rafmacalaba/datause-encoder-data huggingface dataset:
and include the date, time, location (longitude and latitude) of the attack, and basic characteristics of the vessel being attacked, such as type, gross registered tonnage (GRT), etc. The IMB data broadly classify the outcome of the attack into “attempted”, “boarded”, “detained”, “fired upon”, “hijacked”, “missing”, and “suspicious” categories. We define those attacks that ended in hijacks as “successful” attacks.

The second source is the data on ransoms. Information on ransoms comes from the joint data set compiled by the U.N. Office of Drugs and Crime and the World Bank (thereafter referred to as the UNODC-WB data set). The data are compiled from open-source information such as newspaper and reports from national and international law enforcement agencies. Some observations were obtained from interviews with the law firms directly in charge of ransom negotiations. The data set covers 233 vessels hijacked by Somali pirates between April 2005 and December 2012, and contains information on vessel and incident characteristics such as vessel name and date of which allows us hijacking, to match observations with the IMB database. In addition, the data provides information on the most recent (up to December 2012) status of the vessel (whether released, captive, liberated, or sunk), the amount of ransoms paid and the length of the negotiation.

# **4.2 Estimation**

We jointly estimate the model parameters with the method of simulated moments following the ideas outlined in McFadden (1989). There are 23 structural parameters in the model, which we collectively denote as the Θ vector. Our estimation strategy consists of finding the vector Θ that minimizes the weighted distance between a set of simulated moments and their counterparts in the data. Specifically, Θ is defined as: where _n_ = 1 _,_ 2 _, · · ·_ 57 indexes the moments to be matched, and _j ∈{_ 1 _,_ 2 _, · · · , J}_ indexes the number of simulations, _J_ being the total number of such simulations. _mn_ is the _n_ -th moment in the data, and _m_ ̃ _n_ (Θ; _j_ ) is the _n_ th moment in the _j_ th simulation of the model conditional on parameter vector Θ. _W_<sup>�</sup> is the weighting matrix, which is the inverse of the 20
26 Table 1 (cont.) Notes:

-

- D19: a dummy variable. D19 1 indicates that a country has defaulted (or negotiated at concessionary terms) on its national privately held bond debt during 1900-'929.

- D20: a dummy variable defined as D19 but for the 1820-1929 period.

- D30: a dummy variable defined as D 1 but for the 1930s.

- OR: a dummy variable, OR - 1 indica-es that a country **has rescheduled** its debt to official creditors through multilateral agreements during 1956-1968.

- B60 a dummy variable where B60 - 1 indicates that a country has defaulted or continued adjusted servicing of its bond debt during the 1958-1967 period.

- ".": Panama was not sovereign during the entire 1820-1929 period, and there were no private loans to Mexico and Turkey in the 1930s.

- Sources: D 9, D20 and D30 are from Lindert and Morton (1987) OR is from Hardy (1982)

- B60 is from Foreign Bondholders Protective Council Annual Reports.
Changing demographics can also have significant effects on the demand for public sector investments and public services, incentives for private sector investments, social and political conflict, and labor markets. Thus it is important to look at recent changes in demographic patterns in Haiti’s rural and urban areas. The following overview describes demographic changes between rural and urban areas that have taken place from 1982 to 2003.

# **Overview of Demographic Changes**

Haiti is slightly smaller than Wales and its population is growing rapidly (2.2 percent a year). In 1950, the population was estimated at just over 3 million. By 2001, the number had grown to nearly 8 million. With a surface area of just 27,797 square kilometers (km<sup>2</sup> ), Haiti is second only to Barbados as the most densely populated country (306 people per km<sup>2</sup> ) in the Americas. Considering the fact that parts of the vast mountain ranges that traverse the country remain completely uninhabitable, the actual population density is greater still.

After expanding at an annual rate of about 1.5 percent between 1950 and 1982, Haiti’s population increased by 2.2 percent annually in the period 1982-2003 and reached 7.9 million in 2003 (Table 2.1). The current population growth rate of more than 2 percent a year suggests that the country’s inhabitants could total some 12.3 million by 2030.<sup>3</sup> The indications, however, are that the population growth rate is slowing. Overall, the proportion of the population aged below 15 years is gradually declining. This reflects the twin effects of urbanization (fertility rates are lower in urban areas than in the countryside) and gradually declining fertility rates overall (partly the result of increased educational attainment). The median age increased slightly from 18.5 to 18.9 years between 1994-95 and 2000, revealing the incremental pace of this demographic change (DHS 2000).

During 1982-2003, data reveal that the poorest region, the Northeast, together with the West region where the capital, Port-au-Prince, is located, experienced a higher population growth rate than the country’s average of 2.2 percent. The Northeast region reached an annual population growth rate of 2.3 percent and in the West region the population increased at 3.4 percent. This compares to the Southeast and Gran-Anse regions where the population only expanded by around 1.0 percent annually during 19822003 (Table 2.1 and Figure 2.1).

> 3 World Bank: http://genderstats.worldbank.org/hnpstats/HNPDemographic/total.pdf.

4
**Figure 6. Activity in oil-exporting EMDEs**

# **A. Share of oil-exporting EMDEs with increasing/decreasing growth**

# **B. Contribution to oil exporter growth**

<!-- Start of picture text --> Share of oil-exporting EMDEs Percentage point<br>100 Increasing Unchanged Decreasing 0.4<br>0.0<br>75<br>-0.4<br>50<br>-0.8<br>25<br>0<br>GDP Investment Consumption Exports 2015 2016<br>C. GDP changes since 2014, by exchange rate D. GDP changes since 2014, by export<br>concentration<br>Index, 2014=100 Index, 2014=100<br>120 Interquartile range 120 Interquartile range<br>Average<br>Average<br>110 110<br>100 100<br>90 90<br>Floating Pegged Below average Above average<br>2013 2014 2015 2016 2017 2013 2014 2015 2016 2017 2013 2014 2015 2016 2017 2013 2014 2015 2016 2017 Russia Norway Nigeria Canada Saudi Arabia Nigeria Russia Norway Saudi Arabia Canada<br><!-- End of picture text -->

# **C. GDP changes since 2014, by exchange rate classification**

Sources: International Monetary Fund, United Nations Conference on Trade and Development (UNCTAD), World Bank. A. Aggregate growth rates calculated using constant 2010 U.S. dollar GDP weights. Increasing/decreasing growth are changes of at least 0.1 percentage point from the previous year. Countries with a slower pace of contraction from one year to the next are included in the increasing growth category.

B. Blue bars indicate EMDEs, red bars indicate advanced economies.

C. D. Sample includes 31 oil-exporting EMDEs. Figures show average and range for each category.

C. Exchange rate classification is based on the IMF’s Annual Report on Exchange Arrangements and Exchange Restrictions database, in which countries are ranked 0 (no separate legal tender) to 10 (free float). “Hard and soft pegs” refers to countries with a ranking of 1 to 6, while “floating” denotes those with rankings of 7 to 10 and includes countries with horizontal bands and other managed arrangements.

D. “Above average concentration” and “below average concentration” groups are defined by countries above or below the sample average for export concentration in 2014. Concentration index measures the degree of product concentration, where values closer to 1 indicate a country’s exports are highly concentrated on a few products. The average for the sample is 0.6, where 1 is the most concentrated.

20
measuring r_e riskiness of -le borrower, we emploved variables that are similar those in previous s-.u(ties of crodit terms: total debt-to-CNP ratio, debt seryice-to-exparts ratio, imports to GNP ratio, GNP growth. lagged value of investment to GNP ratio, rate of devaluation and rate of inflation.1 In addition, the existence of IMF standby agreemenits between 1955 dnd our sample perod is considered. This variable is incorporated as a measure of economic difficulties of the recent past, and an earlier indicator of recent repayments problems, which may not be captured by the other variabl-s employed.<sup>1 5</sup> The expected signs of these indicators have been discussed extensively in the literature ( for reviews see McDonough (1982), Eaton and Taylor (1986) and Edwards (1984)), hence we will only briefly comment on this issue. Total debt to GNP ratio is expected to exibit a positive sign, since it can be considered as an indicator of solvency for a country. Liquidity problems will be measured by the ratio of debt service to exports and the sign of this coefficient is expected to be positive. Reserves to GNP ratio is an indicator of the level of International liquidity of a country, thus it is expected to have a negative sign. Ratio of investment to GNP will be negatively related to spreads, since it indicates the prospects of a countries future growth. Rat.o of imports to GNP is expected to have a negrtive coefficient as it measureF che vulnerability of the borrower to trade embargos. Alternatively positive sign would be expected to the extent that it measures the vulnerability of a country to outside shocks. Higher rate of growth is argued to result in lower probability of non-payment. High inflation is employed as an indicator of a larger probability of balance of payments crisis. Rate of devaluation is used to measure a countrv's willingness to use exchange rate adjustments to avoid balance of
7

**Figure 1 Real Price of Green Coffee, US cents/lb, constant 1995 dollars**<sup>12</sup>

<!-- Start of picture text --> 600<br>500<br>400<br>300<br>200<br>100<br>0<br>1974 1976 1978 1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000<br>Real price of green coffee<br><!-- End of picture text --> _Source: ICO, WDI; author’s own work_ In light of the dramatic behavior of coffee prices and the significance of agricultural employment in the commodity, it is unsurprising that a wide range of policy interventions have been conceived and applied to coffee. These span international commodity agreements (ICAs), marketing boards, stabilization funds and national price controls.

# _Traditional Commodity Policies in Coffee_

Coffee has known a series of ICAs since the negotiation of the first in 1962. The ICAs were supply retention schemes aimed at maintaining and stabilizing coffee prices within a determined band of prices. The agreement included both producing and consuming countries and, unlike other major commodity agreements such as those for tin, cocoa and rubber that used buffer stock schemes, it functioned via a system of export quotas based on export volume and stocks of the producing countries. Quotas were activated when a composite average price calculated by the International Coffee Organization (ICO) dropped below a series of specified trigger prices. It was, in fact, disagreement about the size and distribution of these quotas that led to a suspension of the first ICA in 1972. In the face of depressed prices induced by supply responses to the post-1976 frost plantings and the failure of other unilateral initiatives at retaining supply, a new ICA was negotiated that took effect in 1980. Interrupted only by temporary price-induced suspensions, this agreement held until its final collapse in 1989. A further effort at an agreement took place with a much reduced membership and no retention clauses in 1994, but the US refused to join even this toothless coffee ICA. At the same time, some coffee producers formed the Association of Coffee Producing Countries (ACPC), which initiated a number of supply retention initiatives in the second half of the 1990s. The most recent supply management measure in effect is the 5% poor quality retention plan of Costa Rica, El Salvador, Guatemala, Honduras, Nicaragua and Colombia.

12 Real price index constructed using annual average ICO indicator prices for the ‘Other Milds’ category, deflated by WDI ( _World Development Indicators_ ) US price deflator.
SPECIAL TOPIC 11: DEMOGRAPHIC TRENDS IN FORMER SOVIET-UNION Lxxi

# SPECIAL **TOPIC H:** DEMOGRAPHIC TRENDS IN **THE** REPUBLICS OF **THE** FORMER U.S.S.R.

Since the previous edition of _World Population Projections_ was issued, the U.S.S.R. has dissolved and become fifteen separate countries for which individual projections are provided in this volume for the first time. The demographic situation in these countries is quite diverse as a result of past differences in vital rates and migration, and their demographic prospects are consequently very different as well. This section focusses on the recent demographic history of and prospects for the former Soviet Republics, which, because they constituted one country, has not been presented before.

## _Populatton growth and dtrnibuton_

Of the fifteen countries, the largest is the Russian Federation with over half of the population of the former Soviet Union, as is shown in Figure SI. The next largest are, in order, the Ukraine, Uzbeklstan, Kazakhstan, and Belarus. The other ten countries contain only 12 percent of the total population of the former Soviet Union. Thus, while the Russian Federation is by far the largest country of Europe, most of the former Soviet republics resemble many other countries in the region in the size of the total population. Land areas of the countries vary even more widely than population totals; as a result, population densities are very uneven, ranging from 129 persons per square kilometer in Moldova, to _&7_ persons per square kilomneter in the Russian Federation, which includes the vast and sparsely populated Siberian regions.

<!-- Start of picture text --> Figure Sl. Composition of the Former Soviet Union<br><!-- End of picture text --> <!-- Start of picture text --> Latvlo CO.91)<br>Ukr& Inn C I7.m d w<br>Arnonis C1.1% ||<br>Kazakhstan C5.0 i<br>_ -_ l R~~~~~~~uss laon Fed . C5 t. 25)<br>Azerbaijan I J on C 2.C5<br>Georgia C1.9x)<br>Kyrgyzatan C1.5W)<br>Turk*nnlstan C /<br>Lithuania (<br>mIoasova C 150\ /<br>TaJ lk stan C t.9<br>8Blarus C.i)_<br>Uxbilklet2cnao5)<br><!-- End of picture text -->
Table 8. Variability and growth rates for export volumes, values,<sup>and unit values and domestic</sup> production of commodities for Tanzania.,

|||19|61-73||_|19|74-87||
|---|---|---|---|---|---|---|---|---|
||Volume|Value|Unit<br>Value|l<br>Production|<br>Volume|Value|Unit<br>Value|Production|
|Coffee|18<br>(6.3)|19<br>(5.7)|10<br>(-0.5)|16<br>(2.5)|14<br>(0.5)|38<br>(-2.2)|40<br>(-2.6)|14<br>(-0.6)|
|Cotton|22<br>(3.9)|22<br>(2.1)|5<br>(-2.1)|17<br>(4.6)|17<br>(-3.3)|25<br>(-8.8)|18<br>_(-5.5)_|26<br>(0.2)|
|Sugar|180<br>(-18.3)|180<br>(-18.4)|58<br>_(5.5)_|9.5<br>(7.1)|_53_<br>(-1.9)|76<br>(-8.0)|26<br>(-7.5)|12<br>(-0.2)|
|Tea|7<br>(8.3)|15<br>(2.0)|6<br>(-6.2)|11<br>(9.0)|15<br>(2.0)|22<br>(-2.6)|23<br>(-4.3)|11<br>(1.4)|
|Tobacco|21<br>(14.7)|24<br>(15.9)|21<br>(4.0)|18<br>(14.6)|25<br>(-5.6)|36<br>(-10.4)|19<br>_(-5.0)_|13<br>(-2.5)|
|Cashew<br>-______||11<br> <br>(8.3)|13<br>(7.4)|12<br>(-0.3)|9<br>(8.0)|36<br>(-18.3)|22<br>(-14.4)|37<br>(3.7)|22<br>(-30.4)|
|Sisal<br>________|11<br>(°3.5)|31<br>(-10.7)|31<br>(-7.2)|8<br>(-2.4)|18<br>(-12.8)|45<br>(-18.6)|34<br>(-6.7)|8<br>(-11.9)|
|Total||11<br>(1.0)||||16<br>(-6.|8)|l|

aCVs for variability and average annual percentage growth (in parenthesis).

27
Policy Research Working Paper 7824

# **Abstract**

This paper introduces a variety of concepts and methods to examine living standards improvements in Nepal in a dynamic perspective. Using data from three rounds of Nepal Living Standards Surveys conducted in the past two decades, together with data from a nationally representative survey that was implemented in 2014 specifically to collect information on social and economic mobility, the paper presents novel statistics on the extent of inter- and intra-generational mobility in Nepal. The findings suggest that there has been appreciable upward mobility in education; that is, Nepalis today are increasingly more likely to be better educated than their parents. However, inter-generational mobility of occupations has been much more muted, with 47 percent of Nepal today remaining in the same occupation as their parents. Upward mobility is higher for younger cohorts and for individuals who move from their rural areas of birth to an urban area. There are also significant differences in mobility by social groups, with Dalits and Terai caste groups having lower upward mobility odds. Examining mobility within generations using synthetic panel techniques, the paper finds that: (a) for every two people who escape poverty, one slides back, suggesting significant churning around the poverty line; (b) a large fraction of those who have escaped poverty remain vulnerable to falling back, with an overall vulnerable population of 45 percent; and (c) the share of the middle class—defined as those with sufficiently low likelihood of falling back into poverty— has increased steadily over the past two decades, reaching 22 percent in 2010–11. However, triangulating subjective well-being data from Gallup, it appears that a majority of even those who constitute the middle class are fundamentally insecure about their economic futures. The prevalence of a large vulnerable population and a nascent, growing but struggling middle class represents a key challenge to consolidating recent gains in moving people out of poverty.

This paper is a product of the Poverty and Equity Global Practice Group. 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 stiwari@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
|UnitedKingdom|69|
|---|---|
|Spain|82|
|Latvia|77|
|Slovak Republic|85|
|Slovenia|84|
|Colombia|55|
|Nicaragua|75|
|Figures for Europe are for 2004, for Latin America for 2000<br>Source: Federcasa (2006),Torche and Spilerman(2007)||

**The U.S. national homeownership rate may never rise much higher than 75 percent.** Regional disparities in ownership rates reflect differing housing costs, with ownership levels somewhat higher in the Midwest (72 percent) and South (69.7 percent) than the Northeast (64.7 percent). Disparities between age groups reflect life cycle factors, with ownership rates higher for older households that would be expected to have saved more and to be more settled. Ownership levels within age groups have been relatively steady for some time. For example, the rate for owners under 35 was 39 percent in 1985, rose to 43 percent in 2003, and dropped to 41.3 percent in the first quarter of 2008. It may be that access to lower down payment loans has made ownership available to as many younger households as can afford to buy or that want to (Census, 2008, 1999, Garriga, et al., 2007).  The greatest remaining disparity is with respect to race, which reflects differences in income among racial groups and, to a lesser extent than in the past, discrimination.<sup>18</sup> The ownership rate for Blacks rose from 46.3 percent in 2000 to 48.1 percent in 2005, and then fell to 47.1 percent in the first quarter of 2008. The Hispanic ownership rate rose from 45.7 percent in 2000, to 49.4 percent in 2005, and then fell to 48.9 percent in 2008. Median household income for Blacks was 61 percent that of whites in 2006, and that of Hispanics was 72 percent that of Whites (Census, 2008). Income disparities between racial groups narrowed between the 1960s and 1990s, and ownership rates among minority groups rose during that period. If incomes and ownership rates among Blacks and Hispanics were to rise to the level of White-headed households, the overall ownership rate would rise to 75 percent.

# **3. Weaknesses in Secondary Market Practices**

Between 2001 and 2006, between 60 and 80 percent of subprime loans were bundled into mortgage-backed securities and sold to investors in capital markets (Inside Mortage

> 18 In the 1930s the Federal Housing Authority (FHA) explicitly referred to African Americans and other minority groups as adverse influences, and many lenders and insurers openly discriminated until such practices were outlawed by in 1968 by a Supreme Court decision and by the 1968 Fair Housing Act. The Community Reinvestment Act of 1977 required banks to offer products to previously underserved neighborhoods. Prosecutions of racial discrimination in lending continue under the Equal Credit Opportunity Act. See Hillier (2003), Department of Justice (2008).

15
the census contains information on the year individuals arrived in each location to account for insample migration in 1980 and 1985 in Columns 3–4 and 5–6. Results do not change. In addition, rates of migration across the boundaries are very low (less than one percent).

Figure 5. Effects of Guerrilla Control on Education Outcomes of the Nonmoving Population Only <!-- Start of picture text --> (a) Wealth Index (b) Average Years of Education<br>.1 .2 .3 .4 .5 .6 .7 .8 .9 1 1.1 1.2 1.3 1.4 1.5 1.6 1.7 1.8 1.9 2 2.1 2.2 2.3 2.4 2.5 2.6 2.7 2.8 2.9 3 3.1 3.2 3.3 3.4 3.5 3.6 3.7 3.8 3.9 4 .1 .2 .3 .4 .5 .6 .7 .8 .9 1 1.1 1.2 1.3 1.4 1.5 1.6 1.7 1.8 1.9 2 2.1 2.2 2.3 2.4 2.5 2.6 2.7 2.8 2.9 3 3.1 3.2 3.3 3.4 3.5 3.6 3.7 3.8 3.9 4<br>Bandwidth (Km) Bandwidth (Km)<br>(c) Literacy Rate<br>.1 .2 .3 .4 .5 .6 .7 .8 .9 1 1.1 1.2 1.3 1.4 1.5 1.6 1.7 1.8 1.9 2 2.1 2.2 2.3 2.4 2.5 2.6 2.7 2.8 2.9 3 3.1 3.2 3.3 3.4 3.5 3.6 3.7 3.8 3.9 4<br>Bandwidth (Km)<br>.5<br>.2<br>0<br>0<br>-.5<br>Coefficient magnitude -.2 Coefficient magnitude<br>-1<br>-.4 -1.5<br>.02<br>0<br>-.02<br>Coefficient magnitude<br>-.04<br>-.06<br><!-- End of picture text --> _Notes:_ The figure illustrates the results for each outcome variable obtained from the estimation of equation 1 using the “stayers” subsample. The gray coloring illustrates 95 percent confidence intervals. Overall, we find that the effects of guerrilla control on the three outcomes are consistent under a wide range of bandwidths (0.1 to 4 km).

26
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