rafmacalaba/datause-encoder-data huggingface dataset:
Table D.24. Effects of Guerrilla Territorial Control on Crimes during the War Period

||Total War Events|Total War Victims|Has a War Event|Has War Victims|
|---|---|---|---|---|
||(1)|(2)|(3)|(4)|
|Guerrilla control|0.00660|-0.258|0.00180|0.00322|
||(0.0894)|(0.490)|(0.00264)|(0.00287)|
|Observations|3,652|3,652|3,652|3,652|
|Bandwidth (Km)|2.266|2.266|2.266|2.266|
|Dependent mean|0.0410|0.2130|0.00100|0.00200|

_Note:_ The table presents the results of estimating equation 1 for our outcomes related to crimes committed in the war period. Columns 1 and 3 report the total of events related to war and its probability, respectively. A war event can be a massacre, combat, bombing, or any other war event that caused victims from war. Columns 2 and 4 show the total number of victims and the probability of the census tract to have war victims. The unit of observation in all Columns is the canton level. The information was recovered from the registry of victims. Controls not shown include a linear polynomial of the distance to the boundary of guerrilla territory, its interaction with whether the tract was under guerrilla control or not, and up to 400 fixed effects representing the closest evenly spaced break in the guerrillacontrolled boundary. The algorithm of Calonico, Cattaneo and Titiunik (2014) was used to set the bandwidth and the estimates weight using a triangular kernel. Robust standard errors in parentheses. *** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1.

Table D.25. Effects of Guerrilla Control on Homicide and Victimization Rates

||Homicides|Victim of Any Crime|Victim of Gang Extortion|
|---|---|---|---|
||(2017)|(2004-2016)|(2004-2016)|
||(1)|(2)|(3)|
|Guerrilla control|-0.0110|-0.210***|-0.193***|
||(0.0562)|(0.0552)|(0.0637)|
|Observations|3,652|94|94|
|Bandwidth (Km)|2.266|2.266|2.266|
|Dependent mean|0.314|0.688|0.0420|

_Note:_ The table presents the results of equation 1 for our outcomes related to current crime. Column 1 shows the number of homicides reported to police for each census tract in 2017. Column 2 shows the share of people within a census tract who reported being a victim of any type of crime in the LAPOP survey. Column 3 shows the share of people within a census tract who reported being a victim of extortion in the LAPOP survey. The unit of observation in all Columns is the census tract. Controls not shown include a linear polynomial of the distance to the boundary of guerrilla territory, its interaction with whether the tract was under guerrilla control or not, and up to 400 fixed effects representing the closest evenly spaced break in the guerrilla-controlled boundary. We use the algorithm of Calonico, Cattaneo and Titiunik (2014) to set the bandwidth and weight using a triangular kernel. Robust standard errors in parentheses. *** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1.

93
# **VI.   EMPIRICAL RESULTS**

As can be seen in the table below, we find that imports from the EU would increase in the range of 10.5 percent for Senegal to 11.5 percent for Nigeria, under a zero tariff / free trade scenario and assuming an elasticity of substitution of 2.0 and an import demand elasticity of 0.7.

In absolute terms, trade diversion and trade creation in Nigeria are by far the largest, with US$ 417.8 million and US$ 486.1 million respectively, due to the magnitude of its total imports and the share of its imports from the EU. Trade creation (in absolute terms) is lower than trade diversion for Nigeria and Ghana, as imports from Non-EU countries make up a relatively large share of total imports, in contrast to Senegal and Cape Verde.

Results of two other sets of simulations associated with lower (0.5) and higher (0.9) import demand elasticity<sup>19</sup> and with lower (1.3) and higher (2.5) elasticity of substitution showed no significant changes (Table 2).

**Table 2:  EU-ECOWAS Economic Partnership Agreement:**

**<u>trade and fiscal revenue effects</u>**

||Nigeria|Ghana|Senegal|Cape<br>Verde|
|---|---|---|---|---|
|**Trade Effects**|||||
|I. Trade creation (million US $)|417.8|93.7|95.4|20.8|
|% of preferred imports (EU)|5.3%|5.1%|6.1%|6.6%|
|II. Trade diversion (millions US $)|486.1|105.8|70.9|15.4|
|% of non-preferred imports|3.7%|3.7%|6.8%|9.0%|
|III. Total Trade effects (million US $)|903.9|199.5|166.3|36.2|
|% of preferred imports (EU)|11.5%|10.9%|10.6%|11.4%|
|**Tariff Revenue Loss**|||||
|million US $|682.0|150.6|154.7|34.3|
|% of Total Government Revenue|2.4%|7.1%|10.4%|15.8%|
|% ofGDP|1.0%|1.7%|2.0%|3.6%|
|Data source: IMF and World Bank, 2004|||||

Tariff revenue losses range from US$34.3 million in Cape Verde to US$150.6 million in Ghana, US$154.7 million in Senegal and US$682 million in Nigeria.

As a share of total government revenue, the loss would be largest for Cape Verde (15.8 percent) and Senegal (10.4 percent), because of their relatively large share of imports from the EU and their higher dependence on tariff revenue. Ghana would also be particularly affected with an estimated decline in government revenue of 7.1 percent, while the impact would amount to 2.4 percent of government revenue in Nigeria.

> 19 Based on empirical work on trade elasticities (Olarreaga et al., 2004).

12
The main results for this question will discussed separately by region. The bar charts always display the calculated average ranking for each obstacle-ranging from 1 (no obstacle) to 6 (very strong obstacle).

# **Developed countries**

Entrepreneurs in developed countries found five out of six major obstacles for doing business related to regulations. They are the following: <!-- Start of picture text --> g. Tax regulations and/or<br>high taxes<br>e. Labor regulations<br>j. Safety or<br>environmental regulations<br>~ : d. Financing<br>a. Regulations for<br>starting business/new<br>1. General uncertainty on<br>costs of regulations<br>h. Inadequate supply of<br>inftastructure<br>n. Corruption<br>m.Crime and theft<br>OECD<br>c. Regulations on foreign<br>(exports, imports)<br>i. Policy instability<br>M > ~~~~~~~~~~~~~~~~~~~k. Inrtation<br>f. Foreign currency<br>-<br>b. Price controls<br>o. Terrorism<br>1.5 2.0 2.5 3.0 3.5 4.0 4.5<br><!-- End of picture text -->

1. Tax regulations and/or high taxes (question 12.g)

2. Labor regulations (question 1 2.e)-the highest ranking among regions.

3. Safety or environmental regulations (question 12j)-also the highest ranking among regions. ranking among regions. None of the other regions ranked environmental regulations higher than 11 on a 15 point scale (rank 1 means the worst obstacle, rank 15- the least).

5. Regulations for starting business/new operations (question 12.a) 6. General uncertainty on costs of regulations (question 12.1) Financing was ranked the forth major obstacle by surveyed businessmen in developed countries.

At the same time, corruption (question 12.n) was ranked number eight out of 15 obstacles considered-the lowest rank in any region. Foreign currency regulations (question 12.f) and price control (question 12.b) were found to be the third and second lowest obstacles and terrorism (question 1 2.o) was ranked the least important obstacle.

The quantitative value of the top obstacle (tax regulations and/or high taxes) was almost twice higher than the value of the second lowest obstacle (price control).

21
# **4. Methodology**

# **_Choice Experiment Surveys_**

Given that we are interested in identifying the opportunity cost of the REDD+ contracts and how the different characteristics of the REDD+ contracts influence the adoption of the contracts we use choice experiment (CE) surveys for this study. CE surveys are based on Lancaster’s (1966) consumer theory and are used to elicit preferences for environmental goods and policies (Boxall et al. 1996, Louviere et al. 2000). Lancaster (1966) proposed that consumers obtain utility from the characteristics of goods rather than the good itself. Therefore, CEs can be considered the analog of hedonic analysis for stated preference valuation methods and they allow the researcher to gain a detailed understanding of the respondents’ preferences for the policy or scenario being analyzed.<sup>3</sup> In a typical CE survey, the respondent repeatedly chooses the best bundle/choice from several hypothetical bundles/choices. The attribute values appearing in each bundle/choice are identified using experimental design techniques to ensure a balanced representation of values across choices. Alpizar et al. (2003) Hanley et al. (2001), Hensher et al. (2005), and Hoyos (2010) provide reviews of the choice experiment methodology.

# **_Survey Instruments_**

The survey for this particular study presents respondents with opportunities to express preferences over hypothetical REDD+ contracts. The attributes of costs and benefits and their levels, presented in Table 1, were selected through the focus group discussions in nine CFs and nine non-CFs. These CFs were purposively represented in both the hill and Terai (plains) regions. In each region, these CFs were selected randomly from the random data set of the CF

> 3 There are some concerns about hypothetical bias in values obtained from choice experiment surveys. See Carlsson, F. and P. Martinsson (2001) for a discussion of this.

14
Argentina than in other emerging markets in Latin America and elsewhere (Table 4.1), and view the fiscal deterioration mainly as a casualty of the recession. The contribution of Social Security refomi to the fiscal imbalances of the 1990s has also attracted attention.<sup>29</sup> In this section we assess those claims.

**Table 4.1. Debt Indicators in Emerging Markets**

||**PLiblic**Debt|Public|DebtInterestPay|ments|
|---|---|---|---|---|
||PercentageofP<br>GDP|ercentageo<br>GDP|fPercentageofP<br>TaxRevenue|ercentageof<br>Debt|
|Argentina|62.2|5.4|30.8|8.7|
|Brazil|65.0|9.5|33.8|15.5|
|Colombia|50.8|5.0|25.3|9.8|
|Mexico|27.7|2.6|25.7|9.4|
|Venezuela|35.3|3.3|18.7|9.3|
|Poland|39.1|2.9|11.0|7.4|
|Russia|52.3|3.0|7.9|5.7|
|Turkey|85.1|23.7|133.1|27.8|

Source: Goldman Sachs and IMF.

Note: Data are for 2000 except for Argentina (2001).

# A. FISCAL POLICY DURING BOOM AND BUST

We begin our inquiry by examining how and when fiscal vulnerabilities developed during the nineties. Most analysts have pointed to the deterioration of fiscal balances (both at the Federal and provincial levels) and the corresponding increase in debt indicators since 1995 and, especially, since 1999, which are shown in Figures 4.1 and 4.2 respectively.<sup>30</sup> 29 See Teijeiro (2001) and Hausm,ann and Velasco (2002).

> 30 Argentina's fiscal balances during the first part of the nineties look better than they actually were, as they include some privatization receipts above the line and hide important fiscal liabilities (in both Federal and Provincial pension systems and other items) that were later partially recognized, especially from 1994 onwards. Public debt decreased up to 1993 thanks to large privatization receipts and the Brady deal, while from 1994 onward the debt path reflects the recognition of some of the previously hidden liabilities (see Teijeiro 2001). Also, from 1995 on the Federal Government absorbed cash flow deficits previously included in the Provincial pension system. This latter change does not affect the consolidated (Federal + Provinces) deficit but only its composition.

29
quarrying and manufacturing we would have good reasons for suspecting similar problems for the other sectors (construction, trade, transportation & communications, and other industries) had statistics other than those of SII been available. To evaluate the overall average for all insured it may still be useful to have a look at the representativeness of the SI1 as compared with the distribution of economically active population according to the population censuses of 1960 and 1980. Since agricultural workers, self-employed, and most government employees by law are excluded from the SII and unpaid family labor is not covered either, Table 3.4 shows the distribution of economically active, for 1980 also of employees, excluding agriculture, services (most of which is government) and persons wiLh occupations not adequately described. These are the distributions that matter in this context, in particular the one for employees in 1980. It is immediately seen from the comparison with employees that relatively the SII in 1980 somewhat overrepresented mining & quarrying, manufacturing, construction and electricity etc., grossly underrepresenting trade, finance etc., and somewhat underrepresenting transportation & communications.' The misrepresentation is clearly worst for trade, finance etc. The comparison is more difficult to make for 1960 because we do not have the distribution of employees. To judge from the distribution of economically active in 1969 as compared with 1980, it would appear that the misrepresentation may have been

- 46 -
From the literature on institutions, we move to trade policy. The effects of trade barriers on growth and income have been studied since the early 1990s. While Dollar (1992), Sachs and Warner (1995) and Edwards (1998), using different measures of openness, in many cases constructed from standard policy measures, showed positive effects of trade on growth, these papers have been strongly criticized by Rodriguez and Rodrik (2001) for the problems with measures of trade openness and the econometric techniques used as well as for the difficulty in establishing the direction of causality. While Rodriguez and Rodrik (2001) have criticized the measure of openness used by Sachs and Warner (1995) as capturing many aspects of the macroeconomic environment in addition to trade policy, Baldwin (2003) has recently defended that approach on the grounds that the other policy reforms captured in the measure, though not trade reforms per se, accompany most trade reforms sponsored by international institutions. Therefore, using such a measure tells us the value of the entire package of trade and accompanying reforms. Wacziarg and Welch (2003) have updated the Sachs-Warner dataset and have again shown the benefit of such reforms in driving growth.

Just as in the case of the literature on the effect of institutions as explained above, the trade literature has also shifted focus to levels from growth rates. Frankel and Romer (1999) look at the effect of trade share in GDP on income levels across countries for the year 1985. They construct an instrument for the trade share by summing up the gravity-model driven, geographybased predicted values of bilateral trade flows across all trading partners. The variables used to predict bilateral trade flows include distance, country size variables such as land area and population and dummies for whether the countries are landlocked, have a common border etc. They find that their instrumental variables approach produces positive effects of trade on income levels that are greater than the estimates produced by ordinary least squares. Irwin and Tervio (2002) apply the Frankel-Romer approach to cross-country data from various periods in the twentieth century to show that this trade-income relationship is indeed highly robust.

Building on two literatures, namely the one on institutions and incomes and the other on trade and incomes, Rodrik, Subramanian and Trebbi (2002) have looked at the simultaneous effects of institutions, geography and trade on per capita income levels. Using a measure of property rights 7
Policy Research Working Paper 7528

# **Abstract**

The World Bank has used an income classification to group countries for analytical purposes for many years. Since the present income classification was first introduced 25 years ago there has been significant change in the global economic landscape. As real incomes have risen, the number of countries in the low income group has fallen to 31, while the number of high income countries has risen to 80. As countries have transitioned to middle income status, more people are living below the World Bank’s international extreme poverty line in middle income countries than in low income countries. These changes in the world economy, along with a rapid increase in the user base of World Bank data, suggest that a review of the income classification is needed. A key consideration is the views of users, and this paper finds opinions to be mixed: some critics argue the thresholds are dated and set too low; others find merit in continuing to have a fixed benchmark to assess progress over time. On balance, there is still value in the current approach, based on gross national income per capita, to classifying countries into different groups. However, the paper proposes adjustments to the methodology that is used to keep the value of the thresholds for each income group constant over time. Several proposals for changing the current thresholds are also presented, which it is hoped will inform further discussion and any decision to adopt a new approach.

This paper is a product of the Data Group, Development Economics Vice Presidency. 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://econ.worldbank.org. The authors may be contacted at nfantom@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
_Table 4. Estimated effect of mangroves at the potential afforestation sites summarized in Table 3. For each case, the effect of a 100-m wide mangrove belt with a slope of 1/100 is evaluated in terms of (1) embankment height reduction, (2) reduction of the thickness needed for slope protection, and (3) reduction of the shear stresses at the toe of the embankment. The estimates are obtained for_ = 1.3 _,_ = 0.1 _trees/m_<sup>_2_</sup> _, and a profile slope of 1/100._ DD,ww vv cc NN

|Site|Polder|Location|Surge<br>[mPWD]|Hm0<br>[m]|100 m of m<br>|angroves with slope o<br>|f 1/100<br>|
|---|---|---|---|---|---|---|---|
|number|||2-M|2-M|Crest height<br>reduction[m]|Slope protection<br>reduction[%]|Stress<br>reduction[%]|
|1|55/4|Galachipa|4,9|3,7|0.08|8|15|
|2|55/3|Galachipa|5,1|2,3|0.09|11|21|
|3|56/57|Bhola|5|3|0.08|9|17|
|4|73/2|Hatiya South|4,9|3,4|0.08|8|16|
|5|66/3|Khangona|4,8|2,1|0.09|12|23|
|6|69|Boro<br>Moheshkhali|4,4|2,7|0.06|8|16|

A wider belt of 500 m over a shallower profile with a slope of 1/1000 would have a more pronounced effect on embankment designs (Table 5) with a crest height reduction of 0.130.19 m, a slope protection thickness reduction of 23%-32%, and a shear stress reduction of 41%-54%. It should be noted that these predictions are indicative of the potential effect of afforesting mangrove belts, but they are highly sensitive to the choice of vegetation properties and drag coefficient. More accurate predictions could be obtained by using the geometric and mechanical properties of the local mangrove species (see Section 5).

_Table 5. Estimated effect of mangroves at the potential afforestation sites summarized in Table 3. For each case, the effect of a 500-m wide mangrove belt with a slope of 1/1000 is evaluated in terms of (1) embankment height reduction, (2) reduction of the thickness needed for slope protection, and (3) reduction of the shear stresses at the toe of the embankment. The estimates were obtained for_ = 1.3 _,_ = 0.1 _trees/m_<sup>_2_</sup> _, and a profile slope of 1/1000._ DD,ww vv cc NN

|Site|Polder|Location|Surge<br>[mPWD]|Hm0<br>[m]|500 m of m<br>|angroves with slope o<br>|f 1/1000<br>|
|---|---|---|---|---|---|---|---|
|number|||2-M|2-M|Crest height<br>reduction[m]|Slope protection<br>reduction[%]|Stress<br>reduction[%]|
|1|55/4|Galachipa|4,9|3,7|0.16|23|41|
|2|55/3|Galachipa|5,1|2,3|0.19|31|53|
|3|56/57|Bhola|5|3|0.17|26|45|
|4|73/2|Hatiya<br>South|4,9|3,4|0.16|24|42|
|5|66/3|Khangona|4,8|2,1|0.18|32|54|
|6|69|Boro<br>Moheshkhali|4,4|2,7|0.13|24|42|

22
WPS4136

# **The Impact of Sea Level Rise on Developing Countries: A Comparative Analysis**

By Susmita Dasgupta<sup>*</sup> Benoit Laplante<sup>**</sup> Craig Meisner<sup>*</sup> David Wheeler<sup>***</sup> and Jianping Yan<sup>**</sup>

## World Bank Policy Research Working Paper 4136, February 2007

_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 view of the World Bank, its Executive Directors, or the countries they represent. Policy Research Working Papers are available online at http://econ.worldbank.org._

* Development Research Group, World Bank.

** Independent consultants, Canada.

*** Senior Fellow, Center for Global Development

Correspondence should be addressed to: Dr. Susmita Dasgupta, World Bank, 1818 H Street, NW, Washington, DC 20433, <u>sdasgupta@worldbank.org.</u> Acknowledgements: Funding for this project was provided by the Canadian Trust Fund (TF030569) sponsored by the Canadian International Development Agency (CIDA). We would also like to extend our special thanks to Piet Buys, Uwe Deichmann and Jillian Kingston for their guidance and valuable help.