rafmacalaba/datause-encoder-data huggingface dataset:
Education and infrastructure are two factors consistently linked to India’s regional development (e.g., Lall 2007, Amin and Mattoo 2008). We measure education level as the district’s percentage of adults with a graduate (post-secondary) degree and through literacy rates. Our infrastructure measures are the share of villages in a district with electricity access or paved roads. Spatial locations relative to major population centers are frequently found to be important. We thus include a measure from Lall et al. (2011) of the driving time from the central node of a district to the nearest of India’s ten largest cities as a measure of physical connectivity and across-district infrastructure.<sup>5</sup> We finally model the strength of the household banking sector for each district, the district’s urbanization rate, and the district’s average income per capita.

Table 3a finds a positive correlation of better education and infrastructure with increases in the business ownership rates of women. These results correspond to several recent studies of India that have found important roles in more rigorous frameworks for education and infrastructure in explaining women’s entrepreneurship (e.g., Mukim 2011, Ghani, Kerr, and O’Connell 2011a,b). The final three rows show that increases in women’s business ownership are occurring most in more urban and wealthier areas of India.

Table 3b provides a similar analysis of industry-level traits. The results are much sharper than the district correlations in Table 3a. Industries that are conducive to small, household-based establishments have seen the most substantial increases in the ownership rates of women. Rather than expanding into industries where they hold a smaller presence, the growth in women’s ownership is strongly associated with women expanding their role in sectors where they have a tradition of activity.

With this background, we measure by district the share of unorganized sector activity (employments or establishment counts) that is held within women-owned businesses. For this calculation, we form the denominator through businesses where we know the gender of the owner (i.e., excluding the small share of activity in other categories). We also calculate the change in the size of the unorganized sector. For this work, we only consider the manufacturing sector. Figure 3 shows that the trend observed for India as a whole in our tabulations is present in the districts. The slope of the trend line is 0.097 (0.046).

> 5 These are Ahmedabad, Bangalore, Bhubaneshwar, Chennai, Delhi, Guwahati, Hyderabad, Kolkata, Mumbai, and Patna. Distances are calculated based on data on India’s road networks gathered using GIS software.

12
# **Figure 1**

# **Net Foreign Asset Positions, 1985-2004**

# **China and India**

<!-- Start of picture text --> 10% China<br>0%<br>-10%<br>-20%<br>India<br>-30%<br>-40%<br>-50%<br>-60%<br>East Asia and G7<br>10%<br>G7<br>0%<br>-10%<br>-20%<br>East Asia<br>-30%<br>-40%<br>-50%<br>-60%<br>Eastern Europe and Latin America<br>10%<br>0%<br>-10%<br>-20%<br>Latin America<br>-30% Eastern Europe<br>-40%<br>-50%<br>-60%<br>1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004<br>1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004<br>1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004<br><!-- End of picture text --> Net foreign asset position expressed as a ratio to GDP. East Asia is the average of Indonesia, Korea, Malaysia, and Thailand. G7 is the average of Canada, France, Germany, Italy, Japan, United Kingdom, and United States. Latin America is the average of Argentina, Brazil, Chile, and Mexico. Eastern Europe is the average of Czech Republic, Hungary, and Poland. The series for the regions are weighted averages where the weights are the countries' GDPs as a fraction of the region's GDP. Source: Authors' calculations drawing on the dataset constructed by Lane and Milesi-Ferretti (2006).
A for more details on the estimation of and the energy efficiency scenarios.

# **3. Data and Assumptions**

A wide range of data is required to calculate MACs for buildings sector. In this section we discuss the data and their sources. The data that is needed to calculate MACC are following:

- Demographic and economic characteristics and drivers (i.e. population and household numbers, area of commercial space, etc.)

- End-use penetration and technology characteristics in base year and their future projections

- Fuel net calorific values and emission factors

- Fuel and technology costs

# **3.1 Data Sources for Armenia**

Major statistics such as historical growth of population, households, household size were taken from National Statistical Service Yearbook 2014 (NSSRA, 2014). Prices for electricity for different users are based on information available in various notifications of the Public Services Regulatory Commission of Republic of Armenia (PSRCRA, 2014). Data related to technological and pricing of inefficient and efficient energy utilizing technologies are obtained from various sources including GEF (2014a), GEF (2014b), EBRD (2014). The database of MARKAL-Armenia model used for Armenia’s low carbon study (USAID, 2014) was also used for data and assumptions related to penetration rates in the baseline and climate change mitigation scenarios.

# **3.2 Data Sources for Georgia**

A large number of secondary sources have been used to collect the required data for energy efficiency MAC analysis for Georgia. Some of the key sources include recent household energy survey carried out by the Winrock International EC-LEDS project (Winrock International, 2014), which provided data on average household area, average household size (number of persons per household), average percentage of heated area in dwellings, share of households using gas for heating, penetration of end-use technologies, both efficient and inefficient (refrigeration, washing machine, lighting bulbs) in households. Energy Audits carried out by EC-LEDS project (Sustainable Development Centre Remissia, 2014) was used to collect information on costs of different insulation 8
markets. Comparing the results of rows 5a, Sb and row 1, we can assess the optimal regional tariff, rest of world tariffs and regional access held constant. We estimate that the differences are rather small, but that Cameroon would be marginally better off if it held its regional tariffs constant at the level of the benchmark equilibrium. The welfare differences are rather small, because regional imports by Cameroon are only about two percent of its total imports. The results from rows 4 and _5_ are reconciled by again appealing to the theoretical result that incremental preferential tariff reduction may be beneficial initially, but eventually becomes immiserizing. in the case of Cameroon, row 4 shows that preferential reduction below the benchmark tariff level is immiserizing if the tariff against the rest of the world is not reduced to the CET level; but row _5_ shows that preferential reduction from the CET to the benchmark level is welfare neutral.

**Other Policy Possibilities.** In rows 6 and 7, we estimate the impact of unilateral nonpreferential trade liberalization by Cameroon. Lowering tariffs to zero provide greater gains than lowering tariffs to the level of the common external tariff of CEMAC, which is to be expected since the bulk of Cameroon's trade is with the rest of the world and Cameroon is a price taker with respect to the rest of the world.

What is most interesting from a policy perspective is that in the most realistic case of regional market power on exports by Cameroon, Cameroon will gain more from lowering tariffs to zero than from full implementation of CEMAC, i.e., row 7 column 7 exceeds row 1 column 7. An effort by Cameroon to unilaterally lower its tariffs to zero may be impeded by a conflict with the common external tariff of CEMAC. If Cameroon chose to drop out of CEMAC, it would have to suffer from reduced access to the markets of CEMAC. These estimates indicate that the losses that Cameroon would suffer from reduced preferential access to CEMAC are more than compensated by the

-23-
projected increases are significantly smaller for _Ceriops decandra_ (Goran) and _Ceriops decandra_ (Goran) - _Excoecaria agallocha_ (Gewa). Our projections for _Heritiera fomes_ (Sundri) and _Ceriops decandra_ (Goran) are in general agreement with estimated changes by Hoque et al. (2006), Potkin (2004); and Blasco and Aizpuru (2002).<sup>13</sup>

# **5.  Implications for Poor Communities in Sundarbans** **_Upazila_ s**

The Sundarbans contributes significantly to both the economy of southwestern Bangladesh and the national economy (Potkin 2004), and forest-based livelihoods have been important in the region. In the past, the Sundarbans was an important source of timber, pulp, firewood, reeds, _Nypa fruticans_ , Phoenix Fronds, honey, bee wax, fish, crab and shrimp. In 1989 the Forest Department imposed a moratorium on the felling of trees and collection of firewood. The remaining timber<sup>14</sup> has potential value, however, and our projections indicate that salinization will further deplete the stock of _Heritiera fomes_ (Sundri), the most valuable species. Many households remain dependent on collection of golpata ( _Nypa fruticans_ ) leaf for roofing, grass for matting and fodder, reeds for fencing, bee-wax for commercial use, honey, fish and crab for consumption (Uddin et al. 2013; IPAC 2010; Gopal and Cahuhan 2006). All of these activities are likely to be affected as advancing salinization changes the spatial distribution of mangrove ecosystems.

The Sundarbans mangrove region includes five _upazila_ s in Khulna Division: Dacope and Koyra in Khulna District; Mongla and Sarankhola in Bagerhat, and Shyamnagar in Satkhira. Many of these _upazila_ s are poor compared to the national standard. We identify the poverty status of the

> 13 Mukhopadhyay et al. (2015) project future mangrove distributions from recent trends in mangrove migration using an approach based on a Markov chain model and cellular automata.  Our approach differs significantly, because we project alternative futures by combining empirical estimates of mangrove species’ salinity tolerance ranges with salinity projections developed from several IPCC AR4 climate scenarios, global circulation model projections, and models of riverine flow and land subsidence in the lower Ganges Delta.

> 14 Although the ban on felling trees in the Sundarbans is likely to remain, estimated Annual Allowable Cuts ranging from 54,000 m<sup>3</sup> to approximately 143,000 m<sup>3</sup> of Sundri were reported in the Integrated Resource Management Plan of the Forest Department of Government of Bangladesh in 2010 (GoB 2010).

20
# Table 5. The effects of flat versus rising carbon taxes (F2cut vs. R2cut)

(High tax cases, revenue recycled by cutting VAT and TK)

|||2015<br>F2CUT:|R2CUT:||2030<br>F2CUT:|R2CUT:|
|---|---|---|---|---|---|---|
||Base<br>case|<br>Flat C<br>Tax|<br>High C<br>Tax|Base<br>case|<br>Flat C<br>Tax|<br>High C<br>Tax|
|Variable|values|% change f|rom base|values|% chan<br>ba|ge from<br>se|
|GDP (billion yuan 2010)|58,956|<br>-0.26|<br>-0.027|126,235|<br>-0.61|<br>-0.74|
|Cumulative GDP (not discounted)|||||-0.50|<br>-0.42|
|Consumption (bil yuan 2010)|25,290|<br>-0.17|<br>-0.017|67,264|<br>-0.54|<br>-0.71|
|Investment (bil yuan 2010)|25,374|<br>-0.41|<br>-0.045|43,747|<br>-0.64|<br>-0.86|
|Capital stock (bil yuan 2010)|138,851|<br>0.00|<br>0.00|386,091|<br>-0.56|<br>-0.51|
|Energy use (million tons of sce)|4,005|<br>-8.9|<br>-1.21|5,958|<br>-7.8|<br>-13.1|
|Coal use (million tons)|3,666|<br>-12.4|<br>-1.71|4,610|<br>-11.9|<br>-20.0|
|Oil use (million tons)|537|<br>-2.2|<br>-0.27|835|<br>-2.4|<br>-4.0|
|Gas use (billion cubic meters)|166,354|<br>-4.3|<br>-0.53|381,163|<br>-5.4|<br>-9.4|
|Electricity (billion kWh)|5,554|<br>-5.0|<br>-0.65|8,951|<br>-4.6|<br>-7.9|
|CO2 emissions(inc cement; mil tons)|9,819|<br>-9.8|<br>-1.33|<br>12,797|<br>-9.5|<br>-16.0|
|Cumulative CO2 (2015-2030)|||<br>|184,840|<br>-9.8|<br>-9.8|
|Carbon tax yuan/ton CO2||¥82.5|<br>¥9.8||¥82.5|<br>¥157|
|Carbon tax as a share of total revenue||5.0%|0.66%||3.3%|5.8%|
|Electricity generation|values|% change f|rom base|values|% chan<br>ba|ge from<br>se|
|Coal|3967|<br>-7.96|<br>-1.04|5782|<br>-8.2|<br>-14.2|
|Gas|152|<br>0.41|<br>0.11|459|<br>-1.1|<br>-2.2|
|Nuclear|190|<br>2.78|<br>0.35|678|<br>2.9|<br>5.4|
|Hydro|902|<br>2.78|<br>0.35|1132|<br>2.7|<br>5.2|
|Other|133|<br>5.22|<br>0.67|271|<br>4.6|<br>8.5|
|Wind|191|<br>0.09|<br>0.01|498|<br>-0.1|<br>0.12|
|Solar|19|0.17|0.02|<br>130|0.1|<br>0.25|

The carbon tax rate in 2030 is ¥149 in the R2lump case but slightly higher at ¥157 in the tax cut case. The lack of an offsetting cut means that the VAT’s in the lump sum case remain high and generate greater distortions. The net effect for many goods is a higher price in R2lump; for example, the price of primary metals rise by 5.7% versus 4.6% in R2CUT, for food manufacturing it is 2.1% versus 0.9%, and for construction it is 2.9% versus 1.8%. These greater price distortions contribute to a smaller GDP. The cut in capital taxes in R2CUT also allows ‐21‐
51. The teachers, however, are not disadvantaged. They have more education, on average, than do teachers in the nation as a whole, reflecting the policy of the Thai government to place new gradua.es in relatively remote, rural schools. They have, on average, experienced more inservice training, but are less likely to have received regular supervision.

Table 1: Descriptive Statistics for National Sample ar.d Sub-Sample

|Variable|National|Sample|Sub-|sample|
|---|---|---|---|---|
||m|sd|m|sd|
|(a) School Level|||||
|Infrastructure|3.09|2.42|1.89|2.23|
|North|0.19|0.40|0.20|0.40|
|Central|0.18|0.39|0.16|0.37|
|South|0.13|0.12|0.07|0.25|
|Bangkok|0.15|0.13|0.08|0.27|
|Northeast|0.33|0.22|0.49|0.50|
|Pupil Teacher Ratio|21.32|6.78|20.49|7.93|
|Enrollment|425.23|515.91|164.77|85.15|
|Remoteness|7.77|2.70|8.49|2.23|
|Principal Age|44.78|8.75|41.13|8.50|
|Equpment and Facilities|<br>0.00|0.19|0.00|0.42|
|Textbooks|26.26|8.41|20.49|7.93|
|Teaching materials|1.94|1.60|1.47|1.33|
|(b) Teacher Level|||||
|Sex|0.37|0.48|0.40|0.49|
|Education|0.59|0.49|0.73|0.44|
|Inservice Training|1.61|0.89|1.84|0.73|
|Internal Supervision|9.16|11.19|7.57|7.51|
|(c) Student level|||||
|Dialect|0.49|0.50|0.32|0.47|
|SES|0.00|0.69|-0.30|0.42|
|Breakfast|0.83|0.37|0.90|0.30|
|Time to School|18.77|15.08|16.48|12.60|
|Pre-primary education|0.49|0.50|0.38|0.48|
|GPA|4.81|1.39|4.49|1.41|
|Sex|0.50|0.50|0.49|0.50|
|Repetition|0.15|0.36|0.20|0.40|
|Instructional quality|45.00|7.24|44.51|9.33|
|<br>Total Achievement|-0.12|11.83|-2.77|8.88|

21
_COTE D’IVOIRE: Growth, Specialization and the Terms of Trade_

**Table A-6: Real GDP Growth Per Capita, Selected African States Experiencing Conflict, 1993-2002**

|Country|1993|1994|1995|1996|1997|1998|1999|2000|2001|2002|Average|
|---|---|---|---|---|---|---|---|---|---|---|---|
|Liberia|(32.98)|(21.76)|(4.27)|12.12|106.28|29.70|22.90|20.40|4.90|3.30|14.06|
|Ethiopia|13.36|3.49|6.12|10.91|5.38|(1.86)|6.20|5.74|8.88|2.72|6.09|
|Eritrea|13.37|21.22|2.86|9.25|7.90|1.78|(0.01)|(13.15)|10.22|1.81|5.53|
|Sudan|4.57|1.01|6.00|5.92|6.34|6.44|6.46|5.14|6.10|5.52|5.35|
|Rwanda|(8.11)|(50.25)|35.22|12.75|13.85|8.86|7.58|5.97|6.72|9.38|4.20|
|Angola|(24.70)|3.50|10.40|11.20|7.90|6.80|3.35|3.04|3.17|15.30|4.00|
|Cote d'Ivoire|(0.19)|0.81|7.13|7.73|5.72|4.75|1.58|(2.47)|0.35|(1.82)|2.36|
|Congo,Rep.|(1.00)|(5.50)|5.00|4.30|(0.60)|3.70|(3.00)|8.20|3.60|3.50|1.82|
|Sierra Leone|1.38|(1.95)|(8.00)|6.09|(17.60)|(0.88)|(8.08)|3.81|5.40|6.30|(1.35)|
|Burundi|(5.71)|(3.86)|(7.27)|(8.36)|0.37|4.78|(0.97)|(0.90)|3.20|3.60|(1.51)|
|Congo,Dem|(13.47)|(3.90)|0.70|(1.02)|(5.62)|(1.62)|(4.40)|(7.00)|(2.00)|3.00|(3.53)|
|Average|**(4.86)**|**(5.20)**|**4.90**|**6.44**|**11.81**|**5.68**|**9.25**|**2.62**|**4.59**|**4.78**|**3.36**|

Peace may bring opportunity to advance. Countries that resolve their conflicts, or at least end or mitigate them, often grow strongly in subsequent years. There are numerous examples, including Liberia and Sudan and Rwanda recently, and Uganda in earlier years. There can be a distinct “bounce-back” effect; countries resolving their conflicts often recover by growing rapidly for a period of several years, albeit from a lowered base. If Cote d’Ivoire can resolve it’s difficulties, the result may be growth.

Many of these African countries experienced very severe conflict, and still managed to grow faster than Cote d’Ivoire. That simple fact may suggest that the recent conflict is not the sole or single major cause of Cote d’Ivoire’s slow growth, which after all has persisted now for more than two decades.

According to statistics released by the Center for Systematic Peace, the civil war in Cote d’Ivoire started in 2000 and as of 2002 was still continuing, with about 2,500 “directly related” deaths<sup>36</sup> . They list it as a low-intensity conflict, with a new outbreak in 2002, with “conflict magnitude” or severity rated as only “2” on a scale of 1 to 10<sup>37</sup> . This is a rather small conflict by African standards. In nearby smaller Guinea-Bissau 6,000 died in the 1998-99 coup and civil war. Some 100,000 Eritreans died in the 1998-2000 war with Ethiopia. More than 1.5 million died in Congo (Zaire) between 1996 and 2002. Some 500,000 died in little Rwanda in 1994 alone. Civil war claimed 25,000 in Sierra Leone between 1991 and 2001.Over a million have died in the last twenty years in Sudan. So, things could easily get much worse in Cote d’Ivoire if the conflict is not resolved. The outbreak of civil war in 2003 has not yet been rated in terms of “magnitude”, but is presumably much worse than the earlier conflict.

> 36 See the website maintained by M. Marshall, 2003.

> 37 See also Marshall & Gurr, 2003.

40
of share of total exports (Figure 2.5). This shift means typically labor-intensive and femaledominated industries lost share relative to other sectors that are mainly capital-intensive and employ male workers. In 2018, for example, female share of total jobs in top exporting industries such as motor vehicles, manufacture of chemicals, and machinery were 33.4 percent, 21.6 percent, and 11.8 percent, respectively. Conversely, female workers’ participation in sectors with falling exports – textiles, apparel, and agriculture – were considerably higher with 59 percent, 49 percent, and 30 percent, respectively.

**Figure 2.5: Share in total exports shifted from labor-intensive to capital-intensive industries**

<!-- Start of picture text --> Percentage of total exports<br><!-- End of picture text --> <!-- Start of picture text --> Machinery and Electronics 40<br>Chemicals<br>35<br>Transportation<br>Vegetable 30<br>Textiles and Clothing<br>Food Products 25<br>20<br>15<br>10<br>5<br>0<br>2000 2002 2004 2006 2008 2010 2012 2014 2016 2018 2020<br><!-- End of picture text -->

**Source:** Authors’ elaboration using data from the World Integrated Trade Solutions (WITS). Note: Product groups appear as reported by WITS.

Trade agreements, domestic industrial and economic policies, and China’s positioning as an export competitor to other developing economies have combined to change Morocco’s composition of exports. First, the EU-Morocco trade agreement committed Morocco to reduce tariffs on imports of EU-manufactured goods to zero, harming national production by creating incentives to import light manufactures. Second, internal Moroccan policies incentivized foreign direct investment in specific capital-intensive sectors, such as transportation, machinery, and electronics, which are predominantly male-intensive (Chauffour, 2018). In 2004, the country introduced the Industry Upgrade Plan and the Emergence Plan that provided support to sectors in which the country envisioned an emerging comparative advantage (e.g., automobile industry, aeronautics, and electronics).<sup>4</sup> Additional measures included the 2014 “Industrial Acceleration Plan” that created a fund to provide subsidies to industries. Third, the emergence of China as an export power, 4 Morocco’s structural transformation since the early 2000s has been rather weak. Specific sectors including automotive, aeronautics, and renewable energies, however, reported an impressive performance. The automotive industry has been a leading export sector since 2014 (Chauffour, 2018).

5 | P a g e
expected to increase poverty in each woreda. The econometric methodology applied for this step does lead to unbiased estimates of poverty, but is likely to not be accurate enough to be implemented as actual scale-up rules for the PSNP. However, since the objective of this paper is not to propose a concrete set of rules, per se, but rather to illustrate how financial analysis could be conducted for rules-based approaches more generally, we proceed with these crude rules. (In Section 4 we do also conduct sensitivity analysis to illustrate how the cost of different risk financing strategies would change if different rules were chosen.) These rules were then applied to historical WRSI data to calculate the number of PSNP beneficiaries there would be in each woreda under these rules if rainfall patterns in each of the years 2001-2015 were to repeat themselves in future. This 15-year data period was chosen due to the availability of consistent rainfall data which was applicable in estimating poverty in a baseline year. It is assumed that the PSNP core caseload is covered by the existing PSNP budget and therefore these numbers for total beneficiaries were then converted to numbers of beneficiaries from the scalability component of PSNP by subtracting the 8 million core caseload from the historic poverty estimates for each year in 2001 to 2015.<sup>8</sup> These figures for the number of beneficiaries were converted to expenditures using an assumed expenditure per beneficiary of $US47.25 (US$45 plus 5% to account for the cost of delivery), and US$45 was chosen to represent an expenditure of US$1.50 per additional beneficiary per day, for five days per month, six months per year. The results of this analysis are displayed in Figure 2.1.

**Figure 2.1: Number of additional beneficiaries and additional expenditure under assumed hypothetical rules for scaling of PSNP**

<!-- Start of picture text --> 6<br> 250<br>Expected transitory poverty if<br> 5 rainfall levels from this year<br>were to repeat in future<br> 200<br>Average<br> 4<br> 150<br> 3<br> 100<br> 2<br> 50<br> 1<br> ‐ ‐<br>2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015<br>Year of crop loss<br>Million)<br>Additional population in poverty (Millions)<br>Additional cost of population in poverty ($USD<br><!-- End of picture text --> 8 The 2001 year refers to the 2001-2002 agricultural year that commences in 2001, with the same terminology applying to any other references to the years.

5