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

**Table 13. SBEE’s hidden costs**

|**Year**|**Power**<br>**billings **|**System**<br>**losses**|**Collection**<br>**ratio**|**Average**<br>**costs**|**Average**<br>**effective tariff**|<sup>**Total hidden costs**</sup>|<sup>**Total hidden costs**</sup>|
|---|---|---|---|---|---|---|---|
||(GWh/year)|(%)|(%)|($/kWh)|($/kWh)|($mill/year)|(% revenues)|
|2006|660|18.3|96|0.19||32|39|
|2007|705|17.0|96|0.21|0.17|37|40|
|2008|810|16.8|96|0.29|0.18|87|82|
|2009|920|17.0|96|0.26|0.19|69|51|

_Source_ : Briceno-Garmendia, Smits, and Foster 2009, World Bank 2009a, SBEE annual report. * Refers only to distributional losses.

**Figure 17. Prospects for long-run cost recovery**

<!-- Start of picture text --> Cents per KWh as of 2009<br>a. SBEE b. CEB<br>30 30<br>25 25<br>20 20<br>15 15<br>10 10<br>5 5<br>0 0<br>Historical total Average Average LRMC trade LRMC trade Historical total Average Average<br>costs effective tariff revenue costs effective tariff Revenue<br>US cents per kWh US cents per kWh<br><!-- End of picture text --> _Source_ : Rosnes and Vennamo 2009; SBEE annual report for 2009; World Bank 2009a; and World Bank staff estimates. LRMC trade = long-run marginal cost with additional regional trade in power.

# Information and communication technologies

## **Achievements**

Benin has improved the legal and institutional environment for the ICT sector through liberalization and private participation. In 1997 the country enacted a telecommunication law that liberalized the sector. In 2007 a transitory sector regulator, ATRPT (Autorité Transitoire de Régulation des Postes et Télécommunications), was established. The country has achieved a genuinely competitive market with no real dominant player, except in the landline market. Benin’s mobile market is one of the most competitive in Africa, with five GSM wireless operators.

31
among donors will lift the “return curve” for donors in Figure 1, _vH e_ , to a higher level, by increasing the donor value attached to mitigation activity, from _vH_ in the non-cooperative case, to a level closer to _nvH_ (the mitigation value ascribed to individual donors under full and efficient cooperation). This makes the transformational range for MF (perhaps much) easier to reach.

Adding to these arguments, and discussed further in Appendix 3, donors’ cooperation can further enhance the efficiency of their MF supply by making it conditional on GHG mitigation efforts by the L countries themselves.<sup>29</sup> Such conditionality is easier to demand and enforce when H countries cooperate in their MF supply.

# **7. Conclusions**

This paper discusses mitigation finance (MF) provision from high-income (H) donor countries to lower-income (L) countries, focusing on H countries’ “willingness to pay” (WTP) to provide such MF. In section 2, we study the conditions for reaching a “transformational” stage and range for MF; this requires moving past an initial range where returns to MF provision for H countries are lower than its costs to these countries. We consider three “modalities” for MF provision: “nontransformational project MF” (“traditional” individual-project support; in section 3); “transformational project MF” (to transform entire energy-intensive sectors from fossil-fuel-based to non-fossil-based energy inputs; section 4); and “transformational policy based MF” (comprehensive carbon tax implementation; section 5). The returns to transformational policybased MF (carbon tax implementation) are particularly high for donors when the L country is compensated directly for the welfare loss of implementing a carbon tax and MF support from H countries is conditional on policy implementation. The returns to transformational project-based finance (applied to complete sectoral transformations in L countries) can also be high when unit costs of non-fossil energy investments have already been driven down to a low level through extensive global R&D efforts and local, regional and global learning effects. This case is in this paper developed only in a rudimentary way and requires development in follow-up work.

The return for donors to non-transformational MF is lower than for the transformational MF modes due to serious inefficiencies with the former. Non-transformational project MF also has less scope

> 29 See Mundaca 2020 for analysis of cooperation for mitigation action among receiving countries, a potentially important issue not included in this analysis.

21
We finally analyze the potential impact of immigration on changes in the overall employment levels and wages of native workers as it impacts occupational composition. In contrast with the large occupational shifts, we find no absolute employment or wage effects in the EU. These results are consistent across education levels.

In many countries, many migrants perform the dirty, dangerous and demeaning jobs – sometimes referred to as “3D jobs” – that natives are unwilling to do. The evidence from the COVID-19 pandemic is pointing in the same direction. As more evidence emerges, especially on labor market outcomes during the pandemic, we expect the immigration policies of the post-COVID era will be shaped accordingly.

32
- **and Alan Fox** , “Output-based allocation of emissions permits for mitigating tax and

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35
directed upwards, poor to non-poor. This is not true for Pakistan in 2010-2016, for instance; even among countries where “good” mobility is also prevalent, the ratio of upward to downward mobility is everywhere less favorable than in Bhutan; this includes India in 2009-2011, which does have the highest upward mobility among the examples provided. Even when looking at the proportion of the poor population in period one who remained poor in period two, Bhutan’s recent experience emerges as the best case scenario among those included in Table 9.

**Table 9. Poverty dynamics in selected countries**

||**Bhutan**<br>**2007-2012**|**Bhutan**<br>**2017-2022**<br>(this paper)|**India**<br>**2009-2011**|**Pakistan**<br>**2010-2016**|**Viet Nam**<br>**2006-2008**|
|---|---|---|---|---|---|
|Poor - poor|8.3|5.8|15.0|17.5|9.9|
|Poor - non-poor|10.5|12.7|15.2|2.5|5.9|
|Non-poor - poor|4.1|3.0|8.9|19.0|4.9|
|Non-poor - non-poor|77.2|78.5|54.1|61.0|79.3|
|All patterns|100.0|100.0|100.0|100.0|100.0|
|Poor|Poor|44.1|31.2|50.0|87.5|62.6|

Sources: Bhutan, 2007-2012: World Bank (2014); India: Dang and Lanjouw (2018); Pakistan: Shabnam et al. (2023); Vietnam: Dang and Lanjouw (2023). Estimates for Vietnam are based on actual, not synthetic, panels.

# **4.2 Profiling of population groups**

This section explores the determinants of poverty dynamics, by identifying the characteristics of the population that are associated with, for example, a higher chance to move out of poverty, or to remain trapped below the poverty line. To do so, we first follow the approach that is conventional in the literature, and present estimates of joint probabilities, _i.e. P_ ( _yi_ ,1 ~ _z_ 1 and _yi_ ,2 ~ _z_ 2 ), computed by population group (Figure 1). These compare with the average population-level probabilities displayed in Table 5 (which are also reported as horizontal dotted lines in the figure, for convenience). The interpretation of each estimate is as follows: taking the top left panel of Figure 1 as an example, we can say that living in urban areas is associated with a lower-than-average probability of remaining poor in both periods, and vice-versa, living in rural areas correlates with a higher-than-average chance of staying poor; belonging to a household where the household head has no education is also associated with a higher chance of staying poor, and vice16
_3. Methodological framework_

# 3. Methodological framework

## **Constructing a global SDG database**

In March 2016, the United Nations Statistical Commission’s Interagency and Expert Group on SDG Indicators (IAEG‐SDGs) agreed on 231 SDG indicators to monitor and evaluate progress on the 17 goals and 169 targets of the 2030 Agenda. Each indicator can contain several data series, for instance if it is disaggregated by gender, location, or age. Recognizing that the level of methodological consistency and coverage differs significantly among the SDG indicators, a _ier_ classification was designed to monitor progress on both the methodological aspects and cross‐country coverage. In this classification, approximately 45 percent of the indicators are classified within _Tier I_ , having both an adequate methodology for measurement and sufficient coverage across countries. About a quarter of all indicators are classified as _Tier II_ , indicating that the methodology is adequate, but coverage across countries is limited. Lastly, around 30 percent of indicators are categorized as _Tier III_ . These indicators lack both an adequate methodology and consistent coverage across countries.<sup>2</sup> Despite the immense challenges posed by limited SDG global coverage, for the purpose of this study, IAEG‐SDG data,<sup>3</sup> the World Bank’s SDG database (WB‐SDGs), and additional external data have been merged, producing a database inclusive of the majority of Tier 1 indicators that can be organized by SDG goal or indicator. To maximize coverage, the most recent observation per SDG has been selected by country and series, within the latest 10 years. Also, some variables originally expressed in absolute values have been transformed into relative units of measurement to make them meaningful, for instance dividing them by a country’s population or GDP. Additionally, each observation is matched with the corresponding GNI per capita value for the country/year in question.<sup>4</sup> Table 1 presents summary statistics of the database in question.

> 2 The distribution of data coverage might play a crucial role in the results presented in this paper; but it is hard to quantify the importance of unknown data. Arguably, the current distribution of data coverage reflects the evolving interests of the development community and policy makers. Under this argument, it is not totally surprising that coverage is biased towards the indicators included in the original MDGs, since most countries established the MDGs as benchmarks for progress.

> 3 The SDG Goals data is from the UN’s Sustainable Development Goals Indicator website, August 17, 2016: <u>http://unstats.un.org/sdgs/</u>

> 4 Data can be found at: <u>https://www.researchgate.net/publication/315497169_Compiled_UN_SDG_Global_Database_update_March_22_2017</u>

4
For the net seller households, the distribution of the growth is pretty even except for the higher growth rates for the poor. The growth incidence curves summarize the growth experiences of households compactly; however, they do not illustrate the importance of the links, which is reported in the next subsection.

# **_4.2 Decomposition_**

The decomposition of income growth for different categories of households is presented in Table 9. The contribution of each source is presented such that their sum equals the total income growth experienced by those households. Confirming our insights from the growth incidence curves, Table 9 indicates that the lower income<sup>22</sup> households experienced a larger income growth compared to middle income and richer households both in rural and urban areas. The majority of the growth for poorer households in rural was due to the increase in agricultural income (50%), a significant part of which is from crop profit (Table A.5). Moreover, the business income and non-agricultural wages contribute a healthy amount to the overall growth underscoring the importance of development effects. On the other hand, business income and non-agricultural wages along with remittances were the driving forces for the growth experienced by lower income households residing in urban areas<sup>23</sup> .

Rural net-buyers and rural households in the South are the only groups that do not experience a negative contribution from agricultural wages (Table A.5). Although rice is a labor intensive good, the nature of the land distribution in Vietnam may be a major reason for the small agricultural labor. The laborers in agriculture are mostly net buyers from rural areas. Hence, the effect of an increase in rice price on the agricultural wages and employment is small and only experienced by a certain group of households.

For net-sellers in rural areas profit from crops is the biggest contributor, with rice corresponding to more than half of it. The business income and other wages also have a significant impact on total income growth. Poorer households again experience a higher growth compared to their middle income and richer counterparts.

> 22 The deciles in these analyses are constructed using per capita adjusted income, and 1993 characteristics (deciles, subgroups) are used to trace the growth for the same group of households.  The lowest 40% in a given group corresponds to all the households that were in the group and also in the lowest 4 deciles of the overall income distribution.

> 23 See Table A.5 in the Annex for a detailed decomposition.

24
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The Dickey-Fuller unit root tests were performed for each country. As shown in Table 5, the null hypothesis that the total portfolio flows series have a unit root (and therefore, are non-stationary) could only be rejected for India and Mexico (at the 10 percent level of significance). This result should, however, be treated with caution since the number of observations (n = I8) makes the power of the test low. That is, one tends to accept Ho when, in fact, it may not be true. Hence, the analysis is conducted using panel data for each region. Although, the focus of this study is on portfolio investment flows by region, nevertheless, the results of the individual country analysis help in checking for the consistency of the results obtained from the subsequent analysis of panel data for each region in the sample.

**Table 5. Unit** Root **Tests** - Total Portfolio Flows

# **1.** Test for Individual **Country** Series: Levels

|**Ho:**p**= I**|Augmented Dick|ey-||||
|---|---|---|---|---|---|
||**Fuller**(ADF)|Levels of S|ignificance|Stati|onary at:|
||Test Statistic|5%|**10%**|5%|10%|
|ARGENTINA|**-**3.23|do not reject|do not reject|no|no|
|BRAZIL|**-** 3.21|do not reject|do not reject|no|no|
|CHILE|-1.84|do not reject|do not reject|no|no|
|MEXICO|-3.54|do not reject|reject|no|yes|
|INDIA|4.18|reject|reject|yes|yes|
|INDONESIA|- 2.59|do not reject|do not reject|no|no|
|KOREA|- 3.06|do not reject|do not reject|no|no|
|THAILAND|- 2.12|do not reject|do not reject|no|no|
||MacKinnon<br>critical values|<br>- 3.7347|- 3.3820|||

II. Test for Panel Series by Region: Levels and First Differences

|Ho: p=O|Dickey-Fuller|Levels of Sign|ificance|Stati|onary at:|
|---|---|---|---|---|---|
||IDF) test Stat.|5%|10%|5%|1 0%|
|LAC<br>ASIA|-1.94<br>-2.93|do not reject<br>reject|reject<br>reject|no<br>yes|yes<br>yes|
|D(LAC)|-12-97|reject|reject|yes|yes|
|D(ASIA)|-11.94|reject|reject|yes|yes|
|Levin and LU|n critical values|-2.30|-1.93|||

20
allow, in certain circumstances, the use of third-market price as the measure of normal value. An internationally competitive environment could be chosen as this third market. The more general basis for latitude is that the WTO rules do not demand that an antidumping duty be equal to the measured dumping margin. The duty cannot be larger, but it can be smaller, even zero. Latin American governments adopted the lesser duty rule, which, in theory, provides that the antidumping duty be set no higher than the level necessary to eliminate injury to domestic producers.

Although the lesser duty rule is sometimes described as being allowed by the WTO antidumping agreement, given that WTO members did not give up their sovereign right to set a lower duty by accepting the agreement, there is no need for a WTO rule to create that right. Within the rules, a WTO member can set an antidumping duty at any level determined with reference to any criterion the government considers appropriate—limited by the measured dumping margin. Because the rules for measuring the dumping margin are generous, once an affirmative determination is returned, the latitude is considerable.

Similarly, these countries included in their antidumping processes a national interest clause that allows the deciding agency to take no action or to modify action if the national interest would thereby be better served. Again, this action is a right not given up by accepting to abide by the WTO agreement; it is not a privilege granted by the agreement. The country studies explain additional criteria and limits governments included in their mechanisms.

# **V. RATIONALE FOR SAFEGUARD MECHANISMS**

Though they have been infrequently used, safeguard regulations are in place in each of the seven countries. The Peru study provides a sharp example of the difference in philosophy behind safeguards and antidumping. Antidumping is a technical matter; a distortion compromises economic welfare (the national economic interest) and therefore should be corrected. A safeguard action responds to a political situation. Technical analysis is needed to gauge the extent of injury and assure that it results from import competition. The decision should, however, balance the importance of the various effects that will follow from the proposed action on consumers, production, and employment, not only in the sector that petitioned for protection but also in other sectors. The decision should also take into account other matters such as how action or inaction would affect the viability of the liberalization process and the possibility of retaliation 33
**Table 3: Summary of key EPZ export statistics**

||**Exports**<br>|**Exports**<br>|**EPZ share**<br>|**of national**<br>|**Gr**<br>**‘‘**|**owth in expo**<br>**‘‘**|**rts**<br>**’’**|
|---|---|---|---|---|---|---|---|
||**(US$m)‐**<br>**2008**|**per capita**<br>**(US$)‐**<br>**2008**|**Non‐oil**<br>**exports**|**Manu‐**<br>**facturing**<br>**exports**|**00‐08**<br>**(CAGR)**|**00‐04**<br>**(avg.)**|**04‐08**<br>**(avg.)**|
|**Bangladesh**|2,430|102|15%|16%|13%|11%|16%|
|**DR**|4,545|462|69%|96%|‐1%|0%|‐1%|
|**Honduras**|4,000(est.)|550|61%|98%|10%|15%|6%|
|**Vietnam**|16,175|188|30%|41%|29%|24%|35%|
|**Ghana**<br>**(Tema)**|281|12|33%|590%|29%|56%|31%|
|**Ghana (single**<br>**units)**|1,019|44||||||
|**Kenya (EPZs)**|145|4||||||
|**Kenya (single**<br>**units)**|265|7|9%|25%|31%|57%|10%|
|**Lesotho**|425|211|64%|64%|1%|19%|‐7%|
|**Nigeria**<br>**(Calabar‐ est)**|100|1|4%|16%|N/A|N/A|N/A|
|**Senegal**<br>**(DIFZ)**<br>**Senegal**<br>**(single units)**|50<br>350|4<br>29|16%|42%|26%|144%|‐3%|
|**Tanzania**|59|1|3%|14%|N/A|N/A|N/A|

Source: UN COMTRADE via WITS and author’s calculations based on data from individual country EPZ authorities Given that many of the African zone programs were established only recently, could the low levels of exports simply be a function of time? Certainly, this is part of the story. Data on growth of exports during the period 2000 to 2008 do indicate that the African zones, by and large, grew relatively quickly (although in most cases from very small bases) during the decade. A closer look at the growth patterns, however, raises some concerns over many of the African zones programs. As can be seen in Table 2, many of the African programs experienced rapid growth during the period 2000 to 2004, but slower growth or even decline since then. This is partly attributable to the base on which they are growing in each of those periods. But there may also be a structural factor at play. This is competition from Asia in general, and specifically, garment sector competition in the post‐MFA environment. In the period 2000 to 2004, African EPZs (particularly Kenya and Lesotho) benefited enormously from trade preferences to the US market granted under the AGOA program. At the same time, they faced limited competition in the US market from Asian producers, who continued to confront quota restrictions until the Multifiber Agreement expired at the end of 2004. At least in the cases of Kenya and Lesotho, declining competitiveness in the aftermath of the MFA is a well‐documented source of export stagnation and employment losses since 2005.

More broadly, if we look at the typical growth path of successful economic zones, it appears that most of the African zones are failing to shift to the “exponential” growth path that typically occurs 17