rafmacalaba/data-use-mentions-extended huggingface dataset:
19 We calculate energy needs using equations specified in the DRIs taking median weights and heights from the WHO growth charts, and assuming an active level of physical activity for most individuals and very active for men 14-59 if reporting a physically demanding occupation (WHO Multicentre Growth Reference Study Group 2006; Schneider and Herforth 2020). We assume breastfeeding practices in line with WHO guidelines and consistent with observed median breastfeeding of 23 months in Malawi, assuming exclusive breastfeeding to six months, and continued breastfeeding to two years. During continued breastfeeding, only some nutrient requirements need to be met with food sources, and all mothers of children under two are assumed to be breastfeeding (Dewey 2005; WHO 2008; National Statistical Office (NSO) Community Health Sciences Unit (CHSU) [Malawi] et al. 2017). We refer to these scientifically defined nutrient requirements as the “individual” requirements, and they are the requirements for which the lower bound (individualized diets) least-cost diet problem is solved.

# <u>Household Nutrient Requirements</u>

To define the shared household nutrient requirement, we consider the nutrient density needs and upper limits for all members aged four and above. To define the minimum amount for each nutrient in the shared diet, we identify the largest nutrient density required by any member. Similarly, for the upper limits we use the most restrictive (minimum) upper tolerance in terms of nutrient density to ensure that the shared diet would not exceed any member’s limits for any nutrient. We compute the total quantity of each nutrient in the household diet as the sum of all members’ energy needs times the defining nutrient density, to get the total quantity of each nutrient.
10 sampling unit were drawn, respectively.<sup>5</sup> In the following, all analyses account for the survey design and population weights are used as recommended by the data providers.

In every selected household, all women of age 15-49 years who were either usual household members or who were present in the household on the night before the interview were eligible for interviewing. In half of all selected households, an additional questionnaire was administered to survey all men aged 15-59 years about their health status. The questionnaire design remained broadly similar across the survey waves. Still, both the number of variables and the sample size increased over time with about 6,500, 10,600 and 11,300 prime age women included in the 1992, 2000 and 2005 survey, respectively.

The RDHS include detailed information on women’s birth histories (permitting the calculation of a fertility indicator to be used as the dependent variable below), maternal and child health, marital history, access to health services, domestic violence, sibling mortality, and women’s socio-economic characteristics, including schooling and main occupation. In contrast to LSMS-type household-surveys, the information captured on the characteristics of other household members and respondents’ partners is limited to age, schooling, and occupation (the latter is only available for current partners). Data on community characteristics were not collected. Due to confidentiality policies we only know the province in which a respondent currently resides, but not the administrative unit below the province level. This prevents us from merging the RDHS data with secondary data on geographical conflict intensity.

Moreover, no information on income or consumption expenditure is recorded and households’ physical asset endowments, such as owning a radio or the quality of roofing materials, is the only implicit measure available on household wealth. Instead, we construct a wealth index based on recorded household assets. Components of the index include durables, such as radio and bicycle, source of drinking water, characteristics of floor materials, and type of toilet facility.
# **1 Introduction**

The COVID-19 pandemic exacerbated many of the hardships faced by impoverished households. Due to their limited resources, households across Sub-Saharan Africa are particularly vulnerable to shocks, such as natural disasters and pandemics. While households cannot control their risk of exposure, they can employ _ex ante_ and _ex post_ coping strategies to mitigate the impact of realized risks and enhance their own resilience. For households in Sub-Saharan Africa, among whom formal insurance is uncommon, these coping strategies often involve reallocation of resources, monies, and labor within the family or household (Ellis, 1998). Households can diversify their incomes to bolster resilience to future shocks and uncertainty - an _ex ante_ coping behavior (Welderufael, 2014; Arslan et al., 2018; Khan and Morrissey, 2023) - or decide to diversify only after a shock exposes their vulnerability - an _ex post_ coping behavior (Asfaw et al., 2019; Mulwa and Visser, 2020; Cely-Santos and Hernández-Manrique, 2021).

We study the impact of livelihood diversification on mitigating food insecurity, a proxy for welfare, during the COVID-19 pandemic. To do this, we use household panel data collected by the World Bank, which combines face-to-face survey data collected in Ethiopia, Malawi, and Nigeria prior to the COVID-19 pandemic with monthly post-outbreak phone surveys. This allows us to establish causal relationships to understand how households coped with the impacts of the pandemic through livelihood diversification. Prior to the public release of the phone survey data, we prespecified our analysis and registered the pre-analysis plan with the Open Science Foundation (OSF) (Furbush et al., 2021). We report on the results of two research questions. First, how has household income composition and livelihood diversification changed since the onset of the pandemic? And second, how does household income composition and livelihood diversification impact household food insecurity amid the pandemic?<sup>1</sup> With respect to our first question, we interrogate the data and produce stylized facts regarding how livelihood diversification changes during
*_enterprises_** .  In addition, (iv) **_expert_** surveys will also help to fill in some details at low cost.

The **_outputs_** should include

- (i) a limited number of national **_basic indicators_** collected on a broadly comparable basis across countries and updated every few years – I tentatively suggest about half a dozen indicative headings under which these could be classified;

- (ii) national **_microeconomic databases_** allowing for detailed research including market research.

The paper is arranged as follows. Section 1 discusses the goals of data collection and the overall conceptual framework that could guide the exercise. The remainder of the paper then proceeds to set out the different types of relevant data that have been collected and to provide an indication of the extent, depth and frequency of coverage. In Sections 2, 3, 4 and 5 I describe what is available respectively from household and enterprise surveys, 2
|0.293|0.489|0.206|
||(0.312)|(0.470)|(0.463)|
|Log of hourly wage|-0.275|-0.104|-0.421|
||(0.478)|(0.511)|(0.842)|
|Log of wage for the last month|-0.078|0.071|-0.286|
||(0.523)|(0.580)|(0.895)|
|Log of total wage for the past 12 months|-0.068|0.397|-0.527|
||(0.678)|(0.733)|(1.183)|

Note: This table reports the coefficient of the instrument variable in regressions of maternal employment on the instrument variable. The 1-month bandwidth sample includes women with children born in December and in January of the following year, and the instrument is a dummy indicating a child born in December.

The 2-month bandwidth sample includes women with children born in November-December and in January-February of the following year, and the instrument is a dummy indicating a child born in November-December.

The 3-month bandwidth sample includes women with children born in October-December and in JanuaryMarch of the following year, and the instrument is a dummy indicating a child born in October -December. Heteroskedasticity-robust standard errors in parentheses. Standard errors are corrected for sampling weights and cluster correlation at the commune level. *** p<0.01, ** p<0.05, * p<0.1.

Source: Estimation from VHLSS 2010, 2012, 2014 and 2016.

36
Additionally, we control for mode of interview and use robust standard errors.

Note that while the two intermediate CATI surveys provide insight into the immediate effects of our intervention, the outcomes measured in these surveys are not directly comparable with the outcomes in the final face-to-face survey since a richer set of questions were asked in the latter.

> 12 Note that the response rates on the first two telephonic surveys were relatively lower, 85% and 76%, respectively.

17
- 9 -

everything else constant, freight costs may be higher if cargos are perishable, such as food. The empty cargos are used as a baseline.

18. Finally, the time-specific fixed-effects, _ct_ , are included. The data covers 11 years, from January 2010 to December 2020. About 130 year-month dummy variables are created with January 2010 used as a baseline. The coefficients will reveal the time trend of average freight costs over the sample period.

# **III. Data**

19. The main data comes from the CAREC Corridor Performance Measurement and Monitoring (CPMM) database collected by ADB. The analysis uses data for seven countries in Eastern Europe and Central Asia: Azerbaijan, Georgia, Kazakhstan, Kyrgyzstan, Tajikistan, Turkmenistan and Uzbekistan. The region is of particular interest because many large transport investments have been made to improve road and rail connectivity since the early 2010s. For instance, Kazakhstan implemented a massive infrastructure investment program, Nurly Zhol, to redevelop public infrastructure to connect its main cities, logistics centers and free-trade zones to the border crossings, including ports on the Caspian Sea dry ports. The country spent about US$36.6 billion for five years: 2013-2017 (UNECE, 2019). By the same token, the entire region is expected to be reconnected for more regional trade and integration. Notably, however, there are only a few micro studies that examined transport freight costs at the regional level. Persyn et al. (2022) find that the expected impact of increased road user costs on road freight prices is particularly high in Eastern Europe.

20. Originally, the CPMM database contains over 187,000 observations, covering both road and rail shipments. Excluding data with missing information and apparent outliers,<sup>5</sup> 51,137 road shipment data are used. A total of 8,952 carriers transported cargos among 397 locations. Each route is georeferenced. Note that the database is developed at the stop-by-stop level. Even on the

> 5
**Figure 23.  Estimated Age – Earnings Profiles. Latvia, 2002**

(Upper panel: males, lower panel: females) <!-- Start of picture text --> 200<br>180 Higher education<br>160<br>Postsecondary<br>140 vocational education<br>120 Secondary education<br>(including technical)<br>100<br>Basic education and<br>80 less<br>60<br>15 20 25 30 35 40 45 50 55 60 65<br>age<br>140<br>Higher education<br>120<br>Postsecondary<br>100<br>vocational education<br>80 Secondary education<br>(including technical)<br>60 Basic education and<br>less<br>40<br>15 20 25 30 35 40 45 50 55 60 65<br>age<br>predicted wage, LVL<br>predicted wage<br><!-- End of picture text --> _Note:_ Earnings are measured as after-tax monthly wage at the main job. _Source_ : Calculations based on LFS data.

**It matters how long ago one has graduated.** Figure 24 relates predicted earnings to “age” of employee’s education rather than her/his own age (which is not controlled for in this model). It clearly shows that “new” education has higher market value, especially in the case of university and general secondary education. For males with basic or technical secondary education the effect is weaker. This result is potentially indicative of problems in the vocational education system in Latvia.

34
consumption surveys. This would imply that cross-country comparisons of inequality levels should take into account these differences in family structure and organisation.

Within-household inequality accounts for nearly 14% of inter-personal inequality in Senegal, which we evaluate to be 42% of the level that would be reached if household heads captured the entire private consumption in households. One of the consequences of such unequal repartition of resources within households is the potential existence of “invisible poor” in households classified as non-poor. Taking intra-household unequal access to resources into account, we assess that as many as 13.4% of the poor individuals live in non-poor households. They are therefore ignored when the poverty status of the household is supposed to apply uniformly to all household members. This could have important consequences for the effectiveness of anti-poverty policies.

When households are large and of a complex structure, as in Senegal and in many subSaharan African countries, where several somewhat autonomous budgetary units cohabit, it is not the case that everyone has access to the same level of resources. In these contexts, coming as close as possible to the individual when measuring welfare is crucial in order to obtain adequate measures of poverty and help anti-poverty policies to efficiently target the poor.

27
poverty is set at 3,288,000 VND/person/year (in national real terms).

# **III. Methodology**

The small area estimation method developed by Elbers, Lanjouw and Lanjouw (2002, 2003) is arguably most popular in the context of poverty analysis. In ELL two data sets, a socio-economic survey and a census are combined through an income or expenditure model. This combination allows us to obtain small area estimates (SAE) of income or expenditure based poverty and inequality. By using the survey alone, we would only be able to disaggregate at the region level.

Typical indicators considered are average expenditure/income, percentage of poor (with expenditure/income below poverty line), poverty density (number of poor per area) and the Gini coefficient. We will determine both the point estimates and the standard errors associated with them. The standard errors are important because they make explicit 5
aggregated for 14 economies, chosen based on the earning call data, and include nine advanced economies (Australia, Canada, Germany, Israel, Japan, Switzerland, Sweden, the United Kingdom, and the United States) and five emerging market economies (Brazil, China, India, Mexico, South Africa). Weights are based on 2018-20 equity market capitalization in US dollars with the United States (46 percent), Japan (13 percent), and the United Kingdom (9 percent) accounting for the majority.

# **4 Results**

## **4.1 Demand and supply sentiment in earning calls**

In this section, we provide a time series of demand and supply sentiment indices, covering both COVID-19 and the Great Recession. As documented before, there were significant supply and demand disruptions during the COVID-19 pandemic (figure 2). Supply sentiment dropped more than ten standard deviations from its long-term average during the first and second quarter of 2020, in line with the collapse of global supply chains amid the virus outbreak. Demand sentiment, on the other hand, dropped only slightly in the first quarter before plummeting in the second quarter as a result of widespread lockdowns and increased precautionary behavior. During the Great Recession of 2008-09, in contrast, demand sentiment was broadly identical to that of 2020, with a mild decline in supply sentiment.

Given the central role of uncertainty in investment decisions as well as broader economic activity (e.g., Bernanke, 1983; Pindyck, 1988; Bloom et al., 2007), we calculate uncertainty scores around demand and supply discussions, as well as in the entire earning calls. Uncertainty around supply and demand discussions were approximately mirrored by the corresponding sentiment levels. Both demand and supply uncertainty peaked in the second quarter of 2020, roughly ten standard deviations higher than the long-term average, when the number of cases reached their first peak and divergences within countries 11
to another. In a first time, people moved to escape the disaster area and, in a second time, they came back home.

Despite its lack of representativeness, the survey does, however, provide very useful information on the allocation of assistance programs after the earthquake. In particular, using asset index as a proxy for household wealth, we can firstly specify the dynamics of poverty after the earthquake. Second, it is possible to assess the targeting performance of assistance programs.

Previous results based on a similar survey in February, a few weeks after the earthquake, highlighted the limited ability of assistance programs to target households according to their preearthquake household wealth or asset losses after the quake. On the other hand, modelling the impact of various covariates on both assistance allocation and food consumption score, we find that the fact of being in a camp had at that time a sizeable effect on assistance, especially for food assistance. Hence, the emergency assistance principle appears to be connected with selfselection strategies since food-insecure people had little choice but to move to the camps in order to have access to food assistance. We also find that both the pre-earthquake asset index and asset losses have a significant impact on food consumption score, with asset losses making up for about half the effect of the pre-earthquake asset index, which demonstrates that households who enforced coping strategies have been able to buffer the impact of the shock.

So, shortly after the earthquake, assistance programs allocation prove not to have been effective in targeting the most vulnerable people in the directly affected area. Five months after the earthquake, it appears that things had not really changed: although food assistance may have contributed to decrease the prevalence of food insecurity over the period, authorities still seemed unable to provide an efficient allocation of assistance programs. The location of households stands out as the main criterion that emerges from the regression analysis, yet this method of targeting is not sufficient: indeed, assistance also appeared to benefit less to families headed by women and less to households with
# **II.** **<mark>Data</mark>**

Government records detail the timing of completion of specific segments of the 5,846 km (3,633 mi) of road connecting the major industrial, agricultural, and cultural centers of India. Work began in 2001 and 23%, 80%, 90%, 97% and 98% was completed by the end of 2002, 2004, 2005, 2007 and 2010 respectively. The distance of economic districts to the highway is calculated as the shortest straight-line distance using official highway maps and ArcMap GIS software. Measuring to district edge or centroid yields similar results.<sup>5</sup> The information on location and timing of GQ upgrades is combined with the plant-level Annual Survey of Industries (ASI), obtained from India’s Ministry of Statistics and Program Implementation, to create two new data sets that allow us to study adjustment dynamics in a more detailed fashion than was previously possible. The ASI is broadly a census of establishments with 100 workers or more, and a rotating sample of one third (or one-fifth after 2004-05) of all other formal plants, defined as those with more than 10 workers.<sup>6</sup> One third (or one-fifth after 2004-05) of the units in each strata of state and 4-digit industries are systematically covered in each annual survey subject to a minimum sample size of 6 units in each stratum. The design ensures that the universe of units is covered in five years.<sup>7</sup> Each wave, referenced by its terminal year, was sampled over a two-year period from 1999-2000 to 2008-2009 which spans the entire period of construction of the GQ allowing before and after comparisons. In any given wave there are between 20,000 and 30,000 manufacturing plants, covering all states and districts.

This allows the creation of a 283 district-level pseudo panel tabulating seven outcome measures and extensive covariates for 90% of the country’
20. **Operational Involvement** . As part of the ESW, a quick review of water-related Project Appraisal Documents (PADs) approved since 2000 has been made using the water project database of the World Bank‘s Independent Evaluation Group (IEG). Searches were carried out with terms such as ―water reallocation‖ and ―water transfer‖. A few project documents mentioned that water reallocation would be considered as a measure of demand management. This was the case, for example, in the India Maharashtra Rural Water Supply and Sanitation Project approved in 2004, with the aim of reducing the need for additional infrastructure for water supply and sanitation. The Brazil Ceara Integrated Water Resources Management Project approved in 2000 included a study of intersectoral water allocation in selected watersheds. The project that most explicitly addressed and supported irrigation water reallocations to higher value uses, including environmental and industrial uses, was the China Xinjiang Turpan Water Conservation Project approved in 2010. With the introduction of basin-wide monitoring and management of consumptive use, it intended to ensure that reallocations would take place in terms of consumptive use, and that compensation programs for farmers giving up water supplies were formulated and implemented.

21. However, beyond the relatively few projects that explicitly mention water reallocations and transfers, there are many others that may implicitly affect allocations to irrigated agriculture, including projects supporting inter-basin or even inter country transfers and involving dams. Also projects promoting efficiency improvements in irrigation systems by lining canals and providing incentives for switching to improved irrigation technologies with the aim of moving the ―saved‖ water to other purposes would fall into this category. Furthermore, many urban water supply projects may tap into water resources that directly or indirectly have benefited other (including agricultural) uses downstream. Often project preparation does not consider at all, or does not try to qualitatively or quantitatively assess, the economic costs and benefits associated with such reallocations.

22. Quantitative assessments are more often carried out when projects provide additional water for irrigated agriculture. As part of the economic analysis,
29.8|44.5|75.7|75.4|48.6|61.8|55.9|
||Large|60.6|63.6|65.8|90.3|72.8|69.8|70.5|

Source: Author’s own elaboration from enterprise surveys _Table 10 Firms Managing Inventory on the Internet (Percentage in terms of Firms with Internet_ _<u>Connection)</u>_

|Categories|Ghana|DRC|Tanzania|Uganda|Zambia|Kenya|Average|
|---|---|---|---|---|---|---|---|
|**Aggregate**|30.6|54.3|89.2|77.5|50.4|63.2|60.9|
|**Main Sectors**||||||||
|Manufacturing|37.1|53.0|93.4|81.5|53.6|65.3|64.0|
|Service|24.1|54.9|87.5|76.7|49.2|62.2|59.1|
|**Size**||||||||
|Small|23.1|50.4|85.2|72.7|53.4|62.2|57.8|
|Medium|36.6|52.9|97.4|86.2|42.2|63.1|63.0|
|Large|46.1|84.0|85.1|94.0|53.4|66.8|71.6|

Source: Author’s own elaboration from enterprise surveys _Nearly 90% of Tanzanian firms with internet