables.

Generate cost per calories by decile in Stata using the Paasche price index and adult equivalence, then collapse and summarize by decile to construct food component of the poverty line.

Learn to construct the food component of the poverty line in Stata by using median calories, Paasche price index, and adult equivalence scales, including labeling and data cleaning.

Learn to construct food and nonfood poverty lines in Stata by merging data, applying Paasche and adult equivalence thresholds, and generating shares and decile summaries.

Construct nonfood and food poverty lines from price indices, derive Paasche and adult equivalence poverty lines, then classify the poor and ultra-poor and prepare a Malawi expenditure poverty dataset.

Rebase consumption aggregates across IHS waves using CPI deflators and base years (2010, 2019), generating deflated poverty-related variables and per-capita expenditures for comparable poverty estimates.