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.

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.

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.

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.

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.

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 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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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 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.

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.

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.

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.

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.

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.

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 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.

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.

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.