rafmacalaba/datause-encoder-data huggingface dataset:
|Name|Description|Unit|
|---|---|---|
|_ci_|Cost of investment in sector _i_|$/yr<br>|
| ̄_ai_|Sectoral potential in sector _i_|tCO2_/_yr<br>|
|_δi_|Depreciation rate of abating capital in sector _i_|yr<sup>_−_1</sup><br>|
|_r_|Discount rate|yr<sup>_−_1</sup><br>|
|_ai,t_|Current abatement in sector _i_|tCO2_/_yr|
|_xi,t_|Current investment in abating activities in sector _i_|(tCO2/yr)/yr|
|_νi,t_|Costate variable (present social value of green investments)|$/(tCO2/yr)|
|_λi,t_|Social cost of the sectoral potential|$/tCO2|
|_μ_|Social cost of carbon (SCC) (present value)|$/tCO2|
|_μe_<sup>_rt_</sup>|Current carbon price|$/tCO2|
|_c_<sup>_′_</sup><br>_i_|Marginal investment cost (MIC) in sector _i_|$/(tCO2/yr)|
|_li,t_|Marginal levelized abatement cost (MLAC) in sector _i_|$/tCO2|
|_pi,t_|Marginal implicit rental cost of capital (MIRCC) in sector _i_|$/tCO2|

**Table 1:** Notations (ordered by parameters, variables, multipliers and marginal costs).

Proof. With infinitely-lived capital ( _δi_ = 0), the generalized Lagrangian reads: The conditions are:<sup>5</sup> The optimal MIC can be written as:<sup>6</sup> The complementary slackness condition states that the positive social cost of the sectoral potential _λi,t_ is null when the sectoral potential _a_ ̄ _i_ is not binding: Each investment made in a sector brings closer the endogenous date, denoted

> 5 The same conditions can be written using a Hamiltonian.

> 6 We integrated _ν_  ̇ _i,t_ as given by (5) between _t_ and _∞_ ; used the relation lim _t→∞ νi,t_ = 0 (justified latter); and replaced _νi,t_ in (6).

6
They number only three for 1784-99 Nueva España (comprising the territories of today’s Mexico, Central America, Cuba and parts of the western United States) and 1880 China. In most cases, the number of social classes is in the double digits. The largest number is in Brazil, where the data from the 1872 Brazilian census include 813 occupations.

The estimated inequality statistics are reported in Table 2. The calculated Ginis display a very wide range: from 23.9 in 1880s China to 63.5 in 1784-99 Nueva España. The latter figure is higher than the inequality reported for some of today’s most unequal countries like Brazil and South Africa (Table 2). The average Gini (using Gini2 where available, otherwise Gini1) from these 14 data points is 45.7, while the average Gini from the nine modern comparators is 43.3. These are only samples, of course, but there is very little difference on average between them, 45.7 (ancient) - 43.3 (modern) = 2.4. In contrast, there are very great differences within each sample: 58.8 (Brazil 2002) - 27.3 (Sweden 2000) = 31.5 among the modern comparators, while 63.5 (Nueva España 178499) - 24.5 (China 1880) = 39 among the ancient economies. In short, inequality differences within the ancient and modern samples is many times greater than the difference between them.

The Gini estimates are plotted in Figure 2 against the estimates of GDI per capita on the horizontal axis. They are also displayed against the _inequality possibility frontier_ constructed on the assumption of a subsistence minimum of $PPP 400 (solid line).<sup>12</sup> In

> 12 This is based on Maddison’s (1998, p.12) assumed subsistence minimum. Note that a purely physiological minimum “sufficient to sustain life with moderate activity and zero consumption of other goods” (Bairoch 1993, p.106) was estimated by Bairoch to be $PPP 80 at 1960 prices. Using the US consumer price index to convert Bairoch’s estimate to international dollars yields $PPP 355 at 1990 prices. Maddison’s estimate allows in addition for expenses above the bare physiological minimum. Our minimum is also consistent with the World Bank absolute poverty line which is 1.08 per day per capita in 1993 $PPP (Chen and Ravallion 2007, p. 6). This works out to be about $PPP 365 per annum in 1990 international prices. Another justification for a subsistence minimum between $PPP 350 and 400 was recently provided

14
The results also show that the probability of asking an essential question or completing an essential examination increases with competence, sometimes dramatically so. Across the different questions, moving from the lowest to the highest quintile of competence increases the probability of completing an essential procedure by 40-70 percentage points. Competence derived from the vignettes is strongly associated with greater knowledge of essential procedures. Also noteworthy is that in India, the probability of completing essential procedures does _not_ vary much across the bottom two quintiles—this was because the vignettes were “too difficult” for doctors in the bottom part of the distribution—something that is discussed in Das and Hammer (2005).

Comparing the diarrhea and the TB panel also shows why comparisons across countries will be difficult. Ideally, competence would not vary across different procedures, and this is indeed what we see in any comparison within each of the three countries. However, comparing India and Indonesia, doctors who are competent in one dimension may be less competent in another. For example, providers in India look much worse than their Indonesian counterparts for diarrhea, where the difference in the probability of completing an essential procedure between the two countries is close to 40 percentage points. For Tuberculosis the difference is again close to 40 percentage points in favor of Indonesia for whether the providers complete a chest examination, but Indian providers are far more likely to ask the patients for a sputum examination—a recommended and critical procedure, without which a diagnosis of Tuberculosis cannot be confirmed. Doctors in India come across looking more or less competent depending upon which procedure we are looking at, which may reflect differences in the disease burden or the medical training across these two countries.

11
23 Table 2: Consumer tax equivalents, OECD Countries, all covered products, 1960 to <u>2007 (percent)</u>

||1960-69|1970-79|1980-89|1990-99|2000-07|
|---|---|---|---|---|---|
|**Western European Countries**||||||
|**European Union**|**71**|**57**|**68**|**38**|**24**|
|**EFTA**|**57**|**52**|**97**|**137**|**113**|
|Austria|82|23|42|64|28|
|Denmark|41|68|74|43|24|
|Finland|128|92|123|124|31|
|France|64|52|69|39|24|
|Germany|101|67|70|38|24|
|Iceland|—|—|172|164|98|
|Ireland|42|84|120|64|35|
|Italy|43|36|52|32|20|
|Netherlands|103|89|97|52|30|
|Norway|—|—|57|115|101|
|Portugal|14|23|29|29|20|
|Spain|19|-2|20|27|18|
|Sweden|120|92|107|77|35|
|Switzerland|—|—|171|179|121|
|UK|55|52|83|49|33|
|**Europe's transition**||||||
|<br>**economies**|—|—|—|**0**|**15**|
|Bulgaria|—|—|—|-15|5|
|Czech Republic|—|—|—|21|22|
|Estonia|—|—|—|-1|15|
|Hungary|—|—|—|16|19|
|Latvia|—|—|—|15|32|
|Lithuania|—|—|—|1|25|
|Poland|—|—|—|3|22|
|Romania|—|—|—|5|34|
|Russia|—|—|—|-12|19|
|Slovakia|—|—|—|14|17|
|Slovenia|—|—|—|53|36|
|Turkey|—|—|—|15|12|
|Ukraine|—|—|—|-12|-1|
|**North America**|**7**|**7**|**11**|**-1**|**-1**|
|Canada|9|13|29|19|16|
|US|7|6|9|-4|-2|
|**Japan & Korea**|**55**|**74**|**117**|**125**|**107**|
|<br>Japan|65|80|113|118|97|
|<br>Korea|10|52|131|161|147|
|**Oceania**|**12**|**11**|**10**|**6**|**2**|
|Australia|17|11|8|7|2|
|NewZealand|3|10|15|3|2|

Source: Authors’ calculations based on data in Anderson and Valenzuela (2008)
Detailed estimation results can be found in the supplemental appendix. Exchange rate as of November 2011: 1 USD = 607 FRW.

_Source:_ Household data set 2011/2012.

Looking at _total energy expenditure_ (Table 6. Expenditures per Month per Category (in FRW)

||Treatment|Control|_ITT_|_p_‐<br>value|
|---|---|---|---|---|
|Candles|42|109|_‐20_|<br>.339|
|Kerosene for lighting|155|609|_‐418_|<br>.000|
|Big batteries (Type D)|358|352|_‐9_|<br>.750|
|Small batteries (Type AA)|30|72|_‐43_|<br>.003|
|Mobile phone charging|407|520|_‐68_|<br>.407|
|Total traditional energy sources (without cooking energy)|993|1,662|_‐557_|<br>.000|
|Total expenditures|37,971|31,334|_7,249_|<br>.276|
|Share of energy expenditure on total expenditures|0.04|0.07|_‐0.03_|<br>.001|

_Note:_ The ITT depicts the difference in means at the follow‐up stage between the whole treatment and control group, including also non‐complying households. We control for all stratification and re‐randomization characteristics. Detailed estimation results can be found in the supplemental appendix. Exchange rate as of November 2011: 1 USD = 607 FRW).

_Source:_ Household data set 2011/2012.

As a consequence, we observe a significant reduction in expenditures for small batteries, but not for the larger batteries since households use their Pico‐PV kit predominantly for lighting but only very seldom to run their radio. The consumption of candles is also significantly reduced. In addition, we find a moderate reduction in expenditures on cell phone charging, although the difference is not significant. Estimating an ATT only among mobile phone users by employing the random treatment assignment as an instrument shows a statistically significant reduction of costs for phone charging of 1,662 FRW (2.74 USD). The average household that pays
_The Creative Wealth of Nations_ <!-- Start of picture text -->
|| Figure 8 || Bank’s Experience – Project Portfolio (as of 2010)<br> “Since the 1970s, the Bank has financed 241 operations (208 lending and 33 non-lending)<br>having components in heritage conservation and promotion of local economy through<br>sustainable tourism, for an investment of US$4 billion .<br> [As of 2010] there [were] 117 operations under implementation (100 lending and 17 non-<br>lending), for an actual investment of US$1.8 billion .”<br>3000<br>2584.57<br>2500<br>2000<br>1500<br>882.88<br>1000<br>443.7<br>500<br>182.3<br>0<br>1970–1973 1980–1989 1990–1999 2000–2009<br>The figures refer only to the actual Bank commitment in culture heritage and sustainable tourism, and not to the overall project amounts.<br>Source: The World Bank, “Cultural Heritage: an Asset for Urban Development and Poverty Reduction.” 192<br><!-- End of picture text -->

On the last, the Bank’s investments in this particular area have been more than USD$ 1 billion from 1970 to 2009. (See figure 8.) In these four decades, however, most of the funds were dispersed between 2000 and 2009. This may be another modest way to suggest that cultural activities are increasingly figuring into the WBG’s work programs. But it may also be seen as a policy area that gained traction because of leadership: Wolfensohn (the Bank’s President from 1995 to 2005) is a proponent of the arts and their relationship to development.

Indeed, to realize any of the strategic options outlined here, the role of leadership at all angles — bottom, middle, and above, direct and indirect — matters. As Noel M. Tichy and Warren G. Bennis note in Judgment: How Winning Leaders Make Great Calls _,_ ideas, no matter how good they are, are worthless if never executed. In a world banked with infinite excuses, leadership may be the decisive factor of what happens to the following suggestions:

# **1) Create a Catalog of the WBG’s Arts in Development Initiatives**

The WBG’s arts initiatives (in terms of information) are generally scattered across the organization. So, to keep track and learn from what is going on, one possible strategy is to do this:

> 192 “Cultural Heritage: an Asset for Urban Development and Poverty Reduction,” The World Bank, - accessed April 15, 2014, http://siteresources.worldbank.org/INTCHD/Resources/430063 <u>1250192845352/CHandslums_Oct.pdf.</u>

<mark>47</mark>
Global Investment Costs for Coastal Defense through the 21st Century Report to the World Bank 5 annual losses (3) Constant relative flood risk -- maintain relative average annual losses (4) Risk intolerance -- keep relative average annual losses below 0.01% percent of local (5) Cost-benefit analysis -- compute the optimum protection level balancing flood damage to assets with protection costs to 2100, including capital and maintenance costs

# **3.1 The DIVA Flood Module**

The methods for assessing global coastal flood risk are taken from Hinkel et al (2014). The impact of coastal extreme events is calculated with the DIVA flooding module (Figure 1) and the key data sets that are defined in Table 3. Impacts are expressed in terms of the mathematical expectation of flood damages under a given protection level for the 12,148 coastline segments defined in the DINAS-COAST database (Vafeidis et al., 2008) which describe the world’s coasts with approximately 100 parameters per segment. Population exposure is obtained by overlaying the elevation data with population data (Table 3). Exposed population is translated into exposed assets by applying sub-national GDP per capita rates to the population data, followed by applying an assets-to-GDP ratio of 2.8. Present extreme water level distributions are assumed to be uniformly displaced upwards with relative sea-level rise, following 20th century observations (see Church et al., 2013). In addition to climate-induced sea-level rise, relative sea-level rise includes glacial isostatic adjustment and deltaic subsidence (Table 3). Current protection levels are taken from Sadoff et al. (2015) (see Table 4), who took protection levels for the biggest 136 coastal cities from Hallegatte et al. (2013) and complemented these through expert judgement for segments not associated to one of these cities. Protection level zero is assumed if the population density in the 1-in-100-years floodplain is lower than 30 people per km2.

For residual damage and population living in the 100-year flood plain, the methods of Hinkel et al (2014) are used.
- 69 -

**Analysis of ROL Rates and Proportional Treaty Equivalents - Eastern Caribbean**

|Insured Val.|||Coverage|Coverage||||Weighted|
|---|---|---|---|---|---|---|---|---|
|Treatya/|EPL|Prem.|Point|Layer|Prob.|ROL|Spread|ROL Rate|
|$100.00|10%|0.6%|5.7%|5.7%|15.00%|17.00%|<br>2.0%|<br>1.0%|
||||10.0%|4.3%|11.00%|14.00%|3.0%|<br>0.6%|
|ROL Eqv.|5.9%||14.3%|4.3%|7.50%|10.79%|<br>3.3%|<br>0.5%|
||||21.4%|7.1%|5.30%|8.30%|3.0%|<br>0.6%|
|less rein.|||28.6%|7.1%|3.50%|6.64%|3.1%|<br>0.5%|
|commis.|4.2%||35.7%|7.1%|2.50%|5.80%|3.3%|<br>0.4%|
||||42.9%|7.1%|1.50%|4.90%|3.4%|<br>0.4%|
||||50.0%|7.1%|1.20%|4.20%|3.0%|<br>0.3%|
||||57.1%|7.1%|0.75%|3.90%|3.2%|<br>0.3%|
||||64.3%|7.1%|0.70%|3.80%|3.1%|<br>0.3%|
||||71.4%|7.1%|0.35%|3.50%|3.2%|<br>0.3%|
||||85.7%|14.3%/,|0.18%|3.40%|3.2%|<br>0.5%|
||||100.0%|14.3%|0.15%|3.35%|3.2%|<br>0.5%|
|||||100.0%||||5.9%|

a/ Assumes after deductible under proportional/quota share reinsurance treaty.

A summary of the above ROL pricing structure at selected probability levels is illustrated below: <!-- Start of picture text --> Prob. Rate<br>X; over indabove 65%losses<br>-<br>0J.35% .0<br>1.50)% 7 v vg > ... , ia,,9__, , 4.90%<br>- -E o ad above-i / Imp-s<br>7.50% 11.0%<br>15.0% .0%<br><!-- End of picture text --> The above, thus, serves to show that reinsurance rates-on-line are consistent with primary premium rates under proportional/quota treaties. Nevertheless, it also shows that well managed low-cost local companies stand to gain from reinsurance commissions
governments concur –“double disapproval pricing”- and only for certain, specified reasons intended to ensure competition.

_Fair and equal opportunity to compete_ : all carriers of both countries may establish sales offices in the other country, can convert and remit earnings in hard currencies at any time. Besides, designated airlines are free to provide their own ground handling services and airlines and cargo consolidators may arrange ground transport of air cargo and are guaranteed access to customs services. According to the model text of Open Skies Agreements, user charges can not be discriminatory and should be based on costs. The text also includes procedures for resolving differences that arise under the agreement.

_Optional 7_<sup>_th_</sup> _Freedom all-cargo rights_ : provide authority for an airline of one country to operate all-cargo services between the other country and a third country, via flights that are not linked to its homeland. Most of the Open Skies Agreements signed by the United States include 7<sup>th</sup> freedom for all-cargo services.

The inclusion of specific provisions for air cargo in the Open Skies Agreements signed by the United States suggests that, irrespective of the size of the air cargo market, the American government is concerned about entry barriers, competition and ultimately prices of air cargo services.

Most of the empirical literature in the area of air transport focuses on the effects that Open Skies Agreements have on passengers. A recent and comprehensive study (Brattle Group, 2002) estimates the effects on passengers of Open Skies Agreements between the United States and countries in the European Union. Button (2002) describes the potential effects that the liberalization of U.S-European air transport market could have on airlines, passengers and labor, but he does not provide any quantitative evidence. The U.S. Department of Transportation (2000) published a report that argues that between 1996 and 1999 average passenger airfares in transatlantic markets declined 10.3% in non-open skies countries and 20.1% in open skies countries. This report neither controls for other factors nor explains the methodology used to estimate the reduction in airfares.

Despite some work on the effects of open skies on passenger fares, a survey of the

> 16 For  a detailed explanation and examples see http://www.state.gov/e/eb/rls/fs/208.htm

8
Gains in labor productivity may be attained due to the reallocation of labor toward sectors with higher productivity. Such reallocation can help overcome the misallocation of factor inputs to comparatively unproductive sectors and firms.<sup>17</sup> Alternatively, labor productivity gains may occur due to workers becoming more productive within their sectors, e.g. due to labor-augmenting capital accumulation or technology improvements.<sup>18</sup> We compare the contribution of labor reallocation across sectors and the within-sectoral productivity gains to explain aggregate improvements in labor productivity for data extending until 2015. Over India’s two phases of high labor productivity growth, within-sector productivity improvement has been the key driver of growth in labor productivity (Figure 6). Until the early 2000s, reallocation contributed only approximately 1 percentage point to annual growth. Even though productivity increases driven by labor reallocation have grown in importance since the early 2000s, the contribution of labor reallocation to total labor productivity gain has remained relatively modest, at around 1.5 percent.<sup>19</sup>

**Figure 6: Labor Productivity Growth in India: Reallocation and within Sector Gains**

<!-- Start of picture text --> 12<br>10<br>8<br>6<br>4<br>2<br>0<br>-2<br>-4<br>Within Sector gains Reallocation of labour<br>Total change in productivity<br>1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017<br><!-- End of picture text --> Note: Central Statistics Office data. Employment statistics are estimates provided by the International Labor Organization, available for 1991 to 2017. Years refer to respective fiscal year.

# **2.5 Long-term Proximate drivers of growth**

In this section we discuss the proximate factors that have likely contributed to India’s steady economic growth. The Growth Commission (2008) identified the following factors as the correlates of high and

> 17 See Hsieh and Klenow (2009) for a discussion of the potential magnitude of these effects in manufacturing. 18 Within sector productivity gains are likely to be substantive on aggregate, as evidence from development accounting exercises points to the fact that cross-country differences in income levels are more likely to be explained by sectoral productivity differences instead of the sectoral composition of the economy (Caselli 2005).

> 19 This contrasts with earlier periods, for which the literature estimates that reallocation contributed approximately 1 percentage point to annual growth until 2001 (Bosworth, Collins, and Virmani, 2007).

14
# **Table 2: Plot characteristics**

||**Total**|**North**|**East**|**Center**|**West**|**Conflict**<br>**pending**|**No conflict**<br>**pending**|
|---|---|---|---|---|---|---|---|
|Number of plots|861|197|232|155|277|199|662|
|House plot or adjacent|49.70%|52.31%|62.50%|47.65%|37.83%|26.67%|52.46%|
|Plot size|5.11|4.62|3.66|6.58|5.85|5.06|5.26|
|**Mode of acquisition**||||||||
|Bought|39.55%|19.47%|38.10%|49.31%|49.63%|44.21%|38.18%|
|Inherited|44.80%|72.11%|52.81%|22.92%|30.52%|36.32%|47.3%|
|Gift|7.77%|2.63%|6.93%|11.81%|9.93%|7.89%|7.73%|
|Other (incl. recent settlement)|7.88%|5.79%|2.17%|15.97%|9.93%|11.58%|6.8%|
|**Tenure status**||||||||
|Mailo|27.57%|1.16%|8.73%|78.63%|40.54%|40.52%|24.19%|
|Freehold|3.11%|1.74%|0.87%|7.69%|4.05%|5.88%|2.39%|
|Leasehold|6.89%|15.70%|6.55%|0.00%|4.05%|4.58%|7.50%|
|Customary/public|58.51%|77.33%|82.97%|11.97%|43.24%|43.14%|62.52%|
|Renting|3.92%|4.07%|0.87%|1.71%|8.11%|5.88%|3.41%|
|**Ownership**||||||||
|Male only|46.81%|45.18%|51.72%|38.07%|48.74%|41.71%|48.34%|
|Female only|9.76%|6.60%|11.64%|4.52%|13.36%|15.58%|8.01%|
|Male and female joint|21.14%|13.71%|25.00%|21.94%|22.74%|20.10%|21.45%|
|Landlord|16.96%|25.38%|10.78%|26.45%|10.83%|2.01%|3.63%|
|Other (incl. relatives)|5.34%|9.13%|0.86%|9.03%|4.34%|17.59%|16.77%|
|**Main crop and output value**||||||||
|Plot cultivated with perennials|31.13%|12.69%|47.41%|32.26%|29.96%|39.20%|48.19%|
|Plot cultivated with annuals|46.11%|73.10%|36.21%|40.00%|38.63%|28.14%|32.02%|
|Plot left fallow|1.05%|1.02%|1.29%|1.29%|0.72%|1.51%|0.91%|
|Output per acre (US $)|175|180|352|79|77|90|201|
|**Impact of conflict (plots w conflict)**||||||||
|No impact mentioned|22.62%|22.22%|18.92%|27.59%|38.89%|||
|Social problems|41.97%|48.15%|44.60%|31.03%|27.78%|||
|Contemporary economic loss|10.82%|6.17%|13.51%|13.79%|0.00%|||
|Reduced investment|24.59%|23.46%|22.97%|27.59%|33.33%|||

Source: Own analysis based on the 2001 EPRC/World Bank survey.

20
31

# **Table 6.  Effects of Remittances on Poverty for Non-Remittance and Remittance-Receiving Households, Ghana, 1998/99**

||Receive no<br>remittances|Receive<br>internal<br>remittances<br>(from Ghana|Receive<br>international<br>remittances (from<br>African or other<br>countries)|Receive<br>internal and<br>international<br>remittances|Internal<br>remittances<br>vs. no<br>remittances|Percent change<br>International<br>remittances vs.<br>no remittances|Internal and<br>international<br>remittances vs.<br>noremittances|
|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|
|Poverty headcount (percent)|33.23|32.91|38.69|32.56|(-2.02)|+16.43|(-2.02)|
|Poverty gap (percent)|14.15|13.95|13.70|13.78|(-1.42)|(-3.19)|(-2.62)|
|Squared poverty gap (percent)|10.26|9.84|6.69|9.82|(-4.10)|(-34.80)|(-4.29)|
|Gini coefficient|0.402|0.416|0.413|0.414|+3.48|+2.70|+2.94|
|Predicted mean per capita household<br>expenditure (including remittances) in<br>Ghanaian cedis|1,424,153|1,523,462|1,460,281|1,534,467|+6.97|+2.50|+7.46|
|N|5,998|5,998|5,998|5,998||||

Notes: Column (1) uses predicted income equations to measure the situation excluding remittances for all 5,998 households. Column (2) measures the situation for all households when only internal remittances (from Ghana) are included in predicted household expenditure. Column (3) measures the situation for all households when only international remittances (from African or other countries) are included in predicted household expenditure. Column (4) measures the situation for all households when both internal and international remittances are included in predicted household expenditure. Poverty calculations made using poverty line of 684,401 Ghanaian cedis per person per year, which is equivalent to poverty headcount index of 39.5 percent that is cited as Ghana poverty line in Ghana Statistical Service (2000: Table 2).

In 1999, US$ 1.00 = 2,394 Ghanaian cedis.

Source: Calculated from 1998/99 Ghana Living Standards Survey (GLSS 4).