rafmacalaba/datause-encoder-data huggingface dataset:
**Figure 10. Urban Housing Amenities Compared to Shelter and Infrastructure Shortages by** **<u>Country</u>**

<!-- Start of picture text --> a) Shelter b) Housing infrastructure AGO<br>AGO<br>ZMB100.0% BFA<br>100.0% BFA<br>ZMB CIV UGA 80.0% CIV<br>80.0%<br>UGA 60.0% CMR TZA 60.0% CMR<br>TZA 40.0% COM 40.0%<br>STP COM<br>20.0% 20.0%<br>STP ETH<br>0.0% SLE 0.0% ETH<br>SLE GHA<br>SEN GHA<br>SEN KEN<br>RWA KEN<br>RWA LBR<br>NGA LBR<br>NGA MOZ<br>NER MWI NER MOZ<br>MWI<br>Shelter Shortage Roof Walls Infrastructure Shortage Water Toilet<br><!-- End of picture text -->

# **3.2 Shelter shortages appear to decrease as income rises but infrastructure shortages persist**

Intuitively, higher levels of per capita income may translate into better housing conditions and higher investments in housing infrastructure, offering insight into cross-country relationships between national income and housing conditions. Figure 11 shows the relationship between GDP per capita<sup>21</sup> and the housing shortage as calculated from the latest available data from each country. While it is difficult to discern a clear relationship between national income and housing shortages because of the tight range of GDP per capita across the countries studied, there is some indication that countries with low levels of GDP per capita also experience higher housing shortages. Lying below the best fit line are Comoros and Senegal which suggests that these two countries have on average shortages below what would be expected at their income level for Sub-Saharan African countries. If we exclude Angola, which appears to be an outlier based on its high income relative to other countries studied, Senegal remains below the best fit line. The relationship between infrastructure shortages and national per capita income is less clear. There does not appear to be a strong relationship between national per capital income and infrastructure shortages. However, these findings must be kept into perspective given the little variation in GDP per capita across the countries in the analysis.

> 21 The income data used are from the World Development Indicators (WDI) 2012 and represents GDP per capita with purchasing power parity in constant USD. Note that the income data are captured in the latest year available from the WDI, while the shortage data reflect the latest SHIP data available for each country (Annex 1).

16
well short of government investment (4.8 percent), a shortfall that is covered by domestic and foreign financing.

# **Table 5.3. Government budget, low‐income archetype country in 2015 (percent of GDP)**

|**Item**|**Value**|
|---|---|
|Recurrent receipts||
|Direct taxes|4.0|
|Social contributions|0.0|
|Activity taxes|0.3|
|Commodity taxes|5.7|
|Tariffs|1.4|
|Export taxes|0.0|
|Domestic transfers|1.8|
|Foreign transfers|0.9|
|Total|14.1|
|Recurrent spending||
|Consumption|11.7|
|Domestic transfers|1.6|
|Foreign transfers|0.1|
|Total|13.4|
|Savings*|0.8|
|Investment|4.8|
|Surplus**|‐4.0|
|Financing||
|Net domestic financing|2.5|
|Net foreign financing|1.5|
|Total|4.0|

*Savings is the difference between the totals for recurrent receipts and recurrent spending. **Surplus is the difference between savings and investment. Source: Authors’ calculations based on Table 3.6.

The production and trade structure of the country is summarized in in Table 5.4. The columns “Exports” and “Imports“ show the share of each sector in total exports and imports, respectively while the columns “Export‐Output ratio” and “Import‐Demand ratio” present, for each sector, the share of exports in output and the share of imports in domestic demands, respectively. In sum, the private sector accounts for more than 96 percent of value added and all of exports while all imports are categorized as private. Within the ‐22‐
Where:

- �: is the subnational region, � is the year and � denotes each event;

- �: is the unit (grid) of observation of wind sped;

- �: is the total number of grids per region (or sub‐national unit);

- ��: is the maximum sustained wind speed;

- ��: is the area of the grid.

The third approach is the Index of Hurricane Destruction (IHD), proposed by Strobl (2012). This index uses the distribution of population as a proxy for economic activity, weighting the MSWS of a given cell by the percentage of the population within the cell (see equation 3). This index further reduces the measurement error, albeit at the cost of potential endogeneity issues because population could make the decision of locate themselves strategically on low‐hazard areas.<sup>8</sup> Where: W�: is the percentage share of the country’s population;

- �: is the damage factor (equal to 3.8);<sup>9</sup>

- �: is the unit (grid) of observation of wind speed;

- �: is the total number of grids per region.

# 2.3. Intensity Threshold

Given the non‐linearity on the relationship between MSWS and the damage on the ground,<sup>10</sup> we evaluate the impact of hurricanes measured by the Saffir‐Simpson (SS) scale.<sup>11</sup> The SS scale classifies events into five categories according to the sustained wind speed measurement, with one being of the lowest and five the highest wind speed. The wind speed is directly correlated with the intensity of the hurricane. The scales are as follow: 1 for hurricanes between 119 and 153 km/h, 2 for hurricanes between 154 and 177 km/h, 3 for hurricanes between 178 and 209 km/h, 4 for hurricanes between 210 and 249 km/h, and 5 for hurricanes above 250 km/h.<sup>12</sup> As Strobl (2012) suggests, it is generally agreed that considerable damages due to windstorm only occur once a hurricane reaches a strength of 3, which is intrinsically how the scale has been created. In the case of Central America, however, it is unclear whether this relationship holds given the high levels of poverty and the fragile residence features in the region.<sup>13</sup> Therefore, this is an empirical question that we address

> 8 We use the GHSL – Global Human Settlement Layer Population dataset for available years (1975, 1990, 2000, and 2015).

> 9 Strobl (2012) suggests that this parameter relates local wind speed to the local level of damages.

> 10 Each additional unit of wind speed does not cause a constant level of damage.

> 11 The National Hurricane Center provides further explanation and a conceptual animation of the SS categories: <u>http://www.nhc.noaa.gov/aboutsshws.php.</u>

> 12 Related categories are tropical storm (63 – 118 km/h) and tropical depression (≤ 62 km/h).

> 13 For example, according to SEDLAC, the share of dwellings of low‐quality materials in Central America could be high as in Guatemala (46 percent), El Salvador (31 percent), Nicaragua (15 percent), and Honduras (14 percent).

6
**V**

- 23 -

Table 3: POLLUTION CHARGES IN SELECTED DEVELOPED COUNTRIES

|Charges -><br>Countrie&|WaLe|air|Solid<br>waste|Hazardous<br>waste|Noise|Products|
|---|---|---|---|---|---|---|
|Australia|X||X||||
|Denmark||||x|||
|France|X|X|X||X|X|
|Germany|X|X|X|X|X|X|
|Hungary|X||||||
|Italy|X||X||||
|Finland|||X||||
|Japan||X|||X||
|Netherlands|X|X|X|X|X|X|
|Norway||X|X|||X|
|Sweden|||X|||X|
|Switzerland|||||X||
|United Kingdom|X||||x||
|United States of America|<br>X|X||X||X|

Source: Hahn 1989.

Hahn's review of charge systems in France, Germany, Netherlands and the USA yields the following Insights:

- The major motivation for implementing an emission fee has been to raise revenues. Most revenues are earmarked for the improvement of environmental quality. Usually, the revenues are used to provide subsidies, grants or loans to private firms and to help support treatment plants.

- O The number of charges that have documented effects on polluter behavior is only a small subset of the total number of charges.

- O The main way charges had a positive impact on environmental quality was through the use of charge revenues for abatement activities.

- O There has been a tendency for charges to be raised over time, even after allowing for inflation.

76. The lack of conclusive evidence on the direct effect of charges on polluter behavior needs to be interpreted cautiously. First, data to test the impact of charges on the environment are generally scarce. Second, most charges have not been large enough to induce dramatic changes in firm behavior. And third, charges have generally been designed to promote environmental quality through the redistribution of funds to these activities. The indirect effect of charges on environmental quality has been significant. Apart from these caveats, an earlier study by Baumol and Oates (1975) found a significant direct positive impact of charges on pollution reduction. The
and stimulate agricultural production in marginal areas, the traditional sector (handicrafts and souvenirs) and transport services.

Assessing the magnitude of the economic impacts of tourism development is not straightforward because of a wide range of activities associated with growth in this sector and the difficulty in defining tourism as an industry. Wide discrepancies across regions and between urban and rural areas, common in developing countries and especially pronounced in Panama, are likely to translate into similarly large variation in the magnitude of the impacts of tourism on growth and poverty by region. This paper estimates the economic impact of the growing tourism sector in Panama at the regional level and assesses the distributional and poverty impacts of the sector’s growth.

# **2. The Role of the Tourism Sector in Panama within the Broader Social and Economic Context**

Panama is attracting tourism not only because of its scenic vistas and vast biodiversity, but also because of its rich cultural heritage. In addition to attracting thousands of business travelers, it is a growing center of coastal, cultural, health, and ecotourism. Surveys of foreign tourists reveal that business and shopping are still the main purposes of the trip for about a quarter to a third of all visitors; recreation is the main purpose for over a third of all visitors, and family visits for around 20 percent. Among activities, shopping, entertainment and business needs are still the main motives for the trip. But adventure, coastal and ecotourism are gaining prominence.<sup>4</sup> One survey reveals that over 40 percent of foreign visitors have spent time at the beach, and 10 percent have participated in ecotourism, among other activities.<sup>5</sup> Another survey suggests that ecotourism is one of the main purposes of the trip for about 15 percent of foreign visitors, while cultural tourism is still in a nascent stage (Table 1).

**Table 1. Activities realized during the trip.**

|_Forei_|_gn tourists_|_Domestic to_|_urists_|
|---|---|---|---|
|Shopping|82%|Recreation|32%|
|Entertainment|46%|Vacation|24%|
|Beach|22%|Family visit|24%|
|Ecotourism|15%|Rest (_descanso_)|10%|
|Etnotourism|2%|Religion|5%|
|Other|11%|Health|2%|
|||Other|3%|

Source: Authors’ calculations using the data from tourism surveys for Satellite Tourism Accounts, 2008. For foreign tourists, 1,626 tourists (and more than one response was permissible); for domestic tourists-1,423 trips (states only the main purpose).

Investments in infrastructure and hotel accommodations, which are not yet sufficient for meeting the growing tourism demand in most areas outside of Panama City, are expected 4 Results for October and November 2007 of the ongoing tourism survey conducted by PSM SIGMADOS. Each month’s sample size is 500 tourists (with the total sample size of 6,000 once all rounds of the survey are completed).

5 Tourism survey conducted in June 2006 by Dichter & Neira Latin Research Network. Sample size: 843 foreign tourists.

3
respondents in these communities may assume that women and young adults living in their households and engaged in the informal economy are not employed or working. The LOA module, on the other hand, may address the potential bias driven by the exposure to high informal employment by presenting to respondents a list of activities that are mostly from the informal sector. As a result, this seems to lead to no differences in the reporting of women’s and youths’ labor market indicators.

# **6.2 Social Norms around Gendered Work Activities**

Occupational and sectoral separation by sex is remarkably persistent over time (World Bank, 2011) and a major contributor to the gender wage gap. The prevalence of working women and men in particular occupations that classify jobs by type and skill and in different sectors of the economy varies greatly (Borrowman & Klasen, 2020). For example, women are more likely to be engaged in a wider range of activities compared to men. They combine unpaid domestic work or caregiving responsibilities, which traditional survey methods may not capture effectively, with income-generating activities. Thus, a hypothesis is that the unpaid domestic work women do (which is partly explained by social norms) is more likely to be underreported (Franck & Olsson, 2014), as well as women’s paid work may be prone to undercounting because it is conducted in shorter periods of time and they may consider that it is not worthy to be reported in household surveys.

Considering the types of activities in which women predominantly engage, especially in contexts where women devote a large portion of their time to domestic obligations, we test whether our survey methods measure women’s work accurately. To this end, we estimate a measure for the respondent’s exposure to social norms associated with domestic obligations. This measure consists of the average amount of time in hours that the women—excluding the respondent—in each EA devote to domestic duties, which we obtain from the main survey’s time use module. We then interact this average time with each treatment indicator in a manner similar to Equation (2), but where is the average time in this exercise, and we estimate the results separately for the sample of men and women. iihss DD We present the results from this heterogeneity analysis in Table 7. Columns (1) and (2) and (3) and (4) present the results for the sample of women and men, respectively. We document that for every hour that other women from the community dedicate to domestic duties, female respondents assigned to the LOA group are 5.9 pp and 6.5 pp more likely to report being 18
country’s initial polity measures. Specifically, we compare a country’s Polity2 in 1990 with 0, executive constraints in 1990 with 5 and political competition in 1990 with 7. If the country’s initial polity measures are larger than the above-mentioned cutoffs, the countries are considered to have better polity. In Appendix Table A10, we use 5-year rolling average Polity. At year _t_ , a country is defined to have better polity if the country’s backward 5-year window Polity average (i.e. from year _t-1_ to _t-5_ ) is larger than the above-mentioned cutoffs. This is to address the potential evolution of countries’ Polity, such as the democratization of many Eastern European and Latin American countries in the 1990s. Finally, we also use a Democracy measure from Freedom House. Our results show that the finding that economic downturns cause reform chatter to increase more in democracies holds.

Our results hold when we consider the higher income and lower income countries separately. Appendix Table A11 presents the results for high and upper-middle income countries, while Appendix Table A12 presents the results for low and lower-middle income countries. Within each income group, reform chatter responds more to downturns in countries with better polity. The results are stronger and more statistically significant for the low and lower-middle income countries, suggesting the overall results are driven primarily by poorer countries.

The results remain robust when crisis years are removed (Appendix Table A13). This robustness check confirms that our results are not driven by crisis years or extreme growth shocks. We also show that the results are not driven by the degree of press freedom. Appendix Table A14 shows that when Press Freedom is included, the interactions of executive constraints and political competition with GDP growth remain highly significant. However, the interaction of the aggregate index Polity2 (Democracy) becomes insignificant. We can conclude that our main results are not affected solely by press freedom but rather by some elements of democracy.

Our main results are not driven by human capital either. Appendix Table A15 shows that while high human capital does make reform chatter increase more in bad times, human capital does not crowd out the effects stemming from a higher level of democracy.

An important caveat is that negative economic shocks are likely to affect media coverage of all economic topics, not specifically economic reforms. It may even be that, as a share of _economic news_ , the number of articles discussing reform decreases in response to economic downturns. To address this concern, we focus on the corpus of articles in Factiva categorized as “Economic News”. Table A16 presents the IV results using this alternative dependent variable. The coefficients on all the interactions are negative and highly significant, suggesting that reform chatter increases even within the sub-sample of economic news articles. In contrast, reform chatter decreases in less democratic countries in response to negative economic shocks.

11
# A short-term cash transfer program had sustained effects on investment in children

Poverty affects the qualities that others believe a person has, and the experience of poverty affects how an individual perceives himself (Fell and Hewstone 2015). Many Americans hold a stereotype that the poor are low in competence and warmth (Fiske et al. 2002), and it can lead them to treat poor people with contempt (Cuddy, Fiske, and Glick 2007). The self-schemas of the poor can make them feel stigma and shame. In a global survey of people living in poverty, respondents emphasized the humiliations: One deeply felt deprivation is not being able to do what is customary in the society ... not being able to entertain visitors or enjoy social life ... shame from not having toilets for visitors, or money to buy a coffin for burying a relative ... In Etropole, Bulgaria ‘people who cannot afford warm clothes for the winter go to work. Then they come back and stay at home under a pile of blankets, shivering with cold. They don't go out. They are ashamed to meet other people. If they run into a friend and are invited for a drink, they must refuse. So they would rather not go out at all’ (Narayan et al. 2000, 38).

Shame is associated with depleted self-efficacy (Baldwin, Baldwin, and Ewald 2006). The psychological dimensions of poverty make it more challenging for the poor to improve their lot.

Exposure to high-achieving role models can sometimes help people develop self-efficacy (Bandura 1997) and increase their productivity (Henrich and Broesch 2011). But poverty tends to put such role models out of reach, since many residential neighborhoods and social networks are income-segregated. As a result, poor people often naturalize the situation they are in (Dalton et al., 2016; Macours and Vakis, 2014; Tanguy et al., 2014; Duflo, 2012; Ray, 2006; Appadurai, 2004). Then they do not aspire to change it.

33
domestic or foreign development agencies, treasuries or constitutional funds, but not by deposits from the general public; these agencies are allowed to invest only in priority areas as defined by the state government. Privatized banks were sold to strategic investors, i.e. foreign or domestic private banks, with the state relinquishing all control; in some cases, however, up to 10% of the shares were reserved for bank employees. Given the traditional importance of state banks in deficit financing, and their politicized use by governors, the above mentioned list represents a likely inverse ranking of what state authorities desired as the outcome for their bank. However, the a-priori preferred option - restructuring a state bank and keeping it - required that the state provide at least half of the restructuring costs and change management.<sup>21</sup> For most state governments, recapitalizing their bank was likely considerably more costly than the political benefits they stood to lose when relinquishing control.<sup>22</sup> Under PROES a good portion of Brazil’s public financial system has been restructured since 1997. Specific outcomes under PROES varied, and not only by state; since some states had more than one bank, outcomes varied by bank as well. Table 2 provides a summary of the status of each of the state banks. Two of the states—Mato Grosso do Sul and Tocantins— had no bank. All other states had at least one state-owned bank. The state of Paraíba opted out of PROES altogether, as did the Federal District. Seven states had more than one state financial institution. The state of Espírito Santo apparently had one bank under PROES, but was able to opt out with respect to its second bank.<sup>23</sup> Six states were able to restructure a bank and keep it, including four states that had more than one bank, though Espírito Santo is currently attempting to privatize. Nine banks have been privatized to strategic investors; five

> 21 The federal government also covered only 50% of the losses in the cases where the state government decided to privatize or liquidate one, but keep and restructure another state bank. 22 Ness (2000), 80.

> 23 <u>Folha de São Paulo 12 May 2000.</u>

10
_70_ _Estimates and Projections of Mortality by Cause_ Table C2L. Expert assessnents of mortality rates (per 100,000) and deaths (in thousands) for some specific causes, by age, sex, and region, 1985

|_Region and_|_Rate_|_s_|_Dea_|_ths_|_Rat_|_e_|_Dea_|_ths_|
|---|---|---|---|---|---|---|---|---|
|_age group_|_A4k_|_Female_|_Mak_|_Femak_|_Male_|_Female_|_Male_|_Female_|
|||Diarr|hea|||Polio|||
|World|||||||||
|Totd|59|61|1439|1457|1.7|1.5|42|35|
|0<br>|1582<br>|1342<br>|977<br>|793<br>|19.4<br>|16.9<br>|12<br>|10<br>|
|1-4|100|193|233|429|12.5|10.8|29|24|
|5-14<br>|23<br>|20<br>|126<br>|105<br>|0.2<br>|0.2<br>|1<br>|1<br>|
|15-44|6|9|72|95|0.0|0.0|0|0|
|45-64|6|7|23|25|0.0|0.0|0|0|
|65+|6|7|7|11|0.0|0.0|0|0|
|Developed|||||||||
|Total|2|2|10|10|0.0|0.0|0|0|
|0|70|58|6|5|0.0|0.0|0|0|
|14|6|8|2|3|0.0|0.0|0|0|
|5-14|0|0|0|0|0.0|0.0|0|0|
|15-44|0|0|0|0|0.0|0.0|0|0|
|45-64|0|0|0|0|0.0|<br>0.0|<br>0|0|
|65+|2|<br>2|<br>1|<br>2|<br>0.0|<br>0.0|<br>0|0|
|Developing|||||||||
|Total|76|80|1429|1447|2.2|1.9|42|35|
|0|1838|1559|971|788|22.7|19.8|12|10|
|1-4|117|226|231|426|14.7|12.7|29|24|
|5-14<br>|28<br>|24<br>|126<br>|105<br>|0.2<br>|0.2|1|1|
|15-4|8|12|72|95|0.0|0.0|0|0|
|45-64|9|11|22|25|0.0|0.0|0|0|
|65+|9|11|6|9|0.0|0.0|0|0|
|Industrial|||||||||
|Total|1|1|2|3|0.0|0.0|0|0|
|0|5|3|0|0|0.0|0.0|0|0|
|1-4|2|3|0|1|0.0|0.0|0|0|
|5-14|0|0|0|0|0.0|0.0|0|0|
|15-4<br>|0<br>|0<br>|0|0|0.0|0.0|0|0|
|45-64|0|0|0|0|0.0|0.0|0|0|
|65+|3|3|1|2|0.0|0.0|0|0|
|Nonmarket|||||||||
|Total|4|3|8|7|0.0|0.0|0|0|
|0|164|136|6|5|0.0|0.0|0|0|
|1-4|11|14|2|2|0.0|0.0|0|0|
|5-14|0|0|0|0|0.0|0.0|0|0|
|15-44|0|0|0|0|0.0|0.0|0|0|
|45-64|0|0|0|0|0.0|0.0|0|0|
|65+|1|1|<br>0|<br>0|<br>0.0|<br>0.0|<br>0|0|
|LAC|||||||||
|Total|77|74|154|148|0.0|0.0|0|0|
|0|1536|1330|89|75|0.0|0.0|0|0|
|14|117|185|25|39|0.0|0.0|0|0|
|5-14|52|48|25|23|0.0|0.0|<br>0|0|
|154|11|9|10|8|0.0|0.0|0|0|
|**45-6**|13|8|3|2|0.0|0.0|0|0|
|65+|10|10|1|1|0.0|0.0|0|0|