rafmacalaba/datause-encoder-data huggingface dataset:
# **High damage storms increase migration**

Unlike average storms, storms in the top 10% of the damages distribution do lead to increased out-migration in the year of the storm. We use data from FEMA on total compensation paid to disaster victims to isolate the most damaging storms – those that result in at least 10 million dollars in compensation. These storms increase out-migration in the year of the storm by 6.16% ( _t_ -stat: 1.69 ) relative to years that do not experience a storm (Table SI-6). The impact on migration increases monotonically as the damage threshold increases. The most damaging storms, representing fewer than 1% of storm-county years in our data, have the largest impacts on out-migration. These effects persist in the years following a storm. Our lags model indicates that the year after a storm also sees elevated out-migration, but this impact begins to decline by year three (Figure SI-2 & Table SI-7).

The migration that occurs after high damage storms does not reduce the risk that migrants face from future storms. Migrants leaving counties impacted by high damage storms move to counties that are at no lower risk than the counties they leave (Table SI-8). Over our sample period, counties that receive migrants from counties experiencing a high damage storm experience 7.72 storms on average while sending counties experience 7.65 storms (difference _t_ -stat: -0.62 ).

Return migration in the years following high damage storms appears to offset much of the out-migration that occurs in the year of the storm for all but the most damaging storms. We restrict our analysis to flows between counties experiencing a high damage storm and counties that receive migrants in the same year. We compare changes in net migration flows between these counties in the year of the storm and subsequent five years to years in which there is no storm in the sending county. On average, net migration (migration into high damage storm impacted county minus migration out of it) is highly negative in the storm year, consistent with our previous results. However, for storms that cause fewer than $80 million in compensated damages, this is offset by what appears to be substantial return migration in the following year (Table SI-9). The total change in net migration over five years is not statistically different from zero. For storms causing over $80 million in compensated damages, return migration does not appear to offset the initial out-migration, but we note that these storms are very rare in our data – fewer than 1% of storm-county years – and estimates of their effects are dominated by the effects of Hurricane Katrina (Figure SI-4).

# **Population-level exposure is growing**

We find that individuals do not migrate county-to-county after experiencing a hurricane, unless the hurricane was particularly damaging. Even in the cases of highly damaging hurricanes, it 8
-2-

That crisis, which is now in its seventh year, has left its mark on all problem debtors and has acquired geopolitical dimensions. Since 1982 virtually all HICs have experienced low GDP growth, declining investment ratios and falling living standards that have rolled back much of the progress of previous decades. Per capita income in most HICs is lower today than in 1980, and absolute poverty and malnutrition are again reaching alarming levels. Even in 1988, when the HICs' real CDP growth reached 2 percent-compared with an average of 0.8 percent from 1980 to 1986--per capita income continued to decline. These circumstances explain why adjustment and economic reform have generally been fitful and uneven, and fraught with domestic social and political tensions.

Table 1: Highly Indebted Countries and the World Economy, 1980-88

||1980|1981|1982|1983<br>Perce|1984<br>ntage<br>rea|1985<br>l chan|1986<br>ge|1987|1988 c/|
|---|---|---|---|---|---|---|---|---|---|
|Economic_rcowth_<br>Indicators||||||||||
|Industrial<br>Country<br>Output|1.3|<br>2.0|-0.4|<br>2.8|<br>4.5|<br>3.1|<br>2.7|<br>3.3|<br>3,9|
|World<br>Trade<br>a/|1.3|<br>2.4|-1.0|<br>3.0|<br>9.9|<br>4.0|<br>2.6|<br>4.3|<br>7.5|
|HIC GOP b/|5.6|<br>0.6|-0.4|-2.9|<br>1.9|<br>3.7|<br>3.4|<br>1.7|<br>2.0|
|HIC Investment<br>b/|9.4|<br>0.4|-13.1|-21.0|-2.1|<br>4.5|<br>1.9|<br>0.8|-2.9|
|HIC Per Capita<br>Consumption<br>b/|3.4|<br>0.3|-2.2|-4.1|-1.7|0.2|<br>2.6|-1.4|-0.6|
|HIC Exports<br>b/|1.1|-6.6|<br>0.0|<br>5.0|<br>9.3|<br>2.2|<br>0.7|<br>0.4|<br>6.4|
|HIC Imports<br>b/|8.2|<br>2.3|-14.1|-20.4|-1.1|-1.6|<br>4.0|-1.7|2.0|
|||||US|S bill|ion||||
|Total<br>External<br>Debt|289.0|351.0|391.0|422.0|438.0|454.0|482.0|527.0|529.0|
|Net Flows<br>to HICs|28.6|43.7|34.6|19.1|13.3|6.0|<br>4.5|<br>6.2|<br>7.6|
|Net Transfers<br>to HICs d/|8.8|18.3|<br>3.7|-9.9|-19.9|-26.5|-25.8|-21.8|-31.1|

a/ Volume.

b/ Constant 1980 US dollars.

**_cl_** Preliminary estimates.

d/ Net resource transfers are defined as the difference tetrwoen new lending and total debt service (interest and principal repayments).

# Source: World Debt Tables 1988-89

By comparison, expansion in the industrial countries is already in its sixth year--the longest period of uninterrupted peacetime growth. In 1988 that growth exceeded all expectations, accelerating to 3.9 percent. The undertone of world trade is equally buoyant with an estimated volume growth of about 7 percent in 1988.
# 1. Introduction

What is the employment status of the world's population? It is this simple question that this paper attempts to answer. By gathering data for many countries and inferring data for countries with missing data, this paper describes the work status of the working age population for the world, and various subgroups thereof. In order to describe the characteristics of the word's working age population one needs make some heroic assumptions about the mapping between available and unavailable data, and the way these aggregate into various country groupings. This paper documents the data, assumptions and methods used to estimate the "World at Work" numbers presented in the _World Development Report 1995: WZorkers inz an Integrating World_ [World Bank 1995].

First, a preliminary note on methodology: The goal is to estimate numbers for the working age population, and shares of the working age population out of the labor force, unemployed, in wage employment and nonwage employment in agriculture, industry, and services for the world, and various income and regional groupings. Clearly there are several ways of doing this. The strategy used here is twofold. First, get global estimates for the three World Bank income classifications (low-, middle-, and high-income countries) and aggregate these into estimates for the world. Second, for each geographic region, estimate the share of the working age population that falls into each category.

In order to aggregate, data on the _shares_ for countries with available data are taken as representative of their income group or region and the weighted average share is projected onto the population data for the group or region. As the distinctions between employment status categories become finer (for example from the share in agriculture to the share of 1
# **_Average loan size and fixed costs_**

Much of our interest is in the pattern of financial variables across institutions in different market segments. We use (normalized) average loan size as a rough proxy for the income level of customers.

_Summary statistics._ Table 1 gives summary statistics on the distribution of average loan size. For the full sample, the average loan size (normalized as described above) is 2.4, but the median is substantially lower at 1.0, reflecting a long upper tail. At the 75<sup>th</sup> percentile, the normalized average loan size is 2.5, so roughly a quarter of the sample is above the sample mean. Table 1 shows how average loan size varies across types of institutions. The row on NGOs, for example, shows a median of 0.5, a figure substantially below the median for banks (3.6).<sup>10</sup> As in previous analyses, NGOs and banks look and behave differently, a motivation for the disaggregation here. The mean (normalized average) loan size for banks is 6.9 and the mean for NGOs is 1.4. We asserted that NBFIs span the space between NGOs and banks, consistent with the mean average loan size for for-profit NBFIs of 2.8 and the mean for non-profit NBFIs of 2.4. Table 2 shows how different the institutions are by the gender of borrowers. The median commercial microfinance bank serves a base that is 50 percent female. The median NGO, in contrast serves a base that is 80 percent female. The NBFIs are again in the middle.

# **_Interest rates_**

> 10 Summary statistics vary slightly in the figures and tables since we truncate extreme values in the figures, as described above. The median normalized average loan size of commercial microfinance banks is 3.4 in figure 2, for example, and 3.6 in Table 1.

11
policy-selectivity increased markedly beginning in the late 1990s, coinciding with the publication of World Bank (1998) and a working paper version of Burnside and Dollar (2000), which both argued that aid is effective provided it is given to countries with a good policy environment. This message has been very influential among policy makers and development practitioners.<sup>9</sup> <!-- Start of picture text --> 1980 1990 2000 2010<br>Year<br>IDA Bilateral Aid<br>0<br>-.5<br>Poverty-Selectivity -1<br>-1.5<br><!-- End of picture text --> Figure 4: Poverty Selectivity of IDA vs. Bilateral Aid This time-series variation in IDA selectivity can be used to provide further tests of whether donor behavior is consistent with the model. If IDA’s major comparative advantage is in its aid selectivity, then we should observe donors making larger contributions to IDA, other things equal, during periods when aid from other donors is less selective. Table 4 provides some supportive evidence. The dependent variable is annual data on total contributions to IDA, for the period 1978-2012, as a share of total ODA (columns 1-4) or as a share of total core contributions to multilaterals (columns 5-6). The independent variables are the (once-lagged) elasticity coefficients computed from annual regressions similar to those in Table 3, either for aid provided by the DAC bilaterals (columns 1, 3-6) or for all non-IDA aid (column 2). When aid from other donors is more policy selective - i.e. the elasticity of non-IDA aid with respect to the CPIA rating is lower - then IDA’s comparative advantage in providing more policy-selective aid is weaker, and donors will have less reason to contribute to IDA. Thus, we expect the

> 9See Easterly (2003) for an insightful discussion of the impact of the Burnside and Dollar paper.

27
# **4 Data**

The data sets compiled for this analysis include household characteristics and energy consumption from three waves of a panel survey, geographical data on the electricity transmission and distribution network, monthly energy prices by region, and financial data for the electricity distribution utility. The combined data sets provide a comprehensive perspective on the energy sector in Uganda.

## **4.1 Household survey data**

The Uganda National Panel Survey (UNPS) is a household panel that began in 2009–10. There were three waves in the original panel: 2009–10, 2010–11, and 2011–12. The sample frame for this survey was the national household survey in 2005–06.<sup>15</sup> The survey recorded both the total amount paid and the quantity of energy consumed for each energy commodity. For kerosene, firewood, gasoline and charcoal, the quantity and the amount paid were asked in two separate sections of the survey: the energy module and the consumption module. This repetition provides a consistency check on the responses.

During the period covered by the panel, there was a gradual increase in the number of households with electricity connections, a substantial increase in the number of households using solar installations for lighting, and a small decrease in the number of households using kerosene for lighting.

In 2011, the dominant fuel used for lighting was kerosene (Table 1). Even a significant number of households with an electricity connection still reported using kerosene for lighting, either in combination with electricity or on its own. Generator ownership is uncommon, and even the households with a generator appear to use it more as a backup rather than as a primary source of energy. By 2011, solar was a more popular form of o↵-grid generation than diesel or gasoline generators.

## **4.2 Electricity coverage**

The decision by a household to obtain an electricity connection depends on the availability of the electricity grid in the household’s community. Extending the grid to a new area would be prohibitively expensive for a single household. We develop several complementary measures of grid availability for each household in the survey data.

> 15The energy section of the UNPS is the most complete of all African Living Standards Measurement Surveys. It has 33 out of 41 possible energy-related questions, the highest of 17 countries considered.

12
Positive self-selection implies that migration generally tends to improve the financial situation of the migrant’s household.<sup>33</sup> The results in Table 4.8 show that youth who migrate are less likely to be unemployed or inactive than those who remain in their region of origin, controlling for attained education. In Haiti nearly all domestic migration has been from rural areas to the metropolitan area. Some migrants leave Haiti; based on flows of remittances there are indications that the highest educated Haitians from the richest households go to the USA or Canada, while less educated emigrants go across the border to the Dominican Republic. Going to the USA or Canada to study or work is a unique opportunity for youth, but few can afford it and have the necessary connections. The situation for Haitian emigrants in the Dominican Republic is a different matter because wages are low and jobs are often precarious (World Bank 2005f).

Although remittances are an important part of income for many families, emigration also leads to lack of qualified workers or brain drain in Haiti. Very few return migrants are observed in the household survey. For youth, the lack of parental role models (particularly fathers) these emigrants represent leaves an even greater gap. Migration may lead to stressful situations for children and youth if they are left behind with only one parent or with another family member or friends. Thus, migration of parents increases the risk of neglect of small children and youth. The following section will look deeper into the family structure.

# **5. Family and Health Issues**

## **Family and Gender**

The family is both the strongest protective factor and the strongest risk factor. Families in Haiti are relatively large, as already indicated by a fertility rate of 4.2 children per woman in 2003. Poor and extremely poor households are on average larger than nonpoor households and, as Table 5.1 shows, poor metropolitan households tend to contain more youth on average than other households. This shows that youth are overrepresented in Port-au-Prince because on average poor rural households tend to be larger (Table 5.2).

Table 5.1: Average Number of Youth per Household in Haiti, 2001

|Income(per capita)|Metropolitan|Urban|Rural|Haiti|
|---|---|---|---|---|
|Extremely poor (<$1 a day)|1.25|1.06|0.87|0.94|
|Poor (<$2 per day)|1.23|1.01|0.89|0.94|
|Nonpoor(>2$per day)|1.07|0.74|0.61|0.85|

_Source:_ Own calculations based on ECVH 2001.

> 33 This does not necessarily mean that they are better off than the average in their new place of residence.

23
There seems to be consensus that the effect that currency substitution may have on the banking system is ambiguous. On the one hand, a high level of dollar deposits in an economy in which the dollar is used as a store of value but not as a medium of exchange will result in higher exchange rate risk in banks' balance sheets, since these banks will take deposits in dollars but will typically lend in domestic currency.'" On the other hand, a system that allows the coexistence of domestic currency deposits and FCDs can help prevent a capital outflow by increasing the buffer that banks have, increasing their ability to absorb bad shocks. In this section we will try to determine which effect dominates.

As preliminary evidence, we investigate how the probability of a banking crisis is influenced by the degree of dollarization in the economy. Using the Caprio et al (1999) database which reports the banking crises for a large set of countries, we can then calculate the probability of a banking crisis for all the countries in our study given a certain level of dollarization. In order these conditional probabilities, we then divide our sample between high andi low dollarized economies."<sup>1</sup> We then find that the probability of a banking crisis, of any kind, increases with the degree of dollarization. In particular, the probability of a banking crisis given that the economy is highly dollarized is 0.381. On the other hand, if the economy is not highly dollarized the probability of a banking crisis is 0.130. We then investigate how the degree of dollarization affects the nature of a banking crisis. Using Caprio's classification and considering all the countries in our study, we compute the probability of having a severe banking crisis given a certain degree of dollarization."<sup>2</sup> We find that the probability of a severe banking crisis increases with the degree of dollarization, presumably because of higher exposure. In particular, the probability of a severe banking crisis given that the economy is highly dollarized is 0.302. On the other hand, if the economy is not highly dollarized the probability of a severe banking crisis is 0.130. Note, the probability of having rmajor banking problems in a highly dollarized economy is roughly three times larger than in a mildly dollarized economy.

In order to present stronger evidence, it is necessary to introduce additional controls. Following Demirgiuc-Kunt and Detragiache (1998), we consider a multivariate logit specification. This approach can identify a number of interesting correlations. However, since we are "This may weaken the Central bank's ability to be an efficient lender of last resort. 1 When the average is above 30% for the period 1990 to 1998, the economy is classified as highly dollarized. This classification is suggested by Balinio, Bennett and Borenstein (1997).

12Caprio et. al. (1999) classify the banking crises into two major groups: systemic banking crises where most or all of banking system capital is eroded, and mild banking crises.

11
Availability of infrastructure (G) is measured differently in the static and the dynamic model. In the former, we use the distance to the next municipality, expressed in hours of travel time using the most common means of transport. This variable also proxies physio-geographic conditions such as ruggedness of the terrain or forest cover which, as pointed out in the literature, can make it easier for rebels to operate. To measure changes over time in the availability of infrastructure, we include information on whether or not a road was constructed during the last 4 years within 50 km of the community, an intervention that is clearly exogenous to the individual household.

Proxies for pre-existing grievances (C) are also constructed in two number of ways. First, in line with the literature, we use an index of ethnic fractionalization from the percentage share of population belonging to the four most irmportant ethnic groups.'<sup>4</sup> A second measure is the perception (as derived from the community survey) on the most pressing problem preventing development in the community. While the large majority of communities pointed towards specific issues (e.g. market access, schooling, health, agricultural technology), about one fourth of communities mentioned pervasive poverty as the key issue, pointing towards a generalized feeling of destitution and lack of perspective in the community which is likely to make it easier for rebels to obtain recruits.

Descriptive statistics on the incidence of civil strife, theft, and attacks, at the community level, are presented in table 1. Surprisingly, the share of households who were affected by civil strife increased during the 1992-99 period, from 5.9% to 9.7%. This is in line with an increase in the share of communities from which such activity is reported from 38% in 1992 to 69% in 1999/2000. Regional disaggregation illustrates that the increase was particularly pronounced in the West where the share of households suffering from civil strife more than doubled, from 5.5% to 12.4%, reaching almost the level of the North (13%) which shows a more modest increase from 9.6% since 1992. While the Center is consistently characterized by the lowest incidence of civil strife (4.3% and 2.5%) in both perods, it is

> 4 Data on etimicity are available only at the community level, preventing us from performing further and more disaggregated analysis (e.g. by differentiating between households who had been bom in the locality and those who migrated in ubsequently). The infonnation given is the share of the population that belongs to the four most important ethnic groups _i,_ Es. Sunming Ei 2 for all _i,_ subtracting it from 1, and normalizing the result _so_ that all values fall between 0 and I gives the measure used in the analysis.

15
||**Figure 10: % Dif**<br>**Stone et al.(2011)**|**ference in posttests per course (VR vs**<br>**[Socio-emotional Skills]**|**non-VR**|**training)**<br>Collaborative learning|
|---|---|---|---|---|
||**Stone et al.(2011)**|||Open communication|
||**Stone et al.(2011)**|||Improvement seeking|
|-60.0|-40.0<br>-20.0|0.0<br>20.0<br>40.0<br>60.0|80.0|100.0<br>120.0|

_Note_ : studies in bold are in the field of engineering, science, and technical education. Underlined studies are in the field of health and safely. Other studies are in the field of general education. The bold vertical dotted line represents the average observed effects. Gray vertical dotted lines represent the standard deviation of the observed effects.

Results from Stone et al. (2011) indicate that students who receive welding training in virtual labs report higher self-reported peer communication and collaborative learning in post-test than students who receive traditional training. This may occur because virtual laboratories can allow for digital peer interactions, which can be repeated multiple times, and allow for real-time / automated feedback to students. However, the authors do not find significant differences in student self-reported improvement seeking of students who received their training in a virtual lab (Figure 10). The remaining 10 experiments assess the effects of VR training on student learning selfreported skills value added, as proxied by the % difference in post-test minus pre-tests for students who participate for VR training (Figure 11). Available results indicate that students who complete VR training tend to report 20% higher levels of confidence and self-efficacy towards learning after they complete their courses. For instance, results from Vincent et al. (2008) indicate that after being exposed to VR training, students report higher levels of confidence in several dimensions (e.g. being effective, making decisions, and using resources) when addressing a health emergency.

Interestingly, results for Buttussi et al. (2018) indicate that such self-reported increases in confidence can vary using different types of VR technology. In particular, the authors find that students exposed to narrow view VR technology (which allows them to focus more on features of the simulation) tend to report higher increases in self-reported self-efficacy addressing a plane emergency than students exposed to wide VR technology (which may expose students to more visual distractions).

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