rafmacalaba/data-use-mentions-extended huggingface dataset:
comprehensive and targeted risk mitigation strategy. Finally, the government should pay special attention to rural areas. Doing so would contribute to a forward-looking poverty reduction strategy in El Salvador, potentially allowing for a more sustainable development trajectory.

**The paper is organized as follows.** Section 2 describes the country context. Section 3 details the definitions and summarizes the recent literature. Section 4 presents the methodology applied. Section 5 presents the empirical results, which include our estimates and their drivers, as well as temporal trends. Section 6 presents several robustness tests and a sensitivity analysis to investigate the validity of our main messages. Finally, section 7 concludes and presents policy insights.

# **2. Country Context**

**Studying vulnerability in El Salvador is highly relevant, given that the country is marked by a variety of different hazards.** The country is exposed to several hazards, which is reflected when comparing the country in its vulnerability globally. El Salvador scores 4.7 out of 10 in the INFORM Global Risk Index 2021 and ranks 58th (DRMKC, 2021). One important dimension of the country's vulnerability is its high emigration rate. In mid-2020, 1.6 million El Salvadorians lived abroad, representing one-fourth of the country's total population, one of the highest numbers worldwide (Migration Data Portal 2021). This significant emigration rate and a slowing demographic dividend pose challenges to the country's development trajectory. It also creates a high dependency on remittances (Robayo-Abril and Barroso, 2022). In general, El Salvador is highly dependent on the US economy, with one-third of its imports originating in the US (Trading Economics, 2022). Similarly, 40 percent of its exports flow to the US (ibid).

**Moreover, the country is vulnerable to the spread of epidemics, and crime rates are high.** The country's high population density makes it prone to epidemics and the spread of illness (Robayo-Abril and Barroso, 2022).
and the case of the _General Quadratic_ approximation. Under such an approximation, the shape of an income distribution is approximated by three parameters, _a_ , _b_ and _c_ (see the Appendix). Any change in the Gini coefficient can thus be associated with infinite combinations of changes in these three parameters; but these combinations have different impacts on the ultimate change in poverty, as they affect the shape of the income distribution differently. With any approximation that uses two or more parameters to approximate the shape of an income distribution, it is therefore not possible to conduct counterfactual simulations as in (2).

In running the simulations we are thus faced with two choices: either we run the simulation using the lognormal approximation – where we can simulate changes both in average per capita income and in the Gini coefficient – or we only simulate changes in average per capita income (keeping inequality constant at its initial level) using, however, a more accurate approximation. Neither approach is perfect, hence, the decision of which one to use ultimately boils down to minimizing approximation errors. For these purposes, in Figure 3 we compare for Latin America (where we have the actual household surveys) the actual changes in poverty against three simulation alternatives that all use the lognormal parameterization:<sup>3</sup> the first approach takes into consideration the actual changes in per capita income, but keeps income inequality at its initial level; the second approach takes into consideration actual changes in inequality, but keeps per capita income at its initial level; and, finally, the third approach takes into consideration both actual changes in per capita income and inequality.

> 3 The simulations use the first and second moments of the actual income distributions as the parameters for the lognormal approximation.

11
coincided with lifting of movement restrictions previously imposed on native populations.

# **3 Data and Empirical Strategy**

To investigate how adverse climatic conditions affect urban extent over the long term, we build a novel panel dataset that covers the entire globe between 1985 and 2014.<sup>1</sup> We rely on the PRIO-GRID v2.0 data structure (Tollefsen _et al._ , 2012) that divides the Earth’s surface into 0.5°cells (corresponding to about 55 _×_ 55 km per cell). These cells serve as our 1The time period under investigation was determined based on data availability at the time of writing.

6
There are two sets of coping strategies in the survey data. One set implies the general household coping strategies used in the past 14 days, and the other indicates the household actions in to the COVID-19 shocks. We use the specifically response first set of coping strategies in the main analysis and cross check the gender differences in COVID-19 specific household actions in the sensitivity analysis.

As shown in the summary statistics displayed in Table A.1 in the Appendix, overall, the most commonly used coping strategy in Kenya is to reduce food consumption. About 38 percent of the households reduced meals when hit by COVID-19. Apart from that, households also take loans and rely on family transfers to cope. Only about four percent of the households have received any help from governments or NGOs.

We first perform descriptive analysis by estimating the probability of using various coping strategies by male-headed and female-headed households in each survey wave. Afterwards, we use linear probability models pooling all survey waves to formally test the gender differences in coping strategies. Specifically, the model takes the following form.

Where on the right hand side, _Copehct_ is a dummy, indicating whether the household _h_ in county _c_ , interviewed in wave _t_ , has used the specific coping strategy in the past 14 days. On the right hand side, there is a constant _α_ , as well as a dummy, _Female Headht_ , indicating whether the head of household _h_ in wave _t_ is female (= 1) or male (= 0). Moreover, several household characteristics might determine the household coping strategies, including the level of wealth, local social norms and institution. In light of this, a set of household controls, **X** _ht_ , is included in the regression model. These include the household head age, household size, the child-adult ratio, a dummy indicating whether the household is located in urban or rural areas, and a dummy indicating the status whether the household’s has a dirt poverty by examining dwelling space floor or not. Lastly
and resulting in a minimum of 1000 battle-related fatalities over the course of a 12 months period (Sarkees and Wayman 2010). The CoW data collection distinguishes interstate and intra-state conflict, both of which are included as disaster events here. International or interstate conflicts are defined as wars between two or more territorial states that are members of the international state system. Intra-state (or civil) wars are defined by sustained military combat within a state and with active participation of the national government. In the CoW database, war begins with the onset of sustained military combat ́, irrespective of a formal declarations of war.

Measuring _natural disasters_ and the resulting damages and casualties over 200 years is challenging. To build a long-run dataset we draw on a large number of sources. First and most importantly, we draw on the EM-DAT International Disaster Database, which contains information on several thousand natural disasters around the globe since 1900, including geophysical (earthquakes and volcano eruptions), meteorological (extreme temperatures and storms), hydrological (floods and landslides), climatological (droughts, famines and wildfires) and biological disasters such as epidemics.

20
# 1. **Introduction**

The Southeast Asian turmoil of the late 1990s brought<sup>to the forefront the role finance</sup> can play in propagating and dampening macroeconomic<sup>fluctuations. In this paper, we</sup> investigate this role, providing empirical evidence<sup>on the link between the depth and</sup> structure of a country's financial sector and the severity<sup>of its business cycles. We have</sup> two important findings: First, countries with more<sup>developed financial sectors experi-</sup> ence less fluctuations in output, consumption, and<sup>investment growth. And second, the</sup> type of finance matters. Private sector finance is particularly important in reducing macroeconomic volatility. Specifically, we show that the<sup>relative supply of credit from</sup> banks is effective in reducing consumption and investment<sup>volatility, while a greater</sup> supply of credit to the private sector in general reduces<sup>consumption and per capita in-</sup> come volatility. Overall, these results suggest that the risk<sup>management and information</sup> processing provided by banks may be particularly important<sup>in reducing consumption</sup> and investment volatility, while the simple availability<sup>of credit to the private sector</sup> helps to smooth consumption and GDP.

We find evidence for these conclusions in two different cross-country<sup>data sets.</sup> Our main data set is a panel of 70 countries spanning the<sup>years 1956 to 1998, which</sup> we divide into four time periods. We observe the<sup>four different measures of financial</sup> development employed by King and Levine (1993a, 1993b) at<sup>the beginning of each time</sup> period and then examine their effects on the
**Figure 2:** Average temperature and precipitation anomalies (2004-2019)

<!-- Start of picture text --> (a) Positive temperature anomaly (heat shock) (b) Negative temperature anomaly (cold shock)<br>2 2<br>1.5 1.5<br>1 1<br>.5 .5<br>0 0<br>2005 2007 2009 2011 2013 2015 2017 2019 2005 2007 2009 2011 2013 2015 2017 2019<br>Year Year<br>(c) Positive precipitation anomaly (flood shock) (d) Negative precipitation anomaly (drought shock)<br>2 2<br>1.5 1.5<br>1 1<br>.5 .5<br>0 0<br>2005 2007 2009 2011 2013 2015 2017 2019 2005 2007 2009 2011 2013 2015 2017 2019<br>Year Year<br><!-- End of picture text --> _Note:_ Average temperature and precipitation anomalies in survey sample, using individual weights. The time period refers to a time bin of 90 days before a household’s date of interview in the EPH survey.

reasons. First, raw data from the CHIRPS and ERA5 data bases are more granular than the SPEI that is downloadable for instance from Google Earth engine. As the district is the lowest level of geographical aggregation in the EPH data, we want to ensure maximum variation of our weather shock variables at the district level, and as districts in (southern) Paraguay are rather small, we opt for the more granular weather data. Furthermore, several studies have shown high and significant correlation between the SPI and SPEI, so our estimates should be effective in capturing drought conditions (Danandeh Mehr, Sorman, Kahya, & Hesami Afshar, 2020; Salimi, Asadi, & Darbandi, 2021; Torelló-Sentelles & Franzke, 2022).

Table 2 displays descriptive statistics for relevant temperature and precipitation variables. Temperature and
# Table 7

# **Regressions of Monthly Earnings (1n) Full** time. Male. Salaried Workers. **Coopleters of Vocational Schools -** Israeli Census. 1983

# n-9798

||Direct Ma|tchings|Wider Ma|tchings|
|---|---|---|---|---|
|Independent|||||
|Variable|Co-efficient|t-statistic|Co-efficient|t-statistic|
|YRS.SCH|0.024|1.51|0.022|1.38|
|EXP|0.025|2.13|0.025|2.06|
|B p2|-0.0007|5.07|-0.0007|4.98|
|EXP*YRS.SCH|0.002|2.37|0.002|2.36|
|Certification:|||||
|P.CERT|0.011|0.44|0.013|0.38|
|S.CERT|0.073|3.10|0.067|2.84|
|BAG|0.112|3.46|0.103|3.23|
|WEEKS (in)|0.302|13.16|0.298|13.05|
|HOURS (in)|0.374|9.66|0.367|0.53|
|ETHNIC|-0.137|11.84|-0.134|11.66|
|Economic Sector:|||||
|IND|0.101|3.08|0.095|2.90|
|ELECT|0.284|6.48|0.285|6.54|
|COMM|0.11|0.30|0.008|0.23|
|FIN|0.133|3.31
0.028]<br>13.39<br>5.72<br>184,888<br>23,188<br>an 500 Organized Violence <br>-0.436<br>-0.394<br>(0.184)<br>(0.268)<br>[0.018]<br>[0.142]<br>7.94<br>4.41<br>141,172<br>17,942<br> Regions of Highest Famine<br>-0.341<br>-0.442<br>(0.139)<br>(0.201)<br>[0.014]<br>[0.027]|11.60<br>5.83<br>164,252<br>18,739<br>om the Ethiopian census <br>TI = mother tongue ins<br> level of schooling instru<br>easures _I_MTI-T<br>_zy_<br>and _I_MT<br>_zy_<br>ns in which two intervent<br> found in Section D.2 of t<br>ecifc linear trends, and a<br>al four columns. Each esti<br>own in square brackets.|
|Non-MTI<br>Regions Only<br>Efect of FPE|Census<br>DHS<br>+ DHS<br>Only<br>(1)<br>(2)<br>K. Only <br>-0.425<br>-0.499<br>(0.087)<br>(0.157)<br>[0.000]<br>[0.001]|15.28<br>6.44<br>78,149<br>13,638<br>L
.3|0.8|1.5|0.2|2.9|
|Average monthly subsidy received<br>(USD)|7.2|5.7|5.8|1.8|14.4|
|Bill as percentage of<br>household income|0.3%|0.3%|0.7%|0.1%|1.0%|
|Subsidy as percentage of<br>household income|1.8%|2.0%|3.8%|0.6%|6.1%|
|* Household survey data matched to utili|ty billing r|ecords for bo|th water an|d electricity.||

# **_Estimates of the distribution of electricity and water subsidies received by households with both_**

# **_private electricity and private water connections_**

Table 9 shows the distribution of electricity and water subsidies received by the 366 households with private electricity and water connections as a function of subsample wealth quintiles. There is substantial variation in household income across the five wealth quintiles. The monthly household income in the highest wealth quintile ($825) was 4 times the monthly income of households in the lowest quintile ($206).

20
# Food deprivation

National averages of severely food deprived children have remained constant in urban and in rural areas over 2009 through 2017. A temporary drop in food deprivation estimates based on the GHS wave 2 data may be a result of data quality concerns. While the data food deprivation throughout all survey waves of GHS and MICS suffers from high proportions of missing information, the consistency of the GHS wave 3 and 4 and MICS estimates give the results little more credibility. Food deprivation is not as widely dispersed between urban and rural as it is between northern and southern regions. Despite widespread subsistence farming in rural areas across regions, which might suggest that rural children have better access to food than urban children, an on average 10 percentage points higher share of rural compared to urban children is food deprived.

Figure 16: National Level Severe Food Deprivation <!-- Start of picture text --> AGGREGATE FOOD DEPRIVATION -GHS AND MICS<br>child child north child south child rural child urban<br>W A VE 1 W A VE 2 W A VE 3 MIC S 2016/2017 W A VE 4<br>23% 28% 17% 26% 15% 11% 13% 9% 13% 8% 23% 30% 11% 26% 16% 24% 30% 8% 28% 16% 22% 30% 8% 24% 16%<br><!-- End of picture text --> Source: Authors’ estimates based on GHS Wave 1-4 and MICS 2016/17 Note: Individuals with missing age were dropped from the analysis. Total refers to all individuals with known age.

In line with the findings of rural children facing higher rates of severe food deprivation, children in the largely rural north are found to be more food deprived than the south. This is consistent across regions as per GHS and MICS data sets.

30
how much, what and to whom you have trade linkages with. Table 1 shows the (median) amount of trade conducted by Sub-Saharan Africa with other world regions (inter-regional trade) and the amount of trade undertaken within the region (intra-regional trade). The data are gathered from the World Bank’s World Development Indicators and COMTRADE.

The (median) value of inter-regional across Sub-Saharan African countries increased over the past two decades from 31.9 percent of GDP in 1990-94 to 34.8 percent in 2010-14, while intra-regional trade grew from 20.04 percent of GDP in 1990-94 to 23.75 percent of GDP in 2010-14. Inter-regional trade grew at a faster pace among resource rich countries from 1990-94 to 2010-14. Inter-regional trade increased by about 8 percentage points of GDP (from 40.4 percent of GDP in 1990-94 to 48.6 percent of GDP in 2010-14) while intra-regional trade grew by nearly 3 percentage points of GDP (from 19.4 percent of GDP in 1990-94 to 21.98 percent of GDP in 2010-14). Finally, inter-regional trade for non-resource rich countries in the region grew by approximately 5 percentage points of GDP (from 26.7 percent of GDP 10
- 26 -

Indeed, the most significant change in the external position was of financial rather than of a *real" nature (meaning the trade account and related measures). This was the elimination of the constant injection of liquidity through the large fiscal deficits which threatened to lead to recurrent financial crises, especially in the foreign exchange markets.

If the changes in the current account were not fundamental, does it mean that the present state of affairs is not sustainable? Not necessarily. First, as we noted earlier, it is not quite clear that the state of the balance of payments in the pre-stabilization era was not sustainable. Secondly, even if a fundamental improvement was indeed required it may still take place in the future with the stimulus of growth provided by a stabilized economy and by the benefits of reforms in the tax structure and in the capital markets.

# III. Evolution of Policies in the Course of Stabilization 14/

# 1. Fiscal Policy

Since the beginning of the stabilization program till the end of 1987 the government maintained consistently a virtually balance budget with domestic deficit running at about 1Z of GNP. The persistent behavior was maintained both with respect to direct domestic public sector demands and net taxes. However since the last quarter of 1987 through the second quarter of 1988 there are indications that the public sector deficit is increasing. This is based on estimates of the central government's domestic deficit, calculated on a cash basis from the Bank of Israel accounts. According to these estimates the domestic deficit in the last three quarters (including real interest payment on the public debt) was around 4Z of GNP. While this
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(1977) and Jasso and Rosenzweig (1986, 1988) are among the first papers to the study migrants’ occupational placement. In a widely cited paper, Jasso and Rosenzweig (1995) identify the differences in occupational placement of migrants entering the United States through family reunification versus skill based visa programs. Powers and Seltzer (1998) and Powers et al. (1998) provide gender-specific evidence of improvement in initially undocumented immigrants’ occupational status on the US market. Green (1999) employs 1981, 1986 and 1991 censuses to show that after a period of adjustment, Canadian immigrants are more likely to be found in professional and more skilled manufacturing occupations compared to natives, but this pattern declines across successive cohorts. Using 2000 US Census data, Mattoo et al. (2008)

> 6 Chiswick, 1978, Borjas, 1995, 2000, Duleep and Regets, 1997, Schoeni, 1997 7

> Funkhouser and Trejo (1995), Lalonde and Topel (1990), and Baker and Benjamin (1994) for Canada

5
MICs<br>30<br>Today's LICs<br>LIC population<br>15<br>0<br>2001 Latest<br><!-- End of picture text --> Source: United Nations, World Bank.

A. Latest reflects 2015 data. Due to data limitations, poverty share for “LICs turned MICs” includes 28 of 35 countries and reflects 94 percent of the sample’s total population number in 2015; “Continued LICs” includes 24 of 29 countries and reflects 84 percent of the sample’s total population number.

B. Latest reflects 2015 data. Due to data limitations, poverty share for “LICs turned MICs” includes 28 of 35 countries and reflects 94 percent of the sample population in 2015; “Today’s LICs” includes 31 of 31 countries.

Today’s LICs face severe challenges that threaten to hold back convergence of their per capita incomes with those in MICs. Today’s LICs also face significantly larger spending needs than nonLIC EMDEs to meet the Sustainable Development Goals by the end of next decade, underscoring the need for sustained and robust growth (Gaspar et al. 2019).

Against this backdrop, this paper examines three specific questions. First, what has driven and supported growth in LICs since 2001? Second, how have these factors affected LIC progression to MIC status? Third, what are the prospects for future progression among today’s LICs? The following findings are presented. First, growth in low-income countries—and, especially those that have progressed to middle-income status—has benefited from a confluence of favorable developments since 2001. Second, prospects for further progress by today’s LICs toward middleincome status are challenging. Compared to the LICs of 2001 that became MICs, today’s LICs 3
LFS, can generate policy relevant insights within about 12-months in a way that is cost effective,<sup>26</sup> scalable, and consistent with the IAEG-SDG mandate of building local capacity for sustainability.<sup>27</sup> Data from the module point towards high levels of tenure insecurity, demand for title, and limits on transferability of land. This suggests that for residential state land, award of (full) title is a viable strategy. At the same time they caution that, without regulatory and procedural reform, responding to such demand in a systematic way may be difficult, desired impacts may not materialize, affordability and sustainability may be limited, and negative gender impacts cannot be ruled out. Our data also show that titling is not a silver bullet to secure tenure in Zambia: 45% of households without title overall and 40% (23%) of those without title who perceive their land to be (very) insecure do not want to acquire title while 29% (20%) of those who have title feel (very) insecure. Unless other avenues to secure land tenure, including recognition of customary boundaries -possibly in return for greater accountability on land management- are available, transparent and effective use of the country’s rural land resources may be difficult to achieve.

To the extent that private owners’ willingness to pay can provide a lower bound estimate for the economic benefits from land title, survey data on demand and willingness to pay for title can help identify villages or regions where such an intervention may be justified and economically viable in principle. Despite high demand, current fees for title issuance are unaffordable and several times above stated willingness to pay. Regulatory reform to streamline processes and reduce fees is thus essential if such an intervention is to be affordable and sustainable and greater interoperability can greatly enhance long-term benefits. At the same time, with LFS data showing that the current titling process reinforces pre-existing gender bias rather than alleviating it,<sup>28<