rafmacalaba/data-use-mentions-extended huggingface dataset:
started rising rapidly. The paper finds that the transfer led to increase in reopening of businesses that had temporarily closed, increase in business profits by a third of the decline observed in the first months of the pandemic, and increase in household food expenditures. Spending on personal protective equipment (PPE) also increased in the treatment sample, but only among those who perceived COVID-19 a major health risk, which suggests potential complementarity between cash transfers and information campaigns to reduce the risk of contagion while minimizing impacts on economic activities.

Abay et al. (2021a) study whether the Ethiopian Productive Safety Net Program (PSNP) played a positive role in protecting beneficiaries of the program from the malign impacts of the COVID-19 pandemic on food security. The authors combine a pre-pandemic face-to-face survey and a phone survey conducted during the pandemic to compare the evolution of food security outcomes of beneficiaries and nonbeneficiaries before and after the onset of the pandemic. They find that food insecurity increased significantly less among PSNP beneficiaries, especially for poor households and those living in remote areas. They also find that PSNP beneficiaries were less likely to reduce expenditure on education, health, and agricultural inputs. Strupat (2021) employs a similar difference-in-differences strategy to study the impacts of the National Safety Net Programme (NSNP) and the Hunger Safety Net Programme (HSNP) in Kenya. The study combines nationally representative in-person surveys before and after the first wave of COVID-19 in Kenya to study impacts of the programs on household incomes, coping strategies and social cohesion. The study finds that beneficiaries experienced lower loss of incomes, and they did not sell their assets to cope with the impacts of the pandemic. Social cohesion, measured in terms of trust in government and parliament as well as cooperation with others to do voluntary work, remained stable among beneficiaries but declined significantly among non-beneficiaries. These findings suggest that existing social protection programs can play a key protective role during covariate shocks such as COVID-19.

Kimani et al. (2020) worked with GiveDirectly to implement an unconditional cash transfer program in
of public debt, differentiating between SIDS with higher and lower debt-to-GDP ratios. Section five discusses what type of external assistance most SIDS need to help them strengthen their resilience to climate change, and whether debt relief measures offer feasible and optimal instruments to deliver this objective. Section six provides a conclusion and main messages.

# **2. Vulnerability to climate related natural disasters**

This section examines the vulnerability of SIDS to climate change-related hazards, which is likely to increase over the long term, as the planet’s climate becomes warmer. SIDS are among the most highly exposed countries in the world to hazards from climate change (Hallegatte et al, 2018). This is illustrated by their ranking in the Notre Dame Global Adaptation Index (NDGAIN). One of the sub-indices of the overall ND-GAIN Index is exposure to climate change, which captures the physical factors external to the system that contribute to vulnerability—it is a component of vulnerability independent of the socio-economic context. Of the 192 countries ranked in this sub-index, 10 of the 19 countries in the most exposed decile are SIDS. Figure 1 shows the ranking of all of 36 EMDE SIDS which are ranked in the ND-GAIN sub-index of exposure to climate change.

3
driven women and unmarried women. by high-skill unemployed unemployed Specifically, we observe a null effect (0.02 with _p >_ 0.10) on travel time and an insignificant effect on time spent on household chores (-0.127 with _p >_ 0.10) for married unemployed women, suggesting the persistence of entrenched gender norms.

# **7 Impact on Employment and Job Search**

In the previous section, we documented that the _Pink Slip_ scheme affects the amount of time that employed women devote to work, varying by their skill level: increasing for high-skilled and decreasing for low-skilled women. By definition, time spent on work is not applicable for unemployed women. Yet, they display a similar pattern: highskilled unemployed women increase travel time, whereas low-skilled women decrease it. Considering that a portion of this travel time may involve searching for job, the _Pink Slip_ scheme could potentially impact women’s labor market outcomes. While we are unable to directly measure travel time spent on job searches in the CPHS data, we could infer the extent to which these searches may impact labor market outcomes based on observed changes in participation, employment or unemployment. We now present these results, followed additional evidence on the and distance of travel from the Delhi by purpose Primary Survey.

**Evidence from the CPHS Data** We estimate equation (7) to investigate whether women are more likely to be in the labor market, more likely to be employed, and more likely to switch from being out of the labor market to actively searching for jobs after the _Pink Slip_ scheme. The results are in columns 1 to 3 of Table A7. We no reported find statistically or economically significant impact on being in the labor market or being employed. However, there is a marginal increase in the job search for women. Women who were previously out of the labor market are 1.1% ( _p <_ 0.15) more likely to start searching for jobs after the scheme,
# 8. References

Balasubramanya, S., Garrick, D., Brozović, N., Ringler, C., Zaveri, E., Rodella, A.-S., Buisson, M.-C., Schmitter, P., Durga, N., Kishore, A., 2024. Risks from solar-powered groundwater irrigation. Science 383, 256–258.

Barron, O.V., Emelyanova, I., Van Niel, T.G., Pollock, D., Hodgson, G., 2014. Mapping groundwater-dependent ecosystems using remote sensing measures of vegetation and moisture dynamics. Hydrol. Process. 28, 372–385. https://doi.org/10.1002/hyp.9609.

- Bonsor, H.C., MacDonald, A.M., 2011. An initial estimate of depth to groundwater across Africa. British Geological Survey Open Report. https://nora.nerc.ac.uk/id/eprint/17907/1/OR11067.pdf. Accessed 15 Dec 2022.

- Center for International Earth Science Information Network, 2018. Gridded Population of the World. https://sedac.ciesin.columbia.edu/data/set/gpw-v4-population-density-rev11. Accessed 22 Apr 2023.

- Chandel, S.S., Nagaraju Naik, M., Chandel, R., 2015. Review of solar photovoltaic water pumping system technology for irrigation and community drinking water supplies. Renew. Sustain. Energy Rev. 49, 1084–1099. https://doi.org/10.1016/j.rser.2015.04.083.

- Esteban, E., Dinar, A., 2016. The Role of Groundwater-Dependent Ecosystems in Groundwater Management. Nat. Resour. Model. 29, 98–129. https://doi.org/10.1111/nrm.12082.

- European Commission, 2021. Copernicus Atmosphere Monitoring Service (CAMS).
a proscribed counter-stereotypical behavior, such as women’s work outside the home, may eventually reduce norm costs associated with the behavior and help erode the restrictive norm. In this way, we consider individual non-conformist behavior as a first-order condition to allow for norms change at the societal level. The _Choices_ program aimed to shift personal gender attitudes and make the returns to gender equality more salient.<sup>21</sup> We measure personal gender attitudes, perceived community norms and conformity through self-reported survey questions and complement these constructs with behavioral measures of perceived norms and social conformity from a lab-in-the-field experiment. Conformity is considered a behavior that is situationally determined rather than an individual trait or characteristic. In our experiment we define conformity as adolescents taking actions or stating beliefs in a public group setting that deviate from what they privately considered to be the optimal action or belief; what they would say or do if peers would not observe them. Specifically, we first measure conformity with respect to gender beliefs (the focus of the _Choices_ training content), whereby we assess whether adolescents switch their private response when in public in front of a randomly selected group of peers. That is, we explore individuals’ likelihood of conforming in public where they may be confronted with an opposing view to their own. Next, we measure conformity in an abstract setting where respondents are asked to match the length of lines as in the Asch conformity experiments. The line match games are used to bolster the argument that what we observe are social dynamics specific to gender norms that were thematized by the program, not a broader influence on conformity. Finally, we examine self-reports of conformist behavior from the surveys.

Societal norms often establish gender differences and there is literature showing differences in the way men and women conform to social influence. See for example, Eagly and Carli (2007) who conduct a meta-analysis and find that women are more persuadable and conforming than men in group pressure situations that involve surveillance. This implies that the gender composition of the group may
The following is the set of 64 countries and economies with official reported deaths used in the analysis: Afghanistan; Albania; Algeria; Argentina; Australia; Austria; Bangladesh; Belgium; Brazil; Canada; Chad; Chile; Colombia; Costa Rica; Czechia; Denmark; El Salvador; England and Wales; Eswatini; Finland; France; Germany; Greece; Haiti; Hong Kong SAR, China; Hungary; Iceland; India; Indonesia; Israel; Italy; Jamaica; Japan; Jordan; Malawi; Mexico; Moldova; Nepal; Netherlands; New Zealand; Nigeria; Northern Ireland; Norway; Paraguay; Peru; Philippines; Portugal; Romania; Scotland; Slovak Republic; Slovenia; Somalia; South Africa; Republic of Korea; Spain; Sweden; Switzerland; Taiwan, China; Togo; Turkey; Ukraine; Uruguay; United States; Vietnam.

**Excess all-cause deaths from vital statistics records** , from the following sources:

Short term mortality fluctuations (STMF) harmonized data series from the Human Mortality <u>Database</u>

- For this analysis; the STMF input files are used. These input files cover 38 countries and economies: Australia; Austria; Belgium; Bulgaria; Canada; Switzerland; Chile; Czech Republic; Germany; Denmark; Spain; Estonia; Finland; France; Northern Ireland; Scotland; England and Wales; Greece; Croatia; Hungary; Iceland; Israel; Italy; Republic of Korea; Lithuania; Luxembourg; Latvia; Netherlands; Norway; New Zealand; Poland; Portugal; Russian Federation; Slovak Republic; Slovenia; Sweden; Taiwan, China; United States.

- Russia does not have data for 2020.

- Chile and Germany do not have data for 2015.

Colombia vital statistics tabulations ( _Defunciones no fetales_ ) were compiled by DANE. 2019 and 2020 tabulations are preliminary.

Historic (2015-2019) Mexico vital statistics microdata are those compiled ( _Defunciones registradas, mortalidad general_ ) by INEGI, 2020-21 deaths microdata based on death certificates compiled by RENAPO
10% respectively. Tapwater supply is limited to provincial towns and large metropolitan cities such as Dhaka, Chittagong, Rajshahi, Sylhet, Barisal and Rangpur (Figure 1).

Access to groundwater-fed water supplies for irrigation is an important indicator for measuring food security in Bangladesh. Upazila-level, groundwater use for irrigation for the year of 2006– 07 Boro rice season is shown in Figure S2c. Groundwater irrigation has been estimated using reported information on irrigated area and the number of irrigation pumps surveyed under the minor irrigation campaign by the Bangladesh Agricultural Development Corporation (BADC). Additional information on irrigation requirement for dry-season rice cultivation under various soil types and their infiltration capacity (Ravenscroft, 2003) has been used to estimate groundwater irrigation.

The social vulnerability is defined here by the lack of nutrition in children under the age of five. Four indicators measuring the level of under-nutrition in children: the number of underweight and severely underweight, and stunted and severely stunted children at the upazila level of Bangladesh (Figure S3) are used here to characterize social vulnerabilities. According to the 2012 Undernutrition Maps of Bangladesh, underweight and severely underweight children are those whose standardized weight-for-age is less than two and three standard deviations, respectively, below the median for the international reference following the WHO standard. Similarly, stunted and severely stunted children are those, whose standardized heightfor-age is less than two and three standard deviations, respectively, below the median for the international reference following the WHO standard.

# **2.5 Groundwater risk mapping at the national scale**

We construct two multi-hazard groundwater risk maps based on the following selection criteria applying on groundwater arsenic (As), salinity (i.e. electrical conductivity, EC) and dry-season depth to shallow groundwater levels (GWD) at the national scale in Bangladesh. Mapped areas of groundwater risks represent four zones with specific ranges of groundwater As and salinity concentrations that are considered risks to public health, and depth to dry-season groundwater levels below or greater than a certain threshold (4-8 m bgl) that is
|-644.83|-116.32|
||(0.63)|(3.22)|(3.36)|(3.76)|(3.81)|(16.83)|
|R<sup>2</sup>|0.04|0.06|0.16|0.09|0.17|0.19|
|Obs with factor weights|168,830,240|168,830,240|168,830,240|168,830,240|168,830,240|168,830,240|
|Obs in dataset|758,388|758,388|758,388|758,388|758,388|758,388|
|Sector FE|NO|NO|NO|YES|YES|YES*|

**Note** : The table presents coefficients from regressions of individuals’ earnings on business size categories and other characteristics. Regressions are estimated separately for each country. Reported coefficients correspond to a weighted average of individual country coefficients, where the weight for each country is the total number of country observations. Standard errors (in parentheses) are the square root from the weighted average of the squares of the standard errors. Regressions exclude workers still enrolled in education. Columns 4 and 5 include one-digit sector fixed effects for all countries. Column 6 includes sector-level fixed effects at the most disaggregate level available for each country. **Sources** : Authors’ calculations done using National Household Surveys for 2019 (2017 for Chile and 2018 for Mexico).

To quantify the importance of workforce allocation across different business sizes in overall earnings inequality, we decompose inequality into its extensive (i.e., allocation) and intensive margins. We do so comparatively for LATAM and for one of our reference economies, the US. The extensive margin captures the role played by the differential allocation of workers across business size categories,
Two other sets of comparisons between the rural and urban areas are worth looking at in order to better interpret the results presented in the following sections. First, Oportunidades participation rates are much lower in urban areas. Two factors explain this: (i) the percentage of eligible households is smaller, and (ii) the take-up rate among eligible households is smaller. On average, rural communities have between 30 percent and 70 percent of the households enrolled in the program, while the urban communities have at most 17 percent (Table 2).

Second, due to the geographic targeting of the program, localities incorporated later in time tend to be relatively better-off. Using the Housing and Population and Count data we analyzed a broad set of socioeconomic variables, including dwelling characteristics (floor and roof materials, access to basic services such as running water and electricity), asset ownership, literacy rates, school attendance, and access to health insurance. Within rural areas, both the mean and the median (not shown) of each characteristic improve in communities that were incorporated later. In contrast, urban communities that received the program earlier look similar to those incorporated later. This is consistent with the fact that the geographic targeting of the program was done only in rural areas.<sup>9</sup>

# **4. Identification strategy**

The identification strategy exploits the combination of three elements: (i) the evidence that nutritional interventions have only modest effects on child height after the first few years of life; (ii) the fact that Oportunidades was gradually expanded over the years; and (iii) the panel dimension of MxFLS. Based on the first two, program exposure is defined as a function of the age

> 9 Even though by looking at some socioeconomic indicators urban localities do not look, on average, better-off as they are incorporated later in time, the expansion was still not random. Therefore, we still think it is reasonable to keep working with the locality classification introduced earlier which uses
case of downward mobility than upward mobility. For example, moving from primary education to secondary education is associated with roughly a 5 percentage point increase in upward mobility but a 7 percentage point decrease in downward mobility for both the Arab Spring countries and the other countries. Figure 6 also shows that while migrants are more likely to be less upwardly mobile (and more downwardly mobile) in Arab Spring countries, the opposite holds for non-ArabSpring countries.

We draw non-parametric curves of the subjective well-being dynamics against individual income (in logarithmic form from the Gallup Poll surveys) in Figure 7. Notably, upward mobility remains rather flat for both the Arab Spring countries and other countries along the income distribution, but downward mobility shows a decreasing trend toward higher income levels. Downward mobility also slightly curves upward at the top income levels, suggesting that the most affluent experienced erosion in happiness levels to an extent similar in magnitude to those of the lower income groups.<sup>27</sup>

# **V. Concluding Remarks**

We provide systematic analysis of welfare dynamics in the Arab countries using both objective measures and subjective measures of well-being. The advantage of the latter is that they capture factors important to subjective well-being such as quality of life, expectations, and changes not yet reflected or not measured well with objective data. The use of alternative welfare measures is essential as the period of interest forms a watershed moment in the history of the Arab region, so objective measures may not reflect adequately the changes affecting people’s perceptions. Although the Gallup World Poll offers the most complete survey data on subjective well-being, the fact that these data were not available in most cases for the period before 2009 prevented us 27 Formal t-tests, however, indicate that the differences between the affluent and the poor are statistically significant. 23
effectively rules out the possibility that changes in macroeconomic expectations do not affect consumption because individuals are unable to follow through on their consumption plans.

# **7 Conclusion**

How do macroeconomic expectations affect individual consumption decisions? To explore this question, we conducted a field experiment with 2,872 credit card customers of a large commercial bank. We then created exogenous variation in macroeconomic expectations through an information-provision experiment in which participants were provided with expert forecasts of inflation and the exchange rate. We measure the effects of these information shocks on consumers’ subsequent macroeconomic expectations (based on survey data), their self-reported consumption plans (measured in survey data), and their _actual_ spending (measured in administrative data). We test several predictions from standard macroeconomic models, such as whether an increase in inflation expectations increases spending on durables. We find that information provision shifts beliefs but does not change consumers’ actual spending behavior.

Our preferred interpretation of the findings is that consumers fail to incorporate revised macroeconomic expectations into their decision-making process, thus not re-optimizing their consumption plans when new information becomes available. Several recent findings support this interpretation. First, related work has found that agents fail to optimize in much simpler economic environments (see Campbell et al., 2011; Beshears et al., 2018). Specific to the context of credit card spending, Ponce, Seira, and Zamarripa (2017) and Gathergood et al. (2019) find that consumers do not borrow using the lowest interest rate card nor prioritize payment of the card with the highest interest rate. Chetty et al. (2020) shows that credit card spending does not react to anticipated income shocks. Second, and in contrast with the competing hypothesis that information provision is not meaningful (for example, because individuals mistrust the source or because it is not salient), growing evidence in similar settings indicates that participants retain information provided in the context of an information treatment for months after the experiment (see Cavallo, Cruces, and Perez-Truglia, 2017; Bottan and
disbursement related to a previously signed investment agreement.<sup>6</sup> All IFC investment data are sourced from the iDesk internal database. Investment data for each country are denominated in US dollars and reported on a quarterly basis. This study examines the 20-year period from the first quarter of 2000 through the fourth quarter of 2019. The raw investment data have been reformatted from the fiscal year to the calendar year and deflated using the US Federal Reserve’s quarterly Consumer Price Index Deflator<sup>7</sup> to eliminate any potential price effects.

# **3.2 Countries Output Series**

Real quarterly GDP is used to model each recipient country’s economic output. The data series are sourced from the IMF’s International Financial Statistics (IFS) database and have been seasonally adjusted using the simple moving averages method.<sup>8</sup> A brief discussion of the theoretical basis for removing seasonality from a time series is presented in the Appendix, along with an empirical example from Brazil.

# **3.3 Extracting Cycles for Correlation Analysis**

This study uses two time series, GDP data and IFC investment data, to determine whether the latter is procyclical, countercyclical, or acyclical. To extract business cycles within the time series, the de-trending method is used to separate the trend from the original series. While many econometric methods can decompose a series into its trend and cycles, this study uses the Hodrick-Prescott (HP) filter.<sup>9</sup> Additional details on de-trending are presented in the Appendix. As a final step, the cyclical component (CC<sup>10</sup> ) of IFC investment variable and country-level de-seasonalized real GDP are analyzed using the Pearson’s correlation coefficient ( _r_ ).<sup>11</sup> For interpretation purposes, a positive _r_ value implies procyclicality, a negative value implies countercyclicality, and a value of zero implies acyclicality.

> 6 The IFC also