rafmacalaba/datause-encoder-data huggingface dataset:
their overall disbursements. Table 2 shows global budget shares for the top 20 bilateral and multilateral donors. What is striking is that two out of the top four donors are multilateral donors: IDA and the European Union combined disburse nearly 30% of all aid. There are other multilateral agencies in this list, however, with substantially smaller aid disbursements. The Global Fund disburses about 3.5% of the global budget and the IMF 1.8%. As expected, the US and Japan lead the ranking in terms of donor size. However, Japan is ranked as a larger donor than the USA in 2010 and 2012 because of the adjustments we make. Removing the bilateral aid that is channelled through multilateral agencies (trust funds) and excluding debt relief, humanitarian and emergency aid changes the ranking of the two donors. Without these adjustments, the US is the largest bilateral donor.

In brackets we indicate the number of recipient countries of a donor in a year. Many donors disburse aid to a large number of recipients. These figures confirm observations made elsewhere pointing out the extent of aid fragmentation among donors (e.g. Knack and Rahman, 2007; Djankov, Montalvo, and Reynal-Querol, 2009; Annen and Kosempel, 2009; Annen and Moers, 2012). Noticeable, though is the small number of recipients for IDA given its size relative to other donors, suggesting that among IDA-recipient countries, IDA may be a very large and important donor.

To assess this issue more precisely, we rank donors in terms of their size in all IDA-recipient countries and calculate a z-score for the average rank of every donor between 1960 and 2012 using DAC disbursement data (DAC Table 2A).<sup>2</sup> A larger z-score indicates a higher-ranked (i.e. relatively large) donor. Figure 1 plots the z-score for IDA and the USA, the two donors with the largest average scores across all years. We find that IDA has the largest z-score in every year since 1984, and either the largest or second largest z-score in every year since 1975. In IDArecipient countries, therefore, IDA is a top donor in terms of size. For example, since 2000, IDA has been on average the largest donor in 37% of its recipient countries, and it has been one of the top three donors in 80% of its recipient countries relative to all bilateral donors, including the US and Japan. Thus, IDA is a very significant donor in the (approximately) 80 IDA-eligible countries, which include most of the world’s low-income countries.

To assess sectoral specialization of multilateral agencies relative to bilateral donors, we compare measures of the sectoral distribution of aid disbursements between the two. We focus on multilateral agencies with more than 1% of the global share of ODA, but include a few smaller, specialized agencies for illustrative purposes.

2 We use a z-score instead of just the average rank because the number of donors has changed across years.

7
9

# Table 3: QUANTICATON OF BASIS RSK

|Cotton Type|PidHedge|Ratio||Basis Rs|
|---|---|---|---|---|
||<br>Using N.Y. No. 2|Cotton Future Co|ntract|_<br>___|
|Cecntral Asian|Aug 85-Jan 93|.38|.30|.70|
|PunjjabSGlSOS5|Aug 88-1an 93|.66|.44|_.56_|
|Chinese 329|May 85-Jan 93|.42|.39|.61|
|Turkish Izmirant|Jan 85-~Apr<br>92|.20|_.05_|_.95_|
|EPA|May 85-Jan 93|.34|.30|.70|
|____________|<br>Using Cot|look AIndex|||
|Cenftral Asian|Aug 85-Jan 93|.98|_.85_|_.15_|
|Punjab SG 1505|Aug 88-Jan 93|1.01|.73|.27|
|Chinese 329|MayS85-Jan 93|.79|.68|.32|
|TurkishIzmirant|Jan 85-Apr 92|.39|.34|.66|
|EPA|1 <sup>May 85-1an 93</sup>|.80|.80|.20|

Note: The Cotlook A Index is published daily by Cotlook Limited, a cotton information service in the United Kingdom. The A index is an avenage of the _5_ lower quotes in USC/lb for cotton being ofli:red in significant quantifies from 14 cotton growin regions in 13 producing countrie. The Index is based on cotton comparable to middling 1-3/32 inch quality by the 'Liverpol" concept, delivered C.I. F. North Europe, cash against documents on arrival of vessel, includin profit and agent's commission. T'he Index is presented as an indication of fth compe-titivl **e** vel of offering prices.
**<u>Table 1: Overall changes yields, output shares, and prices 2001 to 2011, by region</u>**

||**Year **|**Total **|**West**|**East**|**North **|**South **|
|---|---|---|---|---|---|---|
|Wheat|2004|2950|**Panel A: Yields**<br>2594|**of major crops (k**<br>3048|**g/ha)**<br>3259|3098|
||2006|2354|1972|2293|2581|2705|
||2011|3155|3543|3045|3042|3076|
|Corn|2004|3305|3024|2834|3827|3119|
||<br>2006|<br>3298|<br>3776|<br>2216|<br>4072|<br>2396|
||2011|5892|6334|4431|7433|3723|
|Barley|2004|1830|1594|1619|2138|1970|
||2006|1595|1362|1403|1799|1798|
||2011|1675|1796|1321|1641|1880|
|Soybean|2004|1081|1021|952|1080|1353|
||2006|1060|1124|797|1137|989|
||<br>2011|<br>1914|<br>1675|<br>1518|<br>2079|<br>2127|
|Sunflower|2004|942|813|929|962|991|
||2006|1424|1226|1475|1505|1361|
||2011|1948|1827|2160|2159|1614|
|Sugarbeet|2004|19746|19076|20118|20199|21820|
||2006|24855|23654|21698|28071|20857|
||<br>2011|<br>32042|<br>32230|<br>23569|<br>35831|<br>41562|
|||**Pa**|**nel B: Key crops’**|**shares in output**|**value (%)**||
|Grain|2001|0.803|0.820|0.722|0.831|0.792|
||2006|0.679|0.740|0.556|0.711|0.620|
||2011|0.582|0.666|0.485|0.632|0.541|
|Oilseed|2001|0.065|0.006|0.196|0.043|0.117|
||2006|0.181|0.075|0.353|0.154|0.265|
||<br>2011|<br>0.353|<br>0.222|<br>0.485|<br>0.310|<br>0.394|
|Root crops|2001|0.106|0.157|0.057|0.118|0.020|
||2006|0.104|0.159|0.061|0.123|0.017|
||2011|0.035|0.084|0.015|0.047|0.003|
|Fruit & vegetable|2001|0.026|0.017|0.026|0.008|0.071|
||2006|0.051|0.026|0.030|0.059|0.097|
||2011|0.043|0.029|0.015|0.055|0.063|
|Wheat|2001|0.414|0.381|0.411|0.378|0.545|
||2006|0.305|0.332|0.306|0.259|0.334|
||2011|0260|0294|0269|0177|0315|
|Corn|<br>2001|.<br>0.028|.<br>0.018|.<br>0.032|.<br>0.046|.<br>0.016|
||2006|0.051|0.030|0.046|0.089|0.029|
||2011|0148|0151|0108|0282|0036|
|Barley|<br>2001|.<br>0.152|.<br>0.137|.<br>0.160|.<br>0.151|.<br>0.181|
||2006|0.171|0.145|0.167|0.181|0.206|
||2011|0.095|0.080|0.076|0.066|0.151|
|Sunflower|2001|0.065|0.006|0.196|0.043|0.117|
||2006|0.132|0.018|0.339|0.094|0.222|
||2011|0.261|0.060|0.466|0.200|0.317|
|||**Pa**|**nel C: Producer pr**|**ices for key crop**|**s (US$/kg)**||
|Wheat|2001|0.139|0.159|0.119|0.141|0.111|
||2006|0.120|0.126|0.111|0.119|0.115|
||2011|0155|0161|0152|0158|0148|
|Corn|<br>2001|.<br>0.176|.<br>0.167|.<br>0.194|.<br>0.144|.<br>0.235|
||2006|0122|0136|0126|0112|0124|
||<br>2011|.<br>0.150|.<br>0.154|.<br>0.148|.<br>0.146|.<br>0.159|
|Sunflower|2001|0.246|0.253|0.251|0.240|0.246|
||2006|0.213|0.230|0.214|0.208|0.210|
||2011|0.387|0.382|0.387|0.389|0.387|
|No. of obs|2001|9992|3901|1517|2781|1793|
||2006|6835|2301|1086|2120|1328|
||2011|7639|1565|1631|2288|2155|

_Source:_ Own computation from Form 50.

18
13 Decision 135 communes—may prompt local officials to roll out HCFP faster than they might otherwise have done.

# V. **PROPENSITY SCORE ESTIMATION AND BALANCING**

**Table 3** reports the results for the probit model used to obtain the propensity scores.   As well as providing the basis for the propensity scores for the matching procedure, the results in **Table 3** are of intrinsic interest, because they shed further light on the targeting of the HCFP program. As intended, being classified as officially poor significantly increases the chances of having a free health card or having health insurance for the poor. Also, as intended by HCFP, being a beneficiary of Decision 135 (the disadvantaged commune program) and being an ethnic minority household in an officially designated disadvantaged mountainous province also significantly raises the chances of being covered by one or other of the HCFP modalities.

In addition to these three official determinants of HCFP coverage, several other variables included in the model also significantly affect the chances of being covered by HCFP. Household per capita income also exerts a statistically significant effect on the probability of being included, with the better off being less likely to be included, though the effect is not linear. Households with an elderly head are also more likely to be covered by HCFP. Households with illiterate heads are more likely to be covered. The head’s education also affects the probability, but not in a monotonic fashion. Households in urban areas, other things equal, have a significantly lower chance of being covered by HCFP. Households living in regions other than the Northeast and South central Coast are more likely to be covered, other things equal, than households living in omitted region (the Red River Delta).

**Figure 2** shows the histogram for the propensity scores (the predicted probability of being covered by HCFP) for those not covered by HCFP and those actually covered by HCFP. The distribution for the uncovered is heavily skewed, with the bulk of cases having a very small probability of being covered. There is, however, a long right-hand tail. With the large sample size of the survey being used, and the large fraction that fall
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**Table 6: Unemployment and Underemployment by Region, Togo, 2006.**

||**Unemp**|**loyed**|**Undere**|**mployed**|
|---|---|---|---|---|
||**Urban**|**Rural**|**Urban**|**Rural**|
|**Lomé**|8.7%|‐|19.7%|‐|
|**Maritime**|5.9%|2.5%|28.7%|30.0%|
|**Plateaux**|6.1%|1.0%|12.2%|31.1%|
|**Centrale**|4.5%|1.2%|8.0%|10.7%|
|**Kara**|5.0%|1.4%|18.4%|10.6%|
|**Savanes**|4.9%|0.2%|19.3%|30.0%|
|**Total**|7.4%|1.4%|18.4%|25.0%|

_Source_ : CWIQ 2006 and author’s calculations.

**With regard to the sectors of employment, the main difference between Lomé and other urban areas is in the role that agriculture plays for employment (** Figure 22 **).** Less than 1% of workers are employed in agriculture in Lomé, with the same number being 10% and higher in the other urban areas. Otherwise the distribution of urban workers among sectors is similar across regions. Trade and commerce usually employs most of urban workers, followed by services, agriculture, manufacturing and transport.

**Figure 22: Employment by Sector, Urban areas (LHS) and Rural areas (RHS) in Different Regions, Togo, 2006.**

<!-- Start of picture text --> 100% 100%<br>90% Mining 90% Other Services<br>80% Administration 80% administ<br>70%<br>Other Services 70% mines /<br>60%<br>50% Construction 60% construc<br>40% Education 50% transpor<br>30% Transport 40% educatio<br>20% Manufacturing 30% fabricat<br>10% Agriculture 20% services<br>0%<br>10%<br>Services commerce<br>0%<br>Commerce and Trade agricult<br>maritime plateaux centrale kara savanes Total<br><!-- End of picture text --> _Source_ : CWIQ 2006 and author’s calculations.

**Most of the rural workforce is employed in agriculture with the share varying from 56% in Maritime to 92% in Savanes (** Figure 22 **).** Other sectors with important shares in some regions include commerce, financial services and manufacturing.

# **5.4 Employment, Education and the Incidence of Poverty**

**The incidence of poverty is – not surprisingly ‐ higher for workers with a lower education level in both rural and urban areas (** Table 7 **).** Similarly, there are certain sectors that have higher incidences of poverty. In urban areas the ones standing out are Agriculture, and Agriculture and Transport in rural areas.

**Table 8 presents the distribution of poor by education levels and sectors and shows that among the urban (rural) sectors, Commerce (Agriculture) has the highest number of poor workers.** 38% of urban poor work in Commerce, of which more than half has not finished primary school. Commerce and Agriculture together concentrate 69% of the poor in the urban areas. Agriculture together with Commerce, concentrate 91% of the rural poor, with low‐ skilled agricultural workers employing the highest share of poor people in rural areas (54%). 41% of the poor in urban areas have no education and in rural areas 65%.

25
Unfortunately, the input-output data for the Central American economies are not usually available. Barring Costa Rica, for which the input-output tables are available in the OECD database (OECD, 2017), I couldn’t find such data for the other countries. To overcome this challenge, I use the average intermediate shares from eight regional countries (including Costa Rica). In the data appendix, I show that these shares of regional economies lie within close range. Therefore, their average provides a reasonable benchmark for the Central American countries.

Table 1 reports the country-wise coverage of cleaned data obtained from various sources. Column (6) shows the effective period of analysis for each country, which corresponds to the overlapping years in columns (2)–(5).

||**T**|**able 1: Countr**<br>**Value**|**y-wise time co**<br>**Trade**|**verage **|**Effective**|
|---|---|---|---|---|---|
|**Series**|**Employment**<br>**shares**|**added**<br>**shares**|**shares**<br>**(aggregate)**|**Sectoral trade**<br>**shares**|**period**<br>**of coverage **|
|||||**Atlas of**||
|**Source**|**ILO**|**UNSD**|**UNSD**|**economic**<br>**complexity**||
|(1)|(2)|(3)|(4)|(5)|(6)|
|Costa Rica|1980-2017|1976-2017|1976-2017|1995-2016|1995-2016|
|Guatemala|1981-2017|2001-2017|2001-2017|1995-2016|2001-2016|
|Honduras|1991-2017|1970-2017|1970-2017|1995-2016|1995-2016|
|Nicaragua|2003-2012|1987-2017|1987-2017|1995-2016|2003-2012|
|Panama|1970-2017|1970-2017|1970-2017|1995-2016|1999-2016*|
|El||||||
|Salvador|1978-2017|1990-2017|1990-2017|1995-2016|1995-2016|

*1995-1998 is dropped for Panama as the imputed agricultural consumption shares using the linkages are negative.

# **4   Accounting for trends in industrial employment**

This section quantifies the relative importance of the three macro variables p p p � in accounting for the change in industrial shares. Analyzing inter-temporal changes is not only crucial �CC<sup>�</sup> , NN<sup>�</sup> , TT for countries that observe quantitative shifts in the sectoral distribution of the workforce. Significant 11
than optimal (Liebowitz and Margolis, 1995; Church et al., 2002).<sup>9</sup> On the supply side, there could be positive technology spillovers among firms especially in the early stage of new technology diffusion (Stoneman and Diederen, 1994). The development of EV technology requires significant investment but the technology know-how once developed can spread through many channels including worker mobility and the supplier network (Kim and Marschke, 2005; Blalock and Gertler, 2008; Poole, 2013; Serafinelli, 2019). So the private returns of R&D are likely smaller than the social returns, leading to under-investment in the market.

# **2.2.2 Financial Incentives**

Consumer subsidy is the most common policy to promote EV adoption across countries. As a comparison, Table 1 presents the level of subsidies and eligibility rules in US and China. Effective from 2010, US federal government provides a federal income tax credit to new EV buyers based on each vehicle’s battery capacity and the gross vehicle weight rating, with the amount ranging from $2,500 to $7,500, with a phase-out target of 200,000 EVs sold. In addition to the federal level mandate, according to the National Conference of State Legislatures, as of 2017, 46 out of 50 states have published incentives (monetary and non-monetary) to promote the adoption of EVs.<sup>10</sup> These incentives include tax exemptions and rebates for EVs and non-financial incentives such as high-occupancy vehicle (HOV) lane access, toll reduction, and free parking. In addition, federal, state, and local governments also provide funding to support charging station deployment (Li et al., 2017; Li, 2020).

Similarly, Chinese government institutes the policy plan promoting EV adoption at both central and local levels, with the main device being consumer subsidy contingent on vehicle quality attributes. The central subsidy program experienced considerable coverage expansion starting from 2009 with updates in every 3 to 5 years. The policy program followed a phaseout design over each of the effective policy period. Local subsidies are generally pegged to the central subsidy amount by a certain ratio depending on the respective government mandate at the provincial and city levels, some with additional restrictions contingent on various vehicle

> 9Given the nature of the market, each side of the market is unlikely to internalize the external effect on the other side through market transactions. If EVs are produced by one automaker, the automaker would have incentive to offer a charging station network to increase EV adoption. Nissan and GM are the two early producers of EVs but more and more auto makers are entering the competition. Nissan is a large owner of charging stations but GM is not. Tesla is building its own proprietary network for Telsa owners only. This suggests that they recognize the importance of charging stations in EV adoption but this would create duplicate systems. 10Source:

``` https://www.ncsl.org/research/energy/state-electric-vehicle-incentives-state-chart.aspx ``` 8
the standard indices such as the societal headcount ratio violate the most basic welfare-consistency property when preferences are heterogeneous. We show that a simple modification of the societal headcount is better aligned with the theory, and that this proposed switch yields a different picture of the evolution and distribution of income poverty both at the country and world level.

Our paper leaves several questions unanswered and additional research is called for. First, the reference bundle is taken to be exogenously chosen by some social planner. Although such exogeneity is sufficient to provide the foundations for global lines of the ‘societal’ type, it implies that our theory cannot pin down the exact design of the global line. Thus, the construction of a fair and welfareconsistent global poverty measure requires bringing together the insights of our results with those of the literature on global lines (Ravallion, 2020). Furthermore, we demonstrated that the design of the global line depends on the set of admissible preferences as well as the specification of the reference bundle. While this set of preferences could also be taken as an exogenous choice of the social planner, it could alternatively represent the empirical diversity of preferences over own and relative income in the global population. We leave elicitation of such preferences for future research. Second, our results rule out the use of standard poverty indices in our framework, but do not pin down a unique index to replace them. We argue that the hierarchical headcount ratio would be a natural candidate to replace the societal headcount ratio because it strikes a good compromise between simplicity and having desirable properties. However, this is a mere proposal and the selection of the ideal index for our framework remains an open question.

Finally, we motivate our paper based on _global_ income poverty measurement. More generally, our theory would be relevant for any government or regional entity concerned with both subsistence and social inclusion, such as, for example, the European Union (European Commission, 2015).

40
**Figure 6:  Evolution of different categories of textile exports for sub-Saharan Africa**

<!-- Start of picture text --> 2.5% 5% 2016<br>2.0% 4%<br>Agri-inputs<br>1.5% 3%<br>Textile eq.<br>Textile<br>1.0% 2%<br>Apparel<br>0.5% 1%<br>Leather &<br>footwear<br>0.0% 0%<br>1995 2000 2005 2010 2015<br><!-- End of picture text --> Note: The left graph shows the share in global trade for five product categories in the textile GVC, calculated using normal values (USD). The agri-inputs (wool, cotton, silk, etc.) are measured on the right scale, the other categories on the left scale. The total value in 2016 was 4.7 billion USD and the pie-chart on the right shows the breakdown by product category.

On the right, we show for the final year how total exports break down across the different categories. Not surprisingly, textile equipment is extremely small, but even textiles account for barely 5% of total exports. The largest category is apparel, which is the most labor intensive segment of the industry, but also the segment showing the highest trade elasticity in our estimates. Agricultural inputs is the second most important segment, which is a natural fit with the important agricultural sector in Africa.

The second factor that determines the potential for future textile exports from sub-Saharan Africa is the remaining tariff protection on these products. Of course, the previous liberalization episodes that we exploited to identify our export supply elasticities, limit the potential benefits that can be gained from further liberalization of market access. We show the remaining import tariff protections for the EU and the United States for the three textile categories and an average over all products in Figure 7. These statistics refer to the last year of our sample period (2016). We show them separately for the different schemes that countries can belong to, and enumerate below the table the members of the different groups, separately for the two regions.

A number of patterns stand out. If countries do not benefit from any preference scheme, average tariffs for textile products are quite a bit higher than for other groups. The darkest bars show average ad-valorem rates of 4.6% for the EU and 4.3% for the United States overall, but for apparel average tariffs are much more punitive, at 11.3% and 10.5%, respectively. Only a few countries are subject to these rates.

Standard GSP or general AGOA import tariffs, in lighter green, are much lower for leather and footwear, but the benefits they confer for textile or apparel are very limited. The US does not even provide any benefits for textiles and the EU GSP preferences for textiles are much 33