rafmacalaba/datause-encoder-data huggingface dataset:
<!-- Start of picture text --> Urban pollution change Urban bottom expenditures change Urban average expenditures change<br>after 10% increase in pollution after 10% increase in pollution after 10% increase in pollution<br>Median Median Median<br>25%−75% Quartile 25%−75% Quartile 25%−75% Quartile<br>01−2014 01−2019 01−2024 01−2029 01−2014 01−2019 01−2024 01−2029 01−2014 01−2019 01−2024 01−2029<br>date date date<br>Cumulative effect: −17.071% after 15 years, PM25 > 10 Cumulative effect: −5.742%* after 15 years, PM25 > 10 Cumulative effect: −2.645%* after 15 years, PM25 > 10<br>Urban pollution change Urban bottom expenditures change Urban average expenditures change<br>after 10% increase in bottom expenditures after 10% increase in bottom expenditures after 10% increase in bottom expenditures<br>Median Median Median<br>25%−75% Quartile 25%−75% Quartile 25%−75% Quartile<br>01−2014 01−2019 01−2024 01−2029 01−2014 01−2019 01−2024 01−2029 01−2014 01−2019 01−2024 01−2029<br>date date date<br>Cumulative effect: −4.171%* after 15 years, PM25 > 10 Cumulative effect: 5.132%* after 15 years, PM25 > 10 Cumulative effect: 1.221%* after 15 years, PM25 > 10<br>Urban pollution change Urban bottom expenditures change Urban average expenditures change<br>after 10% increase in average expenditures after 10% increase in average expenditures after 10% increase in average expenditures<br>Median Median Median<br>25%−75% Quartile 25%−75% Quartile 25%−75% Quartile<br>01−2014 01−2019 01−2024 01−2029 01−2014 01−2019 01−2024 01−2029 01−2014 01−2019 01−2024 01−2029<br>date date date<br>Cumulative effect: −0.235%* after 15 years, PM25 > 10 Cumulative effect: −0.634%* after 15 years, PM25 > 10 Cumulative effect: 2.146%* after 15 years, PM25 > 10<br>0<br>60 0<br>−2<br>40<br>−2<br>20 −4<br>0 −6 −4<br>% change % change % change<br>−20 −8 −6<br>−40 −10<br>−8<br>−60<br>0 12 2.0<br>−1 10<br>1.5<br>−2 8<br>−3 6 1.0<br>% change % change % change<br>−4 4 0.5<br>−5 2<br>0.0<br>−6 0<br>0.4<br>12<br>0.5<br>0.2 10<br>0.0 8<br>0.0<br>6<br>% change % change % change<br>−0.2 −0.5 4<br>2<br>−0.4 −1.0<br>0<br><!-- End of picture text --> Figure 4: IRF plots for exogenous shocks in pollution, bottom household expenditures and average household expenditures PM2 _._ 5 _>_ 10. Effects that exclude zero in the final year, are marked by<sup>_∗_</sup> .

The results suggest several feedback mechanisms. First, average growth is non-inclusive. Second, pollution lowers primarily after bottom expenditures increase, while average growth is less effective in reducing pollution. Third, average household expenditures are more resilient to pollution effects. Taken together, these three effects compound in downwards pressure on bottom growth, subsequently also slowing pollution clean-up, and creating an environment in which heavily polluted urban poverty traps may potentially arise if pollution and poverty are 28
Table A.3: Baseline Parameters (continued)

|Parameter<br>Description|Units|Value|
|---|---|---|
|Forestry Sector|||
|_ξ_<sup>_w_</sup><br>0<br>Forest Harvesting Cost|1000USD/ton|0.019|
|_ξ_<sup>_w_</sup><br>1<br>Forest Harvesting Adjustment Cost|1000USD/Ha|150,000|
|_ξ_<sup>_w_</sup><br>2<br>Forest Conversion Adjustment Cost|1000USD/Ha|300|
|_c_<sup>_p_</sup><br>Forest Regeneration Cost|1000USD/Ha|0.036|
|_κ_<sup>_w_</sup><br>_v_<br>Yield Gains per annum of Vintage v|Share of Yield 0|0.011|
|_ψ_1<br>Merchantable Timber Yield Parameter 1||5.75|
|_ψ_2<br>Merchantable Timber Yield Parameter 2||75|
|_v_<br>Minimum Age for Merchantable Timber|Years|11|
|Timber Processing Sector|||
|_θ_<sup>_w,y_</sup><br>0<br>Timber Processing Technology Index in 2004||15.2|
|_κw,y_<br>Timber Processing Technology Growth Rate p.a.||0.022|
|_c_<sup>_w,y_</sup><br>Timber Processing Cost|1000USD/ton|1.74|
|Ecosystem Services Sector|||
|_ρe_<br>CES Parameter for Ecosystem Services||0.123|
|_θ_<sup>_r_</sup><br>Technology Parameter for Ecosystem Services||0.64|
|_α_<sup>_C,r_</sup><br>Share of Agricultural Lands in CES Function||0.02|
|_α_<sup>_P,r_</sup><br>Share of Pasture Lands in CES Function||0.14|
|_α_<sup>_F,r_</sup><br>Share of Managed Forest Lands in CES Function||0.26|
|_θ_<sup>_R_</sup><br>Eectiveness of Protected Lands||10|
|_κR_<br>Decline in Costs of Managing Protected Lands||0.022|
|p.a.|||
|_c_<sup>_R_</sup><br>0<br>Cost of Managing Protected Land in 2004|1000USD/Ha|0.175|
|Other Goods and Services|||
|_o_0<br>Output of Other Goods and Services in 2004|10000USD|0.95|
|_θ_<sup>_o_</sup><br>0<br>TFP in 2004||1|
|TFPh||0022|
|_κo_<br> growt rate per annum||.|
|GHG Emissions<br><br>|||
|_μ_<sup>_L_</sup><br>GHG Emissions from Natural Land Conversion|tCO2e per Ha|515|
|_μ_<sup>_g_</sup><br>GHG Emissions from Fertilizers|tCO2e per ton|4.066|

10
For the purposes of the evaluation exercise, we treat the census-based EBP estimates as the gold standard. The census-based estimates are compared to: (a) small area estimates under both survey weighted and unweighted versions of model (1) with the outcome defined at household level and the geospatial covariates defined at grid cell level; (b) small area estimates under an area level (Fay-Herriot) model, with geospatial covariates aggregated at the target area level; and (c) small area estimates under a grid cell-level model where both the outcome and the geospatial covariates are defined at grid cell level. It is important to note that the survey data was collected from the same harmonized WAEMU survey instrument as the survey data for the other four countries we consider in this paper.

Figure 3 and Table 3 summarize the results of these comparisons. Across all Communes in Burkina Faso, we find a high correlation equal to 0.799 between the estimates under the household-level model with geospatial covariates and those derived under the household-level model with census covariates. However, there is a large difference in this correlation between in-sample and out-of-sample Communes. For the 234 Communes included in the sample, which comprise 84 percent of the population of Burkina Faso according to WorldPop estimates, the correlation between the survey and census-based estimates is 0.879. In contrast, the correlation for the 117 non-sampled Communes is 0.457. The in-sample correlation is also remarkably similar to findings from other contexts (Masaki et al., 2022; Newhouse et al., 2022; Van der Weide et al., 2022). The correlation for out-of- sample areas meanwhile, is significantly lower than the out-of-sample correlation of 0.7 reported between geospatial and census-based estimates in Mexico (Newhouse et al., 2022). This may be explained by differences in the nature of the geospatial covariates used in Mexico, which could lead to better out-of-sample predictions, as well as differences in the country context. Perhaps, the lower out-of-sample correlations in this case could be explained by the fact that non-sampled Communes are different from the sampled Communes, as they are more remote, and they are not covered by the survey. The household level, grid cell level, and area-level geospatial models all benefit from conditioning on the same household survey data that was used for producing the census-based estimates, making the census and geospatial estimates (household and area level) more consistent with each other in sampled areas. On the other hand, for out-of-sample areas prediction is purely based on grid cell aggregated covariates that may not be as predictive of poverty as household census covariates.

14
# **Geoparse Tweets - Full Results**

Table S5 shows full results of the geoparsing algorithm. In particular, the table shows the value added of different data sources to build the landmark gazetteer; we run the algorithm using the augmented gazetteer generated from Geonames, Google and OpenStreetMap separately. Results highlight that the algorithm mainly relies on landmarks scraped from Google maps; recall and precision are only slightly worse using Google alone compared to combining all sources. Geonames performs poorly and OpenStreetMap performs better but still worse than Google, achieving about 0.2 and 0.1 worse recall and precision respectively compared to Google when judging whether the algorithm captures the true crash location.

Table S5: Tweet Geoparse Results

||Any Locat<br>Algori<br>True C|ion Captured by<br>thm Close to<br>rash Location|Crash Loc<br>by Algo<br>True C|ation Determined<br>rithm Close to<br>rash Location|Algorit<br>Co<br>True Cr|hm Cluster<br>ntains<br>ash Loction|
|---|---|---|---|---|---|---|
||Recall|Precision|Recall|Precision|Recall|Precision|
|**LNEx**|||||||
|LNEx Aug Gaz|0.674|0.686|0.129|0.132|0.175|0.125|
|**Algorithm - by Source**|||||||
|Aug Gaz - Geonames|0.124|0.326|0.112|0.455|0.124|0.446|
|Aug Gaz - Google|0.79|0.853|0.645|0.811|0.653|0.777|
|Aug Gaz - OSM|0.52|0.691|0.431|0.728|0.446|0.691|
|**Algorithm - All Source**|**s**||||||
|Raw Gaz|0.695|0.757|0.579|0.756|0.591|0.72|
|Aug Gaz|0.798|0.857|0.651|0.811|0.656|0.774|

S20
3

# **Appendix Table 2. Consumer Tax Equivalents of Policies Assisting Producers of 28 Covered Farm Products, All 75 Focus Countries, 1960 to 2004 (percent)**

||**1960-64 **|**1965-69**|**1970-74**|**1975-79**|**1980-84 **|**1985-89**|**1990-94 **|**1995-99**|**2000-04 **|
|---|---|---|---|---|---|---|---|---|---|
|**Grains and tubers**|**23**|**7**|**1**|**7**|**4**|**20**|**15**|**10**|**13**|
|Rice|42|-14|-11|4|1|24|25|22|38|
|Wheat|19|19|2|3|12|27|16|6|2|
|Maize|7|11|7|8|2|4|-3|-2|-2|
|Cassava|0|0|-1|-1|-2|-1|0|3|3|
|Barley|44|39|24|33|10|28|27|11|6|
|Sorghum|62|32|43|20|5|17|7|10|7|
|Millet|-15|-4|-2|0|2|3|4|6|6|
|Oat|39|54|33|68|11|24|17|4|-3|
|**Oilseeds**|**-4**|**-2**|**-8**|**-8**|**0**|**3**|**2**|**4**|**2**|
|Soybean|0|1|-3|-1|3|1|0|7|4|
|Groundnut|-21|-8|-20|-30|-7|26|-6|-12|-15|
|Palmoil|-19|-30|-35|-15|-7|-9|33|-2|-6|
|Rapeseed|3|13|7|5|9|13|15|5|11|
|Sunflower|10|1|-9|-17|-23|-2|-6|-5|-8|
|Sesame|-43|-56|-58|-61|-51|-38|-36|-40|-26|
|**Tropical crops**|**28**|**56**|**-2**|**-2**|**-1**|**11**|**19**|**15**|**27**|
|Sugar|116|175|1|13|19|38|42|44|63|
|Cotton|-8|0|3|-12|-15|-11|-18|-11|-6|
|Coconut|-29|-24|-9|-3|-12|-22|-36|-25|-10|
|Coffee|-16|-30|-30|-32|-49|-35|-18|-14|-4|
|Rubber|-43|-52|-6|-19|-23|-19|-11|2|1|
|Tea|-38|-41|-28|-26|-21|-21|-19|-21|-21|
|Cocoa|-28|-29|-33|-50|-43|-29|-19|-22|-31|
|**Livestock products**|**41**|**43**|**37**|**49**|**31**|**39**|**28**|**26**|**24**|
|Pigmeat|34|47|35|30|-12|-11|0|7|8|
|Milk|96|98|89|137|130|139|69|54|46|
|Beef|19|16|14|16|25|46|30|36|31|
|Poultry|24|23|28|27|28|17|21|18|19|
|Egg|-6|-1|-6|11|8|17|15|17|8|
|Sheepmeat|64|77|107|161|94|70|39|19|19|
|Wool|0|0|6|4|6|2|4|1|0|
|**All of the above**<br>**28 commodities**|**32**|**26**|**15**|**23**|**15**|**26**|**21**|**18**|**19**|

Source: Anderson and Valenzuela (2008), based on CTE estimates reported in national studies covering 75 focus countries.
|1.|**Village Entry**|Doing a thorough power<br>analysis/informal information gathering|Getting a ‘buy-in’ for the project|
|---|---|---|---|
|2.||Social mapping as a means of taking site<br>of knowledge production to the village|Social mapping as a means of arriving<br>at a number of target households|
|3.|**Meetings**|Focusing on collective capabilities|Focusing on individual material<br>outcomes|
|4.||Turning first movers into eyes and ears<br>of the community|Turning first movers into agents of the<br>facilitators|
|5.|**Everyday life of**<br>**Pt**|Ritualization|Rote recitation|
|6.|**rojec**|Engaging head-on with elite capture|Keeping local politics at bay|

# **I. Village Entry**

The first step of a project at the community level is called ‘village entry.’ In this step, it is project protocol to first get approval from key stakeholders in the village and then conduct a social mapping to find the poorest households. Following this, facilitators proceed with gathering women from those identified households into the project’s fold until they reach ‘saturation’ (i.e., when the project has mobilized 80 percent of its target audience).

# **a) Doing a thorough power analysis versus getting ‘buy-in’**

In Phase I, village entry began with a series of informal conversations with the key stakeholders in the village—local politicians, traditional leaders, moneylenders, school teachers, landowners and other influential men and women—as a means of information gathering. In these initial stages, ACs and CCs collected a large amount of information on caste dynamics, land-use patterns, informal moneylending practices and patriarchy, and tried to locate women’s interests and positions in each village. “...it was our main task to figure out how to work within these setups so as to be as least disruptive as possible. We had to avoid being seen as threatening, but we also had to get them to reveal reliable information about the village.”<sup>13</sup> In these initial stages, facilitators repeatedly emphasized that there was no substitute for time. For instance, one of the district level community trainers, formerly an AC in the first phase, explained to us how

> 13 BPM and former AC, Cycle 4, Saifpur.

16
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Our hypothesis is that better performance will be reflected in better forest quality, including carbon sequestration.

Table 4

# Independent variables

|**Independent Var**|**iables of Primary Interest**|**CF Mean**|**NCF**<br>**Mean**|**P value**|
|---|---|---|---|---|
|**Variable Name**|**Variable Description and Coding**||||
|CFdummy|1=CF; 0=NCF|N/A|N/A||
|CanIDFUGyear<br>_dummy|1= Can identify year of forest user<br>groupformation; 0=Cannotidentify|1.0|0.57|0.00|
|CForProposed|1= Either a registered or proposed CF<br>0=NCF that is not a proposed CF|N/A|N/A||
|**Environmental**<br>**Plots by Forest)**|**Variables (Plot or Average Across**||||
|NDVI_1990|1990 average NDVIbyforest|0.41|0.44|0.02|
|Altitude|Altitudein meters|1037.23|509.83|0.00|
|Fire|1=Evidence of fire; 0=none|0.23|0.34|0.11|
|Foresttype|1=Natural,2=plantation|1.10|1.03|0.02|
|Slope|Percent (flat=0)|21.26|10.31|0.00|
|Hill|1=hill; 0=_Terai_|0.67|0.23|0.00|
|Totalforestarea|Forest area in hectares|149.0|106.44|0.11|
|**Community Var**|**iables**||||
|Forestperhh|Forest areain hectares per household|0.90|0.75|0.17|
|HHsinfug|Total number of households in forest<br>usergroup|295.80|296.63|0.70|
|Migrationrate|Fraction of forest user group members<br>thathavemigratedfromthe village|0.094|0.077|0.09|
|**Plot Level Env**<br>**Models Only**|**ironmental Variables in Plot Level**||||
|Soilcolor|1=black/black;<br>0=gray/red/white/yellow/other|0.69|0.6|0.02|
|Clayloam|1=clay/loamsoil; 0=sandy/rocky soil|0.52|0.66|0.00|
|Sal<sup>12</sup>|1=Sal forest, 0=other forest|0.37|0.63|0.00|
|Aspect|N=1;NE/NW=0.75;E/W/flat=0.50;SE<br>/SW=0.25;S=0|0.53|0.50|0.03|
|Erosion|1=yes, 0=no|0.24|0.27|0.37|

> 12 Sal ( _Shorea robusta)_ is a member of the Dipterocarpaceae family.  It is a particularly valuable timber species found in Nepal at lower elevations.  308 of 620 plots are primarily sal. Other species include broadleaf, pine, _bel_ and _chilaune_ .

**19 |** P a g e
Table 12: **Value added shares of exports by sector – Brazil**

|||1992|1995|1997|2004|2007|
|---|---|---|---|---|---|---|
|Brazil:|Primary|0.327|0.356|0.376|0.372|0.379|
|Direct VA share|Textiles and Clothing|0.331|0.324|0.339|0.335|0.342|
||Machinery|0.301|0.296|0.304|0.271|0.278|
||other Manufacturing|0.236|0.251|0.269|0.308|0.318|
||Trade and Transport Services|0.499|0.527|0.567|0.626|0.640|
||Commercial Services|0.722|0.719|0.739|0.666|0.670|
||Public Services|0.636|0.639|0.687|0.670|0.676|
|||1992|1995|1997|2004|2007|
|Brazil|Primary|0.746|0.703|0.720|0.666|0.641|
|Total VA share|Textiles and Clothing|0.508|0.504|0.529|0.478|0.518|
||Machinery|0.470|0.470|0.464|0.366|0.380|
||other Manufacturing|0.606|0.620|0.667|0.643|0.701|
||Trade and Transport Services|1.198|1.084|1.422|2.924|2.956|
||Commercial Services|23.924|18.104|3.713|3.631|2.696|
||Public Services|18.294|2.326|0.727|0.734|0.751|

Table 13: **Relative intensity index based on forward linkages – Brazil**

|||1992|1995|1997|2001|2004|2007|
|---|---|---|---|---|---|---|---|
|Brazil:|Primary|0.278|0.276|0.257|0.238|0.291|0.305|
|RII|Textiles and Clothing|0.047|0.037|0.036|0.029|0.026|0.020|
||Machinery|0.102|0.090|0.105|0.124|0.096|0.090|
||other Manufacturing|0.208|0.211|0.205|0.189|0.191|0.183|
||Trade and Transport Services|0.125|0.154|0.125|0.097|0.154|0.159|
||Commercial Services|0.240|0.231|0.262|0.314|0.230|0.232|
||Public Services|0.000|0.001|0.010|0.009|0.012|0.011|

22
# **Table 2**

**Own revenue versus grant revenue for the three different spheres of government 2003/04** **<u>(R billions)</u>**

|**Sphere**|Own revenue<br>generated|Borrowing|Transfers from<br>national<br>government|All budgeted<br>spending|Actual<br>expenditure<br>excl interest<br>payments|
|---|---|---|---|---|---|
|**National**|300.3|27.1|-173.9|157.8|110.5|
|**Provincial**|6.6|0|161.5|168.1|170.8|
|**Local**|64.8|2.4|12.4|86.0|79.9|
|**Total**|371.70|29.5|0|411.9|361.2|

Source: South Africa (2004a) According to the Constitution, each local government and each province is ‘entitled to an equitable share of revenue raised nationally to enable it to provide basic services and perform the functions allocated to it’. The Constitution specifies broadly what needs to be taken into account in determining the transfers. A permanent, independent Financial and Fiscal Commission, created in accordance with the Constitution, gives advice on all aspects of inter-governmental fiscal relations. The division of revenue has been surprisingly uncontentious since 1994.

The objective has been to ensure that transfers are formula-driven, based on objective criteria, well publicized, and determined well in advance. All transfers from national government have to be legislated. Thus, each year a Division of Revenue Act is passed along with the national budget. This Act explains the basis for the transfers, and sets out grants to each province and municipality for the coming three financial years. The outer years can be adjusted in subsequent budgets, but such adjustments are minimal.

# 4.3. <u>Grants to local government</u>

Transfers to local governments are divided into ‘equitable share’ (unconditional) and conditional grants, the latter include grants for capital purposes. Grants to local governments have been increasing significantly in real terms in recent years as indicated in Table 3.

**Table 3**

**Conditional and unconditional transfers from national to local government** **<u>(R millions)</u>**

|Year|2000/01|2001/02|2002/03|2003/04|2004/05|
|---|---|---|---|---|---|
|Equitable share<br>andrelated|3,201|3,876|4,887|7,352|8,536|
|Conditional|2,336|2,642|3,820|5,038|5,709|
|Total|5,536|6,517|8,706|12,390|14,245|

Source: South Africa (2004a) 7
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19

**Figure 7: Ratio of Flight Capital to GDP by Region World Bank Residual, Stocks 1991**

<!-- Start of picture text --> Per,entge5<br>120<br>100<br>80<br>40<br>20<br>ioE ..i<br>E. t Asia Europe and Latin North South Asia Sub- U.*.._+.-,_ All<br>and the Central America Africa and Saharan Developing<br>Pacific Asia and the the Middle Africa Countries<br>Caribbean East<br><!-- End of picture text --> Ranking the 84 countries by the stock of flight capital to GDP shows that only three are Latin American (Nicaragua, Bolivia and Venezuela), four are African (Gabon, Zambia, Sudan and Uganda) and three are from the Middle-East and North Africa (Egypt, Syria and Jordan). For Gabon, the stock of capital flight is almost triple its 1991 GDP (Figure 8). Much the same picture emerges when the stock of capital flight is compared to external debt, adjusted to exclude the effect of cross-currenicy movements on the dollar-measured debt stock (Figure 9).

**Figure 8: Top Ten Countries for the Ratio of Flight Capital to GDP World Bank Residual. Stocks - 1991**

<!-- Start of picture text --> Percentoage<br>300 r<br>2001<br>1100<br>50 Ii<br>Sudan Egypt, Panama Gabon Bulgaria Nigeria Venezuela Syrian Jordan Uganda<br>Arab Rep. Arab Rep.<br><!-- End of picture text -->
surveillance of the WTO membership’s market access commitments; especially for the monitoring needs of developing countries.

The fear of a global resort to protectionism during the global economic crisis of 2008–2009 created a political opportunity to push forward the monitoring agenda, and this resulted in additional initiatives to monitor multilateral commitments under the trading system. To summarise the chain of events, the financial crisis in mid-2008 and its global spread resulted in a major shock to the international economic system. In particular, the fourth quarter of 2008 saw a sudden and unexpected drop in global trade flows. The year-on-year estimated decline in world trade during this time was over 10%; furthermore, large declines in trade took place simultaneously in each of the major trading regions including a 16% drop for Europe, a 7% decline for North America, and a 5% decrease for Asia (WTO 2009: 3, chart I.5). The first quarter of 2009 saw another estimated (year-on-year) decline in world trade of 30%. The fall in trade during this period of 2008–2009 was so sudden and immediate that it was clearly _unrelated_ to any major new global protectionist initiatives that had taken place up until then. Bown (2009b, table 7.4) provides one set of estimates concerning trade remedy data consistent with this. These results indicate that even under a hypothesised (though unlikely and subsequently unrealised) scenario that each trade remedy investigation initiated from first quarter (1Q) 2008 through 1Q 2009 were to result in prohibitive trade restrictions for the affected products, the upper bound estimate is that this might have affected less than 0.5% of these economies’ imports.

However, by early 2009, the main concern for trade policy was that the global economic recession that accompanied the financial crisis had resulted in widespread 4