rafmacalaba/datause-encoder-data huggingface dataset:
SUDAN’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE

**Figure 23. Hidden costs are high in Sudan relative to other African countries**

<!-- Start of picture text --> 300<br>250<br>200<br>150<br>100<br>50<br>0<br>Nigeria South Liberia Niger Mali Ghana Sudan Burkina Cote Benin Cape Senegal South<br>Sudan Faso d'Ivoire Verde Africa<br>Collection Inefficiencies Underpricing Losses<br>Percentage of revenues<br><!-- End of picture text --> _Source:_ Derived from Briceño-Garmendia, Smits, and Foster (2009); Sudan Central Bureau of Statistics handbooks.

In the long run, Sudan’s power cost-recovery situation looks a little more promising, particularly if the nation’s hydropower potential is developed. Using a model that simulates optimal (least-cost) strategies for generating, transmitting, and distributing electricity in response to demand increases, it is estimated that the long-run marginal costs of producing power in Sudan would be about $0.13/kWh when all cost-effective power strategies are fully developed. Although this is well below the historic cost of around $0.19/kWh, it is still significantly above the current tariff of $0.09/kWh. Thus, there is a need to continue raising the tariff toward these long-term equilibrium levels (figure 24).

**Figure 24. Existing tariffs for power are insufficient to recover huge operating costs and long-run marginal costs**

<!-- Start of picture text --> 20<br>15<br>10<br>5<br>US$ cents per KWh 0<br>Existing total Longrun - Existing Effective tariff Average Existing capital<br>cost marginal Cost operating cost revenue cost<br><!-- End of picture text --> _Source:_ AICD calculations.

Sudan’s rich endowment of hydropower resources could make it a significant exporter of hydropower in the Nile Basin region. To meet its potential, Sudan would need to develop more than 3,100 MW of hydropower capacity beyond its domestic needs, which would represent a doubling of capacity. The nation would also need to develop around 13,500 MW of interconnector capacity, the largest of any country in the region.

> <sup>33</sup>
References Allison, P. D. (1999). Comparing logit and probit coefficients across groups. Sociological Methods and Research 28.2, 186-208. Arrow, K. (1950). A difficulty in the concept of social welfare. Journal of Political Economy 58, 328-346. Bartholomew, D. (1982). Stochastic Models for Social Processes. New York: John Wiley. Baulch, B. and J. Hoddinott (2000). Economic mobility and poverty dynamics in developing countries. Journal of Development Studies 36.6, 1-24. Blanchflower, D. and A. Oswald (2000). Well-being over time in Britain and the USA. National Bureau of Economic Research Working Paper 7487. Brickman, P. and D. Campbell (1971). Hedonic relativism and planning the good society. In M. Apley (Ed.), Adaptation-Level Theory: A Symposium, pp. 287-302. New York: Academic Press. Browning, M. (1992). Children and household economic behavior. Journal of Economic Literature (30), 1434-75. Clark, A. and A. Oswald (2002). A simple statistical method for measuring how life events affect happiness. University of Warick, mimeo. Conlisk, J. (1990). Ranking mobility matrices. Journal of Mathematical Socology 15, 173-91. Deaton, A. (1992). Understanding Consumption. Oxford: Clarendon Press. Deaton, A. (1997). The Analysis of Household Surveys. Washington, DC: World Bank. Diener, E., E. Suh, R. Lucas, and H. Smith (1999). Subjective Well-Being: Three decades of progress. Psychological Bulletin 125, 276-302. Easterlin, R. (1974). Does economic growth improve the human lot? Some empirical evidence. In P. David and M. Rider (Eds.), Nations and Households in Economic Growth. Essays in Honor of Moses Abramovitz. New York: Academic Press. Frey, B. and A. Stutzer (2000). Happiness, economy and institutions. The Economic Journal 110, 918-938.

- Glewwe, P. and G. Hall (1998). Are some groups more vulnerable to macroeconomic shocks than others? Hypothesis tests based on panel data from Peru. Journal of Development Economics 56, 181-206.

- Graham, C. and S. Pettinato (2001). Frustrated achievers: Winners, losers, and subjective well being in new market economies. The Brookings Institution, Center on Social and Economic Dynamics Working Paper Series 21.

- Greene, W. H. (2000). Econometric Analysis. London: Prentice-Hall. Hayo, B. and W. Seifert (2001). Subjective economic well-being in Eastern Europe. Essen University, mimeo.

- Hoetker, G. (2001). Confounded coefficients: Accurately comparing logit and probit coefficients across groups. University of Illinois, mimeo.

- Inman, R. and F. Oberholzer-Gee (2003). Democratic institutions and subjective well-being. University of Pennsylvania, mimeo.
<!-- Start of picture text --> Model1, BAA, Fg Model1, BAA, Fad Model1, BAA, FemeIn Model1, XR, Fg Model1, XR, Fad Model1, XR, FemeIn<br>2 2<br>1 0<br>0<br>-1 -2<br>-2 -4<br>Model2, BAA, Fg Model2, BAA, Fad Model2, BAA, FemeIn Model2, XR, Fg Model2, XR, Fad Model2, XR, FemeIn<br>2 2<br>1 0<br>0<br>-1 -2<br>-2 -4<br>0 5 10 0 5 10 0 5 10 0 5 10 0 5 10 0 5 10<br>Years Years<br>95% CI Cumulative dynamic multipliers 95% CI Cumulative dynamic multipliers<br><!-- End of picture text --> Figure A.2: Cumulative effect of a permanent one-standard deviation increase in the bond spread measure (left panel) and the log of the real US trade-weighted exchange rate (right panel), over the estimated factors, based on the regression model of columns 1-3 of table 8 (top graphs) and the expanded regression model in columns 4-6 of table 8 (lower graphs).

<!-- Start of picture text --> Model1, ChinnIt, Fg Model1, ChinnIt, Fad Model1, ChinnIt, FemeIn Model1, Slope, Fg Model1, Slope, Fad Model1, Slope, FemeIn<br>15 1<br>10 0<br>5<br>0 -1<br>-5 -2<br>Model2, ChinnIt, Fg Model2, ChinnIt, Fad Model2, ChinnIt, FemeIn Model2, Slope, Fg Model2, Slope, Fad Model2, Slope, FemeIn<br>15 1<br>10 0<br>5<br>0 -1<br>-5 -2<br>0 5 10 0 5 10 0 5 10 0 5 10 0 5 10 0 5 10<br>Years Years<br>95% CI Cumulative dynamic multipliers 95% CI Cumulative dynamic multipliers<br><!-- End of picture text --> Figure A.3: Cumulative effect of a permanent one-standard deviation increase in the average over advanced and emerging countries of the ChinnIto capital account openness measure (average of the contemporary value and its 1 and 2 years lags, left panel) and the slope of the yield curve (10year US treasury constant maturity minus federal funds rate, right panel), over the estimated factors, based on the regression model of columns 1-3 of table 8 (top graphs) and the expanded regression model in columns 4-6 of table 8 (lower graphs).

47
**<u>Table 30: Total Cruise Spending as a Percentage of GDP, 2012</u>**

|<br>**Country**|<br>**Total Cruise**<br>**Spending**<br>**(US$ Million)**|<br>**GDP (US$ -**<br>**Million)**|<br>**Total Cruise**<br>**Spending as a**<br>**percentage of GDP**|
|---|---|---|---|
||<br>**Small Island State**<br>|**s**<br>|**(%)**<br>|
|Antigua and Barbuda|40.4|1,007|4.0|
|Dominica|16.0|451|3.6|
|Grenada|13.2|666|2.0|
|St. Kitts and Nevis|65.7|579|11.4|
|St. Lucia|40.8|1,042|3.9|
|St. Vincent and the Grenadines|1.7|593|0.3|
||**Caribbean Hubs**<br>|||
|Bahamas, The|320.8|7,842|4.1|
|Barbados|45.7|2,521|1.8|
|Dominican Republic|17.5|51,990|0.03|
|Jamaica*|89.5|14,755|0.6|
|Trinidad and Tobago*|23|18730|001|
||.<br>**Continental Caribbe**|,<br>**an**|.|
|Belize|51.9|1,245|4.2|

_Source: Cruise Industry News Annual Report (2012) and Word Bank (2013b)._ In terms of number of cruise passengers, The Bahamas leads all Caribbean destinations with 4 million cruise passengers visiting annually (Table 31), making it one of the main destinations for cruises in the Caribbean. Jamaica is the second highest with 1.3 million passengers. Of the Caribbean hubs, Trinidad and Tobago has the lowest number of cruise visitors, receiving only 102,000 thousand passengers annually.

Antigua and Barbuda, St. Lucia, and St. Kitts receive approximately 600,000 passengers, who account for more than half of their total annual number of visitors. In an interesting note, Grenada and St. Vincent, who are both located in the south of the Caribbean, see a huge difference in the number of annual cruise visitors: St, Vincent only receives 66,000 visitors compared to the 320,000 who visit Grenada.

72
# **_Table 6: Effects on taking both screening tests_**

||(1)|(2)|(3)|(4)|
|---|---|---|---|---|
||Both tests|Both tests|Both tests|Both tests|
|Intervention Group One|.15***<br>|.15***<br>|.15***<br>|.156***<br>|
||(.022)|(.022)|(.022)|(.022)|
|Intervention Group Two|<br>.145***|<br>.145***|<br>.145***|<br>.157***|
||(.021)|(.021)|(.021)|(.022)|
|Intervention Group Three|.143***|.143***|.143***|.15***|
||(.021)<br>|(.021)<br>|(.021)<br>|(.022)<br>|
|Intervention Group Four|.308***|.308***|.307***|.312***|
||(.025)|(.025)|(.025)|(.026)|
|Small Facility||<br>.062|<br>.062|<br>-.162*|
|||(.064)<br>|(.064)<br>|(.097)<br>|
|Medium Facility||.062|.063|.039|
|||(.102)|(.102)|(.089)|
|Age|||<br>0|<br>0|
||||(.001)|(.001)|
|Male|||<br>-.035**|<br>-.032*|
||||(.017)|(.017)|
|Urban||||<br>-.213*|
|||||(114)|
|No education||||.<br>.096|
|||||(.128)|
|Technical Vocation, College||||<br>-.04**|
|||||(.02)|
|Higher Education||||-.028|
|||||(.025)|
|Single||||<br>-.063**|
|||||(.028)|
|Divorced||||-.005|
|||||(.053)|
|Widowed||||<br>.016|
|||||(.052)|
|State Employee||||.004|
|||||(.025)|
|Private Sector Employee||||<br>-.023|
|||||(.022)|
|Self Employed||||-.036|
|||||<br>(.031)|
|Income not sufficient for food||||<br>-.055|
|||||(.036)|
|Income sufficient but not for clothes||||<br>.04**<br>|
|||||(.02)|
|Income sufficient but not for savings||||<br>.017|
|||||(.034)|
|Income sufficient for everything||||<br>-.04<br>|
|||||(.061)|
|Y Mean Control|.035|-|-|<br>-|
|SE Control|(.009)|-|-|-|
|Observations|<br>2000|2000|2000|1997|
|R-squared|.076|.076|.078|.088|
|<br>p ig1=ig2|<br>0.855|<br>0.855|<br>0.862|<br>0.955|
|<br>p ig1=ig3|0.783|0.783|0.805|0.822|
|p ig1=ig4|0.000|0.000|0.000|0.000|
|p ig2=ig3|0.926|0.926|0.941|0.775|
|<br>p ig2=ig4|0.000|0.000|0.000|0.000|
|<br>pig3=ig4|0.000|0.000|0.000|0.000|

_Standard errors are in parentheses;_ Facility and Province effects on all models (not shown) and robust standard errors _*** p<.01, ** p<.05, * p<.1_ 19
Policy Research Working Paper 5005

# **Abstract**

The 2005 Paris Declaration on Aid Effectiveness sets targets for increased use by donors of recipient country systems for managing aid. A consensus view holds that country systems are strengthened when donors trust recipients to manage aid funds, but undermined when donors manage aid through their own separate parallel systems. This paper provides an analytical framework for understanding donors’ decisions to trust in country systems or instead to micro-manage aid using their own systems and procedures. Where country systems are sufficiently weak, the development impact of aid is reduced by donors’ reliance on them. Trust in country systems will be sub-optimal, however, if donors have multiple objectives in aid provision rather than a sole objective of maximizing development outcomes.

Empirical tests are conducted using data from an OECD survey designed to monitor progress toward Paris Declaration goals. Trust in country systems is measured in three ways: use of the recipient’s public financial management systems, use of direct budget support, and use of program-based approaches. The authors show using fixed effects regression that a donor’s trust in recipient country systems is positively related to (1) trustworthiness or quality of those systems, (2) tolerance for risk on the part of the donor’s constituents, as measured by public support for providing aid, and (3) the donor’s ability to internalize more of the benefits of investing in country systems, as measured by the donor’s share of all aid provided to a recipient.

This paper—a product of the Human Development and Public Services Team, Development Research Group—is part of a larger effort in the department to determine how aid can be delivered in ways that better promote development. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The lead author may be contacted at sknack@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
Not only injustice perceptions fell during the last decade, but the intensity of beliefs also diminished over time. To see this, Figure 6 shows the evolution of the different possible answers to the question of unfairness perceptions.

_Figure 6. Intensity of unfairness perceptions in LA, 1997-2015_ <!-- Start of picture text --> 60<br>Unfair<br>40<br>Very unfair<br>Fair<br>20<br>Very fair<br>0<br>1997 2000 2005 2010 2015<br>%<br><!-- End of picture text --> Note: This figure presents the average across 18 LA countries of the share of individuals that perceived income distribution as very unfair, unfair, fair, and very fair over the 1997-2015 period.

As can be seen from Figure 6, the decrease in unfairness perceptions was driven mainly by strong beliefs about unfairness (i.e., people who perceived inequality as very unfair). While in 2001, 37.4% of the population thought income distribution was very unfair, this figure decreased to 25% in 2015. In contrast, weak beliefs about unfairness (i.e., the population that responded income distribution was only ‘unfair’), have been more volatile, remaining relatively constant during the 2000s (from 51.4% in 2001 to 49% in 2015). On the other hand, the share of the population believing in a fair distribution increased from a meager 9.5% in 2001 to a sizable 22.6% in 2015, while strong beliefs on fairness (i.e., ‘very fair’), have remained under 5% throughout all the 2000s.

# **3. Is fairness absolute or relative?**

In the previous sections, we showed that a large, albeit decreasing, share of the population believes income distribution is unfair, and that such levels and evolution are consistent with a high, but also declining Gini coefficient. Despite being the most widely used indicator to measure income inequality, the general population’s views on income distribution might, in fact, be better captured with other indices.

The literature on inequality measurement makes a crucial distinction between two types of indicators: the relative (such as the Gini coefficient) and absolute ones (such as the variance). The main distinction between them is that relative indicators fulfill the scale11
**Table 1: Parameter Estimates**

||||Russia 19|94-1998|||Poland 1|993-1996||
|---|---|---|---|---|---|---|---|---|---|
|Parameter<br>|Symbol|Consu|mption|Inc|ome|Consu|mption|Inco|me|
|_Means of logs_||||||||||
|Period I|tic|8.129|(0.018)|7.834|(0.022)|6.231|(0.007)|6.218|(0.008)|
|Period 2|VC2|7.940|(0.017)|7.530|(0.024)|6.173|(0.008)|6.210|(0.010)|
|Period 3|1<sup>1</sup>c3|7.777|(0.020)|7.325|(0.031)|6.204|(0.007)|6.271|(0.009)|
|Period 4|**ptC4**|7.506|(0.020)|7.230|(0.022)|6.240|(0.007)|6.322|(0.010)|
|_Variances of logs_||||||||||
|Period 1|2c|0.678|(0.038)|0.736|(0.045)|0.227|(0.007)|0.335|(0.016)|
|Period 2|C<sup>2 </sup>C2|0.623|(0.023)|0.970|(0.058)|0.239|(0.007)|0.355|(0.016)|
|Period 3|**0**<sup>**2**</sup>**C3**|0.769|(0.038)|1.699|(0.090)|0.240|(0.007)|0.338|(0.018)|
|Period 4|&SC4|0.698|(0.029)|1.110|(0.065)|0.248|(0.007)|0.319|(0.014)|
|_Variances offirst diferen_|_ces of lo_|_gs_||||||||
|Period 2-1|0 <sup>2</sup>AC2|0.678|(0.029)|1.138|(0.072)|0.174|(0.006)|0.321|(0.017)|
|Period 3-2|&2C3|0.686|(0.026)|1.801|(0.112)|0.154|(0.006)|0.294|(0.022)|
|Period 4-3|(32AC4|0.785|(0.033)|1.950|(0.117)|0.161|(0.007)|0.293|(0.023)|
|_Decomposition of shocks_||||||||||
|Var[persistent shock]|**a213**|0.059|(0.022)|0.130|(0.041)|0.022|(0.003)|0.028|(0.013)|
|Var[transitory shock 2]|a2,2|0.256|(0.022)|0.560|(0.066)|0.065|(0.004)|0.135|(0.012)|
|Var[transitoryshock3] 31||0.371|(0.025)|1.111|(0.092)|0.067|(0.004)|0.131|(0.019)|
|_Derived estimates_||||||||||
|Var[underlying level 2]|(52c2-|0.367|(0.030)|0.409|(0.048)|0.174|(0.005)|0.220|(0.015)|
|Var[underlying level 3]|20C3*|0.398|(0.031)|0.588|(0.056)|0.172|(0.006)|0.207|(0.015)|
|Correlation[C*2,T13]|p|-0.094|(0.145)|0.106|(0.116)|-0.188|(0.047)|-0.265|(0.1.26)|
|Fraction persistent||0.137|(0.047)|0.105|(0.034)|0.244|(0.029)|0.178|(0.075)|
|_Sensitivity of "Fraction_|_Persistent_|_" to the_|_assumpti_|_on that 7_|_7i, and eit a_|_re uncorr_|_elated._|||
|Frac. persist. if corr[rlitt,|e]=<br>1.0|<br>0.|081|0.0|65|0.1|84|0.1|01|
|<br>Frac. persist. if**corr[riit,**,,|<br> ]=<br>0.2|0.|120|0.0|93|0.2|25|0.1|54|
|Frac. persist. if corr[Tlit,|eq]= -0.2|<br>0.|159|0.1|19|0.2|68|0.2|10|
|Frac. persist. if corrhNi,|ej,l= -1.0|<br>0.|439|0.2|69|0.5|64|0.7|56|

Note: All measures are for equivalent adults. The equivalence scale is household size to the power of 0.75 (0=0.75). Data **consist of** balanced panels, 1993-1996 for Poland and 1994-1998 for Russia. Fraction persistent is the fraction of the shock between period 2 and **3 that** is persistent. **This** fraction is defined by the ratio of **the** variance of **the** persistent shock to **the** variance of **the** total **shock** between period 2 and 3.

# **_Simulations_**

Because the variances of the transitory shocks for the first and last periods are not identified, the paths of persistent and transitory income and expenditure &an only be simulated for periods 2 and 3. While the estimation of the variances of the various 11
23 Mercosur countries had been liberalizing imports on a most favored nation basis for several years when, in 1991, they introduced their first widespread set of preferential tariff cuts. This is the year for which the UN COMTRADE records indicate that intra-block trade accelerated sharply. If the most dynamnic products in Mercosur's intra-trade, or those that were shifting most rapidly toward the region, had disproportionately high preferences this would suggest that Mercosur trade barriers were a factor in the re-orientation of exports. Evidence relating to this point could come from an analysis of the margins of preference that Mercosur's trade barriers provide member countries. Are these high enough to account for the increases in intra-trade that occurred during the 1991-94 period when tariff preferences on all but a few products were being implemented.

Several, sources of statistics on Mercosur's tariffs and NTBs are available for analyses of these points. First, a cooperative project between UNCTAD and the World Bank, named SMART -- Software for Market Analysis and Restrictions on Trade, compiled statistics on many OECD and developing countries' pre-Uruguay Round trade barriers (see UNCTAD and the World Bank, 1989 for a description of the SMART database and operating system). Since both Brazil and Uruguay's 1988/89 tariffs were included in these records (along with data on Brazil's nontariff measures) they provide partial details on Mercosur's trade barriers at very fine levels of detail. These two countries account for over 60 percent of Mercosur's total imports with the result that the SMART records provide a useful profile of the _structure_ of external protection. However, it should be noted that Mercosur countries (particularly Brazil) have subsequently implemented major unilateral MFN tariff reductions so the earlier statistics are not a reliable guide to current levels of protection. For this reason, post-Uruguay tariff data were drawn directly from the World Trade Organization's Integrated Data Base (IDB). Where there were known exceptions and departures from the reported WTO statistics (as was the case with tariffs and nontariff restrictions on automobiles) these were incorporated in the data. These statistics, like those in SMART, are recorded at
Table 12 Continued: IV regression IT use on firm-level productivity considering China’s competition and initial IT

|Dependent variable: log(sales/worker)|(1)|(2)|(3)|(4)|(5)|(6)|
|---|---|---|---|---|---|---|
|Share of labor with computer|-2.306|-1.565|-1.024|-1.743|-1.544|-1.769|
||(2.678)|(2.579)|(2.794)|(3.031)|(3.035)|(2.455)|
|Share of labor with computer*Competition|20.832*|19.805*|18.794*|20.457*|18.549*|20.300**|
||(10.788)|(10.157)|(10.294)|(10.886)|(10.559)|(9.063)|
|Competition|-1.017|-1.436|-1.445|-1.411|-1.146|-1.647|
||(1.182)|(1.219)|(1.223)|(1.200)|(1.193)|(1.408)|
|Initial IT*Competition|-15.397*|-13.215|-12.697|-14.633|-13.256|-14.190*|
||(9.325)|(8.860)|(8.929)|(9.591)|(9.441)|(7.679)|
|F-frst stage IT|7.5|7.1|5.9|5.9|5.0|5.1|
|F-frst stage IT*competition|12.1|12.1|11.1|11.8|10.6|9.7|
|Sargan|0.17|0.13|0.15|0.15|0.15|0.17|
|Controls|||||||
|Age|No|Yes|Yes|Yes|Yes|Yes|
|Firm size|No|No|Yes|Yes|Yes|Yes|
|Share of white-collar workers|No|No|No|Yes|Yes|Yes|
|Capital/worker|No|No|No|Yes|Yes|Yes|
|Exports/sales|No|No|No|No|Yes|Yes|
|FDI share|No|No|No|No|Yes|Yes|
|Time efects|No|No|No|No|No|Yes|
|Observations|1402|1402|1402|1402|1402|1402|

Standard errors in parentheses Source: Authors’ calculations using data from ENTIC, INEGI

* _p <_ 0 _._ 10, ** _p <_ 0 _._ 05, *** _p <_ 0 _._ 01

33