rafmacalaba/datause-encoder-data huggingface dataset:
evident. We find competitive cities at all levels of pollution, from below WHO guidelines to the highest PM 2.5 concentration levels. This shows that cities can achieve competitiveness without degrading the environment, and the questions that seem to follow are: how are these “cleaner” competitive cities facing this challenge? What are they doing differently? Although these questions motivate further analysis at different levels (for instance, a deeper look at examples of best practices), our first step is to focus on a group of low income and fast‐growing cities.

# A brief look into South Asian and African Cities

Although most of the examples of competitive cities that are also successfully managing pollution are in richer countries (such as Vancouver, in Canada), it is useful to find some cases in the developing world. As mentioned earlier, developing countries often face particular challenges and may need to follow different strategies than what developed nations do.

We now move the discussion to focus on cities located in South Asia and Africa. We choose to focus on this area for two reasons. First, these regions, particularly South Asia, include cities with pollution levels among the highest ones. Second, as previously discussed, most of the cities in these regions are in the phase where they experience a positive relationship between pollution and per capita GDP (EKC analysis). Many of them are starting to face the pollution challenges associated with rapid growth. This exercise aims to identify two types of cities: those that are good at managing pollution that could be held as examples of best practices in the developing world, and those that are struggling with this challenge and may use successful stories of pollution management. After a first characterization, the analysis of the second group of cities may require further research and deep dives studies could help to accomplish this.

Figures 8 through 10 show the relationship between pollution and city competitiveness for cities located South Asia and Africa. We use the same four typologies of cities described before and focus on examples of competitive cities at different stages of pollution management. We find two examples of competitive cities that show considerably low levels of pollution ‐Kigali (Rwanda) and Coimbatore (India)‐ and two examples of competitive cities that are struggling with high pollution levels ‐Patna (India) and Dhaka (Bangladesh).

Kigali, the capital of Rwanda, is a good example of a competitive city experiencing lower pollution levels compared to similar cities. With a population of approximately 1.2 million and a GDP per capita of $1,518 (in 2015), Kigali falls in the Market Town category, specializing in non‐tradable services and some tourism. During the ten‐year period between 2006 and 2015, Kigali has outperformed Rwanda’s national averages by almost 5 percentage points in terms of GDP per capita growth, and 7.5 percentage points in terms of 24
# _5) GRUMP 2005 (pre-release) gridded population dataset_

A global gridded population dataset of approximately 1 km resolution produced by the Center for Earth Science Information Network (CIESIN) at Columbia University. Subnational urban and rural population are allocated to grid cells using an urban extents mask and most recent census data adjusted to reflect U.N. projections for 2005. Data for the year 2005 were provided by ftp upon special request. The GRUMP alpha version for the year 2000 is available for download at: http://sedac.ciesin.columbia.edu/gpw/global.jsp.

# _6) 2005 gridded GDP surface_

A global gridded dataset in which shares of GDP, in 2000 USD, are allocated to 1 km grid cells, using GRUMP 2005 population data, urban/rural extents mask, and, where available, regional accounts data for countries. Regional shares of GDP were standardized using 2005 estimated GDP in 2000 USD and allocated to cells on a rural or urban per capita basis. Data are not currently available for download.

# _7) Globcover 2.1_

A global land cover dataset of approximately 300 m resolution produced by the European Space Agency (ESA). _Globcover_ 2.1 was based on imagery acquired between December 2004 and June 2006 by ENVISAT’s Medium Resolution Imaging Spectrometer (MERIS). The 22 general land cover categories derived by automatic and regionally specific classification include four agricultural classes which are used in this analysis. Data are available for download at: http://www.esa.int/due/ionia/globcover.

Note that the Globcover database covers three different types of agricultural land use indicator. A first indicator includes areas which most of the coverage is rainfed/irrigated/post-flooding cropland. A second indicator includes areas for which 5070% is made of mosaic cropland and the rest is made of grassland, shrubland, and forest. A third indicator includes areas for which 20-50% is made of mosaic cropland and the rest is made of grassland, shrubland, and forest. For purpose of identifying impacted agricultural extent, in this research we have retained solely the agricultural land identified 10
66

|**Country**|**Reference fuel type and location**|
|---|---|
|Italy|Spot FOB cargo prices in Northwest Europe of 95 RON gasoline and diesel with 0.001<br>percent sulfur.|
|Japan|Spot prices in Singapore of 92 RON gasoline, diesel with 0.001 percent sulfur, and<br>kerosene; and monthly Saudi Aramco contract prices of 50/50 propane/butane for LPG.|
|Spain|Spot FOB cargo prices in Northwest Europe of 95 RON gasoline and diesel with 0.001<br>percent sulfur.|
|United Kingdom|Spot FOB cargo prices in Northwest Europe of 95 RON gasoline and diesel with 0.001<br>percent sulfur.|
|United States|U.S. Gulf coast pipeline spot prices of 87 octane index gasoline and ultra-low sulfur<br>diesel,and the monthlyaverage spotprice ofpropane at Mont Belvieu for LPG.|

_Sources:_ Platts 2009 and 2012.

_Notes:_ The research octane number represents the ability of gasoline to resist auto-ignition, or knocking, under city (as opposed to highway) driving conditions. The octane index is the average of research and motor octane numbers (motor octane being the ability of gasoline to resist auto-ignition, or knocking, under highway driving conditions). An octane index of 87 in the United States is equivalent to about 91–92 RON.

Table A2.4: Pass-through coefficients (percent), January 2009–January 2012

|**Country**|**Gasoline**|**Diesel**|**Kerosene**|**LPG**|
|---|---|---|---|---|
|Angola|22|8|-17|-21|
|Argentina|90|101|78|-24|
|Bangladesh|2|21|22|103|
|Bolivia|3|2|1|1|
|Brazil|103|51|—|146|
|Cambodia|127|116|123|91|
|Cameroon|-6|-6|-4|-4|
|Chile|198|123|84|203|
|China|95|98|—|71|
|Colombia|88|96|132|331|
|Costa Rica|103|83|62|150|
|Côte d’Ivoire|38|0|67|-3|
|Dominican Republic|137|130|122|151|
|Egypt, Arab Rep.|-6|-4|-4|-1|
|El Salvador|121|121|—|243|
|Ethiopia|100|90|69|129|
|Gabon|-9|4|-3|-9|
|Ghana|98|93|0|1|
|Guatemala|95|93|61|128|
|Guinea-Bissau|64|80|—|—|
|Honduras|117|108|105|166|
|India|79|30|25|22|
|Indonesia|21|21|12|18|
|Iran, Islamic Rep.|57|68|—|—|
|Iraq|10|-10|39|7|
|Jamaica|127|121|134|183|
|Jordan|85|52|54|6|
|Kazakhstan|34|27|—|—|
|Kenya|94|84|—|—|
|Lao PDR|126|117|—|142|
further distort voucher households’ purchases, perhaps towards food items that can be more easily stored, transported or resold.<sup>16</sup>

## _Why Use Vouchers in eastern DRC?_

Despite the potential welfare loss for _voucher_ program recipients, providing vouchers may be the preferred public policy in a context such as the DRC or other humanitarian contexts. Among the potential reasons cited in favor of in-kind transfers, three appear to be of primary importance in the DRC context. First, while many agencies switched from pure in-kind transfers (e.g., distributions) to vouchers, studies of those programs cited concerns about the consumption of “temptation” goods associated with cash transfers (UNICEF 2012, ODI 2009). Since _voucher_ recipients in our study were prohibited from purchasing certain food items at the voucher fairs (such as meat, doughnuts or beer), this suggests that there might have been an implicit preference for encouraging households to purchase and consume particular food items. Second, international organizations were concerned that local markets did not “offer a wide selection of the goods beneficiaries needed,” suggesting that vouchers were provided, at least in part, to address these concerns (UNICEF 2012). Finally, vouchers were considered to be safer than cash transfers, as they limited the risks associated with transporting and distributing cash.

# IV. DATA AND EMPIRICAL STRATEGY

The data we use in this paper come from three primary sources. First, we conducted several rounds of household surveys and use these surveys to measure the impact of the program on households’ behavior and outcomes. Second, we collected price data from voucher fairs and markets to estimate the value of household assets, as well as the prices that program recipients faced. Finally, we conducted focus group surveys with different actors involved in the program. Before presenting our estimation strategy, we discuss each of these data sources in detail.

## _Data_

> 16 Bazzi et al. (2013) show that the timing and expectations of transfers matter. In our context, the timing of the transfers was the same for both modalities. Since both _cash_ and _voucher_ transfer recipients were informed prior to the program that they would receive three transfers, program recipients should have been able to maximize expenditures subject to these respective constraints, although we do not have the data to address this potential issue.

10
119 ROMANIA Projection (00Os) with NRRuI by 1985

# Suumry for 25 Year Periods

|AGE GROUP|1985|2000|2025|2050|2075|2100|2125|2150|
|---|---|---|---|---|---|---|---|---|
|,,,,,,,,,,,,,..,................|................................|.....................||||,...<br>_|<br>_...........||
|TOTAL M.F|22725|24461|26422|27683|28253|28480|28591|<br>28635|
|MALES|||||||||
|0-4|882|961|935|926|924|924|924|924|
|5-9|1016|934|924|922|922|923|923|923|
|10-14|971|875|908|917|920|921|922|922|
|15-19|1018|869|908|917|919|920|921|921|
|20-24|725|999|932|922|918|919|919|919|
|25-29|884|947|942|924|918|917|917|91?|
|30-34|886|987|910|911|913|913|915|915|
|35-39|739|698|846|892|906|909|912|913|
|40-44|614|849|833|887|902|905|907|908|
|45-49|741|840|947|903|901|899|900|901|
|50-54|708|684|878|896|889|886|886|887|
|55-59|643|545|879|837|852|856|859|860|
|60-64|503|615|580|733|790|807|813|816|
|65-69|248|529|630|653|717|736|742|744|
|70-74|309|406|519|630|626|632|635|637|
|75+|341|386|570|845|948|987|1005|1012|
|TOTAL|11230|12125|13140|13714|13963|14054|14099|<br>14121|
|FEMALES|||||||||
|0-4|840|914|888|881|879|878|878|878|
|5-9|968|889|879|878|878|878|878|878|
|10-14<br>|925<br>|835<br>|864<br>|874<br>|877<br>|878<br>|878<br>|878<br>|
|15-19|97..|831|866|875|877|878|878|878|
|20-24|689|957|891|882|879|878|878|878|
|25-29|852|910|903|885|880|878|878|878|
|30-34|865|957|877|876|877|877|877|877|
|35-39|728|672|819|859|872|875|876|877|
|40-44|620|*830|810|858|872|875|876|876|
|45-49|762|837|927|879|876|874|874|874|
|50-54|728|695|871|883|874|870|870|870|
|55-59|684|578|897|84|854|859|861|861|
|60-64|605|684|607|767|821|<br>839|<br>845|<br>846|
|65-69|340|611|702|722|788|808|815|817|
|70-74|422|509|628|752|744|753|758|760|
|75+|491|627|854|1255|1444|1528|<br>1573|<br>1588|
|TOTAL|11495|<br>12336|13283|13970|14290|<br>14426|14491|<br>14513|
|BIRTH RATE||15.8|14.4|13.4|13.0|12.8|12.7|12.7|
|DEATH RATE||10.6|11.2|11.6|12.2.|12.5|12.6|12.6|
|NET MIGRATI|ON<br>RATE|-. 3|-.1|.0|.0|.0|.0|.0|
|GROWTH<br>RATE||.h9|.31|.19|.08|.03|.02|.01|
|TOTALFERTI|LITY|2.124|2.097|2.080|2.069|2.068|2.067|2.067|
|e(O) - BOTH|SEXES|71.03|73.49|76.12|78.01|78.52|78.86|78.94|
|<br>**I1R**- BrJTH S|EXES|21.4|13.8|10.1|7.6|6.9|6.5|6.4|
# ACKNOWLEDGMENTS

The 2014 Global Financial Inclusion (Global Findex) database was developed by the Finance and Private Sector Development Team of the Development Research Group, by a team led by Leora Klapper under the supervision of Asli Demirguc-Kunt and comprising Saniya Ansar, Rafael Alonso Arenas, Jake Hess, Dorothe Singer, and Peter Van Oudheusden, and assisted by Esther Landines. The work was carried out under the management of Kaushik Basu. The team is grateful to Douglas Randall for helping with the questionnaire design. It is also grateful for substantive comments provided at different stages of the project by Massimo Cirasino, Mario Guadamillas, Jake Kendall, Aart Kraay, Maria Soledad Martinez Peria, Douglas Pearce, Peer Stein, and Rodger Voorhies; World Bank colleagues in the Development Economics Vice Presidency and the Financial Markets Global Practice; and staff at the Bill & Melinda Gates Foundation, the Better Than Cash Alliance, the Consultative Group to Assist the Poor, the GSM Association, and the Office of the United Nations Secretary-General’s Special Advocate for Inclusive Finance for Development (UNSGSA). The team is grateful too for the excellent survey execution and related support provided by Gallup, Inc. under the direction of Jon Clifton.

The team is especially grateful to the Bill & Melinda Gates Foundation for providing financial support making the collection and dissemination of the data possible.

Gerry Quinn designed the report. Alison Strong provided editorial assistance.

> THE GLOBAL FINDEX DATABASE v
# Table 2: (continued)

||OLS<br>(1)|(2)|GSTSLS<br>(3)|GAM<br>(4)|Heckman<br>(5)|
|---|---|---|---|---|---|
|Distance from city hall|0_._094|0_._260<sup>_∗∗∗_</sup>|_−_0_._002||0_._252<sup>_∗∗∗_</sup>|
||(0_._068)|(0_._086)|(0_._065)||(0_._081)|
|Distance (squared)|_−_0_._008|_−_0_._025<sup>_∗∗∗_</sup>|0_._003||_−_0_._023<sup>_∗∗∗_</sup>|
||(0_._007)|(0_._009)|(0_._006)||(0_._009)|
|Declared & Residential|||||_−_0_._492<sup>_∗∗∗_</sup><br>(0_._146)|
|Constant|5_._317<sup>_∗∗∗_</sup>|5_._205<sup>_∗∗∗_</sup>|5_._198<sup>_∗∗∗_</sup>|8_._445<sup>_∗∗∗_</sup>|5_._190<sup>_∗∗∗_</sup>|
||(0_._505)|(0_._699)|(0_._643)|(0_._527)|(0_._653)|
|_λ_|||0_._078<sup>_∗∗∗_</sup><br>(0_._023)|||
|_ρ_|||0_._663<sup>_∗∗∗_</sup><br>(0_._202)|||
|Administrative ward fxed efects||Yes|||Yes|
|Observations|460|460|460|460|528|
|Adjusted R<sup>2</sup>|0.286|0.398||||
|AIC|982.5|917.5||904.6||

Note: Standard errors in parentheses. _∗∗∗p <_ 0 _._ 01, _∗∗p <_ 0 _._ 05, _∗p <_ 0 _._ 1. Reference categories are Wall:metal sheets, Roof:metal sheets, Water:community, Latrine:none, Land:private. GCV score=10.78, Scale est.=9.697, edf for s(longitude, latitude)=17.652<sup>_∗∗∗_</sup> in column (4). Smooth term for carpet area is also added in column (4). _ρ_ = _−_ 0 _._ 552<sup>_∗∗∗_</sup> in column (5).

# Table 3: Estimated total effects of slum declaration

||0.5km<br>(1)|1km<br>(2)|1.5km<br>(3)|2km<br>(4)|
|---|---|---|---|---|
|_Inverse distance_|||||
|No ZOI|0_._191<sup>_∗∗_</sup>|0_._193<sup>_∗∗_</sup>|0_._192<sup>_∗∗_</sup>|0_._193<sup>_∗∗_</sup>|
|100m ZOI|[0_._035_,_0_._377]<br>0_._131|[_−_0_._007_,_0_._345]<br>0_._148|[0_._026_,_0_._353]<br>0_._122|[0_._004_,_0_._352]<br>0_._134|
||[_−_0_._088_,_0_._319]|[_−_0_._077_,_0_._347]|[_−_0_._087_,_0_._336]|[_−_0_._061_,_0_._358]|
|200m ZOI|0_._149|0_._169<sup>_∗_</sup>|0_._136|0_._150|
||[_−_0_._066_,_0_._332]|[_−_0_._011_,_0_._372]|[_−_0_._073_,_0_._303]|[_−_0_._037_,_0_._330]|
|_Exponential distance (α_= 1_)_|||||
|No ZOI|0_._159|0_._187<sup>_∗_</sup>|0_._140|0_._160|
||[_−_0_._030_,_0_._360]|[0_._010_,_0_._388]|[_−_0_._047_,_0_._320]|[_−_0_._034_,_0_._343]|
|100m ZOI|0_._161<sup>_∗_</sup>|0_._189<sup>_∗_</sup>|0_._140|0_._160|
||[_−_0_._020_,_0_._338]|[_−_0_._009_,_0_._367]|[_−_0_._059_,_0_._330]|[_−_0_._036_,_0_._345]|
|200m ZOI|0_._162<sup>_∗_</sup>|0_._191<sup>_∗∗_</sup>|0_._141|0_._162|
||[_−_0_._028_,_0_._339]|[0_._009_,_0_._367]|[_−_0_._028_,_0_._353]|[_−_0_._050_,_0_._364]|
|_Exponential distance (α_= 2_)_|||||
|No ZOI|0_._148|0_._173|0_._154|0_._159|
||[_−_0_._056_,_0_._353]|[_−_0_._034_,_0_._380]|[_−_0_._034_,_0_._363]|[_−_0_._046_,_0_._349]|
|100m ZOI|0_._154|0_._179<sup>_∗_</sup>|0_._158|0_._163<sup>_∗_</sup>|
||[_−_0_._040_,_0_._370]|[_−_0_._025_,_0_._365]|[_−_0_._021_,_0_._352]|[_−_0_._028_,_0_._357]|
|200m ZOI|0_._156|0_._183<sup>_∗_</sup>|0_._161<sup>_∗_</sup>|0_._166<sup>_∗_</sup>|
||[_−_0_._025_,_0_._342]|[_−_0_._001_,_0_._356]|[_−_0_._017_,_0_._350]|[_−_0_._028_,_0_._349]|

Note: 95% confidence intervals in square brackets.<sup>_∗∗∗_</sup> _p <_ 0 _._ 01,<sup>_∗∗_</sup> _p <_ 0 _._ 05,<sup>_∗_</sup> _p <_ 0 _._ 1. ZOI: Zone of indifference.

33
<!-- Start of picture text --> ——-<br>(<br>SEBRAE NEGOCIOG ES<br>Endereca: N* Barro: Cidade .<br>VILA MARINGA<br>VARDELINA<br><!-- End of picture text -->

## Prezado Sr. (a).

E com imensa satisfac3o que o Servico de Apoio as Micro e Pequenas Empresas do Parana (SEBRAE/PR) agradece sua participacao no Programa Negocio a Negécio, realizado com 0 objetivo de contribuir cada vez mais para o sucesso da sua empresa.

Vocé recebe neste momento o resultado de um diagndstico empresarial elaborado com base em dados fomecidos para consultores, chamados também de agentes de orientagao empresarial, que estiveram em sua empresa.

O diagndstico realizado é importante para o sucesso do seu empreendimento, pois contém uma avaliagao do que esta bem e do que precisa ser melhorado na sua empresa. Para apoia-lo na implantacao das melhorias, sugerimos solucgdes do SEBRAE/PR.

Agora é sua vez de coloca-las em pratica!l

## Aproveite a oportunidade e conte conosco!

Estamos a disposi¢ao, por meio da Central de Relacionamento, no 0800 570 0800; Portal do SEBRAE/PR, no www.sebraepr.com.br, ¢ no escritorio mais proximo.

### Sucessol

# FINANCAS

Proximos Passos / Devolutiva Vocé controla as entradas ¢ saidas diarias de [Quando controlamos as entradas e saidas do caixa ja estamos dando dinheiro? inicio ao FLUXO DE CAIXA. Trata-se de um controle que auxilia na visualizagao € compreensao das movimentacoes financeiras num determinadofinanceiro visualizarperiodo. asSua sobras grande ou faltasutilidadede recursos€ permitirantesao gestor mesmo que elas ocorram. Dica: A atualizagao constante permite que as tomadas de decisao sejam mais precisas. Aprimore seus conhecimentos sobre isto fazendo o curso "Fluxo de Caixa" em EAD do Sebrae ou participando do Curso "Pratique Controles Financeiros". Pratique Controles Financeiros. https:/ead.sebrae.com.br/cursos/fluxo_de_caixa Vocé sabe quanto dinheiro deve ter em caixa |A agao preventiva tem um papel importante para a solu¢ao dos para cobrir suas despesas enquanto nado imprevistos financeiros da empresa. Dica: A principal agao consiste na recebe os pagamentos? formag¢éo de reserva financeira para enfrentar as mudancas inesperadas no quadro financeiro da empresa, faga uma previsdo para essa reserva.
other words, for reference year 2012, if a country has a survey anywhere from 2010 to 2014, then it is treated as providing coverage for the estimate. If the coverage rate is less than 60% we do not report the regional poverty estimate. For reference year 2008 and later, the Middle East and North Africa region falls below this threshold and poverty numbers are not reported.

# **4. Updating the international poverty line based on 2011 PPPs**

In April 2013, the World Bank Group announced the goal of ending extreme poverty, which was defined specifically as reducing “the percentage of people living with less than $1.25 a day [...] to no more than 3 percent globally by 2030.” The introduction of the 2011 PPPs creates a potential measurement challenge for faithfully monitoring progress towards this goal. The $1.25 line stated in the goal (essentially the target’s “goal post”) is based on the 2005 PPPs. If 2011 PPPs are used to convert the LCU values of consumption and income distributions, what is the poverty line that most closely reflects the same value as the $1.25 line, but is expressed in 2011 PPPs?

Given the importance of avoiding manipulation (and the appearance of manipulation) of the “goal post”, by either making the goal harder or easier to reach, one option that was given serious consideration was simply to not adopt the 2011 PPPs and continue to use the $1.25 line in 2005 PPP-adjusted USD. But, if the threshold that is used to identify the poor is expressed in the relative price levels as estimated by the 2005 PPP data, then the consumption and income distributions must also continue to be based on the 2005 PPPs. This option was considered and ultimately rejected -- a decision that is in line with the majority of scholarly opinion that the 2011 PPPs incorporate a number of improvements in terms of coverage and methodology, and therefore provide a more accurate reflection of relative price levels in the world.

With the decision to use the 2011 PPPs then comes the responsibility of revising the $1.25 line to 2011 PPP terms. Because the 2011 PPPs revise the relative price levels between countries, there exists no adjustment to the $1.25 line (at least not if we restrict the revision to maintaining a single line for all countries) in 2011 PPPs that would produce the same headcount rate as the 2005 PPPs in each country. Any proposed revision will change the real value of the line in many, if not most, countries. An initial proposal from Dykstra et al. (2014) was to update the line based on US inflation rate. The value of their revised line was USD1.44. But, as discussed earlier in the paper, relative price levels between rich and poor countries were 29
compute the gains in GDP per capita using a range of neoclassical growth models with different aggregate production functions and different assumptions, including different structures of production, the possibly slow response of capital accumulation (in a region with a private sector lacking dynamism) and policy implementation, and potential changes in the skill distribution. The results are summarized for eight sample countries in Figure 9, which suggest that while the considerations are important, the models produce results that are consistent in several areas. We suggest four takeaways.

**Figure 9:** Comparing Gains in GDPPC Across Models

<!-- Start of picture text --> ARE (adj) DZA EGY IRN<br>2.25<br>2<br>1.75<br>1.5<br>1.25<br>1<br>PSE QAT (adj) TUN YEM<br>2.25<br>2<br>1.75<br>1.5<br>1.25<br>1<br>GEGI-SR; short/medium run GEGI-SR; capital share = 0.25<br>LTGM/LTGM-NR (by 2050) CSC-SR; fixed s<br>CSC-LR; fixed s CSC-SR; higher s<br>GEGI-LR; default CSC-LR; higher s<br>Gains in GDP per capita from<br>closing gender employment gaps (baseline = 1)<br><!-- End of picture text --> The first takeaway is that for most countries the economic gains from closing gender employment gaps in MENA are _quantitatively large_ —around 50% in the typical country in the long run (GDPPC is 1.5 times the baseline in Figure 9). Second, the gains are heterogeneous across countries, ranging from 10% or smaller in Qatar and approaching 100% of GDP per capita in Yemen. Moreover, the models generally agree on the ranking of the size of the gains (Yemen’s gains are always the largest and Qatar’s are always the 19