rafmacalaba/datause-encoder-data huggingface dataset:
HIV/AlDS in 2000 increased 700,000 or 67 percent from just a year ago. Most of the newly infected are men, and the majority of them are injecting drug users.

|_Table8:HIVAIDS(figure_|_sfor2001)_||
|---|---|---|
|**Region**<br>**Adultsandchildrenliving**<br>**withHIV/AIDS**|**Adultsandchildrennewlyinfecte**<br>**with HIV**|**dAdultprevalencerate**|
|Sub-Saharan Africa<br>28,100,000|3,400,000|8.4%|
|Middle East&NorthAfrica<br>440,000|80,000|0.2%|
|SouthandSoutheastAsia<br>6,100,000|800,000|0.6%|
|EastAsia&Pacific<br>1,000,000|270,000|0.1%|
|LatinAmerica&Caribbean<br>1,820,000|190,000|0.6%|
|EastemEurope&Central Asia<br>1,000,000|250,000|0.5%|
|Highincome<br>1,515,000|75,500|_0.5%_|
|World<br>41,490,000|5,141,000|1.2%|

Source: UNAIDS

# _Goal 7- Environmental sustainability_

The proposed Millennium Development Goals specify three targets intended to capture the spirit of the Millennium Declaration. The first is very broad and unquantified. It calls for the integration of principles of sustainable development. The second adopts a version of the World Water Forum goal of providing access to a sustainable water source by 2025 to all people. Although the reference dates are not yet agreed, the intention is to reduce by half the proportion of those who lack access to safe drinking water. The third environmental target focuses on urban slum dwellers. At this point, it too lacks a well specified target, but has associated with it indicators of the proportion of people with access to improved sanitation services and the proportion of the population with secure land tenure. Additional work remains to be done to validate the proposed indicators and specify operational targets for the environmental goal.

# _Access to water_

Between 1990 and 2000, about 900 million people gained access to an improved water source. However, this increase in the number of people served was just sufficient to keep pace with population growth. An improved water source refers to any form of water collection or piping used to make water regularly available. While the goal calls for access to "safe drinking water" there is no practical measure of whether water supplies are safe. Even so, connecting households to a reliable source of water that is reasonably protected from contamination would be an important step in improving their lives. In 2000, 1.2 billion people are still without access to an improved water source, 40 percent of whom live in East Asia and the Pacific and 25 percent in Sub-Saharan Africa. Access was far better in urban than in rural areas.
43

# The Energy Sector

# _Structure of Energy Demand_

Primary energy consumption in Indonesia is sourced by commercial and non-commercial energy, where the latter accounts for nearly 60 percent of final demand. The primary sources of commercial energy are oil and gas, with mninor shares of electricity and solid fuels. Sectors using commercial energy include industry (35%M/), transport (28%) and households (21%). Over 80 percent of industrial demand for commercial energy is met by fuel oil and diesel. Another important source is natural gas. Together oil and gas account for over 97 percent of industrial demand. There is a similar pattern of fuel demand by the households, the dominant source being oil, maindy kerosene, with marginal contributions from gas and electricity. Table 3.1 gives the structure of energy demand in Indonesia in 1982, when the first substantial price increase was implemented.

_Table 3.1: Indonesia: Primary Ewergy Consumptfion by Major Sectors, 1982 (thousand of TOE)_

|Indonesia|Solid<br>fuels|<br>Oil|Gas|Eectricity|<br>Non-conmn<br>fuels|. Tota|
|---|---|---|---|---|---|---|
|Total final consunption|131|21210|3694|479|38053|63565|
|_Total industry_|91|7712|917|238|1808|10765|
|Iron<br>andsteel|18|-|-|-|-|18|
|Chemical/petrochem.|73|908|917|-|-|1898|
|<br>Non-spec. industry|6804|-|238|1808||8849|
|_Transportation_|||||||
|Air|40|7192|-|-|-|7232|
|Road|-|256|-|-|-|256|
|Rail|-|6625|-|-|-|6625|
|Intmationalnavigation|40|-|-|-|-|40|
||-|311|-|-|-|311|
|_Other sectors_|||||||
|Residentia|-|5431|2777|241|36245|44693|
|Non-spec.other<br>sect|-|5408|29|-|36245|41682|
||-|23|2748|241|-|3011|
|Non-enrgy uses|-|875|-|-|-|875|

**_Sowrce:_** IEA (1994).
Sierra Leone carries a somewhat positive message. The additional burden for being an ethnic minority woman is less than the sum of the two individual burdens. However, men in the reference group are still 21 percentage points more likely to have completed primary school than ethnic minority women (Table 5) and 4.5 percentage points more likely to have completed secondary school (Table 6).

**Table 5. Primary School Completion Regression Results - Probit**

_Marginal effects, using Male Reference Language Speakers as reference group_

||Peru(2007)|Sierra Leone(2004)|
|---|---|---|
|Age|-.008 ***|-.004 ***|
|Rural resident|-.367 ***|-.306 ***|
|Female|-.087 ***|-.181 ***|
|Minority|-.114 ***|-.056 ***|
|Female * Minority|-.119 ***|.023 ***|
|Obs.|1,289,699|171,963|

Note: Regressions include individuals aged 25 and above. ***p<0.01, **p<0.05, *p<0.10.

**Table 6. Secondary School Completion Regression Results - Probit**

_Marginal effects, using Male Reference Language Speakers as reference group_

||Peru(2007)|Sierra Leone(2004)|
|---|---|---|
|Age|-.007 ***|-.000 ***|
|Rural resident|-.426 ***|-.063 ***|
|Female|-.076 ***|-.040 ***|
|Minority|-.163 ***|-.015 ***|
|Female * Minority|-.143***|.010***|
|Obs.|1,289,699|171,963|

Note: Regressions include individuals aged 25 and above. ***p<0.01, **p<0.05, *p<0.10.

Looking across the five countries included in the sample, the intersectionality variable in the primary school probit regressions is larger in Latin American countries than in African countries (see Table C1 and C2 in Appendix C). In Senegal, for example, the negative impact of each of the three identities are quite small—10.0 percentage points for being female, 1.6 21
Table I.6. Robustness Analysis for the SISBEN Access Outcome

|_Panel A. Polynomial of order zer_|_o_||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|(10)|(11)|(12)|
|1[PEP_i_ = 1]|0.547***|0.379***|0.556***|0.625***|0.707***|0.668***|0.547***|0.379***|0.556***|0.625***|0.707***|0.668***|
||(0.087)|(0.109)|(0.101)|(0.076)|(0.084)|(0.083)|(0.087)|(0.109)|(0.101)|(0.076)|(0.084)|(0.083)|
|Obs. left|500|289|430|472|276|392|500|289|430|472|276|392|
|Obs. right|1,138|546|850|1,778|1,541|1,674|1,138|546|850|1,778|1,541|1,674|
|Mean values (Non-RAMV)|0.0233|0.0233|0.0233|0.0233|0.0233|0.0233|0.0233|0.0233|0.0233|0.0233|0.0233|0.0233|
|_Panel B: Polynomial of order one_|||||||||||||
||(13)|(14)|(15)|(16)|(17)|(18)|(19)|(20)|(21)|(22)|(23)|(24)|
|1[PEP_i_ = 1]|0.567***|0.528***|0.562***|0.653***|0.566***|0.652***|0.507***|0.382**|0.487***|0.608***|0.597***|0.629***|
||(0.127)|(0.143)|(0.130)|(0.097)|(0.111)|(0.096)|(0.144)|(0.183)|(0.158)|(0.104)|(0.134)|(0.106)|
|Obs. left|467|366|457|467|348|452|467|366|457|467|348|452|
|Obs. right|990|713|934|2,053|1,594|2,127|990|713|934|2,053|1,594|2,127|
|Mean values (Non-RAMV)|0.023|0.023|0.023|0.023|0.023|0.023|0.023|0.023|0.023|0.023|0.023|0.023|
|_Panel C: Polynomial of order tw_|_o_||||||||||||
||(25)|(26)|(27)|(28)|(29)|(30)|(31)|(32)|(33)|(34)|(35)|(36)|
|1[PEP_i_ = 1]|0.562***|0.635***|0.464***|0.567***|0.592***|0.596***|0.519***|0.556***|0.487***|0.469***|0.459***|0.482***|
||(0.147)|(0.230)|(0.156)|(0.125)|(0.153)|(0.129)|(0.164)|(0.150)|(0.158)|(0.104)|(0.110)|(0.095)|
|Obs. left|578|366|500|375|362|376|578|366|457|375|362|376|
|Obs. right|1,232|713|1,138|1,735|1,808|2,060|1,232|713|934|1,735|1,808|2,060|
|Mean values (Non-RAMV)|0.023|0.023|0.023|0.023|0.023|0.023|0.023|0.023|0.023|0.023|0.023|0.023|
|Bandwidth type|mserd|mserd|mserd|msetwo|msetwo|msetwo|cerrd|cerrd|cerrd|certwo|certwo|certwo|
|Kernel|triangular|uniform|epanechnikov|triangular|uniform|epanechnikov|triangular|uniform|epanechnikov|triangular|uniform|epanechnikov|

_Notes:_ All columns include department (Antioquia, Atl ́antico, Bogot ́a, and Norte de Santander) and sampling-city fixed effects. Individual controls include: age, gender, and years of education before migration. Labor history in Venezuela controls include: ever worked [=1], type of job, had a written contract [=1], and gap between last job and migration. Household controls in Venezuela include: number of children; household size; if had energy, water, and sewage [=1]; owner of dwelling [=1]; and had smartphone [=1]. Migration decisions controls include: had family/friends in Colombia before migrating [=1], knew of job opportunities before migrating [=1], if the head migrated for health reasons [=1], and time of settlement in Colombia. Standard errors are reported in parentheses and False Discovery Rate (FDR) q-values are reported in brackets.<sup>_∗∗∗_</sup> significant at the 1%,<sup>_∗∗_</sup> significant at the 5%,<sup>_∗_</sup> significant at the 10%.
**Table 4: The Impact of Migration on the Education of Males Aged 12 to 15**

||(1)<br>OLS|(2)<br>2SLS|(3)<br>Ordered<br>Probit|(4)<br>IV-Ordered<br>Probit|(5)<br>Censored <br>Ordered<br>Probit|(6)<br> IV-Censored<br>Ordered<br>Probit|
|---|---|---|---|---|---|---|
|Child is in a Migrant Household|0.151|-0.438|0.013|-0.362|-0.037|-0.512|
||(2.13)*|(1.41)|(0.30)|(2.24)*|(0.55)|(2.20)**|
|Age of Child|3.655|3.779|2.094|2.176|0.203|0.376|
||(6.37)**|(6.62)**|(5.92)**|(6.07)**|(0.21)|(0.39)|
|Age of Child Squared|-0.113|-0.118|-0.061|-0.064|-0.008|-0.014|
||(5.26)**|(5.49)**|(4.64)**|(4.80)**|(0.22)|(0.40)|
|Mother's Years of Schooling|0.205|0.207|0.128|0.129|0.161|0.163|
||(18.56)**|(18.96)**|(22.48)**|(23.39)**|(11.51)**|(11.54)**|
|Proportion of rural households owning land in 1910|-0.066|-0.044|-0.039|-0.024|-0.091|-0.070|
||(1.68)|(1.15)|(1.70)|(1.18)|(3.99)**|(3.11)**|
|Male School Attendance in 1930 (6 to 10 year olds)|0.002|0.008|0.001|0.004|-0.005|0.000|
||(0.42)|(1.27)|(0.15)|(1.26)|(1.09)|(0.10)|
|Gini of Income in 1960|-1.305|-1.155|-0.766|-0.673|-1.560|-1.365|
||(1.76)|(1.43)|(1.65)|(1.46)|(2.90)**|(2.61)**|
|Number of Schools per 1000 population in 1930|-0.154|-0.383|-0.092|-0.239|0.240|0.026|
||(0.96)|(1.84)|(0.99)|(2.12)*|(2.01)**|(0.17)|
|Gini of Male Years of Schooling for 15-20 year olds in 1960|-4.336|-4.251|-2.536|-2.486|-2.625|-2.517|
||(2.92)**|(3.19)**|(3.05)**|(3.19)**|(3.08)**|(2.99)**|
|Average Male Years of Schooling in 1960 for 15-20 year olds|-0.451|-0.471|-0.249|-0.261|-0.300|-0.313|
||(2.90)**|(2.79)**|(3.00)**|(3.31)**|(2.73)**|(2.83)**|
|Observations|6451|6451|6451|6451|3226|3226|

# Robust t-statistics in parentheses clustered at the state level. Instrument is the 1924 state-level migration rate.

Censored ordered probit regressions are carried out on a 50% random sample, and use school attendance as censoring variable. * significant at 5%; ** significant at 1% 36
have seen a large growth in the civil service, particularly given that the civil service census of 2001 stated that there were a total of 6,500 civil servants just a decade ago.

**_Chart 5.2  Recruitment of Present Civil Servants from 2000-2011, by Union and Ngazidja/Anjouan_**

<!-- Start of picture text --> 1400<br>1200<br>1000<br>800<br>994<br>Ngazidja & Anjouan<br>600<br>Union<br>523<br>744<br>400<br>371<br>289 209 426 242<br>296<br>200 371 343 3% turnover<br>0 35 34 6794 145 257 208 106 98 115 182 269 176 (for 11,500 civil servants)<br> |<br> 2001 Constitution of Comoros<br> |<br> 2003-4 Ngazidja Island Government established and staffed<br> |<br> 2008 Comorian military with AU enter Anjouan<br> |<br> 2009 Constitutional reforms approved<br> |<br> 2010 President and Governor elections<br><!-- End of picture text -->

Source: Author database, 2011.

Note: the numbers inside the bars represent the number of civil servants in the questionnaire recruited in that year by either the Union or Island governments. Note: 2011 data is incomplete as the questionnaire was completed in a rolling fashion over that summer.

Chart 5.2 clearly shows several trends in the recruitment of civil servants. First, in numerous years of the last decade, the annual recruitment exceeded the normal 3% annual turnover that may be expected in an administration, consisting of retirements, voluntary departures and natural growth. Second, the vast majority of recruitment takes place in the Island Governments of Anjouan and Ngazidja, which together recruited 3,073 agents from 2006-2011, versus 943 agents in the Union Government. Third, an analysis of the breakdown of recruitment by year shows that recruitment is not consistent every year, but rather comes in peaks. The data from the last decade point to three peak years of recruitment into the civil service. By far, the year of the greatest amount of recruitment is 2010, when a total of 1,263 individuals entered the civil service, of which 994 joined the island governments of Ngazidja and Anjouan, and 269 in the Union Government. Interestingly, elections were held in 2010 for both the Union President and Island Governors. Such a correlation between the election and extensive recruitment suggests a political motive, a story that is further reinforced in the following sections of the paper. Historical explanations for the other peak years, 2004 and 2008, emerge from an analysis of the island recruitment years.

10
26

**Table 9:  Changes in the Asset Allocation of Life Companies and Pension Funds**

||**Domestic**<br>**Bonds**|**Foreign**<br>**Bonds**|**Domestic**<br>**Equities**|**Foreign**<br>**Equities**|**Investment**<br>**Trusts**|**Other**<br>**Assets**|
|---|---|---|---|---|---|---|
|1998|54.4|1.3|13.4|12.3|1.9|16.7|
|1999|46.6|2.1|12.6|18.3|3.5|17.0|
|2000|44.7|4.8|11.3|18.5|4.6|16.1|
|2001|45.1|10.7|8.6|12.5|6.3|16.8|
|2002|53.0|13.6|4.6|6.9|6.2|15.7|
|2003|51.3|12.4|4.4|8.0|8.1|15.8|
|2004|43.2|14.6|4.6|8.3|12.3|17.1|

_Source_ : DFSA

**Figure 4**

<!-- Start of picture text --> Asset composition in Danish Pension companies<br>1999 - 2004<br>80<br>70<br>Bonds<br>60<br>Shares<br>50<br>Real<br>40 Estate<br>Other<br>30<br>20<br>10<br>0<br>1999 2000 2001 2002 2003 2004<br>Source: Danish FSA, Market Development for Life-assurance<br>Companies and Nation-w ide Pension Funds<br>percent<br><!-- End of picture text --> The increased use of derivatives and the changes in strategic asset allocation significantly improved the position of pension funds in relation to unfavorable interest rate movements, as indicated in the simulations shown in Table 10. While in 2001 and 2002, an interest rate fall of 1 percentage point produced a net loss of more than DKK 15 billion, the new portfolio structure had transformed a negative 100 basis point decline into an estimated net gain of DKK 15 billion in 2003 and DKK 8 billion in 2004.
- **FATF, Financial Action Task Force** , “Financing of the terrorist organisation Islamic State in Iraq and the Levant (ISIL),” FATF Report 2015.

- **Freund, Caroline and Diana Weinhold** , “The Effect of the Internet on International Trade,” _Journal of International Economics_ , 2004, _62_ (1), 171–189.

- **Gaibulloev, Khusrav and Todd Sandler** , “What We Have Learned about Terrorism since 9/11,” _Journal of Economic Literature_ , June 2019, _57_ (2), 275–328.

- **Gartenstein-Ross, Daveed** , “Jihad 2.0: Social Media in the Next Evolution of Terrorist Recruitment. Hearing before the Senate Committee on Homeland Security & Governmental Affairs (7 May 2015),” 2017.

- **Gassebner, Martin and Simon Luechinger** , “Lock, stock, and barrel: A comprehensive assessment of the determinants of terror,” _Public Choice_ , 2011, _149_ (3-4), 235.

- **Gohdes, Anita R** , “Repression technology: Internet accessibility and state violence,” _American Journal of Political Science_ , 2020, _64_ (3), 488–503.

- **Greenberg, Karen J.** , “Counter-Radicalization via the Internet,” _The ANNALS of the American Academy of Political and Social Science_ , 2016, _668_ (1), 165–179.

- **Gunitsky, Seva** , “Corrupting the Cyber-Commons: Social Media as a Tool of Autocratic Stability,” _Perspectives on Politics_ , 2015, _13_ (1), 42–54.

- **Guriev, Sergei and Daniel Treisman** , “Informational Autocrats,” _Journal of Economic Perspectives_ , November 2019, _33_ (4), 100–127.

- **, Nikita Melnikov, and Ekaterina Zhuravskaya** , “3G Internet and Confidence in Government,” _The Quarterly Journal of Economics_ , 12 2020.

- **Hegghammer, Thomas** , “Should I Stay or Should I Go? Explaining Variation in Western Jihadists’ Choice between Domestic and Foreign Fighting,” _American Political Science Review_ , 2013, _107_ (1), 1–15.

- **Hjort, Jonas and Jonas Poulsen** , “The Arrival of Fast Internet and Employment in Africa,” _American Economic Review_ , March 2019, _109_ (3), 1032–79.

20
_Ei_ is national employment level in industry _i, e_ is total regional employment, E is total national employment.

While there can be many explanations for high and low concentrations of industries in a given region, high LQs suggest regional comparative advantage for the industry, and therefore they point to industries that might be important exporters and sources of regional product, and low LQs suggest that these industries might be underrepresented in a region and hence their products might need to be imported, hence accounting for some negative entries in regional balance of payments. These speculations depend on a range of assumptions about industry mix, consumer tastes and preferences, and the like, so LQs are clearly only broad-brush indicators, but can be useful in pointing the analyst to industries that warrant further attention. Other methods discussed below will relax some of the assumptions that underpin LQ interpretations.

# **_Related Measures of Concentration_**

A related measure that focuses on a single industry’s concentration across all regions is the Coefficient of Localization (CL). The CL relies not on the ratios of ratios, but instead is computed as one-half the sum of the absolute differences of between numerators and denominators in the LQ calculations for the industry in all regions. The closer this value is to unity, the more concentrated the industry is in the nation. An example for a fictitious industry and nation can be seen in the table below, where the LQ indicates that the industry is relatively concentrated in regions 4 and 5, and the value of the CL is 0.21. Industries with low CL might be less tied to specific regional attributes, and therefore might be good candidates for regional diversification in regions where the LQ is less than one. Diversification strategies will be discussed further, below.

||All Regi<br>|ons<br>||||||
|---|---|---|---|---|---|---|---|
||Region<br>1|Region<br>2|Region<br>3|Region<br>4|Region<br>5|Region<br>6|Total|
|A) Region's share of<br>national industry<br>employment|0.050|0.070|0.150|0.250|0.290|0.190|1|
|B) Regions share of<br>national total<br>employment|0.100|0.150|0.200|0.150|0.180|0.220|1|
||A-B|*0.5|0.025|0.040|0.025|0.050|0.055|0.015|0.21|

25
<!-- Start of picture text --> Polley Resorch<br>Education and Employment<br>VWPS 1118<br><!-- End of picture text --> is part of a series of state-of-

- - This paper a product of the Education and Social Policy Department is part of a series of state-ofthe-art studies on employment and labor market issues and reform programs. Copies of the paper are available free from the World Bank, 1818 H Street NW, Washington, DC 20433. Please contact the Education and Social Policy Department, room S6-035, extension 33680 (March 1992, 52 pages).

In multiple-pillar retirement systems, the government provision of old-age income support plays a very different role from vehicles for encouraging private retirement savings - and for the government regulation and insurance of private savings.

Despite the diversity of public and private retirement systems, and despite their wealth and their potential impact on labor and capital markeis, they are often overlooked in structural analyses of country problems and prospects.

Mitchell examines important institutional features cf retirement systems in industrial and develoP'i.g countries and outlines what is known about their economic effects. She also identifies ways in which public and private retirement systems affect economic adjustment, paying special attention to the costs and benefits of encouraging early retirement.

She finds that a coherent plan for retirement referu must identify how much old-age income security is affordable, how the govenmment and private sector can address failures in the private market to provide this security, and how these cbjectives can be attained given available financing mechanisms.

She finds evidence that many retirement systems will be forced to change a great deal in the next few decades. In some cases:

* Retirement benefits will have to be reduced (perhaps by imposing a means test).

* The age for early retirement wil have to be raised.

* Multiple-pillar plans will have to be integrated and streamlined to rationalize work incentives.

* Incentives and opportunities for private saving will be increased.

In any case, using high-cost, long-term retirement systems to mitigate short- and medium-term unemployment problems will probably prove costly and inefficient as a solution to problems faced by economies in transition.

|'The Policy Research Workmg PaperSerd es dissemiates the fmdigs ofwork under way Cn theBank An objectiveof the seriesr is to get dlese findings out qliickly, e /en if presentations are less than ftuly polished. The fmdings, interpretations, and conclusions in these papers do not necessarily represent official Bank policy.|

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6 theoretical foundations of our empirical model. Section 4 explains the econometric techniques employed and presents the results of our estimations under random effects and dynamic panel data methods. Section 5 summarizes the main findings and implications of this study.

# **2. The Data**

The main sources of our data are the IMF’s International Financial Statistics database and the multi-country dataset on foreign assets compiled by Milesi-Ferreti and Lane (2007). Data for GDP per capita (our dependent variable), investment and trade are obtained from the former source. Financial globalization indexes were constructed based on Milesi-Ferreti and Lane database. Our dataset initially spans from 1970 to 2004. We decide, however, to shorten the sample and concentrate on the period from 1986 to 2004 following the suggestion by Kose et al. (2006) and Kose et al. (2008) that the recent wave of financial globalization started in the mid-1980s.

Rather than assessing together industrial and emerging markets (EMEs), we compare the performance of both groups separately. In our view, comparing the performance of emerging economies with that of industrial economies might not be prudent. Firstly, developing economies and industrial economies started to experiment with IT at different times. Except for Chile and Israel, most EMEs started to target inflation in the second half of the 1990s, while almost every targeter industrial economy adopted the regime in the early 1990s. Secondly, because there might be long lags until the full effects of greater credibility---associated with the adoption of IT---are fully felt in the real economy, it is possible that comparing emerging _vis-à-vis_ industrial economies