rafmacalaba/data-use-mentions-extended huggingface dataset:
Amhara and Afar in late June 2021.<sup>26</sup> Thus, Tigray is the region affected in the first phase of the war. The other regions in Ethiopia that were not affected by the first phase of the war serve as controls. We then relax this assumption using the granular data from ACLED and apply alternative definitions of control group to probe the robustness of our results. We also restrict this control sample to the three highland regions of Ethiopia (i.e., Amhara, Oromia, and SNNP) given their similarities to Tigray in both agroecological and livelihood contexts. As shown in Section 3, these regions share comparable trends in food security and participation in livelihood activities before the war. If the identifying parallel trends assumption holds in our context, the parameter associated with the interaction term, 2, identifies the impact of the war on food security and labor market outcomes. _h_ r is an error term and captures other unobservable factors that may affect food αα security and labor market outcomes. εε Multiple pre- and post-war-onset round data allows us to estimate time-varying effects of the conflict and relax the assumption of homogenous and time-invariant treatment effects needed to identify the difference-in-difference parameters in equation (1) (de Chaisemartin and D’Haultfœuille, 2020; Goodman-Bacon, 2021). Moreover, multiple pre-war round data allow us to test the parallel trend assumption in the pre-war period. For these purposes, we also estimate an alternative DID specification with month-specific controls and outcomes: _h_ h + ∑ (2) MM1 MM2 rrmm _h_ mm mm=1 mm rr mm=MM1+1 mm rr YY = αα + αα δδ TTWWTTW TT × 11(DD= WW) + ∑ γγ TTWWTTW TT × 11(DD= rrmm WW) + εε where _h_ is a measure of food insecurity and labor market outcomes associated with household _h_ living in region rrmm _r_ observed in month _m_ , h represents household-specific fixed effects, and YY is a vector of month or round dummies. Pre-war survey rounds
, XX tt εε represented by , due to decreased demand for labor. In the following period, this wage reduction tt XX lowers household purchasing power, diminishing food demand and affecting prices. Over time, this tt YY initial drought shock propagates through the economy, influencing wages and prices (represented by for h> 1), which can have long-term effects on food security outcomes. tt+h Finding insignificant coefficients for climatic impacts in the food security outcome equations, while YY observing significant coefficients in the wage or price equations, does therefore not imply that food security is unaffected by climatic shocks. Rather, it suggests that these shocks indirectly affect food security through their impact on the agricultural economy. Similarly, variables not explicitly included in the food security outcome equation can still influence the system. For instance, may capture the price impacts of conflict shocks, which can subsequently propagate through . YY tt εε tt FF

# 4.2 Regression Specification

For the food security outcomes, we employed two logit specifications to capture different levels of food insecurity. The first specification uses a binary indicator as the dependent variable, set to 1 when the IPC phase is 2 or higher (food stress). The second specification denotes 1 when the IPC phase is 3 or higher (food crisis). For food prices and agricultural wages, the dependent variables are the percentage changes in local domestic food prices and local agricultural wages, respectively.

The primary independent variables are drought and flood anomalies derived from rainfall data and vegetation health, measured using NDVI anomalies. We also include a range of control variables such as fuel prices, global food prices, demographic factors, seasonal dummies, and time trends. Compared to the conceptual model, this expands (climatic shocks) by introducing (additional economic variables like exchange rates and global food prices that enter contemporaneously and with lags) , tt tt (additional economic variables that enter with lags) and XX (time-invariant controls), to allow for WW tt additional covariates to enter the model differently.
).

Looking down the variety of characteristics contained in Table 4.1a for Nigeria (column 9), it is evident that there are no substantial differences between the full sample and phone survey frame, suggesting coverage bias is not a major concern for Nigeria. This is not surprising however, since the share of households without contact information is very small (less than 1%) reflecting the relatively high penetration of mobile phones in the country.

However, the results presented in columns 3, 6 and 12 (Table 4.1a) provide a clear indication of coverage bias in the Ethiopia, Malawi and Uganda samples. Households with contact information are generally richer (as measured by per capita consumption expenditure), more likely to own key assets (TVs, refrigerators, and mobile phones), more likely to live in dwellings with improved features such as a modern roof and floor, improved water source and toilet facilities, and electricity, as well as more likely to have a financial account. In addition, the household heads in the eligible sample tend to be better educated and more likely to working in more formal wage employment (in Ethiopia and Malawi). However, when the Ethiopia sample is disaggregated into urban and rural areas (the two domains for that sample) in Table 4.1b, many of the indications of coverage bias are not observed, particularly for the urban sample. This suggests that coverage bias is not a serious concern in urban areas, but may be a more serious concern in rural areas where mobile phone penetration is much lower and as a result 38 percent of ESS rural households are ineligible (compared to just 7% of urban households).

> 11 In order to save space, a subset of characteristics analyzed are presented in the results that follow. The results for the full set of characteristics considered can be found in the online appendix.

11
**Figure 3:** Public Expenditure in Infrastructure (% of total expenditure)

<!-- Start of picture text --> 40%<br>35%<br>30%<br>25%<br>20%<br>15%<br>10%<br>5%<br>0%<br>0% 10% 20% 30% 40%<br>1990<br>Low Lower-middle Upper-middle 45-degree<br>Income Income Income Line<br>2000<br><!-- End of picture text --> Source: IMF Government Finance Statistics, various years Ultimately, many of the adjustments in public financing and ODA largely reflect the fact that the expectations of private sector participation in the financing of infrastructure needs were overoptimistic. The private sector commitments during the 1990s amounted to about $807 billion in developing countries or about $67 billion/year with strong fluctuations during the 1990s.<sup>xvi</sup> This is 20-22% of the investment realized and only 1015% of the estimated annual needed investments in developing countries. Moreover, as seen from the estimates of private sector commitments to invest in infrastructure in less developed countries (LDCs) during the 1990s summarized in table 7, the bulk of this commitment went to energy and telecoms in Latin America, East Asia and, to a lesser extent, Eastern Europe. These investments were also highly concentrated – over the 1990s, about 70% of private participation in infrastructure occurred in just 10 countries – 20
**Figure 7: Currency composition of allocated reserves of Figure 8: Net foreign purchases of South African monetary authorities reporting to IMF COFER domestic government bonds**

<!-- Start of picture text --> 6 10 ZAR bn ZAR bn 200<br>USD trn Possible<br>8 WGBI<br>5 Other 6 inclusion<br>announced<br>Daily 150<br>CHF 4<br>4<br>2<br>GBR<br>0 100<br>3<br>JPY<br>-2<br>2 EUR -4<br>50<br>-6<br>USD<br>Cumulative<br>1 -8<br>(rhs)<br>-10 0<br>0<br>Source: IMF (COFER database) Source: Johannesburg Stock Exchange data via<br>Bloomberg<br>1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012<br><!-- End of picture text --> If sustained, even small changes in the asset allocations of major investors not traditionally focused on EM debt would significantly drive up demand, given the scale of assets under management in the investment industry. According to the EM debt and currency team at Investec, there are US$80 trillion in conventional AUM globally and allocations to EM debt remain modest (e.g. estimates of an average of 2.2% of AUM allocated by pension funds in the U.S., U.K. and Europe); a reallocation to EM debt of just 1% of assets would therefore generate inflows of US$800bn.<sup>16</sup> It is probably impossible to make a reliable prediction of future global asset allocation and foreign demand for EM local currency debt. What is clear, however, is that there is a multiplicity of sources of this demand, and there is evidence that a
||**Table A2 – continued from previo**|**us page**||
|---|---|---|---|
|**Country**|**Provider and Survey Type**<br>**Size Cutof**|**Year**|**Observations/Year**|
|Kenya|Kenya<br>National<br>Bureau<br>of<br>Census of all formal frms.|2010|Raw<br>data:|
||Statistics (KNBS)- Census of||2,089|
||Industrial Sector.|||

35
(DMSP) Operational Linescan System (OLS) sensors i.e. instruments. These fly on board 6 separate satellites in our 1992-2013 sample, named F-10, F-12, F-14, F-15, F-16, and F-18. For several years, there are OLS observations from two separate satellites; for example, F-12 and F- 14 overlap over 1997-1999. Second, we use data from the Visible Infrared Imaging Radiometer Suite (VIIRS) instrument, which has flown on board the single NASA/NOAA SNPP satellite since its launch in 2011. While the available data sample for VIIRS technically begins in 2012, the more reliable stray light-corrected version begins in 2014, which is the sample we utilize.<sup>2</sup> Throughout, we use the acronyms OLS and VIIRS to disambiguate between instruments, which is only one-to-one with satellite in the case of VIIRS.

With this in mind, Table S1 Column HSW is a reference to Henderson et al. (2012)’s baseline result, which appears in their Table 1, Column 1 (p. 1,012) and Table 2, Column 1 (p. 1,015). This estimate used the now dated v. 6.1 of the Penn World Tables (PWT), which is revised between vintages (Johnson et al., 2013). Table 2 Column 2 is our replication of the same specification, but updated using the newest available version of the PWT, v. 10.0. Column 3 uses the same model, but now with since-available inter-calibrated OLS data.<sup>3</sup> Column 4 repeats the same exercise, but extended to the end of the OLS data we include in the analysis, in 2013. This is the first instance of the estimated lights coefficient changing notably, and R-squared also increases. Column 5 repeats this specification, but includes satellite fixed effects. These are advisable since variability across satellite measurements even within the OLS instrument may be substantial, owing to differences in sensor settings
not all of them had received the program, we can use a difference in difference approach with household fixed effects. As the household panel goes back to 1982, we can also test for parallel pre-treatment trends in key outcome variables between treatment and control group. Second, data on labor supply and wages at individual level allow us to explore heterogeneity by gender and initial land ownership. Finally, we estimate a production function and use administrative data on the intensity of MNREGS implementation for a subset of villages to go beyond average treatment effects and make inferences on potential transmission channels.

With respect to labor markets, we find that in the short term MNREGs contributed to wage growth and employment diversification, especially for females and those with limited land endowments. Based on our estimates, the program increased wages by 10 points and reduced hired agricultural labor demand by 8.3 days, mainly in the pre-harvest period, a decrease that was more than outweighed by more intensive use of family labor (by a total of 18 days). The program triggered more intensive use of machinery (by 5.5 points), irrigation (by 7.9 points), and fertilizer (by 11.7 points) and contributed to an increase in the number of crops grown (by 0.17 in the first and 0.41 in the third season). Together with a shift from rice and wheat towards high value crops for small farmers, this points towards program-induced diversification of income sources and productivity growth.

To explore if this can be attributed to productive assets or resource transfers through MNREGS, we use, for a subset of villages, the amount of days spent on all and irrigation-specific MNREGS works. The elasticity of agricultural output with respect to the former is, at an estimated 7.1 to 10.3 percentage points, larger and more significant than the estimated coefficient on MNREGS time spent on irrigation-related works. We take this as suggestive of short-term productivity impacts of the program being largely due to resource transfers rather than improvements in
51

# **Policy Levers (L)**

Section 4 describes four individual policies we use to construct integrated flood risk management strategies:

- Groundwater management

- Rainwater capture

- Relocation of vulnerable areas

- Elevating homes

Here, we describe these policies in greater detail.

Subsidence is a major contributor to increasing effective height of the Saigon River. High rates of groundwater extraction, in turn, contribute to subsidence, though other factors are also at play. We therefore consider groundwater management and recharge as one potential method of reducing hazard by reducing rates of subsidence.<sup>32</sup> The effects of groundwater extraction on subsidence, and the potential for groundwater management to mitigate subsidence, depends greatly on a variety of factors such as land use and soil composition, and is not well understood for Ho Chi Minh City. For the purposes of this study, we are agnostic towards the groundwater management policies used to reduce extraction and encourage recharge. Instead, we examine the risk effects of slowing the rate of subsidence, regardless of how it is brought about. We use a subsidence rate reduction of 0.33 cm/year, amounting to a maximum of 10 cm of avoided subsidence over the entire 30 years of our study.

Rainwater capture may offer a second method of reducing the flood hazard by reducing the amount of rain water that reaches the drainage system during the extreme rainfall event. Potential rainwater harvesting systems in Vietnam range from small plastic bins that can store 100 liters (0.1 cubic meters) of water to large cement tanks that can store 10,000 liters (10 cubic meters) (Patrick _et al._ 2009). While we were unable to find surveys of rainwater technology adoption in Vietnam, we drew on related studies from neighboring Thailand which found that ceramic jars with a 2000 liter (2 cubic meter) capacity were most popular and sufficient for storing water for a family of six (0.33 cubic meters per person).<sup>33</sup>
# **Sustaining Poverty Gains: A Vulnerability Map to Guide the Expansion of Social Registries**<sup>a</sup>

Oscar Barriga-Cabanillas<sup>b</sup> Thomas Bossuroy<sup>c</sup> Paul Andres Corral Rodas<sup>c</sup> Carlos Rodríguez-Castelán<sup>d</sup> Emmanuel Skoufias<sup>e</sup>

**Keywords:** Poverty, Vulnerability, Poverty maps, Targeting, Social protection. **JEL codes: I32, F63** C15, R11

a The authors wish to thank Daniel Valderrama and two anonymous reviewers at the World Bank Research Department who provided substantial comments on this version of the paper. The authors would also like to thank Prospere R. Backiny-Yetna and Djibril Ndoye for their support in gaining access to census data of the Senegal’s National Agency of Statistics and Demography. The findings, interpretations, and conclusions in this paper are entirely those of the authors and do not necessarily represent the views of the organizations to which the authors are affiliated, nor the World Bank Group, its Executive Directors, or the countries they represent.

b World Bank, Washington, DC; email: obarriga@worldbank.org (corresponding author).

c World Bank, Washington, DC.

d World Bank, Washington, DC. and IZA, Boon.

e Lee Kuan Yew School of Public Policy, National University of Singapore, Singapore.
implying that the administration of the socioeconomic household questionnaire to a sampled household would cost, on average, $131, and that the administration of the multi-topic individual questionnaire to an adult household member would cost, on average, $23. Constructing the unit cost estimate per minute of data collection promotes comparability in cost estimation given the cross-country differences in questionnaire design - unlike previous attempts that had to report, due to lack of paradata, country-specific cost estimates per completed interview. This approach also allows for a cross-country consistent cost assessment of specific modules and questions. The resulting estimates are helpful in understanding the budget implications of questionnaire design decisions for future surveys.

Finally, we use the paradata to measure the interviewer effects on variation in interview duration. Multi-level models permit the estimation of the ICC, which, through its decomposition, allows us to capture the extent to which the residual variation in module duration is attributable to interviewers. The estimated interviewer effects are significantly larger than the previous estimates from high-income contexts and reveal a high degree of cross-country consistency. The discussion focused on the identification of household and individual questionnaire modules that may benefit from additional interviewer training, fieldwork supervision and data quality monitoring – based on the modules that are among the top 5 in terms of total variance in duration and that register elevated estimates of the interviewer component of the ICC. We see that food consumption, household roster and non-farm enterprises consistently emerge among the top 5 household questionnaire modules in terms of total variance in duration, with 5 to 50 percent of the variability being attributable to interviewers. Similarly, labor, health and land appear among the top 5 individual questionnaire modules in terms of total variance in duration, with 6 to 50 percent of the variability being attributable to interviewers. There is in fact a near-perfect overlap between these modules that are singled out in the discussion of interviewer effects and those that are identified as the top time-consuming household and individual questionnaire modules. And these modules are central to understanding monetary poverty and inequality in these contexts and to monitoring a
GFW’s most comprehensive activity is near-real-time recording of fishing effort by vessels which broadcast their locations using the global automatic identification system (AIS). GFW stores daily estimates of vessel-specific fishing hours for the period 2012-2020 in georeferenced daily files at a resolution of 0.1 decimal degrees.

with 0.25 degree resolution for faster computation. We have constructed a fishing intensity index by overlaying this raster with the occurrence map for each marine species and computing mean AlS-based estimates are incomplete because many fishing vessels do not use AIS consistently. In response, GFW has recently collaborated with several research institutions to derive more accurate estimates from high-resolution satellite imagery (Paolo et al. 2024). The authors note industrial fishing activity is concentrated. We have downloaded the summary raster at 0.10 degree resolution and resampled to 0.25 degrees for faster computation. Then we have constructed our species and computing the mean value for raster cells within the map boundary.

2.3.3 Coastal Population Influence Human settlements in coastal areas influence offshore species via recreation, tourism, small-scale fishing, and exploitation of marine resources in other ways. Although quantification of specific have constructed a general index of population influence with a spatial kriging algorithm that replaces offshore missing values in our global terrestrial population raster (Section 2.2.1) with projected values from proximate onshore populations.

https://figshare.com/articles/journal_contribution/Satellite_mapping reveals_extensive_industrial_activi We use the inverse distance weighting (idw) function in R, with a maximum projection distance of 5 decimal degrees. We spread the effects of highly-concentrated urban populations by including 100 neighboring points in the projections and setting the distance power parameter (the exponent of distance
# V. References

Almond, Douglas, Janet Currie, and Valentina Duque. 2017. “Childhood Circumstances and Adult Outcomes: Act II.” Working Paper 23017. National Bureau of Economic Research. https://doi.org/10.3386/w23017.

- Al-Samarrai, Samer, Pedro Cerdan-Infantes, and Jonathan Lehe. 2018. “Mobilizing Resources for Education and Improving Spending Effectiveness: Establishing Realistic Targets Based on Past Trends.”

- Alsan, Marcella, and Claudia Goldin. 2015. “Watersheds in Child Mortality: The Role of Effective Water and Sewerage Infrastructure, 1880 to 1920.” Working Paper 21263. National Bureau of Economic Research. https://doi.org/10.3386/w21263.

- Altinok, Nadir, Noam Angrist, and Harry Anthony Patrinos. 2018. “Global Data Set on Education Quality (1965-2015).” _World Bank Policy Research Working Paper_ , 78.

- Barham, Tania, Molly Lipscomb, and Ahmed Mushfiq Mobarak. 2011. “Development Effects of Electrification: Evidence from the Geologic Placement of Hydropower Plants in Brazil.” 8427. CEPR Discussion Papers. C.E.P.R. Discussion Papers.

- https://ideas.repec.org/p/cpr/ceprdp/8427.html.

- Bui, Anthony L, Rouselle F Lavado, Elizabeth K Johnson, Benjamin PC Brooks, Michael K Freeman, Casey M Graves, Annie Haakenstad, Benjamin Shoemaker, Michael Hanlon, and Joseph L Dieleman. 2015. “National Health Accounts Data from 1996 to 2010: A Systematic Review.” _Bulletin of the World Health Organization_ 93 (8): 566-576D. https://doi.org/10.2471/BLT.14.145235.

- “Dartmouth Atlas Project.” 2019. Dartmouth Atlas of Health Care. 2019. https://www.dartmouthatlas.org/.

- Duflo, Esther. 2000. “Grandmothers and Granddaughters: Old Age Pension and Intra-Household Allocation in South Africa
# **_C: Healthcare_**

<!-- Start of picture text --> 2010-2012 2014-2016 2018-2019<br>62%<br>55%<br>46%<br>43% 43%<br>39% 40%<br>36%<br>35%<br>32%<br>25%<br>22%<br>Baghdad Sunni Shia Kurds<br><!-- End of picture text --> Source: Gallup World Poll, 2019.

# **6. Regression Analysis**

Our regression analysis estimates the relationship of socio-economic characteristics and three broad categories of variables measuring respondents’ perceptions of: 1) their quality of life, 2) national institutions, and 3) quality of service delivery. We control for individual-level characteristics including gender, income, and ethno-religious group classifications (shown), plus employment, education level, marital status, rural classification, and age (not shown). We include these variables to relate perceptions with vertical and horizontal inequality. We assume that ethno-religious groups proxy for regions and therefore we do not include governorate level fixed effects. The regression analysis explores this relationship over our three time-periods: pre-ISIS (2010-12), ISIS-control (2014-16), and post-ISIS (20182019). Results are shown in the tables in the Appendix.

# **_<u>Vertical inequality</u>_**

Individual-level characteristics such as gender, age, education, and income can play an important role in determining perceptions. Among those, we specifically look at two characteristics discussed in the previous section: gender and income, measured as adults living in the poorest 40 percent of households.

18