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# Annex C: Taxonomy of Topic Areas

The following procedure is taken to tag the topic coverage of each micro-level dataset in the WB and UNHCR MDL. First, extract the information on the questionnaire modules in the metadata of the identified FDP datasets as well as all the non-FDP micro-level datasets in the WB MDL.<sup>37</sup> Second, calculate the frequency of words mentioned in those modules for both FDP datasets and non-FDP datasets. Third, map each frequently mentioned word to one of the 14 thematic areas. All words that are mentioned at least twice in FDP datasets and five times in non-FDP datasets<sup>38</sup> are manually mapped to these thematic areas. Table C 1 presents the list of keywords by topic areas. Finally, we use this information to tag each dataset by topic area based on whether its survey modules as described in the metadata mentioned any of the keywords listed in Table C 1. For example, if a given dataset includes a module containing at least one of the keywords listed under food insecurity (e.g., food, nutrition, malnutrition, stunting or nutritional), this dataset is tagged as relevant to the topic of “food insecurity”.

## Table C 1: Keywords and Thematic Categories

|Topic|Words|
|---|---|
|Agriculture,<br>livestock and<br>fishery|agricultural, agriculture, agropecuaria, agrícolas, crop, crops, farmer, farmers, farming, fertilizer, fish, fishery,<br>fishing, harvesting, harvests, irrigation, livestock, planting, plot, poultry|
|Assets|asset, assets, dwelling, dwellinghousing, dwellings, housing|
|Consumption<br>and welfare|consumption, expenditure, income, nonfood, purchase, welfare|
|Coping<br>mechanisms|coping, copingshocks|
|Education|classroom, educación, education, educational, educationliteracy, learning, literacy, maternelle, mathematics,<br>numeracy,school,schooling,schools,teachers,teaching,éducation|
|Finance,<br>credit,<br>and<br>debt|bank accounts, banks, borrow, borrowing, credit, debt, finance, finances, financial, lender, loans, saving, savings|
|Food<br>(in)security|alimentation, dietary, food, food insecurity, food security, height, hungry, malnutrition, micronutrients, nutricion,<br>nutrition,nutritional,nutritionnel,nutritious,stunting,underweight,vitamin,weight|
|Gender|domestic violence, female, femme, gender, gender violence, género, mujer, mujeres, woman, womans, women,<br>womens|
|Health|aids, allaitement, anaemia, anemia, antenatal, anticoncepción, anticonceptivos, breastfeeding, cancer, clinical,<br>clinician, contraception, contraceptive, contraceptives, contracepção, covid, covid19, covid19related, deworming,<br>dhémoglobine, diabetes, diarrea, diarrhea, diarrhoea, diarrhée, disease, diseases, emergencyhealth, fecundidad,<br>fecundidade, fertility, fertilitybirth, fièvre, fécondité, genital, haemoglobin, health, healthcare, healthy,<br>hemoglobin, hemoglobina, hiv, hivaids, hospitalization, illness, immunisation, immunization, immunizations,<br>infecciones, infections, inmunización, maladies, malaria, malariabednet, materna, medical, medicine, medicines,<br>menstrual, morbidity, morbilidad, mortalidad, mortalidade, mortality, mortalité, mosquito, nets, paludisme,<br>patients, postnatal, postnatals, pregnancy, prénatales, prénatals, reproduction, reproductiva, reproductivas,<br>reproductive,santé,saúde,treatment,tuberculosis,vaccination,vaccinations,vaccine,vacunación,vih,vihsida|
|Labor<br>and<br>employment|business, businesses, earnings, empleo, emploi, employment, enterprise, enterprises, job, labor, labour,<br>nonagricultural,nonfarm, pekerjaan,travail,unemployment,wage,wages,work,workers|
|Protection|protected, protection|
|Security and<br>conflict|conflict, conflicts, safety, threats, violence, violencia|

37 For this analysis, we excluded those datasets that were either project specific or collected before 2010.

38 These thresholds are determined to ensure that we cover at least 90 percent of all words mentioned in FDP and non-FDP datasets.