rafmacalaba/datause-encoder-data huggingface dataset:
**Figure 7. Real Estate Prices in Croatia (2007=100)**

<!-- Start of picture text --> 100<br>95<br>90<br>85<br>80<br>75<br>2000 2002 2004 2006 2008 2010<br><!-- End of picture text --> Source: Global Property Guide and authors’ calculations.

Notwithstanding these caveats, Table 2 shows the results on _EH_ and the _LGD_ using (i) the two different vulnerability thresholds described earlier as well as (ii) the different shock definitions discussed in this paper—a more likely combined shock (Figure 5, Panel D) and an arbitrary 35 percent devaluation that also includes interest rate and unemployment rate shocks (Figure 6).

In conclusion, even though the increase in NPLs among Croatian households is a source of concern, it remains manageable given the country’s well-capitalized banking sector. Risk loans relative to all banking system assets increase from about 8 to, at most, 12 percent. And the losses given default are even more limited: only 20 percent of these problem loans are likely to result in losses given the limited decline in housing prices; NPLs among households with debt are by themselves unlikely to constitute a threat for financial stability (i.e., losses of 2-21⁄2 percent of all banking system assets). Also, only one-third of these losses are as a result of the shocks that followed from the global financial crisis. The remainder constitutes _risk loans at origination_ that can be traced to weaknesses in the HBS—and these weaknesses have an impact on the levels, but not the change, of risk loans. Of course overall financial stability depends on developments throughout all asset classes, including developments in commercial real estate and corporate loans. But households by themselves are unlikely to constitute a threat to financial stability.

**Table 2. Non-Performing Loans and Potential Bank Losses**

||No sh|ocks||Combine|d shock||35% de|valuation|plus othe|r shocks|
|---|---|---|---|---|---|---|---|---|---|---|
||Arbitrary<br>threshold|Financial<br>margin|Arbit<br>thres|rary<br>hold|Financia<br>thres|l margin<br>hold|Arbi<br>thres|trary<br>hold|Financi<br>thre|al margin<br>shold|
|||threshold|Level|Change|Level|Change|Level|Change|Level|Change|
|EH (% of HH debt)|31.3|27.2|41.7|10.4|34.5|7.3|48.4|17.2|38.9|11.7|
|in % banking assets|8.8|7.7|11.8|2.9|9.7|2.1|13.6|4.8|11.0|3.3|
|in % GDP|11.6|10.1|15.4|3.9|12.8|2.7|17.9|6.3|14.4|4.3|
|LGD (% of HH debt)|6.3|5.4|8.3|2.1|6.9|1.5|9.7|3.4|7.8|2.3|
|in % banking assets|1.8|1.5|2.4|0.6|1.9|0.4|2.7|1.0|2.2|0.7|
|roatia HBS and authors’ c<br>in % GDP|alculations.<br>2.3|2.0|3.1|0.8|2.6|0.5|3.6|1.3|2.9|0.9|

Source: Croatia HBS and authors’ calculations.

14
# **Box 1.4: Definition of Selected Natural Hazards**

## **Geological Hazards**

**An earthquake is** a sudden tremor of the earth's substrata that may be caused by the movement of large masses of rocks or tectonic plates along fault lines in mountain ranges or mid-oceanic ridges.

A **tsunami** is a wave train or senes of waves generated in a body of water by an impulsive disturbance (such as earthquakes) that vertically displaces gigantic water columns. Tsunamis may reach a maximum run-up or above-sealevel height of 10, 20, or even 30 meters.

Slides are a downward slope movement of soil, rock, mud or snow because of gravity. One of the most common sources of slides **is** prolonged torrential downpours of rain or the accumulation of heavy snow. Mass displacement of large mud, snow or rocks can also be triggered by seismic waves.

Lahars are mudflows that are caused by the melting of the icecap by lava from a volcano or the downhill run-off of volcanic ash because of heavy rainfall.

A **volcanic eruption is** the process whereby molten lava, fragmented rocks or gases are released on to the earth's surface through a deep crater, vent or fissure.

## **Meteorological Hazards**

**Hurricanes** and **Tropical Storms** are large-scale, closed circulation system in the atmosphere with low barometric pressure and strong winds that rotate counter clockwise in the northern hemisphere and clockwise in the southem hemisphere.

Floods are a temporary inundation of normally dry land by overflowing lakes or rivers, precipitation, storm surges, tsunami, waves, mudflow, lahar. They may also be caused by the failure of water retaining structures, groundwater seepage and water-back up in sewer systems.

**Drought** is a lack or insufficiency of rain for an extended period that can cause a considerable hydrological imbalance and, consequently, water shortage, crop damage, stream-flow reduction and depletion of groundwater and **soil** moisture. It occurs when, for a considerable penod, evaporation and transpiration (the release of underground water into the atmosphere through vegetation) exceeds precipitation.

Forest fires are uncontrolled fires whose flames can consume trees and other vegetation of more than 6 feet (I .8m) in height. These often reach the proportions of a major conflagration and are sometimes begun by combustion and heat from surface and ground fires.

Source IDNDR (1992); Bell (1999); Swiss Re (1988, p. 16); Pidwimy (1999); and Encyclopedia Bntannica [http./www Bntannica.com].
||Targeted allowance for house readjustment|X|||
|---|---|---|---|---|
|Family and|Monthlybenefit for bringingupa child upto oneyear old.||X||
|Children|Monthly allowance for a child up to the completion of secondary education but no<br>more than 20years of age||X||
||Lumpsum benefit forpregnancy||X||
||Targeted allowance for schoolchildren enrolled in 1st grade at a state or municipal<br>school(income-tested before 2021)||X||
||Monthly benefit for bringing up children accommodated in families of relatives and<br>friends.|||X|
|Social|MSA Monthlysocial allowance(MSA)|||X|
|exclusion|Heatingallowance|||X|
|Education|Scholarshipforpupils||X||
||Scholarshipfor students,PhD students,andpost-graduate students||X||

Source: ESSPROS database, OECD (2020), Tosheva et al. (2022)

**The Guaranteed Minimum Income (GMI) Indicator was determined by the Council of Ministers until mid-2023 and lacked anchoring in any national or international poverty, consumption, or income measure indicative of an adequate living standard, indicating a discretionary indexation** . The level set was notably low and was not updated for years, staying at BGN 75 (EUR 38) – leading to insufficient coverage and adequacy of the Monthly Social Allowance scheme, heating allowance, and other associated social payments. Beyond the low benefit level, the GMI design failed to adapt or link social assistance to fluctuations in purchasing power and general living standards, as it remained unadjusted and unindexed for inflation. Consequently, this has resulted in diminishing benefit adequacy over time, with GMI indicators as a percentage of the AROP poverty line decreasing from 21.4 percent in 2016 to 14.3% in 2021 (Figure 3). Given this, the design of the GMI-linked basic income support has long called for improvement. Possible policy choices included anchoring the GMI indicator to a poverty line (preferably an absolute poverty measure) or a basic consumption basket and establishing ad-hoc indexation rules to establish changes over time. Such a parametric reform was expected to result in higher coverage of the poor, better targeting, and potentially higher impacts on poverty. The magnitude of the impacts naturally depended on the parametric design features.

**Figure 3. Monthly GMI Indicator compared to at-risk-of-poverty line**

<!-- Start of picture text --> 80 30.0<br>60 21.4 21.4 20.0<br>18.2<br>40 16.6 14.9 14.3<br>10.0<br>20<br>0 0.0<br>2016 2017 2018 2019 2020 2021<br>Monthly GMI Indicator<br>GMI Indicator (% AROP) or adequacy relative to the poverty Line<br>GMI Indicator (BGN) GMI Indicator (% AROP)<br><!-- End of picture text --> 8
# **Acronyms**

|AAGR|average annual growth rate|
|---|---|
|ASYCUDA|automated system for customs data|
|AW|ASYCUDA World|
|AUSAID|Australian Agency for International Development|
|CATT|Customs Assessment Trade Toolkit|
|CIDA|Canadian International Development Agency|
|CARTAC|Caribbean Regional Technical Assistance Center|
|FAS|free along side|
|GDP|gross domestic product|
|IFC|International Finance Corporation|
|IMF|International Monetary Fund|
|LAC|Latin America and the Caribbean|
|LCR|Latin America and the Caribbean Region|
|LIAT|Leeward Islands Air Transport|
|OECD|Organisation for Economic Co-operation and Development|
|OECS|Organization of Eastern Caribbean States|
|PPP|public-private partnership|
|RMF|risk management framework|
|SEMCAR|Supporting Economic Management for the Caribbean|
|SCASPA|St. Christopher Air and Sea Ports Authority|
|SLASPA|St. Lucia Air and Sea Ports Authority|
|SLMTL|St. Lucia Maritime Terminals Ltd.|
|SMF|strategic management framework|
|SOP|standard operating procedure|
|STS|ship to shore|
|TEU|twenty-foot equivalent unit|
|WCO|World Customs Organization|
|WTO|World Trade Organization|

3
**<u>Table A.6. The district fixed-effect RDD-DD regressions of employment of individuals aged 15-64</u>**

||||Dependent<br>|variables<br>|||
|---|---|---|---|---|---|---|
|Explanatory variables|Currently<br>working<br>(yes=1, no=0)|Self-employed<br>farm work<br>(yes=1, no=0)|Self-employed<br>non-farm<br>work (yes=1,<br>no=0)|Having wage<br>job (yes=1,<br>no=0)|Log of<br>monthly wage<br>(wage<br>workers)|Log of<br>monthly wage<br>(all workers)|
||(1)|(2)|(3)|(4)|(5)|(6)|
|Program * Post-program period|-0.0189|-0.1021*|0.0561***|0.0270|0.1280|0.5345*|
||(0.0209)|(0.0569)|(0.0158)|(0.0546)|(0.2707)|(0.3071)|
|(Poverty rate – 50) * Post-program|-0.0028|0.0066|-0.0060***|-0.0034|-0.0054|-0.0319|
|period|(0.0027)|(0.0059)|(0.0019)|(0.0044)|(0.0336)|(0.0294)|
|Program * (Poverty rate – 50) * Post-|0.0048*|-0.0023|0.0049**|0.0021|0.0035|-0.0039|
|program period|(0.0028)|(0.0064)|(0.0019)|(0.0051)|(0.0349)|(0.0325)|
|Age|0.0417***|0.0114***|0.0062***|0.0240***|0.0435***|0.2651***|
||(0.0023)|(0.0021)|(0.0010)|(0.0012)|(0.0064)|(0.0127)|
|Age squared|-0.0005***|-0.0001***|-0.0001***|-0.0003***|-0.0006***|-0.0037***|
||(0.0000)|(0.0000)|(0.0000)|(0.0000)|(0.0001)|(0.0002)|
|Male|0.0045|-0.0822***|-0.0076**|0.0942***|0.0886***|1.0887***|
||(0.0047)|(0.0075)|(0.0032)|(0.0057)|(0.0197)|(0.0628)|
|Ethnic minorities (yes=1, Kinh=0)|0.0590***|0.1939***|-0.1021***|-0.0328*|-0.3696***|-0.2361|
||(0.0132)|(0.0283)|(0.0113)|(0.0180)|(0.0514)|(0.1705)|
|District fixed effects|Yes|Yes|Yes|Yes|Yes|Yes|
|Year fixed effects|Yes|Yes|Yes|Yes|Yes|Yes|
|Constant|0.1246***|0.4184***|0.0093|-0.3031***|6.8637***|-2.9807***|
||(0.0463)|(0.0455)|(0.0168)|(0.0275)|(0.1376)|(0.2782)|
|Observations|118,369|118,369|118,369|118,369|27,361|118,369|
|R-squared|0.174|0.191|0.070|0.187|0.202|0.167|

Note: This table reports district fixed-effect RDD-DD regressions of employment of individuals using individual-level observations. Robust standard errors in parentheses. Standard errors are clustered by district and village-year levels. *** p<0.01, ** p<0.05, * p<0.1.

56
Supplemental Table 4. The association between baseline receptive vocabulary and follow-up Swahili receptive vocabulary

||s1|s2|s3|s4|s5|s6|
|---|---|---|---|---|---|---|
||b/se/p|b/se/p|b/se/p|b/se/p|b/se/p|b/se/p|
|Receptive vocabulary in English (_z-_score)|0.2214****||||0.0675|0.0431|
||0.0452||||0.0413|0.0448|
||<0.001||||0.1036|0.3372|
|Receptive vocabulary in Swahili (_z-_score)||0.5031****||0.4578****|0.4798****|0.4476****|
|||0.0419||0.0461|0.0452|0.0473|
|||<0.001||<0.001|<0.001|<0.001|
|Receptive vocabulary in Luo (_z-_score)|||0.2912****|0.1114**||0.0998*|
||||0.0481|0.0473||0.0509|
||||<0.001|0.0191||0.0508|
|Constant|–0.0064|–0.0011|–0.0011|–0.0013|–0.0029|–0.0024|
||0.0475|0.0406|0.0464|0.0405|0.0406|0.0405|
||0.8923|0.9793|0.9805|0.9747|0.9436|0.9524|
|_R_-squared|0.0508|0.2429|0.0843|0.2532|0.2471|0.2548|
|N. of cases|442|442|442|442|442|442|

Notes. Receptive English, Swahili, and Luo vocabulary scores are age-adjusted _z-_ scores for children ages 2 to 6 years, measured using separate assessments based on the British Picture Vocabulary Scale (BPVS). Baseline and follow-up were conducted approximately 5 weeks apart. * _p_ < 0.1, ** _p_ < 0.05, *** _p_ < 0.01, **** _p_ < 0.001.
# _Traffic with Europe and other Caribbean Islands_

Small island states’ traffic with Europe and other Caribbean island is similar in magnitude but trends are significantly different. Despite the impact of the global slowdown and the European crisis, European capacities show a positive trend and have shown solid recovery since the crisis (Figure 21a). About 16% of the seat capacity of the small island states is on direct routes with Europe. The primary routes are with the U.K., dominated by British Airways and Virgin Airlines. Condor provides another link out of Frankfurt, Germany, but at much smaller volumes.

Traffic between the small island states and other Caribbean nations, and tourism from the Caribbean to the small island states, has been in decline, and is, as mentioned in an earlier section, less than 13% of the traffic within the small island states. This is an overall pattern within the small island states: Both tourism and trade with other Caribbean countries has gone down by a reported 30% (Figure 21b).

**Figure 21: Small Island States’ Traffic with non-American Markets**

<!-- Start of picture text --> (a) Traffic to and from Europe (b) Traffic to and from all other islands in the Caribbean<br>600 600<br>500 500<br>400 400<br>300 300<br>200 200<br>100 100<br>0 0<br>2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012<br>Estimated Seats, thousands Estimated Seats, thousands<br><!-- End of picture text --> _Source: Authors’ calculations based on data from Diio (2014). Note: Puerto Rico is excluded._

# <u>Hub Countries</u>

For the entire group of hub countries, international traffic is generally for their own tourism sector. The success shows a clear size differentiation: the Dominican Republic and Jamaica showed both positive rates of growth in recent years and in the long term, while the smaller islands, including Trinidad, have seen recent declines. None of the islands are engaged in large scale international hub operations involving out-of the region carriers and transferring within the Caribbean.

For three countries, The Bahamas, Jamaica, and the Dominican Republic, the majority of traffic is related to the burgeoning tourism sector. All three islands are large enough to host a variety of tourism attractions, with direct routes from the US, Canada, and Western Europe. Though shows that there may be some hub and spokes operations centered in these countries, they do not affect the network within the Caribbean, since the number of realistic airport pairs served is negligible. Table 17 below shows that 50
The estimated relationship between exports and employment at the local level is the result of decisions of individual firms. Firms might react to an exogenous increase in export demand in three different ways. Existing non-exporters could become exporters (the extensive margin), existing exporters might increase labor demand (the intensive margin), or existing exporters might increase exports by exporting products previously designated for the domestic market (having no net effect on labor-market outcomes). Additionally, exports might affect FLFP in similar ways. Female-intensive firms could either enter or exit the export market or increase production at different rates than male-intensive firms. Twosector trade models with heterogeneous firms and differences in factor intensity across sectors (Bernard et al. 2011) predict that rising export demand in one sector will increase demand for the workers intensively employed in that sector throughout the economy. While most manufacturing exports tend to be male-intensive, rising (falling) exports from female-intensive sectors could increase (reduce) demand for women and expand (reduce) female employment.

Simple models that incorporate firm heterogeneity and exports suggest that changes in employment concentrate along the extensive margin as new firms enter export markets. In this section we illustrate this result and then draw upon the rich firm-level data to complement our analysis. To illustrate these firm dynamics, we draw upon the simplified general equilibrium model by Melitz (2003) used in Berg et al. (2022). This two-sector model starts with a “reserve” sector _a_ , in which firms are homogeneous and produce using only labor with decreasing returns-to-scale technology: (4) aa The parameters and _λ_ characterize the labor demand function, and firms are assumed to ll = ff(κκ, λλ, ww) (4) The parameters and _λ_ characterize the labor demand function, and firms are assumed to be small and take the market wage as given. κκ The other sector is an “exporting” sector. In the exporting sector, firms are heterogeneous and differentiated by a firm-specific productivity parameter _φ._ They decide whether to shut down, produce for the domestic market, or produce for the export market.

After entry into the market, the firm-specific productivity parameter _φ_ is first revealed (i.e., it is unknown prior to entry). The ex-ante productivity parameter distribution is described 2 by _g_ ( ~~φ~~ , σφ ) . In practice, we assume that the productivity parameter is drawn from an exponential distribution.<sup>9</sup> For simplicity, assume that production is a function of labor and can be represented as , in which _j_ ∈ ( _a_ , _b_ ) indicates the subsector, _γ_ represents jj ll αα jj QQ= γγll

> 9 Note that the exponential distribution is closely related to the Pareto distribution. For example, if _x_ follows a Pareto distribution with a minimum of _a_ , then _y =_ log( _x/a_ ) (Halliday, Lederman, and Robertson 2018).

21 | P a g e
_Factor-use Distortions:_ To illustrate the impact of taxability, Table 1 provides estimates of marginal effective tax rates on capital for two different cases: fully taxpaying case and tax loss case. The fully taxpaying case assumes that Ontario Hydro will generate taxable income under the proposed new system. The tax loss case is modeled as one particular situation whereby Ontario Hydro is able to carry forward its prior years' losses to shelter future income from corporate income taxation.<sup>17</sup> Capital and property taxes are payable even though the company may not pay corporate income taxes until a later time.

Appendix A provides details of the model for calculating marginal effective tax rates. It is assumed that Ontario Hydro will be subject to corporate income, capital and property taxes according to the normal tax laws. It is also assumed that Ontario Hydro will have a commercially acceptable capital financing structure with a debt to assets ratio at about 33 per cent (generation) or 40 percent (transmission and distribution). If Ontario Hydro is able to carry forward losses from earlier years, it is assumed that it will take seven years to write off such losses.'<sup>8</sup> As shown in Table 1, there would be quite significant differences in effective tax rates on capital for particular assets. For the tax-paying case, machinery used for distribution bears the lowest effective tax rate (36%) while nuclear structure for generation would be subject to the highest effect tax rate (68% for the corresponding case). Structures tend to be highly taxed since they are provided a relatively low capital cost allowance (4% or 5% on declining balance basis) which is not altogether that much different than economic depreciation.

Moreover, such assets are subject to relatively high property and capital taxes. Similar results hold for the non-tax paying case (Table 2).

> 17 There are other possible scenarios such as Ontario Hydro shifting from taxpaying to non-taxpaying states frequently. See Altshuler and Auerbach [1990] for further discussion. I This estimate is adopted from Chen and Mintz [1993b].

27
# **Appendix 1: Instrumental variable estimations using migrants’ characteristics**

We employ three different variables related to migrants’ characteristics as alternative instruments for remittances: _Average time abroad, Average time abroad_<sup>_2_</sup> and _Kinship relationship._ The variable _Average time abroad_ denotes the average time spent abroad by the household members who migrated. For each member of the household residing abroad, the survey details the time spent in a foreign country. We take the average time across all members overseas for those households with more than one person living in a foreign country. Following the results of Rodriguez (1996) and Aggarwal and Horowitz (2002), we expect a positive influence of time abroad on remittances. The rationale is that migrants might take some time to settle in the host country before they start sending remittances. However, as Rodriguez (1996) asserts, the flow of remittances could decrease due to long absences. Hence, we expect an inverted U relationship between _Average time abroad_ and both the amount of remittances and the likelihood of receiving remittances. Hence, we include _Average time abroad_<sup>_2_</sup> – the square of _Average time abroad_ - to allow for this possibility<sup>12</sup> _Kinship relationship_ is an index that measures how close the kinship relationship between the migrants and the household is. The mentioned index equals 2 if at least one of the migrants is a member of the nuclear family (i.e., head of household, spouse or son); equals 1 when the household has relatives living abroad, but none of them are members of the nuclear family; and equals 0 when the household does not have any member living abroad. Based on the results of Rodriguez (1996), we expect the chances of receiving remittances to be higher if the migrants are members of the nuclear family.

Table A1 presents the results of the first stage regressions using as instruments _Average time abroad, Average time abroad_<sup>_2_</sup> and _Kinship relationship_ . These variables are significant and have the expected signs: _Average time abroad_ shows an inverted U relationship with remittances, and _Kinship relationship_ has a positive coefficient. In order to test the relevance of our

> 12 Rodriguez (1996) analyzes the determinants of remittances flows to Philippines using a survey of overseas workers carried out in 1991. He finds an inverted U relationship between the time spent away and: (i) the decision to remit, and (ii) the remittance amounts. He also shows that remittances tend to be higher when the migrant is part of the nuclear family. Aggarwal and Horowitz (2002) investigate the migrants’ motivations to remit using data from Guyana for 1992 and 1993. They find that households that spent more than one year abroad are more prone to send remittances.

31