rafmacalaba/datause-encoder-data huggingface dataset:
ECOWAS‘S INFRASTRUCTURE: A REGIONAL PERSPECTIVE The overall rate of return **Table 3.7 Rate of return to power trade at country level** on power trade for power exporters in WAPP is 18 Net power trade percent; for importers, 1 Price gain percent. For the WAPP Country (US$/kWh) (TWh) exporters as a whole, regional _Exporters_ power trade represents a gain Côte d‘ Ivoire 0.06 0.9 of 18 percent annually for a Guinea 0.05 17.4 one-time investment of $8.1 _Importers_ billion over 10 years in Burkina Faso 0.01 1.5 interconnection and additional Togo 0.01 1.5 hydropower capacity. For Niger 0.05 1.2 importers, a one-time Benin < 0.01 1.7 investment of $580 million Ghana < 0.01 12.8 over a decade produces a Mauritania 0.01 0.8 return of 1 percent annually Sierra Leone 0.01 1 (table 3.7). The overall rate of Mali 0.03 1.8 return on the investment for Guinea Bissau 0.07 0.2 WAPP as a whole is 33 Liberia 0.06 1.3 percent. While this is a good Senegal 0.04 3.5 rate of return, it is substantially WAPP (exporters) 20.4 lower than the returns to trade WAPP (importers) 27.3 in other power pools, notably Source: AICD calculations. SAPP, which stands to make a <mark>Note: n.a. = not</mark> <u><mark>applicable.</mark></u> return of 168 percent on power trade due to the exceptionally cost-effective hydropower resources available in Democratic Republic of Congo.

**Table 3.7  Rate of return to power trade at country level**

|Country<br>Price gain<br>(US$/kWh)|Net power<br>trade<br>(TWh)|Annual<br>benefits<br>(US$ millions)|One-time<br>investment<br>(US$m)|Rate of<br>return<br>(%)|
|---|---|---|---|---|
|_Exporters_|||||
|Côte d‘ Ivoire<br>0.06|0.9|64.8|270|24|
|Guinea<br>0.05|17.4|1,044|7,860|13|
|_Importers_|||||
|Burkina Faso<br>0.01|1.5|15|0|n.a.|
|Togo<br>0.01|1.5|15|0|n.a.|
|Niger<br>0.05|1.2|60|0|n.a.|
|Benin<br>< 0.01|1.7|0|0|n.a.|
|Ghana<br>< 0.01|12.8|0|50|n.a.|
|Mauritania<br>0.01|0.8|8|100|8|
|Sierra Leone<br>0.01|1|10|70|14|
|Mali<br>0.03|1.8|54|260|21|
|Guinea Bissau<br>0.07|0.2|14|50|28|
|Liberia<br>0.06|1.3|78|20|390|
|Senegal<br>0.04|3.5|140|30|467|
|WAPP (exporters)|20.4|1462|8150|18|
|WAPP (importers)|27.3|394|580|1|
|Source: AICD calculations.<br>Note: n.a. = not applicable.|||||

Individual countries stand to make higher returns. For power importers, the trading decision can be thought of as an investment in cross-border interconnection that yields an annual return in terms of access to lower-cost power. On this basis, it is possible to calculate returns to trade for individual importers. Burkina Faso and Togo stand to make exceptionally high returns, while Liberia, Niger, and Senegal make a solid 30 percent return on such investments. Returns for other importers are much lower due to the high cost of interconnectors. For power exporters, the trading decision can be thought of as an investment in additional generation capacity and cross-border interconnection that yields an annual return in terms of revenues from power export. For example, Guinea could expect to make annual export revenues of $1.4 billion on a one-time investment of $7.9 billion, yielding a rate of return of 19 percent.

54
Figure 2: NAIS Historical Farmer Premium Income and Insurance Claims by State, 2000-2008 <!-- Start of picture text --> 80 Gujarat<br>70 Andhra Pradesh<br>60<br>50<br>40 Karnataka<br>Bihar Maharashtra<br>30 Rajasthan<br>West Bengal<br>20 Madhya Pradesh<br>Uttar Pradesh<br>Orissa<br>10<br>Jharkhand<br>-<br>- 5 10 15 20 25<br>Average Premium Income 2000-2008 (USD millions)<br>(USD millions)<br>Average Claim Outgo 2000-2008<br><!-- End of picture text --> Source: Data from AICI Following Mahul and Stutley (2010) one measure of the value of the NAIS to participating farmers is the producer loss ratio, taken to be total claim payments to farmers divided by total farmer premiums. Since its inception, the producer loss ratio has been always higher than 100 percent, i.e., the total claim payments paid to farmers exceed the premiums received (including premium subsidies). This is a direct consequence of the caps imposed on the premium rates of oilseeds and food crops, described in the previous subsection. Between 2000 and 2008 the producer loss ratio was 3.5, but this hides a large disparity between non-borrowing farmers with producer loss ratio of 6.4 and borrowing farmers with producer loss ratio of 3.0 (see Figure 3). This disparity illustrates the impact of adverse selection: non-borrowing farmers choose to insure their riskier crops (James and Nair 2009). It should also be noted that the producer loss ratio of small and marginal farmers has tended to be less than the producer loss ratio of all farmers, perhaps suggesting that small and marginal farmers have been less adept at selecting against the insurer.

Figure 3: NAIS producer loss ratio (claims/farmer premiums), 2000-2008 <!-- Start of picture text --> 16<br>14<br>12<br>10<br>Non-loanee<br>8<br>6<br>Total<br>4<br>2 Small and<br>0 marginal<br>2000 2001 2002 2003 2004 2005 2006 2007 2008<br>Year<br>farmer premiums)<br>Producer Loss Ratio for NAIS portfolio (Total claims/Total<br><!-- End of picture text --> Source: Data from AICI There is also a wide variation in producer loss ratios by crop, with higher average producer loss ratios for most food crops than most cash crops, and producer loss ratios above four for Groundnut, Maize, Urd and Jowar (Figure 4). Two crops, paddy and groundnut, represent 40 percent of the total farmer premium volume.

6
The 1985-1994 Global Real Estate Cycle. Page 6 Figure 3. Japan: Changing Composition of Bank Lending and Land Inflation <!-- Start of picture text --> Japan: Share of Bank Raa Il Estate Loans<br>17.6<br>11.6<br>1673 1671 13?? 1676 1631 1633 166h 1637 1333 1331<br>-Shaer R. E. Loans in Banks Porlolior,<br><!-- End of picture text --> Source: R Wermer [1994] and Ueno -

-1992].

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|Land Wealth as Fraetlon of Privata Net Worth and ONP| 6.9~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~.<br>_ - - - - - - - - - - -<br>CC ISta -SS =lollat8 17 1* lesss sea 19.a99<br>1L.0 ------------------------------------------- ----------<br>-SargWie! R.E. Loans -~ in B ankior 0o NP<br>R W3-sce [1994]<br>Source: R. Wemner (19941 n co 9)<br><!-- End of picture text -->
75

# Tanzanla 1: PFP- Prolectlons and Actuals

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# Tanzanla 3: PFP- Projectlons and Actuals

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# Tanzanla 5 - PFP Projectlons and Actuals

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# Tanzania 2:.PFP- Projections and Actuali

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# Tanzania 4 - PFP Projectlons and Actu

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Policy ReseaRch WoRking PaPeR 4920

# **Abstract**

This study discusses potential economic implications for Nigeria of an Economic Partnership Agreement with the European Union. It uses the World Bank’s Tariff Reform Impact Simulation Tool to assess the effects of preferential tariff liberalization with respect to the European Union. The results suggest that the impact of an Economic Partnership Agreement on total imports into Nigeria will be slight. This is in part because the Agreement will likely allow the most protected sectors to be excluded from liberalization, and also because where substantial tariffs are involved much of the increase in imports from the European Union will occur at the expense of other suppliers of imports. It is this trade diversion, arising from the discriminatory nature of the EPA, which generates a negative welfare impact of the tariff reforms. One way for Nigeria to limit these losses is to pursue non-preferential trade liberalization before implementing an EPA. The paper looks at the large number of import bans in Nigeria and argues that the positive impact on welfare of removing these import bans is likely to be substantial. Their removal would undermine a major reason for cross border smuggling and pave the way for a return to normal regional trade flows. The paper shows how an Economic Partnership Agreement presents an opportunity for accelerating the reforms that are needed to support a strategy to increase regional and global trade integration. Such an agreement is more likely to have positive and significant impacts when integrated into a comprehensive strategy toward competitiveness and alleviation of the supply constraints that have stifled the impact of previous trade agreements. Key issues that should be addressed include liberalization and regulatory strengthening of services sectors to ensure that all firms in Nigeria have access to efficiently produced backbone services and initiatives to address the country’s poor trade logistics performance.

This paper—a product of the Africa Technical Families, Africa Region—is part of a larger effort to analyze trade agreements and trade policy issues. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The authors may be contacted at sandriamananjara@worldbank.org and pbrenton@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
# Does Democracy Faicilitate the Economic Transition? An Empirical Study of Central and Eastern Europe and the Former Soviet Union

## Jean-Jacques Dethier, **Hafez Ghanem, Edda Zoli* The World Bank**

**Keywords:** Transition, Democracy, Political Economy. **JEL Classification:** E6, P3

> * This paper was presented at a serninar at the World Bank in June 1999. We thank you all the participants for their useful cormments and suggestions.
**Table 8: long-term impacts of the 4P program on gender and GBV/DV norms - ITT**

||Agreement<br>|Pc of men|Pc of women|Wo<br>|men<br>||||
|---|---|---|---|---|---|---|---|---|
||with gender<br>equitable<br>statements<br>(KKL z-score<br>average)|<br>who agree<br>with a gender<br>equitable<br>statement|<br>who agree<br>with a gender<br>equitable<br>statement|Pc of men<br>who disagree<br>with wife-<br>beating|Pc of women<br>who disagree<br>with wife-<br>beating|Disagreement<br>with child-<br>beating (KKL z-<br>score average)|Pc of men<br>who disagree<br>with child-<br>beating|Pc of women<br>who disagree<br>with child-<br>beating|
|ITT|-0.034|-0.039|-0.033|0.005|-0.000|-0.013|0.002|-0.016|
||(0.38)|(0.13)|(0.20)|(0.83)|(0.99)|(0.89)|(0.95)|(0.65)|
|Observations|436|436|436|436|436|436|436|436|
|Adj. R2|-0.00|0.00|0.00|-0.00|-0.00|-0.00|-0.00|-0.00|
|Control mean|0.042|0.597|0.689|0.797|0.859|-0.037|0.581|0.638|
|||||M|en||||
||Agreement<br>with gender<br>equitable<br>statements<br>(KKL z-score<br>average)|Pc of men<br>who agree<br>with a gender<br>equitable<br>statement|Pc of women<br>who agree<br>with a gender<br>equitable<br>statement|Pc of men<br>who disagree<br>with wife-<br>beating|Pc of women<br>who disagree<br>with wife-<br>beating|Disagreement<br>with child-<br>beating (KKL z-<br>score average)|Pc of men<br>who disagree<br>with child-<br>beating|Pc of women<br>who disagree<br>with child-<br>beating|
|ITT|-0.048|0.030|-0.019|-0.008|0.006|-0.106|-0.004|-0.024|
||(0.20)|(0.30)|(0.54)|(0.73)|(0.79)|(0.22)|(0.91)|(0.43)|
|Observations|435|435|435|435|435|435|435|435|
|Adj. R2|0.00|0.00|-0.00|-0.00|-0.00|0.00|-0.00|-0.00|
|Control mean|0.003|0.585|0.694|0.790|0.833|0.054|0.593|0.624|
|P-values inparen|theses: *p<0.1 *|*p<0.05 ***p<0.0|1||||||

31
Table A1: Summary statistics by country

||AGO|BFA|CIV|ETH|GAB|GHA|GIN|GNB|
|---|---|---|---|---|---|---|---|---|
|Number of households|11,822|6,651|11,589|30,255|7,914|14,009|8,243|5,291|
|Survey years|2018_/_19|2018_/_19|2018_/_19|2015_/_16|2017|2016_/_17|2018_/_19|2018_/_19|
|Lowest geographic unit avail-|Bairro|PSU|PSU|HH|HH|PSU|PSU|PSU|
|able<br>|||||||||
|**DOU urban classifcation:**|||||||||
|Urban center|0.49|0.20|0.27|0.10|0.63|0.24|0.31|0.28|
|Urban cluster|0.13|0.12|0.22|0.17|0.14|0.24|0.12|0.15|
|Rural|0.38|0.68|0.51|0.74|0.24|0.52|0.57|0.57|
|**DB urban classifcation:**|||||||||
|Core|0.60|0.34|0.42|0.26|0.74|0.27|0.44|0.41|
|Suburb|0.05|0.08|0.07|0.46|0.09|0.16|0.06|0.08|
|Town|0.19|0.41|0.41|0.11|0.11|0.24|0.37|0.29|
|Other rural<br>|0.35|0.58|0.51|0.28|0.17|0.56|0.50|0.52|
|**Consumption**<br>**(per**<br>**day**|||||||||
|**USD in 2017 PPP terms):**|||||||||
|Real per capita consumption|5.67|4.42|5.11|3.70|11.83|4.01|4.44|4.10|
|expenditures|||||||||
|Nominal per capita consump-|5.90|4.67|5.19|0.99|11.88|4.03|4.49|4.22|
|tion expenditures|||||||||
|**Demographic:**|||||||||
|Household size|6.44|8.93|6.21|5.76|5.90|5.64|6.26|11.34|
|Age of household head|42.87|48.63|44.92|22.53|44.88|46.97|46.28|49.21|
|Household head is male|0.74|0.91|0.84|0.80|0.71|0.71|0.83|0.83|
|**Marital status (of house-**|||||||||
|**hold head):**|||||||||
|Married|0.10|0.90|0.78|0.84|0.27|0.65|0.89|0.80|
|Never married|0.04|0.02|0.15|0.02|0.25|0.06|0.02|0.06|
|Living together|0.66|0.01|0.00|0.00|0.38|0.10|0.00|0.01|
|Divorced/Separated|0.12|0.01|0.02|0.04|0.03|0.09|0.01|0.02|
|Widowed|0.09|0.06|0.06|0.09|0.07|0.10|0.08|0.10|
|**Education (of household**|||||||||
|**head):**|||||||||
|No education|0.16|0.76|0.50|0.51|0.19|0.27|0.55|0.40|
|Primary incomplete|0.30|0.10|0.19|0.33|0.09|0.11|0.13|0.28|
|Primary complete|0.05|0.03|0.03|0.05|0.08|0.04|0.00|0.09|
|Secondary incomplete|0.40|0.08|0.18|0.04|0.42|0.41|0.17|0.07|
|Secondary complete|0.02|0.00|0.03|0.02|0.07|0.06|0.02|0.08|
|Post secondary but not uni-|0.00|0.00|0.03|0.03|0.00|0.07|0.00|0.00|
|versity|||||||||
|University incomplete and<br>complete|0.06|0.03|0.05|0.03|0.16|0.05|0.12|0.07|
|Can read and write|0.75|0.32|0.53|0.46|0.91|0.52|0.40|0.57|
|**Employment (of house-**|||||||||
|**hold head):**|||||||||
|Not in labor force|0.09|0.07|0.08|0.08|0.21|0.02|0.06|0.09|
|Unemployed|0.06|0.00|0.00|0.00|0.04|0.02|0.01|0.00|
|Employed in Agriculture|0.34|0.66|0.52|0.72|0.14|0.42|0.41|0.47|
|Employed in Industry|0.09|0.08|0.09|0.05|0.14|0.14|0.15|0.10|
|Employed in Services|0.42|0.18|0.31|0.15|0.48|0.40|0.38|0.34|
|**Access to basic services**|||||||||
|Improved water|0.68|0.81|0.78|0.57|0.88|0.59|0.79|0.78|
|Improved sanitation facility|0.46|0.30|0.33|0.04|0.32|0.20|0.29|0.37|
|Access to electricity|0.47|0.53|0.80|0.36|0.91|0.81|0.44|0.58|

Source: International Urban Poverty Database. Note: Real per capita consumption expenditures are deflated using a spatial deflator calculated based on the WorldPop 250m dataset and the DOU method. 40
_Age:_ Figure 9 disaggregates the total ninis trend by age subgroup, splitting ninis into those aged 15 to 18 and those aged 19 to 24. The central result that emerges from this picture is that while the share of ninis is declining across age groups in LAC (an average decrease of 2.9 and 3.9 percentage points for those aged 15 to 18 and 19 to 24, respectively, between 1992 and 2010, and a consistently downward trend across countries), the rising total number of ninis in LAC has been driven by a slight increase in ninis in the 19 to 24 age bracket. While the number of ninis aged 15 to 18 in LAC remained roughly constant from 1992 to 2010 at between 5.8 and 5.9 million, the number of those aged 19 to 24 increased by 1.7 million over the same period. Moreover, the majority of ninis are concentrated in the 19 to 24 age group (which accounted for 68 percent of total ninis in 2010), both because of the wider age bracket it covers and because this older group is not covered by compulsory schooling laws; as a result, the increasing number of ninis within this subgroup has more effect on aggregate nini trends.

**Figure 9: Total number and percentage of youth who are ninis in LAC by age, 1992-2010**

<!-- Start of picture text --> Panel A: Percentage of youth who are ninis Panel B: Number of ninis in LAC by age<br>in LAC by age group group<br>18.0 18.2 18.2<br>27.3 27.0 17.1<br>26.5 16.4<br>24.3<br>23.4 23.2 23.4<br>22.6 12.1 12.3 12.3<br>20.5 10.6 11.1<br>19.3<br>18.4 18.3<br>17.6<br>15.6 15.5 5.8 6.0 5.9 6.0 5.9<br>1990 1995 2000 2005 2010 1990 1995 2000 2005 2010<br>Year Year<br>% ninis 15-18 % ninis 19-24 Ninis 15-18 Ninis 19-24<br>% ninis 15-24 Ninis 15-24<br>30.0 20.0<br>25.0 15.0<br>20.0 Ninis (Millions) 10.0<br>Ninis (% relevant age group)<br>15.0 5.0<br><!-- End of picture text --> Source: authors’ own computation with data from CEDLAS and The World Bank (2015) _Gender:_ Figure 10 presents a breakdown of nini trends by gender in both percentage and absolute terms, and for both the 15 to 18 and the 19 to 24 age groups. The results show that while detachment from both education and the labor market is still more common among women than among men, female ninis have decreased both as a share of young women and in absolute terms over the period from 1992 to 2010. Moreover, this reduction in female ninis drove the overall reduction in the share 16
# **8. International Institute for Sustainable Development (IISD)**

IISD has been working on measurements and indicators since 1995, with the aim of making significant local, national and international contributions to the implementation of sustainable development. While focused on sustainability indicators their research emphasizes the importance of linking assessment and measurement work to decision-making and policy-making. IISD produces a broad list of sustainable development activities.

# **9. International Sustainability Indicators Network and ‘Sustainable Measures’**

The International Sustainability Indicators Network is a web based network that shares experiences in creating indicators for sustainability. Despite its clear focus it seams that the Institute has not been active recently since the last update registered in their website is 2004.

Similarly, ‘Sustainable Measures’ is an American website based company that develops indicators that measure progress toward a sustainable economy, society and environment.

# **10. Inventory of World Cities**

Globalization and World Cities (GaWC) Study Group and Network, Research Bulletin Number 5 as published in Cities, 16 (6), 1999, pp 445-458, constructed an inventory of world cities based upon their level of advanced producer services (accountancy, advertising, finance, and law). Cities were rated as Alpha, Beta and Gamma (world cities) and cities showing evidence of world city formation (cities rated from a high of 12 to low of 1).

||**Table 7- In**<br>|**ventory of World Cities**<br>|
|---|---|---|
|**Classification**|**Rating**<br>**12:highest**<br>**1: lowest**|**Cities**|
|**A. Alpha World Cities**<br>**(full service world cities)**|12|London, New York, Paris, Tokyo|
||10|Chicago, Frankfurt, Hong Kong, Los Angeles, Milan,<br>Singapore|
|**B. Beta World Cities (major**|9|San Francisco, Sydney,Toronto,Zurich|
|**world cities)**|8|Brussels,Madrid,Mexico City, SaoPaulo|
||7|Moscow, Seoul|
|**C. Gamma World Cities**<br>**(minor world cities)**|6|Amsterdam, Boston, Caracas, Dallas, Düsseldorf, Geneva,<br>Houston, Jakarta, Johannesburg, Melbourne, Osaka,<br>Prague, Santiago,Taipei, Washington|
||5|Bangkok,Beijing,Montreal,Rome, Stockholm, Warsaw|
||4|Atlanta, Barcelona, Berlin, Budapest, Buenos Aires,<br>Copenhagen, Hamburg, Istanbul, Kuala Lumpur, Manila,<br>Miami,Minneapolis,Munich, Shanghai|
|**D. Evidence of World City**<br>**Formation**|||
|**D(i) Relatively strong**<br>**evidence**|3|Athens, Auckland, Dublin, Helsinki, Luxembourg, Lyon,<br>Mumbai, New Delhi, Philadelphia, Rio de Janeiro,<br>Tel Aviv, Vienna|
|**D(ii) Some evidence**|2|Abu Dhabi, Almaty, Birmingham, Bogotá, Bratislava,<br>Brisbane, Bucharest, Cairo, Cleveland, Cologne, Detroit,<br>Dubai, Ho Chi Minh City, Kiev, Lima, Lisbon,<br>Manchester,Montevideo, Oslo,Riyadh,Rotterdam,|

36
||enrollment rate values for the fiscal year<br>covered by the Enterprise Survey were taken.<br>In cases where the fiscal year spanned more<br>than one calendar year, the enrollment figure is<br>the weighted average over the calendar years<br>covered, where the weights used are the<br>proportion of months covered in the calendar<br>year. In the second step, annual values were<br>taken from the first step and averaged over the<br>three years prior to the (final) year covered by<br>the Enterprise Survey. Three years average<br>was taken to avoid too many missing values of<br>secondary enrollment rate. The final figure is<br>divided by 100 to avoid too small coefficients<br>in the regressions.<br>Source: World Development Indicators,<br>World Bank.|
|---|---|
|Rule of Law (lagged)|Rule of Law indicator from Worldwide<br>Governance Indicators. Higher values of the<br>variable imply better enforcement of rules and<br>laws. The variable is lagged by 2 years from<br>the fiscal year covered by the Enterprise<br>Survey in the country. In cases where the fiscal<br>year spanned more than one calendar year, the<br>Rule of Law figure is the weighted average<br>over the calendar years covered (lagged by 2<br>years), where the weights used are the<br>proportion of months covered in the calendar<br>year.<br>Source: Worldwide Governance Indicators,<br>World Bank.|
|Freedom From Regulation (lagged)|<br>Freedom from Regulation indicator from<br>Fraser Institute’s Economic Freedom of the<br>World database. Higher values of the variable<br>imply less regulation (of formal businesses).<br>The variable is lagged by 2 years from the<br>fiscal year covered by the Enterprise Survey in<br>the country. In cases where the fiscal year<br>spanned more than one calendar year, the<br>figure used is the weighted average over the<br>calendar years covered (and lagged by 2<br>years), where the weights used are the<br>proportion of months covered in the calendar<br>year.<br>Source: Economic Freedom of the World,<br>Fraser Institute.|

60