rafmacalaba/datause-encoder-data huggingface dataset:
**Figure 16: Intra-Regional Traffic for Suriname**

<!-- Start of picture text --> 160 4<br> 140<br> 120 3<br> 100<br> 80 2<br> 60<br> 40 1<br> 20<br> - -<br>2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013<br>Year<br>Estimated Seats Airport Pairs<br>Estimated Seats, thousands<br>Intra-Regional Airport Pairs Served<br><!-- End of picture text --> _Source: Authors’ calculation based on data by Diio (2014)._ As with Suriname, Guyana connects mainly through Trinidad, though there is also some traffic to Barbados. Caribbean Airlines dominates the Trinidad route, and the much smaller Barbados route is served by LIAT. This rise in airport pairs served from two to seven comes together with low-volume routes that may not remain sustainable. For example, Jamaica with 13,455 seats per route for 2013, Suriname with 9,828 seats per route for 2013, and Antigua with 1,326 seats per route for 2013 are all below the 30k-45k seats/route considered sustainable (Figure 17). The difference in traffic with the rest of the Caribbean can most likely be attributed to language differences: though Guyana is a lower middle income country, and Suriname is upper middle income, there is more traffic between Guyana and the rest of the Caribbean, since most Caribbean countries are English speaking, as is Guyana, while Suriname is Dutch speaking.

**Figure 17: Intra-regional traffic for Guyana**

<!-- Start of picture text --> 500 8<br> 450 7<br> 400<br> 6<br> 350<br> 300 5<br> 250 4<br> 200 3<br> 150<br> 2<br> 100<br> 50 1<br> - -<br>2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013<br>Year<br>Estimated Seats Airport Pairs<br>Estimated Seats, thousands Intra-Regional Airport Pairs Served<br><!-- End of picture text --> _Source: Authors’ calculation based on data by Diio (2014)._ 46
**-** **<u>Table 6.3 IV estimated impact on enrollment and child development outcomes (Below average parenting score at baseline)</u>**<sup>**a**</sup>

||IVMode|l:Baseline|IVMod|el:Midline|IVMod|el:Endline|
|---|---|---|---|---|---|---|
||Actua|l Batch 1|Actual|Batch 1|Actua|l Batch 1|
||Coeff|Robust SE|Coeff|Robust SE|Coeff|Robust SE|
|**Enrollment in ECED Programs**|||||||
|Was child enrolled in project playgroups in survey year (1=Yes)|0.153***|(0.039)|0.207***|(0.062)|0.265***|(0.071)|
|Was child enrolled in non-project playgroups in survey year (1=Yes)|-0.002|(0.018)|-0.027|(0.033)|-0.029|(0.035)|
|Was child enrolled in Islamic Kindergarten in survey year (1=Yes)|-0.016|(0.015)|-0.026|(0.027)|-0.060|(0.042)|
|Was child enrolled in Kindergarten in survey year (1=Yes)|-0.003|(0.020)|-0.083|(0.051)|-0.141*|(0.075)|
|Was child enrolled in any ECED programs in survey year (1=Yes)|0.128***|(0.045)|0.042|(0.063)|0.004|(0.049)|
|Was child enrolled in Primary school in survey year (1=Yes)|NA|NA|0.007|(0.012)|-0.001|(0.009)|
|Cumulated number of months of project playgroup|1.445***|(0.386)|3.148***|(0.809)|4.398***|(1.110)|
|Cumulated number of months of non-project playgroup|-0.038|(0.173)|-0.181|(0.369)|-0.467|(0.480)|
|Cumulated number of months of Islamic Kindergarten|-0.159|(0.145)|-0.407|(0.373)|-0.689|(0.602)|
|Cumulated number of months of Kindergarten|-0.085|(0.195)|-0.958|(0.629)|-2.267*|(1.261)|
|Cumulated number of months of any ECED programs|1.163***|(0.447)|1.603*|(0.940)|0.975|(1.303)|
|Cumulatednumberof months of Primary school|NA|NA|0.070|(0.116)|0.156|(0.649)|
|**Standardized Child Development Outcomes**|||||||
|EDI: Physical Health & Well-Being|-0.115|(0.128)|0.044|(0.110)|-0.007|(0.138)|
|EDI: Social Competence|0.059|(0.132)|-0.139|(0.103)|-0.137|(0.106)|
|EDI: Emotional Maturity|-0.046|(0.129)|0.058|(0.106)|-0.112|(0.119)|
|EDI: Language & Cognitive Development|0.071|(0.068)|-0.005|(0.087)|-0.037|(0.046)|
|EDI: Communication & General Knowledge|0.006|(0.096)|-0.091|(0.060)|0.049|(0.128)|
|SDQ: Emotional Symptoms|-0.174|(0.120)|-0.066|(0.106)|-0.040|(0.127)|
|SDQ: Conduct Problems|-0.154|(0.111)|0.044|(0.107)|-0.111|(0.109)|
|SDQ: Hyperactivity & Inattention|-0.003|(0.115)|-0.099|(0.111)|0.009|(0.109)|
|SDQ: Peer Problems|-0.081|(0.127)|0.029|(0.123)|0.043|(0.113)|
|SDQ: Pro-social Behavior|0.013|(0.127)|0.065|(0.106)|0.002|(0.115)|
|SDQ:Total Difficulties|-0.175|(0.123)|-0.028|(0.113)|-0.049|(0.120)|

Notes: *** p<0.01, ** p<0.05, * p<0.1;<sup>a</sup> IVs are two dummy variables indicating original batches 2 or 3 with original batch 1 as a reference group. Each row is the result of a separate regression. Models are estimated separately for baseline, midline and endline. Controls include: child’s age, gender, household size, household wealth and mother’s education level. Robust standard errors are clustered at the village level. Sample size for each year varies from 669 to 675.

36
**Table 2: Key Data Used in Economic Analysis**

|**Technology**|**Overn**|**ight**|**Plant**|**Capacity**|**Source**|
|---|---|---|---|---|---|
||**Constr**<br>**(US$/k**|**uction Cost**<br>**W)**|**Economic Life**<br>**(years)**|**Factor (%)**||
|**Solar PV**|Min|2878|25|21|NEA/IEA|
||Max|7381|25|20|NEA/IEA|
|**Solar CSP**|Min|4347|25|34|NEA/IEA|
||Max|5800|20|26|Lazard|
|**Wind**|Min|1223|25|27|NEA/IEA|
||Max|3716|25|23|NEA/IEA|
|**Gas CC**|Min|538|30|85|NEA/IEA|
||Max|2611|30|85|NEA/IEA|
|**Gas CT**|Min|483|25|85|NEA/IEA<br>(2005)|
||Max|1575|20|10|Lazard|
|**Hydro**|Min|757|80|34|NEA/IEA|
||Max|3452|20|50|CPUC|
|**IGCC w CSS***|Min|3569|40|85|NEA/IEA|
||Max|6268|40|85|NEA/IEA|
|**Supercritical^**|Min|1958|40|85|NEA/IEA|
||Max|2539|40|85|NEA/IEA|
|**Nuclear**|Min|3389|60|20|EIA|
||Max|8375|20|90|Lazard|

Note: * IGCC with carbon capture and storage. ^Supercritical coal.

Figure 4 presents the results of the levelized cost analysis. Although the costs of solar energy have come down considerably and continue to fall, the levelized costs of solar energy are still much higher compared to conventional technologies for electricity generation, with the exception of gas turbine<sup>9</sup> . For example, the minimum values of levelized cost for solar technologies (US$192/MWh for PV and US$194/MWh for CSP) are more than four times as high as the minimum values of the levelized cost of supercritical coal without carbon capture and storage (US$43/MWh). Among renewable energy technologies, wind and hydropower technologies are far more competitive with fossil fuel and nuclear power plants.<sup>10</sup>

> 9 In electricity systems, which face high natural gas price, the levelized cost of simple cycle gas turbine technologies is much higher as compared to that of other conventional technologies because the utilities dispatch this technology only when other technologies are not available, thereby resulting in a small capacity utilization factor.  However, in some system where natural gas is the major source for electricity generation, a gas fired power plant could be also used to serve base load. In such cases, the capacity factor could be as high as 85% and its levelized cost would be lower.

> 10 The costs estimated here are close to that compiled in Arvizu et.al (2011).

12
28

# Table 11. Components of migrant intake 1986-87 and 1987-88 Year Ended 30 June

|Component|1987||1988||
|---|---|---|---|---|
||no.|t|no.||
|Family Migration|28550<br>|23.13<br>|30145<br>|19.20<br>|
|-<br>Spouses, minors,<br>fiance(e)s & special|16936|(13.72)|19096|(12.16)|
|-<br>Parents|11614|(9.41)|110 9|(7.04)|
|Skilled Labour &|20925|16.95|22570|14.38|
|Business Migration|||||
|-<br>Skilled labour|9037|(7.32)|7147|(4.55)|
|-<br>Employer nominees|8353|(6.77)|8201<br>|(5.22)<br>|
|-<br>Business Migrants|3535|(2.86)|7222|(4.60)|
|Independenti<br>|33374|27.04|51492|32.80|
|Concessional|||||
|-<br>Independent|1639|(1.33)|12068|7.69|
|-<br>Concessional|31735|(25.71)|39424|25.11|
|Special Eligibility|1398|1.13|1815|1.16|
|Refugee and Special<br>Hlumanitarian Program|11102|9.00|11076|7.06|
|Total visaed migrants|95349|77.26|117098|74.59|
|Non-visaed migrants|17960|14.55|26392|16.18|
|-<br>New Zealand Citizens|15731|(12.75)|24158|(15.39)|
|-<br>Australian Children|||||
|Born Overseas|1561|(1.26)|(15861|(1.01)|
|-<br>Other|668|(0.54)|(648)|(0.41)|
|Grant of Resident Status|10107|8.19|13494|8.60|
|Total|123416|100.00|156984|100.00|

Sources: Department of Immigration, Local Government and Ethnic **Affairs,** Statrstics Monthly, and unpublished **statistics.**
<!-- Start of picture text --> Oil-exporting Countries: Real Effective Exchange Rate<br>200<br>180 1997-2001<br>160<br>140 2002<br>120 2003<br>100<br>80 2004<br>60<br>2005<br>40<br>20 2006<br>0<br>AngolaCameroon ChadCongo, Rep ofCote d’ivoireEquatorial Guinea Gabon<br>Annual average index 100 (2000)<br><!-- End of picture text --> Source: IMF, African Department database, March 2007.

The traditional exports stagnated during the last few years; because producers’ profits were adversely affected by the nominal appreciation of the CFA franc vis-à-vis the U.S. dollar. However, another crucial underlying factor for stagnation in traditional exports was the loss of immigrant farm labor<sup>20</sup> , which was exacerbated by the abandonment of the farms in search of more lucrative employment in the oil and related sectors and, the poor yields of aged plantations.

<!-- Start of picture text --> Equatorial Guinea: Export Revenues<br>7000<br>6000<br>5000<br>Petroleum<br>4000<br>US$ Million Timber<br>3000<br>Cocoa<br>2000 Reexports<br>Other<br>1000<br>0<br>2000 2001 2002 2003 2004 2005<br>Years<br><!-- End of picture text --> Source: Equatoguinean authorities The traditional export sector which has often been in difficulty due to the recurrent shortage of labor, is obviously further affected by the real effective exchange rate appreciation, consequence<sup>21</sup> of favorable terms of trade developments particularly world oil prices and other fundamentals, such as the strong overall fiscal position which itself is good for the overall economy; given the current structure of Equatorial Guinea economy, and the importance of the oil sector and the real effective exchange rate appreciation for Equatorial Guinea, the Dutch Disease is likely to accelerate the shifting

> 20 Indeed, there is continued shortage of labor in EQG.

> 21 Among other things; the noticeable persistent large inflation differential with the major trading partner countries.

15
<!-- Start of picture text -->
-a<br>-ey Ae<br>-ef<br>; er ———CCS<br>40 | — Predicted exports —— Acaial exports<br>:0 SF — |<br>1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006<br>300<br>: ——Predicted cxports ——Actualcxports LoTMN<br>;FN<br>-aa<br>Ae<br>;0ee<br>1990 1991 1992 1993 1994 1995 199€ 1997 1998 1999 2003 2001 2002 2004 2004 2005 2006<br><!-- End of picture text -->
growth.<sup>26</sup> Does the prevalence of deals affect firm growth? Does the orderliness of such deals affect firm growth? This is a new question as while many studies have examined the impacts of various dimensions of the policy environment on firm performance they have focused on the _average_ not the _uncertainty._ A very recent exception is Sequira and Djankov (2010) who, in a detailed study of ports in Southern Africa find that firms will ship to a much more distant port in order to avoid corruption. Their estimates suggest that firms will incur additional shipping costs that are eight times the average bribe paid, suggesting a very large willingness to pay to avoid the uncertainty induced by (coercive) bribes.

To measure the ‘deals environment’, we again use the location-sector-size cells of the firms. The regressions include both a measure of the average level of the deals indicator and the standard deviation of that indicator within the cell. To the extent there is greater variability within the cell, this is an indication of the disorderliness of the deals – not everyone is able to get the same deal on the same terms. This variation could also be an indicator of uncertainty about whether the deal struck will in fact be honored. If uncertainty is the underlying concern, we would expect the results on the variation measures to be particularly strong.

The dataset includes information on employment at the time of the survey and three years prior to the survey. In calculating employment growth, we follow Haltiwanger’s (2009) approach of using the difference divided by the average. This gives

> 26 Firm level regressions would also raise concerns about possibility endogeneity – whether expanding firms attract the attention of officials and become targets for officials seeking additional payments.  Using location-sector-size averages has been used as a technique to address this concern (Dollar et al 2006, Aterido et al. forthcoming).  Here, our interest is different, how the averages and extent of variation themselves affect firms’ decisions.

35
some purposes it is useful to combine information from many individual indicators into some kind of summary statistic, while for other purposes the disaggregated data are of primary interest. Even where disaggregated data are of primary interest, however, it is important to rely on a number of independent sources for validation, since the margins of error, and the likelihood of extreme outliers, is significantly higher for a disaggregated indicator.

An excessively narrow emphasis on "actionable" indicators detailing specific policy interventions immediately under the control of governments can divert attention from equally-important discussions of which of these indicators are "action-worthy" in the sense of having significant impacts on outcomes of interest. And the answer to which indicators are most “action-worthy” is rarely obvious a priori, and often context-specific. Focusing excessively on “actionable” indicators, while downplaying the scrutiny on outcome indicators, may result in undue emphasis on measures that may not translate into concrete progress on outcomes.

_Use indicators appropriate for the task at hand_ . As with all tools, different types of governance indicators are suited for different purposes. In this survey we have emphasized governance indicators that can be used for regular cross-national comparisons. While many of these indicators have become increasingly specific over time, often they remain blunt tools for monitoring governance and studying the causes and consequences of good governance at the country level. For these purposes a wide variety of innovative tools and methods of analysis have been deployed at the country level in many countries worldwide, and reviewing these is well beyond the scope of this survey. Examples among these in-country tools include the World Bank’s Investment Climate Assessments (ICAs), the World Bank Institute’s Governance and AntiCorruption (GAC) diagnostics, the corruption surveys carried out by some chapters of Transparency International (TI), and the institutional scorecard carried out by the Public Affairs Center in Bangalore, India. And disaggregating further to the level of individual projects, many project-specific interventions and diagnostics are possible to measure governance carefully at this level.<sup>20</sup>

> 20 One of the best-known and best-executed recent studies of this type is a study of corruption in a local road-building project by Olken (2007).

31
SOUTH SUDAN’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE South Sudan has not attracted as much private finance into infrastructure as other African peers. Over the early 2000s, private flows into ICT and a 2006 seaport concession near Juba were equivalent to slightly less than 1 percent of GDP. Many other African countries have done significantly better in this area, suggesting there is room for capturing additional resources vis-à-vis the size of the economy (figure 25). Countries such as the Democratic Republic of Congo, Liberia, Nigeria, Uganda, Kenya, and Senegal have all captured between 1.8 and 2.5 percent of GDP, while the most successful country in this regard— Guinea-Bissau—has captured in excess of 3 percent of GDP.

**Figure 25. Numerous African countries capture more private investment than South Sudan**

<!-- Start of picture text --> 3.5<br>3.0<br>2.5<br>2.0<br>1.5<br>1.0<br>0.5<br>0.0<br>Power ICT Transport Water<br>% GDP<br><!-- End of picture text --> _Source:_ PPI Project Database. (http://ppi.worldbank.org), in current $ millions.

China and India might be sources of additional funding given the track record of these investors in neighboring countries, particularly Sudan in the power sector. In fact one project financed by China ($200 million) has already been identified—though not confirmed—for developing power distribution for villages and rural areas in the White Nile State. The role of donors is also expected to increase, which is critical in making the funding gap manageable.

Adopting lower-cost technologies could substantially reduce the cost of meeting the posited infrastructure targets, and help make the funding gap manageable. Meeting the MDGs for water supply and sanitation with lower-cost technologies than previously used (such as stand posts, boreholes, and improved latrines), could reduce the associated price tag from $348 million to $223 million. Similarly, meeting transport connectivity standards using lower-cost road-surfacing technologies (such as singlesurface treatment) as well as providing rural accessibility access to the currently active agricultural land, could reduce the associated price tag from $711 million to $232 million in South Sudan. The overall savings from these measures would amount to a sizable $605 million for South Sudan (table 23).

**Table 23. Savings from innovation**

|$ millions|Before innovation|After innovation|Savings|Savings as % of<br>sector funding gap|
|---|---|---|---|---|
|WSS appropriate technology|348|223|125|37|
|Roads appropriate technology|711|232|479|155|
|Total|1,059|454|605||

_Source:_ Derived from Foster and Briceño-Garmendia, AICD Flagship Report, 2009. _Note:_ WSS = water supply and sanitation.

45
Most of the control variables in equation (1) are the same as those used in other studies of MFI performance and outreach (Ahlin and Lin, 2006; Cull, Demirgüç-Kunt, and Morduch, 2007). _Yield_ is the real gross portfolio yield, a measure of average interest charges faced by customers. Because loan losses are not netted out of the revenues, this measure is intended to capture the ex-ante interest rate charged by the lender rather than the ex-post interest rate realized on the portfolio. In other studies, portfolio yields have been positively linked to profitability measures (Cull, Demirgüç-Kunt, and Morduch, 2007, 2009a). The ratios of _capital costs_ and _labor costs_ to assets are included in the regressions as measures of the productive efficiency of microfinance institutions. The same studies find that those costs measures are negatively linked to profitability.

We control for the lending methods of the microfinance institutions with dummy variables for _village bank lending_ and _solidarity group lending_ . Solidarity group lenders employ contracts based on joint liability. Loans are made to individuals, but the group, which has between 3 and 10 members depending on the institution and location, shoulders responsibility for a loan if a member cannot repay. Village bank lenders tend to make loans to larger groups. In some cases, each branch forms a single, large group and is given a degree of self-governance. In Uganda’s village banks, for example, joint liability loans are made to groups of twenty or more. There is no extensive screening, no collateral is required, loans are smaller, and interest rates are higher than for other lenders (McIntosh, de Janvry, and Sadoulet, 2005). The omitted category in our regressions is therefore “individual lenders” (shorthand for microbanks that lend to individuals using standard bilateral contracts between the lender and a single borrower).<sup>13</sup> Institution-specific characteristics are captured by controls for the _age_ and size (measured by total _assets_ ) of microfinance institutions. Well-established microbanks might have a different profile than recent entrants, especially in terms of profitability. Similarly, larger microbanks might be better able to take advantage of scale economies that improve their profitability, though they may rely on larger loans to do so.

> 13 In the regressions that interact the individual lending dummy with the bank penetration variables, we change the omitted category to include both the solidarity group lenders and the village banks.

13
32 Table 7 Pensions in the Baltics and Central Europe

||Pension<br>recipient<br>percent <br>populati<br>|s as<br> of the<br>on<br>|Average <br>average <br>ratio<br>(in %)<br>|pension- |<br>wage<br>|Real<br>average |<br>pension |<br>(1987= <br>100)<br>|Total real<br> pension<br> spending<br>| (1987=100)<br>|
|---|---|---|---|---|---|---|
|P||87-88||92-93|87-88||92-93|92-93|92-93|
|Baltics|<br>22.0|<br>[244|<br>[38|<br>[3s|[ss<br>[|56|
|Hungary<br><br>|<br>|<br>|<br>|<br>|||
|[potand<br>[<br> <br>|.4<br>|[22a<br>|[as<br>|[oe[<br>|ios<br>|ta?<br>|
|Czechrepublic<br><br>|<br>[27.8<br>|<br>[294<br>|<br>[56<br>|<br>[ao|<br>|<br>f<br>|<br>as<br>|
|<br><br>[stovakia<br><br>|<br>20.5<br>|<br>[5.3<br>|fae<br>|<br>[as<br>|[6<br>|fo<br>|
|<br>[stovenia<br>2<br>|<br>3.8<br>|<br>[a8<br>|<br>[sa<br>|<br>[se<br>|<br>[<br>|70<br>|
|CentralEurope|<br>|22.8|<br>|26.9|<br>[52|<br>|ss_|<br>[so<sup>[</sup>|<sup>or</sup>|

Note: Regional means are unweighted averages.

Source: World Bank PRDTE data files.

How were Baltic countries able to change pension rules and contain pension costs? The contrast between the Baltics and Central European countries (including Poland) is striking because their original starting points were either similar, or Central European countries’ position for cost-cutting was more advantageous. First, the Baltics started the transition with a low pension-wage ratio (under 40 percent), while Poland, Hungary, Slovenia and former Czechoslovakia had pension-wage ratios between 45 and 60 percent (see Table 7). Four years into the transition, the ratios for the Baltics have gone further down, while those in Central European countries, with the exception of the Czech republic, have stayed the same or gone up. Second, since the wage (the denominator in our pension-wage ratio) declined even faster in the Baltics, the real cut in the pensions in the Baltics was even more substantial than indicated by the pension-wage ratio alone. Between 1987-88 and 1992-93, the average pension and pension