rafmacalaba/data-use-mentions-extended huggingface dataset:
(Bossu and Rossi 2019). Central banks' reserve management operations have always been part and parcel of their monetary policy function. Initially, most central banks had currencies backed by gold, and their reserves primarily consisted of gold bullion. With the abolition of the gold standard in 1971, central banks started diversifying their reserves into foreign asset pools.

Holding foreign reserves has various purposes. Most notable are self-insurance for balance-ofpayments crises and support of exchange rate policies. In some countries, reserves also support financial stability by providing lender-of-last-resort functions in foreign currency. As a result, reserves protect the country against disruptions and volatility in international capital markets. Over the past two decades, successive financial crises have solidified and validated this approach. Recent research indicates that emerging market central banks holding relatively higher foreign reserves experienced less currency depreciation, smoother credit growth, and more stable credit ratings. Countries with higher levels of reserves also had better access to external funding during the global financial crisis of 2008–09 (Arslan and Cantú 2019). Furthermore, empirical research identifies a relationship between political instability and weak institutions in emerging markets,

> 3. For a compendium of such legislation globally, see International Monetary Fund, Central Bank Legislation Database, https://cbld.imf.org/#/. Ashraf Khan’s 2017 IMF Working Paper (Khan 2017) provides an analysis of the data.

> 4. See IMF (2020) for a global accounting of the monetary and fiscal policy actions taken by governments worldwide.

3
We utilize this administrative database for two years, 2017 with 7,442 observations and for 2018 with 8,146.

Because income information is not available in the survey, we compare the administrative data, specifically the taxable portion, with the consumption estimation. This allows us to obtain a more comprehensive understanding of the income distribution. In some cases, we complement missing survey information with administrative data. Because this data set is disaggregated by gender and occupational category, both with values for the taxable income, the administrative data prove useful. Additionally, the administrative data enable us to compare the income distribution to ensure data consistency and improve the exercise's macroeconomic validation. The result is an accurate set of information that makes constructing the taxable income distribution possible.

# **4. Methodology**

This paper uses the Commitment to Equity (CEQ) methodology to assess the distributional impact of the fiscal system in Grenada. According to Lustig (2018), “The CEQ Assessment is a diagnostic tool that uses fiscal incidence analysis to determine the extent to which fiscal policy reduces inequality and poverty in a particular country” (62). Fiscal redistribution effects refer to the process by which a state collects revenue from individuals and households (through taxation) and allocates this revenue to finance direct transfers, subsidies, and in-kind benefits individuals and households enjoy (Lustig and Higgins 2018). Because this study focuses on the distributional effects on the population, state interventions that affect firms or other privatesector institutions are excluded from the methodology. The CEQ framework aims to answer four main questions: (1) How much income redistribution and poverty reduction is being accomplished through fiscal policy? (2) How equalizing and pro-poor are specific taxes and government spending? (3) How effective are taxes and government spending in reducing inequality and poverty? and (4) What is the impact of fiscal reforms that change a particular tax or benefit's size and/or progressivity? (Lustig 2018).

To calculate the distributional effects of fiscal
# _3.1.3 Are the Bans Effective in Normalizing Sex Ratios at Birth?_

It is difficult to evaluate the effectiveness of bans in normalizing SRBs since the bans are not randomly assigned and enforced. The non-randomness in the assignment of the “treatment” (the ban in this case) makes the construction of the counterfactual scenario of what might have happened in the absence of the ban difficult. The country-specific studies below outline some of the attempts at estimating this treatment effect (for an overview, see Table 2).

**Table 2.   Summary of studies that examine the impact of a ban on prenatal sex selection on SRB**

|**Author (Date)**|**Ban (details)**|**Data**|**Type of study**|**Observed impact**<br>**on SRB**|
|---|---|---|---|---|
|Nandi and<br>Deolalikar<br>(2013)|Prenatal Diagnostic<br>Techniques Act, India,<br>1994|Village and town<br>level Indian<br>census data, 1991<br>and 2001|Quasi-<br>experimental<br>(difference-in-<br>difference)|Less imbalanced<br>child sex ratio, but<br>can’t discern impact<br>on SRB|
|Park and Cho<br>(1995)|Ban against prenatal sex<br>selection, Korea, 1987<br>(suspension of medical<br>licenses of miscreant<br>doctors in 1990)|Vital statistics<br>and World<br>Fertility Survey|Correlation-<br>based/Trend<br>analysis|Drop in SRB in<br>1991, but this was<br>more likely due to<br>parents’ adherence to<br
Revealingly, in Betts et al.’s (2013) survey of refugees in Uganda, 96% of all interviewed households in the capital and 70% outside the capital said they owned and used a mobile phone. They use this mobile phone to communicate with customers and suppliers, to get market information and to transfer money. Half of the urban refugees and 11% of rural refugees also have access to the Internet.

# **4. Refugees As a Burden?**

As pointed in Section 2, most refugees in SSA are hosted in neighboring countries. Most of these hosting countries are likely among the least developed countries. It has been argued that these refugees may constitute an additional burden in terms of economic development in hosting countries (Mabiso et al. 2014). UNHCR (2014: 17) implicitly recognizes that potential burden by suggesting that the ratio of the size of the country’s hosted refugee population to its average income level can provide a proxy measure of the burden of hosting refugees. SSA counts 13 out of the 20 countries hosting the larger number of refugees per 1 USD GDP (PPP) in the world.<sup>4</sup> While such figures stress that SSA hosts a fair share of the refugees in the world and underline that refugee flows are mainly a South‐South phenomenon, we shed doubt on this view of refugees described as a burden. We even argue in Section 6 that such representation is not conducive to the right policy framework in refugee‐hosting areas.

> 4 The other major host countries per USD GDP are all developing countries, with Pakistan (1st ), Jordan (8th ), Bangladesh (9<sup>th</sup> ), Yemen (10<sup>th</sup> ), Iran (14<sup>th</sup> ), Lebanon (17<sup>th</sup> ), and India (20<sup>th</sup> ).
be predictive of actual future migration decisions – something we test in section 6 using data from a follow-up survey.

# **4. Testing Classic Theories of Income Maximization and Migrant Self-Selection**

We begin by examining how classic migration theories predict migration behavior in the two destination case. For participant _i_ with randomly assigned skill level _si,_ unobserved skill at destination 1 of _e1i_ , and unobserved skill at destination 2 of _e2i,_ the wages available at the two destinations were specified as: (5) 1ii ii 1ii ww = 500 + 300ss + 3ee (6) 2ii ii 2ii As noted, we consider destination 1 as the home destination. There is thus no cost of moving to ww = 1000 + 100ss + ee this destination and the participant always has full information about this destination and no risk of unemployment. Destination 2 has a higher base wage rate, but a lower return to both observed

> 4 Recall that order bias is not a concern here, since participants were randomly assigned to be asked “home or abroad” versus “abroad or home”.

10
if a student dropped out of any school in our study in the 2019 academic year. _Math grades_ is a dummy that equals 1 if the student’s behavior report is above the school median. All outcomes were obtained from administrative data sources (namely, teachers’ reports). Sample size in each specification varies according to the number of observations available for each outcome. Estimations include all individual controls for which there is an imbalance at baseline. All regressions include randomization-block (strata) fixed effects. Strata are defined as country and violence level (high or low) of the community in which the school is located. Wild bootstrap standard errors are shown in parentheses, and adjusted _p_ -values are in brackets. _∗∗∗ ∗∗ ∗ p <_ 0 _._ 01 _, p <_ 0 _._ 05 _, p <_ 0 _._ 1 48
14

# **Figure 3**

## **Growth Rates of GDP per Capita and Household Consumption per Capita in Tanzania, National Accounts Data**

<!-- Start of picture text --> 5.0%<br>4.0%<br>3.0%<br>2.0%<br>GDP per Capita<br>1.0% Hhold Cons. per Capita<br>(National Account Data)<br>0.0%<br>-1.0%<br>-2.0%<br>-3.0%<br>1988 1990 1992 1994 1996 1998 2000 2002 2004<br>Year<br>Growth Rate<br><!-- End of picture text -->

# **4.4 Sources of Error in the Simulation Analysis**

Several sources of error enter the simulation analysis. First, there is the usual sampling error associated with the survey data. Second, there is uncertainty associated with the national accounts growth estimates. Third, there is drift between the national accounts GDP levels and household consumption levels, i.e. the growth rates of the two may not be equal. Fourth, the true year-by-year changes in the distribution of consumption differ from the changes assumed in the simulations. Fifth, the uniform application of national population growth rates to the rural and urban sectors appears inconsistent and sector specific growth rates might be preferable. Of all of these, only sampling error is readily quantifiable, and the associated standard error terms are calculated for all simulated poverty rates. It is important to recognize, however, that these standard errors are only an extreme lower bound for the true error. The fifth source of error, finally, is considered in Section 5.4.

# **5 Simulation Analysis**

The simulation analysis is conducted under a variety of scenarios, with varying assumptions, first using the Datt and Walker approach and
roles vacated by the men. Overall, male migration increases both household and community responsibilities for women.

Many South Asian countries are either experiencing conflict or are in post-conflict situations, calling for special focus on how conflict affects women’s workforce participation. Conflict can either cause women to seek employment or discourage them from working: that is, women may enter the labor force to augment household income—the “added-worker “effect—or they may withdraw from the labor force because of the unsafe environment. Whether the added-worker or the security effect dominates is unclear based on the literature. Kuhnt and Pasha (2017) found that conflict imparts a short-lived negative impact on women’s employment in Pakistan based on temporary fear created by violence. Berrebi and Ostwald (2014), using a global panel dataset of 165 countries and terrorism data from 1980 to 2007, found that terrorist attacks decreased female labor force participation. In contrast, Menon and Rodgers (2011) found evidence of increased likelihood of women’s employment associated with civil conflict in Nepal.

# 2. **Data and Methodological Challenges**

6
# **Appendix C: Data Sources and Descriptions**

## Notes:

1. Lower numbers in all “state” variables are associated with smaller government.

2. Lower numbers in “institutions” correspond the effective monitoring capacity.

3. Lower numbers in corruption index correspond to less corruption.

|**Corruption**<br>|||
|---|---|---|
|Corruption|1998 Corruption Perceptions Index of Transparency International. The Index includes data<br>from the Economist Intelligence Unit, Gallup International, the Institute for Management<br>Development, the Political and Economic Risk Consultancy, the Political Risk Services,<br>World Development Report, and the World Economic Forum. Some of these sources are<br>based on poll of experts, some others on investor and public surveys. The Index combines<br>assessments from the past three years to reduce abrupt variations in scoring. None of these<br>sources differentiate between special forms of corruption, such as political or administrative.<br>So, they aim to measure the same phenomenon. All indices are strongly correlated with each<br>other. The reliability is further improved by including only countries that have been included<br>into three polls at minimum. Simple average method is used to aggregate the data.|Transparency International|
|**Size of Governmen**<br>|**t**<br>||
|Size of Government|Ratio of nominal public domestic investment (fixed capital formation) to nominal GDP.|World Development Report 1997 and<br>OECD|
|Tax System|||
|Burden of Tax<br>Regulations|“Tax Regulations and/or high taxes are major problem areas”|World Bank: World Development<br>Report 1997<br>Q-12G|

31
LAC, data from Paraguay Central Bank show that banks allocate around 30 percent of their total credit portfolios to the agricultural sector. This allocation is larger than the exposure to all transition-sensitive sectors in the rest of our sample of countries. Banking systems less exposed to transition risks are in El Salvador, Costa Rica, Colombia, and Chile.

LAC firms operating in transition-sensitive sectors already present signs of stress, especially those in the fossil fuel and agricultural industries. In the event of materialization of transition risks, the effects on the banking sector may be greater if ex-ante firms are financially stressed.<sup>38</sup> Available firm-level data show that 26 percent of the LAC firms that operate in transition-sensitive sectors had annual interest payments exceeding profits as of 2019 (Figure 9). This share is six percentage points higher than firms operating in non-transition-sensitive sectors. In terms of individual sectors, fossil fuels have the higher share of firms with an ICR below or equal to one, at 32 percent, and heavy industry the lowest at 23 percent.<sup>39</sup> When we look at the share of outstanding debt that financially distressed firms hold, firms' higher vulnerability in transition-sensitive sectors is more pronounced at 38 percent, which is 12 percentage points higher than firms in

> 36 Costa Rica commits to an absolute maximum of net emissions by 2030 of 9.11 million tons of carbon dioxide equivalent (CO2e) including all emissions and all sectors covered by the corresponding National Emissions Inventory. 37 We consider emissions from carbon dioxide, methane, nitrous oxide, and F-gases measured in tonnes of carbon dioxide equivalents (CO2e). Emissions other than carbon dioxide (CO2) are particularly relevant for LAC. CO2 emissions represent 75 percent of GHG at the global level, a figure 20 percentage points lower for LAC (Data obtained from Climate Watch).

> 38 See Grippa and Mann (2020) for an exercise that uses firm-level information to conduct transition risk stress testing.

> 39 Compounding our findings, Ramírez
<!-- Start of picture text -->
* Percent of governing body that belongs to: government @<br>* Percent of governing body that belongs to: private sector @<br>Update / review admin data more than once per year @<br>General or specific knowledge test is an admission requirement @<br>Interview is an admission requirement r )<br>* Percent of faculty with BA degree<br>* Percent of faculty with graduate degree<br>Evaluate faculty more than once per year<br>Industry helps with student evaluation or curriculum design @<br>Collect data on employment or employers' satisfaction more than once per year @<br>HEI trains students for job interviews @<br>HEI provides job market information @<br>HEI has received funding from government @<br>Loans by banks are used by some students @<br>Collect student satisfaction data more than once per year @<br>Teaches numerical competencies e<br>Analyze student performance to solve problems more than once per year @<br>Test required for graduation @<br>>30% classes can be taken online<br>-10 -5 0 5 10<br>Percentage points<br>Infraestructure @ Training and curriculum @ Costs<br>@ Link with productive sector Faculty @ Other Practices<br><!-- End of picture text -->

<!-- Start of picture text --> Interview is an admission requirement s<br>Update / review admin data more than once per year &<br>Class planning is very important in faculty evaluation<br>Students and peers’ informal comments are very important in faculty evaluation<br>* Percent of faculty that are women<br>* Percent of faculty with graduate degree<br>Classroom observation is very important in faculty evaluation<br>Peer evaluation is very important in the faculty evaluation<br>Evaluate faculty
Policy Research Working Paper 10004

# **Abstract**

Ethiopia is currently embroiled in a large-scale civil war that has continued for more than a year. Using unique High-Frequency Phone Survey data, which spans several months before and after the outbreak of the war, this paper provides fresh evidence on the ex durante impacts of the conflict on the food security and livelihood activities of affected households. The analysis uses difference-in-differences estimation to compare trends in the outcomes of interest across affected and unaffected regions (households) and before and after the outbreak of the civil war. The findings show that seven months into the conflict, the outbreak of the civil war increased the probability of moderate to severe food insecurity by 38 percentage points. Using the Armed Conflict Location and Event Data on households’ exposure to violent conflict, the analysis shows that exposure to one additional battle leads to a 1 percentage point increase in the probability of moderate to severe food insecurity. The conflict has reduced households’ access to food through supply chain disruptions while also curtailing non-farm livelihood activities. Non-farm and wage-related activities have been the most affected by the conflict, while farming activities have been relatively more resilient. Similarly, economic activities in urban areas have been much more affected than those in rural areas. These substantial impact estimates, which are likely to be underestimates of the true average effects on the population, constitute novel evidence of the near-real-time impacts of an ongoing civil conflict, providing direct evidence of how violent conflict disrupts the functioning of market supply chains and livelihoods activities. The paper highlights the potential of phone surveys to monitor active and large-scale conflicts, especially in contexts where conventional data sources are not immediately available.

This paper is a product of the Development Research Group, Development Economics. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors
This paper provides an in‐depth case study of floods, poverty, and climate change in Vietnam and Ho Chi Minh City, examining the exposure of the total population, and poor people in particular to current and future flood risk. It makes two contributions; the first is that it combines state‐of‐the‐art hazard maps with socioeconomic data to examine distributional impacts of floods at the national‐level in Vietnam. Most previous analyses of floods and climate change in Vietnam at the national‐level have focused on hazard mapping and not its distributional impacts (Institute of Strategy and Policy on Natural Resources and Environment 2009; Ministry of Natural Resources and Environment 2009). The second contribution is the paper’s analysis of flood exposure and poverty at national and local levels: most previous analyses have focused on one or the other (Winsemius et al. 2015; World Bank and Australian AID 2014).

# **3. Data**

To examine population and poverty‐specific exposure to floods, we employ spatial data defining flood hazard and a number of socioeconomic characteristics representing poverty and population density.

# **3.1. Flood hazard data**

# **3.1.1. Flood hazard maps for Vietnam developed for this study**

For this study, we developed flood hazard maps representing riverine, flash‐flood and coastal flood risk for Vietnam. These flood hazard maps estimate the inundation depth at a grid cell level of 3 arc‐seconds, (~ 90m) and provide coastal surge hazard layers, along with pluvial and fluvial layers. The maps provide information on the extent and depth of flood hazard for a specific location. For the coastal component, we explicitly model four return periods – 25, 50, 100, and 200 year events, under current and future climate conditions.

There is a significant amount of uncertainty with regards to how much sea level will rise. For that reason we model three future climate scenarios per return period: a low, medium
women of different wealth levels is driven by financial constraints or willingness to pay, rather than by knowledge of which facilities are better.

Table 5 investigates the extent to which wealth gradients exist within facilities. Within‐facility gradients could emerge due to discrimination if providers exert less effort when treating women with lower educational or socioeconomic status than theirs. Alternatively, within‐facility gradients could be a result of the separate fees some facilities charge for consultations, consumables, laboratory tests, and drugs. Poorer women might choose a more limited package of care, or providers might not offer services for which they believe poorer women would not be able to pay.

Table 5, columns 1 and 2, restrict the sample to women who reported attending the health center serving their catchment area and include catchment area dummies in the regressions. As the content of care reported in the household data is from the entire sequence of consultations during the women’s pregnancies, and wealthier women attend more consultations, we control for the number of consultations women reported attending. We observe a statistically significant within‐facility relationship between wealth and content of antenatal care. A standard deviation increase in wealth is associated with a 1.6 percentage point increase in the proportion of actions taken. When controlling for women’s characteristics, the coefficient only slightly changes to 1.4 percentage points (p‐value < 0.01).

Table C.5, in appendix C, presents the results of similar regressions with individual components of care as the dependent variables. We find a statistically significant association at least at the 90 percent level for six of the 13 elements of care reported by the women in the household survey. These elements include services that could require additional fees, such as HIV testing, iron supplementation, and antimalarial drugs. We also find a statistically significant relationship between wealth and receiving advice on nutrition during the pregnancy, having the due date estimated, and measurement of uterine height.

Table 5, columns 3 and 4, explores within‐facility