rafmacalaba/data-use-mentions-extended huggingface dataset:
** Dataset assembled by projecting prevalence of IWS found in IBNET onto JMP measures of access to piped-on-premise water supplies. *** LMI = Low-middle income. **** 95% confidence intervals within parenthesis._

**Figure 6: Impact of Intermittent Water Supply on health.**

<!-- Start of picture text --> 35<br>Annual infections (millions)<br>30 Annual diarrheal cases (millions)<br>25<br>20<br>15<br>10<br>5<br>0<br>Source : Bivins et al. (2017)<br>Africa nean Europe Pacific Global<br>Americas Eastern Mediterra South & East Asia Western<br><!-- End of picture text --> 13
for model(_p_-value)|0.000|0.000|0.000|0.000|
|Adjusted R squared|0.87|0.87|0.88|0.89|

Note: Relative income share and population share are fractions (all regions sum to a national total of 1). Relative regional income is mean regional income divided by mean national income (both calculated from household surveys). Regional Gini, calculated from household surveys, is expressed in percentages (ranges from 0 to 100). Significance levels of 5% and 1% are indicated by two and one asterisks respectively.

In regression 3, we add to the model an interaction term between conflict and regional inequality. The signs and significance of coefficients on relative income, population share, and region’s natural wealth are basically unaffected. The coefficient on conflict now becomes negative as posited in the model with very high statistical significance. An important new result is that the effect of inequality on sovereignty is indirect and a higher regional inequality, contingent 29
part a reallocation shock, estimating that 42% of COVID-related layoffs may be permanent.

The rapidly growing literature on COVID-19 sheds initial light on factors that have enabled firms to cope with and survive the shock, but thus far none has focused on management. Among these factors, prior studies suggest that access to government support plays an important role. For instance, short-term government support has been shown to speed up economic recovery, especially for SMEs (Bruhn, 2020; De Mel et al., 2012). Initial studies of the Paycheck Protection Program in the United States showed that receiving grants was associated with increased employment and business survival, but allocation of these grants was skewed towards larger firms (Humphries et al., 2020). Perhaps the only study to consider a broad range of internal and external factors using a large data set in the COVID-19 setting is Ding et al. (2020), which finds that a stronger pre-COVID-19 financial position, less exposure to COVID-19, non-financial corporate (as opposed to hedge fund) ownership, more corporate social responsibility activities, and less entrenched executives were associated with higher post-lockdown stock market returns. The relationship between management and firms’ COVID-19 responses has not yet been well studied.

# **3 Data and Stylized Facts**

## **3.1 Data Preparation**

We rely on two waves of data collected by the World Bank Enterprise Surveys (WBES). The first wave of data was conducted in 2018 or 2019, which we combine with observations of the same firm in a short follow-up COVID-19 survey conducted after April 2020. Our sample therefore provides a snapshot of firm status before and after the onset of the pandemic (approximately six months to 1.5 years apart). The first wave includes detailed information about a firm’s productive activities, financial situation, employment, and management practices. Of the 29 countries covered by the WBES in the COVID-19 follow-up surveys, we focus on those 16 countries where a survey module on management practices was adminis5
compared to the poverty rate of 47 percent in 2018. Coupled with a high degree of insecurity, loss of livelihoods, and displacement, these results on households’ vulnerability to poverty illustrate the importance of urgent humanitarian action and investments in shock-responsive social safety nets.

Our contributions are as follows: First, our paper contributes to the literature on the impacts of climate change and weather shocks on welfare outcomes in a fragile and conflict-prone context (see section 2 for details). We show that droughts, measured by temperature and NDVI anomalies as deviations from long-run weather distribution in the country, adversely affect consumption and poverty rates in a conflict-affected region.

Second, from a methodological perspective we show that rainfall deficits are not ideal for measuring drought in arid and semi-arid regions that receive little to no rainfall. In Afghanistan, we show the distribution of rainfall anomalies (and absolute levels of precipitation) are tight, with little variation across clusters (less spatial variation) or between years. That is, rainfall-based measures do not adequately capture the full intensity of droughts. This finding has two implications: (i) past literature that uses rainfall deficits in arid contexts (Sub-Saharan African nations, for instance) may be underestimating the impact of droughts, and therefore, (ii) in such regions, other measures, such as temperatures and vegetation, seem to capture the drought-like conditions more appropriately, as illustrated in this paper.

Finally, our paper contributes to the growing literature that uses a combination of remote sensing satellite and household data to quantify households’ vulnerability to extreme weather events, events exacerbated by climate change.<sup>6</sup> Remote sensing methods are particularly valuable in a context with little on-the-ground data, such as conflict-affected Afghanistan. These data allow us to capture the spatial and temporal variation in weather shocks with higher precision to quantify households’ vulnerability to such shocks to inform future policy decisions.

The rest of the paper is organized as follows. Section 2 briefly presents related literature. Section 3 describes the socioeconomic and
.8%|10.7%|11.5%|9.9%|10.5%|11.5%|
|Cooperative|5.8%|6.3%|8.3%|7.7%|9.2%|8.7%|7.2%|7.7%|

Sources: For Private Credit/GDP, Beck, Demirguc-Kunt and Levine (2009). For real GDP Growth and nominal GDP, World Development Indicators. Data for all other calculations are from the Central Bank of Kenya.

3
**|0.091|(0.051) *|
|Road density|0.045|(0.003) ***|0.063|(0.004) ***|0.063|(0.004) ***|
|Transport cost to port|-0.005|(0.003) *|-0.014|(0.003) ***|-0.016|(0.003) ***|
|ln Population|-0.225|(0.084) ***|0.556|(0.093) ***|0.604|(0.092) ***|
|ln Population density|-0.417|(0.048) ***|-0.826|(0.076) ***|-0.856|(0.074) ***|
|ln Local wage|0.300|(0.081) ***|||0.305|(0.078) ***|
|Secondary school enrollment|||0.012|(0.001)***|0.011|(0.001)***|
|Obs.|43808||43808||43808||
|Wald chi2|426.15||643.33||676.14||
|Pseudo R-squared|0.066||0.095||0.099||

7 According to the 2007 BEEP data, about 17 percent of the firms surveyed import materials and equipment from abroad. About 5 percent of the firms export their products.
; entrepreneurship; family background; shocks). Most importantly, since the returns to undobservables can differ across the distribution and can change over time as consequence of a shock as we document henceforth, using a panel Fixed Effect estimation leads to loose crucial information. The comparison of the two surveys taken separately and the use of non-parametric techniques captures rather than disregard this heterogeneity.

> 14The full escalation of the insurgency, with mass displacement and heavy fighting between insurgents and Nigerian Army starts de facto in June 2013, few months after our data collection. Proof that, during 2013, households were only marginally affected by the conflict is that in the third round of the panel survey (2015/2016), most of the households residing in Borno and Adamawa states visited in 2013 could not be re-interviewed due to the poor security situation.

11
ratio that is substantially larger than what the DHS show. For example, in Chad the WDI imply a male/female ratio of 1.80 whereas.the DHS imply a ratio of 1.53. Relying on the WDI one would overstate the male "advantage" by almost 30 percentage points. Outside of this region, the data for Bangladesh,

> **13** The primary net enrollment rate is defined as the percentage of children of primary school age who are indeed in primary school. Unlike the 6 to 11 years cutofl used in Table 3 the definition of primary school age varies across countries. In 8 of the 41 countries the range in 6 to 11, in another 8 it is 7 to 12, in another 5 it is 6 to 10, in another 4 it is 6 to 12, and another 4 it is 7 to 13. The rest are somewhere around a similar range.

13
||0.266***|
|– Developing||(0.010)||(0.032)||(0.021)||(0.089)|
|Robots||-0.001||-0.004||0.008||0.043*|
|– Developed||(0.006)||(0.010)||(0.010)||(0.025)|
|Kleibergen-Paap<br>F statistic|||115.8|32.64|||115.8|32.64|
|# Observations|897,260|897,260|897,260|897,260|897,260|897,260|897,260|897,260|

_Note: Robots reflects log robot stock. The instrument reflects robot diffusion in (initial) foreign export partners, in other world regions, defined using 2000 export flows. Quality refers log quality estimated via Khandelwal et al (2013), unit prices are in logs. Country income status is defined by the World Bank (2000). Controls include (log) employment and (log) real value added per worker at the country-industry-year level. The regressions also include exporter*10digit product and exporter*year fixed effects. The first stage has been omitted for brevity and is available in Appendix Table A5. Robust standard errors clustered at the exporter-industry level are reported in parentheses. *** p<0.01, ** p<0.05, * p<0.1._

# **_ii. Heterogeneity across product types_**

The results of the previous section reflected regressions pooled across all product types. This section examines the extent to which there are heterogeneous effects of robots across different products. First, we explore if robot adoption has resulted in a catch-up or a divergence of product quality over time, by contrasting the quality growth
(0.06)|
|Number of Observations|2654|2654|1260|1260|2657|2440|2323|2666|2790|2670|
|Number of Firms|475|475|446|446|475|475|468|475|475|475|
|P‐values testing:|||||||||||
|Cash Treatments Equal|0.193|0.093|0.351|0.540|0.083|0.691|0.124|0.109|0.013|0.007|
|In‐kind Treatments Equal|0.228|0.048|0.769|0.435|0.578|0.494|0.382|0.513|0.874|0.827|

Notes: All expenditure data truncated at the 99.5th percentile of the data. All estimation includes wave effects which vary by gender, and by category in panel B. Standard errors in parentheses, clustered at the firm level. High and Low profits refers to groups defined on pre‐treatment profits. Trimmed sample used. OLS estimation includes dummies for the matched quadruplets. *, ** and *** denote significant at the 10%, 5% and 1% levels.
.8|40.9|
|Hours worked, female|37.5|45.4|36.1|38.3|37.7|34.7|38.3|38.0|
|Years of education|3.4|4.5|5.2|5.5|5.7|4.9|5.9|6.3|
|Years of education for females in T&G|4.5|5.5|6.1|6.1|5.7|5.9|6.3|6.9|
|Years of education for men in T&G|5|7.5|7.3|8.3|7.3|6.9|7.8|8.2|
|Average years of education in T&G|4.6|5.8|6.3|6.5|6.1|6.1|6.5|7.1|
|_Employment Share of the Industry_|||||||||
|Agriculture (percent)|74.3|63.5|60.7|51|45.2|57.4|46.8|48.4|
|T&G (percent)|1.6|4.2|4.3|4.7|5.5|5.3|6.5|7.8|
|Share of female in T&G (percent)|81.7|82.8|81.1|82.3|78.7|82.6|85.2|84.9|

_Source:_ Calculations based on the Cambodia Socio-Economic Survey (CSES). _Note:_ T&G = textiles and garments.
**Decoupling and recoupling**

returns in the host country. As internal instruments we use the first lag of the dependent variables. Results are in line with our previous estimations (only for currencies we fail to recover a significant effect): indeed, the interaction terms for equity and credit gest bigger at the expense of the simple betas, suggesting that the amplifying effect could be even larger than reported in the previous tables.

Evidence of asset co-movement induced by the incidence of benchmarked international funds can also be seen in the investment decision of fund managers. Country weights tend to remain close to their benchmarks, and at any rate they adjust slowly even in times of turmoil. Alternatively, flows in and out of individual countries mimic those stable weights, which, in times of liquidation, induce correlated sales and price action.

The underpinnings of benchmarking can be illustrated based on EPFR ́s individual fund data on flows and country weights. For the sample of emerging market-dedicate funds, panel regressions of today ́s country weights against last period ́s country weights and country returns (with and without time effects, and with country-time effects) show that the time correlation of weights is close to one ( **Table 12** ). This correlation reappears almost as strongly when we compute weights using the funds aggregate (which includes EM and country-dedicated as well as global funds –the same sample used in the decoupling regressions of **Table 10** ): monthly weight changes tend to be quite small. As can be seen in columns 2 and 6, the persistence of country weights is not altered in crisis periods.

In turn, it is easy to show how the influence of market weights on the cross-country correlation of fund flows increases in times of massive swings in fund allocations. After netting out monthly fund returns, we can decompose individual fund flows to a given country as: which, following the results of Table 6 and expressing !!!!!!!!"!!!!"
of high costs. Pictorial diaries and other methods have been proposed to partly overcome problems of illiteracy (Wiseman _et al._ 2005). Also, respondent‟s fatigue raises concerns about administering extensive (consumption) diaries, particularly in urban areas and for relatively long reference periods, potentially resulting in selective survey compliance. In developing countries contexts, consumption diaries are being increasingly used and their accuracy _vis à vis_ recall methods tested (Ahmed _et al._ 2006, Beegle _et al._ 2010, Gibson 2002). However, as noted by Wiseman et al (2005) “much of the literature on diaries focuses on _empirical_ results as opposed to _methodological_ insights”. Moreover, despite their potential advantages, particularly for intercropping, crops (e.g. tubers and vegetables) where the harvest stretches over longer spells, and for plots cultivated by different individuals within the household, diaries have rarely been used in agricultural production. Whether they can complement or even provide an alternative to recall or crop cutting methods is remains an empirical question.

# **2.2. The diary experiment and modalities of its implementation in Uganda**

A high incidence of intercropping and prevalence of continuously harvested crops such as banana ( _matooke_ ), cassava, and sweet potato imply that accurately measuring crop output in Uganda has long been a challenge. Production statistics published by international organizations such as FAO and figures from national household surveys differ widely from each other. Such errors may be exacerbated by issues of survey design and implementation including interviews that occur long after the reference period, respondent fatigue, or poor enumerator training. To explore ways to improve agricultural production statistics and analyze potential sources and magnitudes of errors in standard household surveys, the Uganda Bureau of Statistics (UBOS) complemented the 2005/06 Uganda National Household Survey (UNHS) with a harvest diary. The UNHS is a standard multi-purpose household survey with a sample of some 7,500 households from 750 enumeration areas (EAs) which, in rural areas are mostly equivalent
are robust across specifications. These effect estimates show meaningful increases in mild food insecurity. Considering a baseline mean of 0.61, in columns (5) and (6), our estimates show that the coronavirus pandemic increased mild food insecurity in Bamako by 16 percent relative to the rest of Mali.

Panel C of Table 1 reports results using a dichotomous dependent variable indicating “moderate food insecurity” defined as equal to one of the raw FIES score is greater than three (Smith _et al._ , 2017). Although columns (1) and (2) show no evidence of an increasing time trend of moderate food insecurity in Mali associated with the pandemic, our difference-in-difference estimates in columns (3) and (4) highlight that in urban areas moderate food insecurity increased by 8 percentage points more than in rural areas. This represents a 33 percent increase in moderate food insecurity in urban areas relative to rural areas. These results represent the strongest estimates in this paper. This is important because existing research using the FIES across a variety of contexts consider this measure of “moderate food insecurity” as the primary measure of food insecurity (Smith _et al._ , 2017). This measure, which identifies “moderate food insecurity” if a household responds affirmatively to at least three out of the eight FIES questions, more accurately represents the multidimensional reality of experienced food insecurity for households. Finally, we find qualitatively similar results on average in columns (5) and (6), when considering differences between Bamako and the rest of Mali, though these effects are less precisely estimated.

Finally, Panel D of Table 1 reports the results using a dichotomous dependent variable indicating “severe food insecurity” defined as equal to one if the raw FIES score is greater than seven (Smith _et al._ , 2017). This definition therefore requires that households have indicated affirmative responses on all eight of the FIES questions and is commonly associated with individuals experiencing physiological hunger (Nord, 2014). Perhaps unsurprisingly,
# **Supplementary files**

||**Tabl**|**e A1: Sample Siz**|**e**||||
|---|---|---|---|---|---|---|
||**Burkina Faso**|**Kenya (KIHBS)**|**Kenya (RDD)**|**Malawi **|**Nigeria**|**Tanzania**|
|Pre-COVID F2F Survey / RDD frame|7,010||92,999,970|3,181|4,976|12,812|
|With phone numbers|6,877|9,007|92,999,970|2,337|4,934||
|Attempted to contact|2,199|9,007|5,000|2,337|4,440|5,750|
|Reached|1,951||4,075|1,729|3,152|2,251|
|Phone interviews completed|1,847|4,561|1,075|1,447|2,922|2,193|

Note: Sampling frame in Kenya consisted of re-contact sample from the Kenya Integrated Household Budget Survey 2015/16 (KIHBS) and a sample obtained from random digit dialing (RDD).

||**Tab**<br>|**le A2: Estimated**<br>|**vaccine acceptanc**<br>|**e**<br>||
|---|---|---|---|---|---|
||BurkinaFaso|Kenya|Malawi|Nigeria|Tanzania|
|Overall|74.4|95.1|75.1|78.4|63
coverage, in terms of the number of farms, compared to Form 29, for the below 120 ha group (with 3.8 and 1.1 times the number of farms in the <50 and 50-120 ha groups, respectively) but worse coverage for farms that are larger than 120 ha, with SAR containing only

> 13 The survey was implemented by the Kyiv International Institute of Sociology, with financial support from the European Commission.

> 14 For example, the SAR automatically gathers information on any outstanding debts to the state (which would legally disqualify them from receiving state support) and on farmers’ registered livestock from the animal registry. The government plans to add information from other registries, including the mortgage registry and the registry of court cases, in the near future.

> 15 The PSG that targeted farmers who cultivated fewer than 120 ha provided a cash grant equivalent to US$100 for each hectare of land cultivated during the 2022 agricultural season in non-conflict-affected areas. Information from the SAR was first used to establish whether the farmer was below the 120 ha threshold. After subtracting land parcels registered in the names of farmers located in conflict-affected areas, maps with registered parcel boundaries were used to cross-check cultivation status against a crop map elaborated based on remotely sensed imagery to compute the total grant amount. The €50 million available under the program was fully disbursed by November 2022 and an evaluation that aims to compare differences over time in parcels by successful as compared to unsuccessful PSG applicants is currently underway. Beyond this, several donors are using the system to implement programs such as for the provision of short-term grain storage and delivery of seeds, fertilizer, or critical capital equipment (generators).

> 16 The SAR simplifies program implementation by providing reliable and current information from official registries, simplifying program application by eliminating the need for repeated filling of paper forms, and leveling the playing field and increasing competition in factor markets by creating a base of potential clients to which service providers or banks can market their services.

16
between employment of men and the work status of wives in urban areas, but a strong and roughly equal positive association between the employment of husbands and wives in rural Korea, which likely reflects joint decisions to retire from farming among older rural Koreans.

**_Employment and Spouse Health Status_** . Inclusion of Spouse Difficulty-ADL and Spouse Unable-ADL z-scores among covariates in model (3), and presented in Table 3, allow us to shed light on the relationship between spouse health status and respondent work decisions. Similar to the relationship with own health status, we find no correlation between spouse health status and employment in urban China. In rural China, by contrast, increases in the number of functions that a spouse has difficulty performing (Spouse Difficulty-ADL z-score) are positively associated with labor supply, suggesting an added worker effect dominates. A one standard deviation increase in the Spouse Difficulty-ADL z-score is associated with 3.1 and 5.2 percent increases that men and women, respectively, will be employed. The gender difference in the added worker effect, evident in rural areas of China and Indonesia, stands in contrast to the results using data from the US Health and Retirement Study, which suggest that shocks to health of a spouse have a small positive effect on labor force participation of men and no effect on women (Coile, 2004). While results for the US can be interpreted as suggesting that there is little opportunity to smooth income loss associated with health shocks through the labor market (as Coile concludes), the employment response for both genders in in rural China and men in rural Indonesia likely reflects both a stronger need to smooth income and fewer constraints to returning to work on the family farm than when looking for formal wage work.

# **Conclusions**

As in other regions of the developing and developed world, population aging in China raises the prospect that both formal and informal mechanisms for supporting the elderly will come under strain over the next twenty years. In common with Indonesia and other developing countries,
–2019; Harmonized Survey on Household Living Standards 2018–2019), Living Standards Measurement Study, World Bank, Washington, DC, https://microdata.worldbank.org/index.php/catalog/4292; RGPHAE 2013 (2013 Recensement Général de la Population et de l'Habitat, de l'Agriculture et de l'Elevage; Population and Housing Census, 2013) (dashboard), National Agency of Statistics and Demography, Dakar, Senegal, https://www.ansd.sn/enquete-et-etude/recensement-general-de-la-population-et-de-lhabitat-delagriculture-et-de-lelevage.

_Note:_ In the baseline results, a household is considered vulnerable if it has a probability of falling into poverty above 29 percent in one year (50 percent in two years). This applies to both census- and survey-based estimations. Additional cutoffs consider a household as vulnerable if the probability of falling into poverty is above 13 percent in one year (25 percent in two years) (panel a) or 50 percent in one year (75 percent in two years) (panel b).

17
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