rafmacalaba/datause-encoder-data huggingface dataset:
In Haiti, cruises dock at Labadee Port located in the northern side of the island. The port is located by the Labadee resort, leased by Royal Caribbean International (RCI). The only two cruise lines using this port are Royal Caribbean and Celebrity Cruise lines. <mark>The site is a private resort for cruise lines owned by RCI. The cruise ships anchor offshore and passengers are ferried to the resort.</mark>

**Table** **<u>32: Port Infrastructure for Cruise Ships</u>**

|**Country**|**Port**|**Facility**|**Cruise**<br>**Ship**|**Length (m)**|**Maximum**<br>**Deth (m)**|
|---|---|---|---|---|---|
||||**Berths**||**p**|
||'|**Small Island States**<br>'||||
|Antigua and Barbuda|St. Johns|HeritageQuayand Port of St. Johns|4|366|11|
||Portsmouth|Cabris Cruise Ship Berth|1|91|15|
|Dominica|Roseau<br>Roseau|Woodbridge Bay Port<br>Roseau Cruise ShipBerth|1*<br>1|244|10<br>12|
|Grenada|St. George's|Melville Street Cruise Terminal|1|335|9|
|St. Kitts and Nevis|Basseterre|Port Zante|1|335|10|
|St. Lucia|Castries|Port of Castries*<br>Pointe Seraphine|5*|503|11|
|St. Vincent and the<br>||||||
|Grenadines|Kingstown|Kingstown Cruise Terminal|2|360|9|
|||**Carribean Hubs**<br>||||
|The Bahamas|Freeport<br>Nassau|Freeport Harbour<br>New Providence Port|3<br>13|625<br>2084|12|
|Barbados|Bridgetown|Bridgetown Port|5|1524|12|
||La Romana|La Romana|2|478|10|
|Dominican Republic|Santo Domingo|Don Diego<br>Sans Souci|1<br>1|395<br>247|9<br>9|
|Jamaica|Ocho Rios<br>Montego Bay<br>Port Antonio|Ocho Rios<br>Montego Bay<br>Port Antonio|3<br>6<br>1|727<br>1280<br>62|14<br>10|
|Trinidad and Tobago|Trinidad<br>|Port-of-Spain<br>|1|305|10|
||Tobao|Scarborouh Harbour|1|181|9|
||g|g <br>**Continental Caribbean**||||
|Belize|Belize City|Belize CityHarbour|5||12|
|Guyana|Georgetown|La Penitence Terminal|1*|292|6|
|Suriname|Paramaribo|Nieuwe Haven|1*|200|6|
|||**Fragile State**||||
|Haiti|Labadee|Labadee|**-**|**-**|**-**|

_Source: Cruise Industry News Annual Report (2012). *Berth also used for container cargo._

# _ii. Main Cruise Lines_

There are around 30 cruise lines that serve the Caribbean, each one having several ships going into each island serving different routes. The ships size and routes vary depending on the length of the trip. Trips can vary from 3 days to 15 days within the Americas and around 20 to 40 days if the ships are coming from Europe. The longer the length of the trip the higher the number countries included in a route (Table 33). Schedules are planned and sent to each port two years ahead of time.

77
Policy Research Working Paper 10077

# **Abstract**

Better understanding the geography of women’s labor market outcomes within countries is important to inform targeted efforts to increase women’s economic empowerment. This paper assesses the extent to which a method that combines simulated survey data from urban areas in Mexico with broadly available geospatial indicators from Google Earth Engine and OpenStreetMap can significantly improve estimates of labor force participation and unemployment rates. Incorporating geospatial information substantially increases the accuracy of male and female labor force participation and unemployment rates at the state level, reducing mean absolute deviation by 50 to 62 percent for labor force participation and 25 to 52 percent for unemployment. Small area estimation using a nested error conditional random effect model also greatly improves municipal estimates of labor force participation, as the mean absolute error falls by approximately half, while the mean squared error falls by almost 75 percent when holding coverage rates constant. In contrast, the results for municipal unemployment rate estimates are not reliable because values of unemployment rates are low and therefore poorly suited for linear models. The municipal results hold in repeated simulations of alternative samples. Models utilizing Basic Geo-Statistical Area (AGEB)–level auxiliary information generate more accurate predictions than area-level models specified using the same auxiliary data. Overall, integrating survey data and publicly available geospatial indicators is feasible and can greatly improve state-level estimates of male and female labor force participation and unemployment rates, as well as municipal estimates of male and female labor force participation.

This paper is a product of the Poverty and Equity Global Practice and the Social Protection and Jobs Global Practice. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank. org/prwp. The authors may be contacted at dnewhouse@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
The charts in Figure 2 provide a detailed account of developments in household debt. Four features are worth noting. First, albeit from low levels, Croatia experienced high credit growth in the decade preceding the global financial crisis (Panel A). Second, a large share of this increase was in the form of housing-related loans (Panel B). Third, most household debt in Croatia is linked to foreign currency (Panel C).<sup>3</sup> Finally, most loans are extended at variable interest rates and this makes households vulnerable to changes in banks’ funding costs (Panel D).<sup>4</sup>

**Figure 2. Household Debt Characteristics in Croatia**

<!-- Start of picture text --> Panel A. Household Credit Growth and Level Panel B. Types of Loans<br>50 100%<br>90%<br>40<br>80%<br>Other<br>30 70%<br>% GDP 60% Consumer<br>20 50% loans<br>10 Growth 40% Housing<br>(in %) 30%<br>0 20%<br>10%<br>-10<br>0%<br>2000 2001 2002 2003 2004 2005 2006 2007 2008 2009<br>2000 2001 2002 2003 2004 2005 2006 2007 2008 2009<br>Panel C. Currency Types Panel D. Fixed versus Flexible Interest Rates<br>100% 100%<br>90% 90%<br>80% 80%<br>70% 70%<br>60% Swiss 60% Long-term<br>50% franc 50%<br>40% Euro 40% Short-term<br>30% 30%<br>20% Local 20%<br>currency<br>10% 10%<br>0% 0%<br>2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009<br><!-- End of picture text --> Source: European Credit Research Institute, Croatian National Bank, and authors’ calculations.

# **Data and Methodology**<sup>5</sup>

The primary purpose of the paper is to stress test the capacity of households to service their debt obligations. To this end, indebted households are exposed to different types of economic shocks. The shocks not only impact households’ welfare, but also increase the probability of loan default. Thus, there is a direct link between these stress-testing exercises and Croatia’s financial stability risks. The rest of this section describes the data and stress-testing methodology.

> 3 Initial conditions have a bearing on interest differentials. Mitra, Selowsky, and Zalduendo (2009) argue that cross-country differences in currency composition are explained by interest rate differentials that in turn reflect differences in initial conditions. 4 The average distribution between flexible and fixed interest rate mortgages is, respectively, 90 and 10 percent. While the effect of changes in interest rates might be mitigated by caps on interest rate adjustments, information on caps is not available. 5 An important caveat should be raised. The assessment of household welfare in response to economic shocks is approached as a partial equilibrium exercise; i.e., changes in household behavior are disregarded and a general equilibrium framework is absent. The methodology on stress testing household debt holdings and on assessing the share of vulnerable households follows Holló (2007), Żochowski and Zajączkowski (2008), Johansson and Persson (2006), Vatne (2006), and Tiongson et al. (2010).

4
exports of high-tech and deep processed products and discourage the exports of pollution and resource intensive products and primary products. To curb the exports of resource-intensive products such as rare earths, for example, China reduced their rebate rates in 2004, leading to higher unrefunded export VAT rates (t-r) for these products. Because these resource-based products are also more likely to be state-owned than other products and SOEs are more likely to export indirectly as shown previously, this may also lead to a positive correlation between indirect export share and (t-r). Based on the classification for primary products and resource-based products as in Lall (2000), we find that the average unrefund rates for primary products and resource-based products in our sample are indeed higher than that of other products (7.06% and 5.84% vs. 4%), and the average indirect export shares of primary and resource-based products are also higher than that of other products (35% and 33% vs. 31%).<sup>35</sup> On the contrary, high-tech products have been encouraged by the Chinese government and received relatively higher rebate rates (in other words, lower (t-r)). If high-tech products which are usually associated with higher quality are less likely to be exported indirectly according to Tang and Zhang (2012), this can also lead to a positive correlation between (t-r) and the share of indirect exports. Based on China’s official classification of advanced technology products as published on the 2005 China Statistical Yearbook on Science and Technology<sup>36</sup> , we find that the average unrefund rate for advanced-technology products in our sample is higher than that of other products (5.84% vs. 4.45%), but the average indirect export share of advanced-technology products is only slightly higher than that of other products (32.5% vs. 31.6%).

Although including HS4 product fixed effects can help to alleviate the above concerns, we also take them as problems of omitted variable bias by checking the robustness of our

> 35 These data are based on the sample used in the first regression in Table 1. The three groups of products (Primary Products, Resource-based Products, and others) are mutually exclusive, accounting for 840, 1317 and 4,852 observations respectively.

> 36 The original primary product and resource-based product data in Lall (2000) are in SITC 3-digit level. We concord them to HS 6-digit. The original ATP data are at 4-digit Chinese Industrial Classification (CIC). We first concord CIC to 4-digit ISIC, and then to HS 6-digit. Although we have access to other classifications of high-tech products such as those in Lall (2000) and Ferrantino et al. (2007), we choose to use China’s own official definition because that is what the rebate rates are actually based on.

26
indicators of student risk and taking preventative actions tailored to students’ needs – are in place in at least 30 U.S. states and the majority of European countries (European Commission 2013; O’Cummings and Therriault 2015). As more low and middle-income countries invest in developing reliable education management information systems, student data that forms the foundation of an early warning system is becoming increasingly available. However, little evidence exists on whether and how such systems can effectively reduce dropout in countries with limited capacity to respond to students’ needs. In Guatemala, the pilot program, named ENTRE ( _Estrategia Nacional para la Transición Exitosa_ – National Strategy for Successful Transition), was designed to utilize recently developed data systems to identify students at risk and activate preventative, evidence-based measures at the school level. ENTRE focuses on three main factors identified as both particularly important for causing dropout in the primary to secondary transition in Guatemala and addressable through relatively low-cost interventions: a lack of knowledge among key primary school actors about effective measures to help students stay in school; a lack of knowledge among these same actors about which students are most at risk of dropping out; and a lack of prioritization of dropout as a problem to be addressed, as school actors have myriad responsibilities and dropout in the transition to lower secondary occurs after students have left their primary schools.

To assess the effectiveness of this early warning system approach, implementation of ENTRE was designed as a four-arm randomized experiment (three treatment arms and one control arm) among one-fifth of all the public primary schools in the country. In the first treatment arm, school principals and sixth grade teachers receive a user-friendly guidance manual and half-day training on specific, evidence-based methods to help students make the transition to lower secondary school – in essence, providing them more information on _how_ to help students stay in school. A letter signed by the Minister of Education accompanied the guidance manual, emphasizing the importance of every Guatemalan child completing at least nine years of basic education (primary and lower secondary) and the responsibility of schools to support them in doing so. The second treatment arm adds to the first a list of sixth-grade students at high risk of dropping out based on a prediction model using administrative data– providing them more accurate information on _who_ needs the most help. The third treatment arm adds to the first two small behavioral nudges to try and keep dropout prevention top-of-mind for principals and teachers. A fourth, control arm included schools that received no intervention or information about ENTRE.

4
- Kim, J., and J. Rao (2012) ‘Combining data from two independent surveys: A model-assisted approach.’ _Biometrika_ 99, 85–100

- Lanjouw, J., and P. Lanjouw (2001) ‘How to compare apples and oranges? poverty measurment based on different definitions of consumption.’ _Review of Income and Wealth_ 47(1), 25–42

- Ligon, E., and T. Sohnesen (2016) ‘Using reduced consumption aggregates to track and analyze poverty.’ _mimeo_

- Magnus, J., and H. Neudecker (2007) _Matrix Differential Calculus with Applications in Statistics and Econometrics: Revised Edition_ (John Wiley & Sons)

- Matloff, N. (1981) ‘Use of regression functions for improved estimation of means.’ _Biometrika_ 68, 685–689

- Ministry of Planning, and United Nations World Food Programme (2002) ‘Estimation of poverty rates at commune-level in cambodia: Using the small area estimation technique to obtain reliable estimates.’ , Ministry of Planning, Royal Government of Cambodia and United Nations World Food Programme, Phnom Penh, Cambodia

- Neyman, J. (1938) ‘Contribution to the theory of sampling human populations.’ _Journal of the American Statistical Association_ 33, 101–116

- Palmgren, J. (1987) ‘Precision of double sampling estimators for comparing two probabilities.’ _Biometrika_ 74(4), 687–694

- Pape, U., and J. Mistiaen (2015) ‘Measuring household consumption and poverty in 60 minutes: The Mogadishu high frequency survey.’ mimeo, The World Bank

- Pettersson, H., and B. Sisouphanthong (2005) ‘Cost model for an income and expenditure survey.’ In ‘Household Sample Surveys in Developing and Transition Countries,’ vol. ST/ESA/STAT/SER.F/96 of _Series F_ (Department of Economic and Social Affairs, United Nations Statistics Division) studies in methods 13, pp. 267–277

- Rao, J., and R. Sitter (1995) ‘Variance estimation under two-phase sampling with application to imputation for missing data.’ _Biometrika_ 82(2), 453–460

34
|Pakistan: PromotingGirls' Education in Balochistan(PGEB)|Umbreen Arif|
|---|---|
|Pakistan: PromotingGirls' Education in Balochistan(PGEB)|Umbreen Arif|
|Pakistan: PromotingGirls' Education in Balochistan(PGEB)|Umbreen Arif|
|Senegal Disaster Risk Management and Climate Change Adaptation|Isabelle Celine Kane|
|Senegal Disaster Risk Management and Climate Change Adaptation|Isabelle Celine Kane|
|Senegal Disaster Risk Management and Climate Change Adaptation|Isabelle Celine Kane|
|MU -Second Public Sector Performance DPL|Rafael Munoz Moreno|
|MU -Second Public Sector Performance DPL|Rafael Munoz Moreno|
|MU -Second Public Sector Performance DPL|Rafael Munoz Moreno|
|Integrated System of Household Surveys|Victor Sulla|
|Integrated System of Household Surveys|Victor Sulla|
|SOMALIA: Drought Management and Livelihood Protection|Stephane Forman|
|SOMALIA: Drought Management and Livelihood Protection|Stephane Forman|
|Improved PolicyMakingand Institutional Framework for People wi|tManuel Salazar|
|Improved PolicyMakingand Institutional Framework for People wi|tManuel Salazar|
|Improved PolicyMakingand Institutional Framework for People wi|tManuel Salazar|
|Improved PolicyMakingand Institutional Framework for People wi|tManuel Salazar|
|Improved PolicyMakingand Institutional Framework for People wi|tManuel Salazar|
|TP-Education Sector Support Project(ESSP)Additional FinancingII|Susiana Iskandar|
|TP-Education Sector Support Project(ESSP)Additional FinancingII|Susiana Iskandar|
|TP-Education Sector Support Project(ESSP)Additional FinancingII|Susiana Iskandar|
|TP-Education Sector Support Project(ESSP)Additional FinancingII|Susiana Iskandar|
|TP-Education Sector Support Project(ESSP)Additional FinancingII|Susiana Iskandar|
|BT: DPC 2|Genevieve F. Boyreau|
|SL Public Sector Pay& Performance|Smile Kwawukume|
|SL Public Sector Pay& Performance|Smile Kwawukume|
|SL Public Sector Pay& Performance|Smile Kwawukume|
|SL Public Sector Pay& Performance|Smile Kwawukume|
|SL Public Sector Pay& Performance|Smile Kwawukume|
|SL Public Sector Pay& Performance|Smile Kwawukume|
|SL Public Sector Pay& Performance|Smile Kwawukume|
|SL Public Sector Pay& Performance|Smile Kwawukume|
|SL Public Sector Pay& Performance|Smile Kwawukume|
|SL Public Sector Pay& Performance|Smile Kwawukume|
|SL Public Sector Pay& Performance|Smile Kwawukume|
|SL Public Sector Pay& Performance|Smile Kwawukume|
|Improving QualityInfrastructure and Investment Climate|Ruvejda Aliefendic|
|Improving QualityInfrastructure and Investment Climate|Ruvejda Aliefendic|
|Improving QualityInfrastructure and Investment Climate|Ruvejda Aliefendic|
|Improving QualityInfrastructure and Investment Climate|Ruvejda Aliefendic|
|Improving QualityInfrastructure and Investment Climate|Ruvejda Aliefendic|
|Improving QualityInfrastructure and Investment Climate|Ruvejda Aliefendic|
|South Sudan Private Sector Development Project|Michael Corlett|
|South Sudan Private Sector Development Project|Michael Corlett|
|South Sudan Private Sector Development Project|Michael Corlett|
|South Sudan Private Sector Development Project|Michael Corlett|
|South Sudan Private Sector Development Project|Michael Corlett|
spells that are non-overlapping, and with limited durations (e.g. spells of 3 years or less, spells shorter than 5 years).<sup>16</sup> In all of our analyses, we treat the country as the unit of analysis and therefore weight each country equally. Where countries have varying number of observations of matched survey and national accounts data, each observation is weighted as the inverse of the number of observations for each country, so that the total weight given to each country, in each subsample, sums to one.<sup>17</sup> This ensures that our analysis does not assign more weight to countries simply because they have more household surveys available, which is what would occur in an unweighted analysis. Our decision to treat the country as the unit of analysis is based on our interpretation of this literature as being fundamentally about the performance of country-level statistical systems, not people. Nonetheless, we do provide, supplemental tables with populationweighted and unweighted gaps in both levels and growth rates between surveys and national accounts as this can help explain how these gaps affect aggregate measures such as global changes in living standards or poverty.<sup>18</sup>

# **3. Assessing the Gaps**

There are many reasons why there are gaps between survey and national accounts estimates, but they largely fall into three categories – (i) measurement error in surveys, (ii) measurement error in national accounts, and (iii) conceptual differences in what each are designed to measure, including differences in their primary objectives. The evidence of measurement error in surveys is vast and indicative of substantial levels of noise in estimated levels of consumption and income. Examples demonstrating the sensitivity of estimated consumption to questionnaire 16 The analysis excludes 1 percent (top 0.5 and bottom 0.5) of estimated gaps in our main analyses of both levels and growth. Even after careful review of deflator and currency conversion issues, a few outlier observations remain, which are not suitable for inclusion in the main analyses as they reflect unrealistic levels of growth, and they reflect discrepancies likely due to currency, deflator or other errors.

17 Weights are re-calculated for each subsample. For example, if a country has two surveys -- one consumption and the other income, each survey is given a weight of 0.5 in the pooled sample, while they each get a weight of one for the analysis when gaps for consumption are estimated separately from the gaps for income.

18 Comparable reviews, such as Deaton (2005) and Ravallion (2003) have used other weighting schemes than our preferred method. Supplemental tables with alternative weighting are available in Appendix 2.

10
households. By contrast, more than half of Rwandan households have an account. This is also reflected in a relatively higher share of formal loan experience: 16 percent have received a loan from a commercial bank or a microfinance institution in the past 3 years. In Senegal and Burkina Faso, people rely more on informal credits, e.g. at a shop, from relatives or friends, or informal savings associations (tontines).

On average, total household expenditures—expressed in USD—are almost twice as high in Senegal as in Rwanda, and are also 14 percent higher than in Burkina Faso. Adjusting for purchasing power, these differences reduce to 80 percent and 3 percent, respectively. However, Rwandan households have the highest per capita expenditure in the sample and Senegalese households have the lowest. Comparing these values to national income data, this sample does not reflect the differences at the national level, where per capita income is highest in Senegal and lowest in Rwanda.<sup>8</sup> The Senegal sample is hence a relatively poorer sample than the Burkinabe sample and especially the Rwandan sample when compared to the overall population of the respective country.

**Table 4: WTP electricity consumption levels**

|Scenario|Consumption level|Corresponds to<br>SE4All access tier|Corresponds to<br>technology|
|---|---|---|---|
|1|Electric lighting inside the house|< 1|Solar lamp|
|2|Electric lighting, radio, and TV, charging a<br>mobile phone|2 or 3|Pico‐PV kit/SHS|
|3|Electric lighting, radio, and TV, charging a<br>mobile phone, fridge, electric stove|3 or 4|Grid connection|

# _3.2 Contingent valuation design_

In all three data sets, respondents were asked about their monthly WTP for one of

> 8 According to World Bank data, GDP per capita in 2013 was 1,072 USD in Senegal, 684 USD in Burkina Faso, and 633 USD in Rwanda. Based on purchasing power parity (PPP), the 2013 figures become 2,269 international $ for Senegal, 1,634 $ for Burkina Faso, and 1,452 $ for Rwanda.

17