rafmacalaba/data-use-mentions-extended huggingface dataset:
and Herforth et al (2021) advise that caution is needed in using this in high income countries to indicate nutrient adequacy. The two GDRS subcomponent scores (NCD-Protect or NCD-Risk) perform better than MDDW in relation to compliance with WHO dietary recommendations (Herforth et al. 2020). In general, the underlying continuous food score data are preferable as they provide more information than a dichotomous summary of a score, although this may require going back to the original datasets rather than the summary statistics available in online databases.

Caution should be exercised when linking diet metrics with the burden of malnutrition and diet-related NCDs as many of these dietary metrics still need additional validation with health outcomes. This issue should be further explored when applying the approach at the country level.

# 10. Discussion and Conclusions

Evidence on the hidden costs of undernutrition in children is well understood, and there is a large literature on the cost-benefit of investing in reducing undernutrition in young children, implemented in actions aimed at reaching the Millennium and Sustainable Development Goals. It is also well understood that unhealthy diets are associated with increased NCD risk. However, despite policy efforts, there were no examples of countries which have successfully reduced overweight and obesity out of 188 countries studied over 33 years from 1980 to 2013 (Ng et al. 2014). Short- and medium-term solutions to reduce undernutrition have in some cases failed to emphasize the basic principles of a healthy diet, namely adequacy, diversity, balance, and moderation.

Although current methodologies for estimating the hidden costs of child undernutrition and of adult overweight and obesity are informative, it is important to note that other risk factors are at play beyond unhealthy diets. Anthropometric measurements such as weight cannot entirely be associated with diets. For 14
*I. (1967): U.S. (1967).

- a. icludes private physicians, hoapitals. pharmacies, nd any other private, non-MO. b. Unspeciied source, may encoepas tuOt wben private, nonprofit$ **are** not **a** category, and my include coercial **where** it **Is** not **a sparate** catetgry.

- C. Source allocation date **are** for 1979. d. Based on nonusers **as vell as users. .** includes currently mrried winen oly.

- f. Thirty percent uncertain **as** to ource of contraceptives. g. Source allocation data **are** tor 1963. h. Preotailia only. t. Oly 40 percent of usrs **use** modern contraceptive **mthods.** j. Prevalence data ist tre 1962.

VP: Table 12t.1
<mark>major urban areas, namely Dushanbe (the capital), Khujand, Istaravshan, Kurganteppa, Kulyab, and Horog. Because of few observations in the household surveys for individual urban areas we group all urban areas into one. We estimate composite urban vulnerability using the same methods and data as for rural areas with the exception that for urban areas the agricultural variables are not included in the calculation of the sensitivity index.</mark><sup>2</sup>

# **<mark>4. Results</mark>**

<mark>The results show that vulnerability varies according to socio-economic and institutional development in ways that do not follow directly from exposure, geography, or elevation. Urban areas are by far the least vulnerable while the eastern RRS mountain zone is the most vulnerable and the remote GBAO mountains rank in the middle. In the following we first present results for rural agro-ecological zones, then for the urban areas, and finally for oblasts.</mark>

# **<mark>Results for agro-ecological zones</mark>**

<mark>The results show substantial and sometimes surprising variation in vulnerability and its components. Overall vulnerability varies much less than exposure, sensitivity, and adaptive capacity; this is because the sub-indices tend to cancel each other out. For example, while the remote GBAO highlands experience the highest exposure, it also benefits from the highest adaptive capacity of any area combined with medium sensitivity. Overall, GBAO therefore has medium vulnerability according to these estimates. The lesson is that a full understanding of the determinants of vulnerability alters the results from what analysis of exposure to the impacts of climate change alone would have led to; such analysis would have placed GBAO in the top as the most exposed region.</mark> <mark>The most vulnerable areas are the eastern RRS (Region of Republican Subordination) mountains, Southern Sughd hills, and Khatlon hills and lowlands (Figure 2 and Figure 3). These are areas of
_Notes:_* Net oil &gas exporters,|icators(WDI) (i,v<br>** Significant oil|ii),World Ba<br>andgas expor|nk Wealth of N<br>ts. Global aver|ations(WoN) <br>ages are unw|(ii,v,vii)<br>eighted.|||
|_~_UAE incorporates Abu|Dhabi,Ajman,D|ubai,Fujairah|,Ras al-Khaim|ah,Sharjah an|d Umm al-Quw|ain.||

# **_Autocracy to Democracy?_**

11. The MENA countries have long been distinguished by the absence of political inclusivity (Diamond 2010). Figure 2 displays global democracy trends by region since 1960; values on the x axis indicate the mean Polity scores of countries in the region. Polity is the most widely-used measure of democratic accountability in the field of political science. Notice that MENA has been either the most or second most undemocratic region in the world since 1960. Despite a slight democratizing trend since the late 1970s, the gap between MENA and all other world regions has grown larger. The lack of democracy, but more critically the extent to which MENA lags behind other regions, represent a special feature of the region in thinking about prospective transition dynamics moving forward. The way oil has interacted with political regimes in the past, and the way in which it may do so across particular country settings in the future, are important factors for the region meriting further scrutinity. While other regions have in the past experienced more of less rapid transitions to democracy, few would probably have predicted the on-set and speed of these transitions.

5
# **2.1 Construction of components**

For each pixel, we construct an estimated value of production based on high spatial resolution information of the five components that serve as priors in the modeling process: crop, livestock, forestry, fishing, and hunting. Given the lack of information on the hunting component, we disaggregate the forestry component into two parts: timber and non-timber products of forestry. The non-timber products of forestry includes an even distribution of hunting. The construction of the five components is described below in four subsections: crop, livestock, forestry (timber and non-timber) and fishing.

## **2.1.1 Crop value of production**

The crop component in the gridded AgGDP is generated by multiplying the quantity of production from the global SPAM 2010 version 1 dataset<sup>2</sup> (You, Wood-Sichra, et al., 2018) with the producer prices at the country level from FAOSTAT (FAO, 2016) for each crop and then summed together.<sup>3</sup> As mentioned earlier, SPAM is a cross-entropy model, which calculates a plausible allocation of crop areas and production to approximately 10 km pixels, based on agricultural statistics at national and sub-national levels, combined with gridded layers of cropland, irrigated areas, population density and potential crop areas and yields (Yu et al., 2020). SPAM’s output distinguishes between 42 crops (33 individual crops, 9 aggregated crops) that together add up to practically all cultivated crops in a country with four parameters including production, yield, physical area and harvest area.

For aggregated SPAM crops (such as other cereals, other pulses, vegetables, fruits, etc.), we computed their prices by taking the weighted average of their components, as follows: where _Jagg_ is the aggregated crop group, _j_ is any crop that belongs to _Jagg_ , _PriceJagg_ is the price of the aggregated crop group, _pricej_ is the price of crop _j_ , and _prodj_
0.811|0.807|63.1|
|**Least developed countries**|0.41|0.429|0.69|0.692|56.8|
|**Sub-Saharan Africa**|0.393|0.419|0.599|0.601|50.8|
|**Developing economies**|0.163|0.166|0.235|0.238|44.4|
|**Developed economies**|0.067|0.066|0.162|0.166|29.5|

Source: Calculated using data from UNCTAD database.

12. **A more diversified exports basket plays in favor of smooth earnings and reduces volatility.** A more diversified export portfolio will have a more stable stream of export revenues (Samen, 2010). Indeed, if a country’s exports are perfectly concentrated in one product, then export earnings will fluctuate with the international prices of that product. Such fluctuations, especially in the case of commodities,<sup>3</sup> can be very large with strong macro-fiscal consequences. Countries

> 2 The equation of the relative standard deviation, given as a percentage is as follows: %���� �̅� ∗100, where _s_ is equal to the standard deviation, and �̅ is equal to the mean. A lower percentage indicates a lower variability in the data set. Equally, a higher percentage indicates the data set is more varied.

3 Commodities include the following HS numbers: 0201, 0202, 0203, 0204, 0206, 0207, 0209, 0210 (meats); 03 (fish, excluding 030110); 0803 (bananas and plantains); 080510, 080512 (oranges); 0901 (coffee); 0902 (tea); 1001, 1003, 1005, 1006 (grains); 1201, 1020, 1026 (oil seeds / groundnuts)
Figure A.1. Population shares of school-age children (%) <!-- Start of picture text --> 60<br>48.4<br>50<br>43.7<br>41.3 41.0<br>37.7<br>40 33.534.6 32.6 33.1<br>30.1<br>30<br>20 16.718.1 17.8 17.2 16.6<br>12.2<br>7.4 7.9<br>10 5.0 5.2<br>0<br>Boys Girls Boys Girls<br>IDP Non-IDP<br>Ethiopia N.E. Nigeria Somalia South Sudan Sudan<br><!-- End of picture text --> Source: Authors’ calculations using the Ethiopia SPS (2017), Nigeria IDP Survey (2018), Somalia HFS (2017), South Sudan HFS (2017), and Sudan IDP Profiling Survey (2018).

Figure A.2. Population shares of primary school-age household members (13+) (%) <!-- Start of picture text --> 50 45.2 47.0<br>45 41.0 40.4<br>38.1<br>40 35.9<br>32.7 33.7 32.8 34.5<br>35<br>30<br>25<br>20 17.1 16.0 17.5 15.7<br>13.3 12.7<br>15<br>10 5.8 6.8 6.5 7.3<br>5<br>0<br>Males Females Males Females<br>IDP Non-IDP<br>Ethiopia N.E. Nigeria Somalia South Sudan Sudan<br><!-- End of picture
Table 2: Municipality-level Summary Statistics, by Higher Education Choice

|Variable|Short-Cycle<br>Program|Bachelors’<br>Program|Not Enrolled|All|
|---|---|---|---|---|
|Total GDP (billions COP)|27.379|25.114|18.853|21.493|
|Ratio of Urban/Rural Population|1.403|1.364|1.030|1.163|
|Homicide rate (per 1,000 inhabitants)|0.354|0.356|0.393|0.379|
|Total Population (in millions)|2.279|2.085|1.597|1.805|
|Area (per 10,000 squared km)|0.090|0.105|0.098|0.099|
|_Availability of Higher Ed_|_ucation Instit_|_utions (HEI)_|||
|_Z_: HEIs only ofer SC programs|0.737|0.646|0.562|0.603|
|_H_: Other type of HEIs|0.844|0.842|0.732|0.775|
|N|30,803|96,504|201,051|328,358|

Note: The sample corresponds to the universe of students who took the high school exit exam (Saber 11) in 2005. The information on local characteristics comes from the Municipal Panel from CEDE (Center of Studies on Economic Development). The information on higher education programs and institutions is from SNIES (National System of Information on Higher Education).

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and learning from the people one is researching, and reflecting their interests, their insights, and their perspectives in the research through a process of iterative learning. Most work in field work in economics tends to be much more top-down, focused on testing an intervention or conducting an experiment which is not based on bottom-up learning. Similarly, not all qualitative work is empathetic – a great deal of it is unfortunately, perfunctory, and careless; employing delegated teams who summarize information from focus groups that replicate the prior biases of the researcher. In a lot of so-called mixed methods work, qualitative information of this kind primarily serves as means of providing color to “hard” quantitative analysis.

On the other hand, field-oriented economists (including me) seem to collect qualitative information that they do not report, or indeed fully analyze, because the profession does not treat it as data worth writing about. This is a mistake. Narrative insights from the deep, grounded interviews and field visits can greatly enrich and complement econometric work, help contextualize findings – they are not merely “anecdotes.” I turn next to a brief overview of research that analyzes data from narratives - open-ended interviews, transcripts of group meetings, social media posts, and the analysis of newspapers, to demonstrate this point.

# B. <u>Narrative Data</u>

People do not talk in numbers, they talk in words. A survey interview where the respondent is directed to take a vague notion and emerge with a precise numerical response, or pick one of a small set of multiple choices when none might exactly fit what they are thinking, is engaging in an unnatural interaction that can lead to a misinterpretation of what they are trying to communicate. Yet almost all the data analyzed by economists comes from interviews of this kind. If economics is to become more reflexive and learn from the people it is trying to study, it needs get more comfortable with analyzing information from narratives.

A nice example of the value of narrative analysis is the use
of the interview dates, it was impossible for the survey team to reach all of the events with enough time to conduct the baseline survey.

> 30 If these characteristics also determine the quality of the training, this non-random sampling of events may bias our estimates upward and overstate the true impact of the program. One mitigating factor is that in every cohort our sample includes a high fraction of the T&E providers contracted by EF.

> 31 More details about the characteristics of the study population are found in a separate baseline report, available upon request from the authors.

> 32 For reference, the 2001 census indicated that Janajatis made up 37% and Dalits made up 13% of the total Nepali population.

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- **Jalota, Suhani and Lisa Ho** , “What Works For Her? How Work-from-Home Jobs Affect Female Labor Force Participation in Urban India,” _Working Paper_ , 2024.

- **Jayachandran, Seema** , “Social norms as a barrier to women’s employment in developing countries,” Technical Report, National Bureau of Economic Research 2020.

- , “Social Norms as a Barrier to Womenâs Employment in Developing Countries,” _IMF Economic Review_ , September 2021, _69_ (3), 576–595.

- **Klasen, Stephan and Janneke Pieters** , “What Explains the Stagnation of Female Labor Force Participation in Urban India?,” _The World Bank Economic Review_ , January 2015, _29_ (3), 449–478.

- **Kleven, Henrik** , “The Geography of Child Penalties and Gender Norms: A Pseudo-Event Study Approach,” June 2022.

- **, Camille Landais, and Gabriel Leite-Mariante** , “The Child Penalty Atlas,” August 2023.

- **, , and Jakob Egholt Soegaard** , “Does Biology Drive Child Penalties? Evidence from Biological and Adoptive Families,” _American Economic Review: Insights_ , June 2021, _3_ (2), 183–198.

- **, , Johanna Posch, Andreas Steinhauer, and Josef Zweimueller** , “Child Penalties across Countries: Evidence and Explanations,” _AEA Papers and Proceedings_ , May 2019, _109_ , 122–126.

- **, , , , and** , “Do Family Policies Reduce Gender Inequality? Evidence from 60 Years of Policy Experimentation,” _American Economic Journal: Economic Policy_ , May 2024, _16_ (2), 110–149.

- **Nandi, Arijit, Parul Agarwal, Anoushaka Chandrashekar, and Sam Harper** , “Access to affordable daycare and womenâs economic opportunities: evidence from a cluster-randomised intervention in India,” _Journal of Development Effectiveness_ , July 2020, _12_ (3)
# **Table 6: Differences between the 12 months and 7 day recall main employment activity among the working** **<u>population in Uganda, 2009/10</u>**

||**Gend**<br>Female|**er**<br>Male|Uganda|
|---|---|---|---|
|**_Different 12 month and 7 day recall primary activity_**|9.0|10.7|9.8|
|**_Same 12 month and 7 day recall activities_**|90.0|89.3|89.2|

_Source: Author calculations based on the UNHS 2009/10. Note: Based on a direct question on whether the 7 day main activity is the same as the main activity in the past 12 months for ages 15 to 65._ Our first finding is that in a 12 month survey (such as an LSMS), recall period does not affect the aggregate indicator on structure of primary employment. As Table 7 shows, the structure of employment calculated from primary employment status data shows that at an aggregated level, and when the data were collected over a 12 month period, the individual differences between activity reported during the 7 day recall and the 12 month recall wash out. _Thus, for macro analyses, using aggregates, the difference can be ignored_ . Note that the UNHS is collected in a 12 month period thereby minimizing the effects of seasonality, and the country has a largely bimodal rainfall season which shortens the duration between agriculture seasons. In other country settings and with surveys of shorter duration, the result might be different. For example, in the case of a Labor Force Survey (LFS) which typically is _not_ conducted over 12 months, these differences could matter even in the aggregate. For this reason, it is extremely important for LFS and other surveys not carried out over 12 months to include
constitutes too large a bucket including several, diverse activities. We found that some consecutively performed activities, such as fetching water, cooking, eating, and cleaning are often misclassified with each other. Since the motion patterns belonging to these activities should not be similar, the most logical conclusion is that the reported activities are overlapping in time. A key finding here is that misclassifications do not always behave symmetrically. Eating, for example, is often confused with cooking, but the opposite (cooking being confused with eating) is less common. This can be indicative of a systematic (mis)timing offset in one direction.

We evaluated potential differences by age and gender as well. In terms of predictions, we found that there are no significant differences by age or gender, after correcting for any size dependence intrinsic to the data. This suggests that the model is unbiased with respect to gender or age. When applying the model on the whole dataset, we see we pick up noticeable gendered differences in time allocation, which align well with the observed time use data.

One final interesting discovery is that background information, such as participant’s age and gender and information on the time of day and the day of week, plays a role in improving model predictions. By running multiple models with different subsets of the data as input, that is, pure sensor signal data, pure background data, and the complete data we were able to disentangle the impact of background information on model predictions. The analysis shows evidence of a fixed boost in model performance when background information is used in combination with the physical activity sensor signal data.

We conclude that it is feasible to predict time use data with high accuracy and that obtainable improvements in the quality of both time use and physical activity data collection hold promise of significantly elevating the predictive capabilities of the underlying models. Based on these results, we offer a few recommendations for the design of future data collection campaigns and suggest some priorities for future research to advance the agenda toward scalable
measure both untreated hypertension (using blood pressure in the survey sample) but also to include treated hypertension (presumably by asking the respondent about medications at the time blood pressure is measured). It is possible that surveys are less good at capturing treated hypertension. The NCD-Protect score has the expect effect on reducing hypertension (significant) and is negative but not significant in the equation for diabetes. There are significant regional differences for diabetes. The NCD variables are likely to be affected by the quality of the health system. NCDs are more likely to be detected and treated in higher income countries. Thus, although one might expect incidence of NCDs to rise with income, premature mortality from NCDs (prior to age 70) is higher in LMICs. The results, given the data constraints noted, provide some encouragement for finding the expected effects of diet quality variables within individual countries.

# 7. Potential Testing of the Methodology Using National Datasets

National health and nutrition survey data provide potential for further testing of the methodology. National data have several advantages. Within countries there is less variation in the ability of health services to screen and treat diseases including NCDs, although there remain problems of socio-economic gradients and rural/urban differences affecting decisions to seek care and obtain access. In a large dataset, it is possible to conduct analysis separately for different age cohorts. Given the progression of the nutritional transition, different age cohorts will face different conditions throughout their lifespan, and cross section data can permit modeling of this. Six datasets were noted above as being potentially suitable, namely health and nutrition surveys for Brazil, Mexico, China, Philippines, Andhra Pradesh (India), and South Africa. These include three lower-middle income countries/states and three upper-middle income countries.

A disadvantage is that, to our knowledge, there are no low-income countries with such datasets. A suggested methodology for how to apply this to low-income countries in the same region is as follows. One could use the health and nutrition dataset from a given region to estimate
also striking that in a city where household income has grown so rapidly for the last several years<sup>18</sup> , the stock of compliant formal housing is still quite small.

# **Recommendations for national policy**

# The case studies of Ahmedabad and Mumbai demonstrate that:

> 17 This housing would not be compliant with today’s standards but is still considered formal.

> 18 Estimated at 12.5 per cent in NCAER (2010)

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