rafmacalaba/datause-encoder-data huggingface dataset:
6 goods, namely: foodstuffs; agricuitural raw materials; ores,<sup>minerals and nonferrous metals;</sup> mineral fuels; and manufactured goods.

One major poist that emerges from Table 2 concerns<sup>the importance of raw</sup> materials and non-temperate zone foodstuffs in African<sup>exports and the unimportance of</sup> manufictured goods. This product orientation has impcrtant<sup>implications since most studies (see</sup> Laird and Yeats, 1987, for example) show that OECD<sup>tariffs and other tradel barriers are</sup> relatively high for manufactures but are generally zero or<sup>relatively low for raw material</sup> products of thle sort Africa exports. Non-fur clothing (SITC 841) normally faces highly .estrictive barriers in O ICD markets and corntitutes a large share of many developing countries' exports, but these products only account for abou;. 4 percent<sup>of total African non-oil exports</sup> which mostly originate in Mauritius (this country accounts<sup>for about 90 percent of all African</sup> clothing expoits to the OECD).<sup>6</sup> Sugar and honey (SITC 061) exports face restrictive trade barriers in OECD markets (including strict quotas and variable<sup>import levies), as do exports of</sup> ferrous metals and fish: but for the most part the products in Table<sup>2 generally encounter lower</sup> 'The statistics on "manufactures" exports in Table 2 may misrepresent the importance<sup>of these items</sup> in African exports due to anomalies in two product groups. Pearls and precious<sup>stones (SITC 667)</sup> account for about 10 percent of African non-oil exports, but there is disagreement<sup>as to whether these</sup> items are "manutactures" (as they are recorded in Table 2). The problem<sup>is that the SITC does not</sup> diffe:entiate between cut and polished gems (which could be considered<sup>a manufactured good) and raw</sup> unprocessed stones which are a qvestionable manufacture. Mainly<sup>the latter are mainly exported from</sup> Africa, while polished gems typically are exported from more industrialized<sup>countries (like Israel).</sup> Second, ships and boats (SITC 735) account for about 6 percent of African<sup>exports, but these transactions</sup> largely reflect transfers under "flags of convenience" and not true manufacturing<sup>activity. Convention</sup> normally defines manufactured goods as all items classified in SITC groups 6<sup>through 8 less 6£, although</sup> it is recognized that there are problems with this procedure (See Yeats<sup>1992).</sup>
Table 3: Value Added Content of Exports **H**<sup>_′_</sup> , Italy % share in 2007

||||||_su_|_pply sect_|_ors_|||||
|---|---|---|---|---|---|---|---|---|---|---|---|
||S1|S2|S3|S4|S5|S6|S7|S8|S9|S10|Total|
|_demand_<br>_sectors_|prim|proc<br>foods|text<br>cloth|chem<br>petro|autos|other<br>mach|other<br>manuf|util<br>consl|mkt<br>servs|pub<br>servs||
|S1 primary<br>production|10.56|0.18|0.02|0.23|0.01|0.12|0.23|0.28|1.79|0.09|13.51|
|S2 processed<br>foods|0.91|1.94|0.01|0.08|0.00|0.03|0.16|0.12|1.03|0.06|4.35|
|S3 textiles,<br>clothing|0.26|0.02|1.92|0.12|0.00|0.03|0.12|0.12|0.86|0.05|3.50|
|S4 chemicals,<br>petrochems|1.79|0.09|0.04|5.35|0.01|0.11|0.40|0.56|2.68|0.19|11.23|
|S5 autos|0.15|0.03|0.03|0.20|2.92|0.29|0.67|0.18|1.82|0.13|6.41|
|S6 other<br>machinery|0.51|0.09|0.06|0.50|0.05|12.04|2.07|0.59|5.59|0.42|21.92|
|S7 other<br>manufacturing|0.90|0.09|0.07|0.39|0.02|0.20|8.48|0.68|3.32|0.19|14.33|
|S8 utilities,<br>construction|0.05|0.00|0.00|0.02|0.00|0.02|0.11|0.71|0.24|0.01|1.18|
|S9 market<br>services|0.36|0.18|0.03|0.40|0.04|0.17|0.44|0.51|19.61|0.29|22.03|
|S10 public<br>services|0.02|0.01|0.00|0.01|0.00|0.01|0.02|0.04|0.20|1.23|1.54|
|Total|15.52|2.62|2.17|7.30|3.07|13.04|12.69|3.80|37.13|2.66|100.00|

_source: own calculations, using GTAP8 data._ For Korea, commercial services accounted for only 3.7 percent of Korean exports on a gross value basis, and 8 percent on a direct value added basis, but for 22.4 percent of all value added contained in exports when embodied exports are included. China proves a notable example as well. While China has opened up and become a dominant exporter of manufactured goods (especially machinery), the contribution of services to total export value added has actually decreased. Indeed, there is an interesting dynamic to China’s service intensity. As China experienced a dramatic increase in exports of manufactured goods, the initial stages used for production saw a drop in total domestic value added in exports, with a rising share of manufactured goods. However, as China has progressed to higher stages of processing, with machinery replacing textiles and clothing as a major export category, there has been a recovery in the total service intensity of China’s exports, from a low of 8.2 percent in 1997 to 13.3 percent in 2007.<sup>9</sup> In this section, we have highlighted the importance of services as inputs to manufacturing, and how this relates to the activity content of exports. In terms 9China has seen a dramatic drop in share terms for services, but this is not something linked to the level of services exports per se, but rather to the very dramatic increase in manufacturing exports since 1992. In 1992, almost half of exports were textiles and clothing, while services were a large share of a small base. Services exports have grown little since – China’s exports are now dominated by machinery exports, on both a value added and gross value basis. See Francois, Manchin and Tomberger (2012) for further discussion.

10
15 Figure 1 (continued): Indexes of real international prices of rice, wheat, maize and groundnut oil, 1972-76 (1972 = 100) and 2006-10 (2006 = 100)

# (c) Maize

<!-- Start of picture text --> 400<br>350<br>1972‐1976<br>300<br>2006‐2010<br>250<br>200<br>150<br>100<br><!-- End of picture text -->

# (d) Groundnut oil

<!-- Start of picture text --> 400<br>350<br>1972‐1976<br>300<br>2006‐2010<br>250<br>200<br>150<br>100<br><!-- End of picture text --> Source: Authors’ derivation drawn from World Bank _Pink Sheets_ of nominal prices deflated by the Unites States GDP implicit price deflator (see Anderson and Nelgen 2010, Appendix Table A).
<!-- Start of picture text --> 8= vy a7 Sex st 8= as Educationa 95 3= 86Consumption1 quintile7 os a7<br>78 76 79 73 83 79 en tds Er}<br>2 re 70 ne nt<br>be 68 cr)<br>32 32 58 32 ‘ sal<br>283 28a 50 Fd28 so<br>3. 3 6 r)<br>od 2 e<br>FY o 2 a 2 FY . - 20<br>13<br>7<br>o Female Male o Preschool/Primary Secondary University o 1 2 3 4 5<br>(ER Piped water «I improved water<br>(EI Basic water = I) improved water on premises<br><!-- End of picture text --> <!-- Start of picture text --> =J Sex Education Consumption quintile<br>= i=]= = J<br>w~ w~ 5 wo~ 73<br>3232zB258<br>aa a<br>3 3 47 a7 3 3 3 3<br>3# 5# 42 3# 42<br>cry<br>a 26 25<br>20 a a e 19<br>1" "1 6<br>7<br>4<br>1<br>o Female Male o Preschool!Primary Secondary University o 1 2 3 4 5<br>(ER Limited and basic sanitation (I) Basic sanitation<br><!-- End of picture text --> <!-- Start of picture text --> 8 Sex 8 Education 8 Consumption quintile<br>wo~ wo~ s wo~ 73<br>w 61 PY w s<br>: 58 : 57 s7 2 58 s7<br>: 3 50 41 : 8 £8 © 43<br># # “ * #<br>uM<br>ES) * * 28<br>»NwyN»N<br>510<br>. 5 1 _ a * 0 4<br>o co os<br>Female Male Preschool/Primary Secondary University 1 2 3 4 5<br>GEE sw sevice EEE) clectncity<br>[EEE] The internet<br><!-- End of picture text -->
<!-- Start of picture text --> All studies Studies with cost effectiveness data<br>1<br>.8<br>.6<br>.4<br>.2<br>0<br>-.2<br>LAYS Gained<br>Giving information on educationbenefits, costs, and quality Interventions to target teaching instruction by learninglevel not grade (in or out of school) (TaRL) Structured lesson plans w/ linked materialsand ongoing teacher monitoring & training Community involvement in school management Health products:sanitation, deworming, malaria Teacher accountability and incentive reforms Targeted intervention to reduce traveltime to schools Giving merit-based scholarships to disadvantagedchildren and youth Early childhood development (ECD) School Choice and PPP Cash transfers (as a tool to improve learning) Additional inputs alone (textbooks, class size,laptops/tablets, grants, libraries, etc) General-skills teacher training (in-service) Nutrition Incentive<br><!-- End of picture text --> _Notes: Each category of education intervention shows the learning-adjusted years of school (LAYS) gained from a given intervention or policy across over 150 impact estimates in 46 countries. The boxplot describes the 25th and 75th percentile. The boxplot is ordered in the same order as Figure 3 to provide a direct analogy, with the exception of the “nutrition” category which has no cost-effectiveness data and does not appear in Figure 3._ Figure 4: Learning-Adjusted Years of School (LAYS) Gained by Intervention Category vouchers for mind-spark adaptive learning software in India (0.36 LAYS); preschool attendance in Uruguay (0.35 LAYS); merit scholarships for girls in Kenya (0.34 LAYS); and ability grouping using extra teachers in Kenya (0.29 LAYS). By contrast, about half of all interventions produce no significant effects; those interventions are not included in the figure.

These findings point to a few overall lessons. In this sample of studies, the most effective programs are: multidimensional programs (a combined intervention in The Gambia, Camfed in Tanzania, and Tusome in Kenya); programs that are carefully targeted to a local need, such as scholarships (for girls), information (when returns to schooling are not widely known), and school construction in under-serviced remote areas; pedagogical instruction that is pitched to students’ levels of learning, not based on a rote syllabus or an over-ambitious curriculum; and programs that facilitate early childhood development.<sup>14</sup>

> 14Related approaches to teaching at the right level include reforming the curriculum so that it focuses on foundational skills and aligns better with students’ actual pace of learning. Such reforms have been evaluated in Tanzania with promising effectiveness (Rodriguez-Segura & Mbiti, 2022).

20
9 approach to wayward debtors, the actual nine arrears cases have suffered enough to appreciate the disastrous consequences of imprudent macroeconomic policies.

In sum, the "rights" approach is a step forward towards zeintegrating the protracted arrears members back into the fold. Nonetheless, like the earlier "support group" strategy, it may well respond to the needs o:^ some debtors but is unlikely to meet the circumstances of others. If that proves to be the case, more flexible and innovative arrangements should be conisidered.

# II. The Bretton Woods Agencies and Private Debt

Considerable progress has been made in addressing the debts owed by SubSaharan Africa to official, bilateral agencies.5 Less progress, however, has been realized in dealing with the debts owed to private creditors. While these obligations eccount for a lesser percentage of the region's total debts than is the case for Latin America, the burden is more significant than is often assumed. Debts owned to private creditors account for 28 percent of Sub-Saharan Africa's total long-term debt-. If one includes short-term debts, the percentage owed to private creditors rises to about 34 percent.- 1rivate debts account for over 20 percent of the long-term debt of twelve Sub-Saharan African countries (Benin, Cameroon, Congo, Guinea-Bi.ssau, Cote d'Ivoire, Gabon, Kenya, Maulritius, Mozambique, Niger, Nigeria, and Zimbabwe), and for over 30 percent for six of these (Benin, Congo, Cote d'Ivoire, Gabon, Nigeria, and Zimbabwe, see table 5).

5. For an analysis of the "Toronto terms" for official bilateral reschedulings, see World Bank, World Debt Tables 1989-90, Washington,DC: World Bank, Volume 1, pp.<sup>47</sup> -<sup>48</sup> .

6. World Bank, World Debt Tales 1989-90. Washington,DC: World Bank, 1989, Volume I, pp.82-85. Short-term private credits are derived as the difference between short-term debt and interest arrears on long-term debt outstanding.
<!-- Start of picture text --> Panle A.<br>60%<br>50%<br>40%<br>30%<br>20%<br>10%<br>0%<br>2008 2009 2010 2011 2012 2013 2014 2015 2016 2017<br>Title Firms None Title Firms<br>Panel B.<br>70%<br>60%<br>50%<br>40%<br>30%<br>20%<br>10%<br>0%<br>2008 2009 2010 2011 2012 2013 2014 2015 2016 2017<br>SOE Titling Firms None SOE Titling Firms<br><!-- End of picture text -->

**Figure 3. Firm Investment over time.**

**In Panel A, t** he sample of titling firms (grey bars) includes all 43 listed firms that receive land titles in the land titling program in Shenzhen. The sample of non-titling firms (while bars) includes all listed companies that are located in Shenzhen and that do not receive land titles in the land titling program. The figure presents the average investment (annual capital expenditure divided by PP&E) between 2008 and 2017.

**In Panel B** , the sample of SOE titling firms (grey bars) includes 30 listed SOEs that receive land titles in the land titling program occurred in Shenzhen in 2009. The sample of none SOEs (while bars) includes 13 listed nonSOEs that receive land titles in the land titling program in Shenzhen. The figure presents the average investment (annual capital expenditure divided by PP&E) between 2008 and 2017.

**Resource:** CSMAR

44
6

**Figure 5: Thailand’s Share in World Cassava Exports, 1961-2007**

<!-- Start of picture text --> 100%<br>90%<br>80%<br>70%<br>60%<br>50%<br>40%<br>30%<br>20%<br>10%<br>0%<br>Starch Dried cassava<br>1961 1963 1965 1967 1969 1971 1973 1975 1977 1979 1981 1983 1985 1987 1989 1991 1993 1995 1997 1999 2001 2003 2005 2007<br><!-- End of picture text --> _Source:_ FAOSTAT Agriculture TradeSTAT accessed 13 Nov 2009 Cassava plays an important role in the Thai economy. In 2007, it was the second most important crop after rice in terms of value and the third after sugarcane and rice in terms of volume (FAOSTAT, Agriculture production data, accessed 13 Nov 2009). At $948m in 2007, combined dried cassava and cassava starch exports were its third biggest agricultural export after rubber and rice (FAOstat, Agriculture trade data, accessed 13 Nov 2009). Unlike other major producers of cassava, there is little domestic direct consumption of cassava as a food product in Thailand, where almost all output is used as an intermediate product in other sectors. In 2008, an estimated 66 percent of all Thai cassavas was exported, 26 percent was utilized domestically and the rest was kept as stock (TTSA, 2009).This represents a somewhat unusual story of an “alien” food crop being introduced into a low-income economy (in this case, dating back to the 1950s) and initially thriving solely as an exported intermediate product used in the food industry in other economies.<sup>2</sup>

# **_2.1. The Cassava Value Chain in Thailand_**

There are essentially two families of products in the Thai cassava sector (Figure 6). These are the dried cassava value chain and the starch value chain.

<u>The dried cassava value chain, in turn, comprises two product segments – dried chips</u> and cassava pellets.

> 2 Thailand’s rapid expansion of cassava exports reflected two factors. The first was the welldeveloped external marketing capabilities of its Chinese trading community. The second was the heavy investment in infrastructure in the north=eastern region during the 1960s and 1970s, designed to counter the political influence of communist insurgents. The north-east has become the major cassava growing region in the country.
the capital and O&M cost components. This learning curve translates into a fixed percentage cost reduction for each percent increase in global cumulative installed capacity of a technology, a relationship known as Wright’s law. In practice, for solar energy it is assumed that for every percent increase in global cumulative production there is a 0.25% reduction in cost, in line with historical trends (IRENA 2023).

The learning curve feature allows assumptions about global decarbonization trajectories to be easily converted into domestic cost trajectories. If the world decarbonizes more quickly, solar in the modeled country will become more competitive with time, making a domestic solar-based net zero policy cheaper. Conversely, a slower global expansion of solar would have an adverse effect on the domestic cost of decarbonization.

# **3.3 Reduced data requirements**

In addition to implementing the technology diffusion process described above, FTT-FLEX substantially reduces the data requirements as compared with FTT or other global energy systems models. Table 1 summarizes data requirements in FTT-FLEX (power sector).

_Table 1: Summary of FTT-FLEX data requirement_

|**Country specific data requirement**|**Country specific policy variables**<br>**(default = zero)**|**Global data assumptions (provided)**|
|---|---|---|
|Starting share of power generation by<br>technology|Carbon tax rate and carbon tax rate<br>exemptions by fuel|LCOE (means and standard deviation)<br>by technology broken down to<br>•<br>Capital<br>•<br>O&M<br>•<br>Fuel<br>(Assumed 7% discount rate and zero<br>carbon tax). Source: IEA|
|Total electricity demand projections|Fuel tax by fuel-type|CO2intensity by power generation<br>technology.<br>Source: IEA|
||Investment subsidy by technology|Global baseline cumulative power<br>production by technology<br>Source: Our World In Data (history)<br>and IEA three future scenarios, World<br>EnergyOutlook 2022|
||Minimum and maximum generation<br>potential by technology (default 0 Gwh<br>and 3000 Gwh)|Lifetime of different kinds of power<br>plant (years)<br>Source: IEA|
|||Lead construction time of different<br>power plant types (years)<br>Source: IEA|
|||Learning rate by technology<br>Source: Main FTT|
|||International fuel price assumptions<br>based on IEA three future scenarios.<br>Source: World Energy Outlook 2022,<br>IEA|

FTT-FLEX utilizes technology-specific cost data from the IEA's Projected Costs of Generating Electricity (2020) database, which includes capital, operating and maintenance, and fuel costs. Average costs and 7
market or time fixed effects leads to collinearity issues and lack of power and thus they are not included. We note, however that in Table 4, the inclusion of fixed effects does not significantly affect the results.

The odds ratio reported in Column 1 of Table 9 is 5.83 (p-value=0.016) This odds ratio is substantially larger than the one in the Democratic Republic of Congo in Table 4. The point estimate for the female client dummy implies a marginal effect of 12 additional percentage points in the probability of transacting with a female agent for female clients (marginal effects are reported at the bottom of the table and evaluated at the average probability of a client being female). In Column 2, we include as regressors the type of transaction (an indicator for withdrawals) and the log of the transaction amount. The logit point estimates and marginal effects are virtually unchanged. The marginal effects estimated for the data in Senegal are about 12 percentage points, a bit larger than our estimates for the Democratic Republic of Congo using similar specifications, which range from 8.1 percentage points in Column 1 to 8.4 percentage points in Column 3 of Table 4.<sup>9</sup> Table 10 displays the results from the dyadic regressions in equation (2) using all of the transaction-level data from Senegal (instead of the sample used in the case of the Democratic Republic of Congo). Our findings are remarkably similar to those obtained for the Democratic Republic of Congo, suggesting again that both males and females have gender homophily. Transactions between clients and agents of the same gender are 1.6 times more likely than transactions between individuals of different gender. Similarly, we also find that clients are more likely to make transactions with those agents located in their same area. As in the case of the Democratic Republic of Congo, there is no gender differential effect, and this is reflected in the fact that the coefficient associated with the binary variable that indicates that both the client and the agent are women is not statistically significant. Since the data from Senegal do not include the age of clients or agents, we cannot include the difference in age as a regressor.

In Table 11, we run the analysis of the transaction amount, analogous to that in Columns 1 and 3 of Table 6. In particular, we run OLS regressions where the dependent variable is the log of the transaction amount (in US dollars of January 2015) against an indicator for female agents, female clients and their interaction. Again, standard errors are clustered at the client level and, due to collinearity issues, we do not include market nor time fixed effects. In Column 1 of Table 10 the estimates suggest that female agents on average perform transactions 72% or 70$USD smaller than male agents when dealing with male customers, but that difference vanishes when the female clients transact with them (p-value of the test that

> 9 When we use the sample of matched agents in Column 10, the marginal effects are 12.8 and 12.5 percentage points for the specification in columns 1 and 3, respectively (results not shown).

14
pattern: Median maintenance costs vary from $5,900/km in West Africa to $18,200/km in Central Africa. We should stress that these variations are projected for the same project mix in each region.

**Table 13: Regional Variations in Projected Unit Costs ($’000 US/km)**

|||Upgrading<br>|||
|---|---|---|---|---|
|Region|N|Min|Median|Max|
|West|16|70.2|135.0|309.0|
|Central|10|109.2|418.3|1,588.3|
|East|9|113.3|146.5|387.9|
|Southern|7|80.0|189.7|621.9|
|Total|42|70.2|167.7|1,588.3|
||M<br> <br>|aintenanc<br>|e<br>||
|Region|<br>N|Min|Median|Max|
|West|16|3.1|5.9|13.4|
|Central|10|4.8|18.2|69.1|
|East|9|4.9|6.4|16.9|
|Southern|7|3.5|8.3|27.1|
|Total|42|3.1|7.3|69.1|

**Table 14: Projected Total Network Upgrading Costs**

|Work Type|Benchmark|Low<br>(-1SE)|High<br>(+1SE)|
|---|---|---|---|
|Upgrading<br>($USBillion)|20,715|15,601|28,380|
|Maintenance<br>($USMillion)|901|683|1,226|

Table 14 summarizes total cost results for our benchmark, high and low cases. For upgrading, our benchmark projection is $20.7 billion, with standard error bounds at $15.6 billion and $28.4 billion. These estimates are roughly compatible with previous estimates by Heggie and Vickers (1998) for upgrading all Sub-Saharan roads currently classified as 33