rafmacalaba/datause-encoder-data huggingface dataset:
|PrimaryEducation Project|Anna Maria Sant'Anna|
|---|---|
|Public Enterprise Reform Loan(PERL)Project||
|Public Enterprise Reform Loan(PERL)Project||
|Public Enterprise Reform Loan(PERL)Project||
|Public Enterprise Reform Loan(PERL)Project||
|Industrial Sector PolicyLoan(ISPL)Project||
|Industrial Sector PolicyLoan(ISPL)Project||
|Industrial Sector PolicyLoan(ISPL)Project||
|Transport AirQualityManagement Project for the Mexico CityMet|rCarl-Heinz G. Mumme|
|Transport AirQualityManagement Project for the Mexico CityMet|rCarl-Heinz G. Mumme|
|Transport AirQualityManagement Project for the Mexico CityMet|rCarl-Heinz G. Mumme|
|Transport AirQualityManagement Project for the Mexico CityMet|rCarl-Heinz G. Mumme|
|Transport AirQualityManagement Project for the Mexico CityMet|rCarl-Heinz G. Mumme|
|Transport AirQualityManagement Project for the Mexico CityMet|rCarl-Heinz G. Mumme|
|Transport AirQualityManagement Project for the Mexico CityMet|rCarl-Heinz G. Mumme|
|HousingMarket Development Project|Richard L. Clifford|
|HousingMarket Development Project|Richard L. Clifford|
|HousingMarket Development Project|Richard L. Clifford|
|HousingMarket Development Project|Richard L. Clifford|
|HousingMarket Development Project|Richard L. Clifford|
|HousingMarket Development Project|Richard L. Clifford|
|HousingMarket Development Project|Richard L. Clifford|
|Road Transport and Telecommunications Sector Adjustment Loan P||
|Road Transport and Telecommunications Sector Adjustment Loan P||
|CommunityForestryProject|Daniel R. Gross|
|CommunityForestryProject|Daniel R. Gross|
|CommunityForestryProject|Daniel R. Gross|
|CommunityForestryProject|Daniel R. Gross|
|CommunityForestryProject|Daniel R. Gross|
|CommunityForestryProject|Daniel R. Gross|
|CommunityForestryProject|Daniel R. Gross|
|CommunityForestryProject|Daniel R. Gross|
|CommunityForestryProject|Daniel R. Gross|
|On-farm & Minor Irrigation Networks Improvement|Michael G. Carroll|
|On-farm & Minor Irrigation Networks Improvement|Michael G. Carroll|
|On-farm & Minor Irrigation Networks Improvement|Michael G. Carroll|
|On-farm & Minor Irrigation Networks Improvement|Michael G. Carroll|
|On-farm & Minor Irrigation Networks Improvement|Michael G. Carroll|
|On-farm & Minor Irrigation Networks Improvement|Michael G. Carroll|
|On-farm & Minor Irrigation Networks Improvement|Michael G. Carroll|
|On-farm & Minor Irrigation Networks Improvement|Michael G. Carroll|
|On-farm & Minor Irrigation Networks Improvement|Michael G. Carroll|
|Decentralization and Regional Development Project(02)|Julio Cordoba|
|Decentralization and Regional Development Project(02)|Julio Cordoba|
|Decentralization and Regional Development Project(02)|Julio Cordoba|
|Decentralization and Regional Development Project(02)|Julio Cordoba|
|Decentralization and Regional Development Project(02)|Julio Cordoba|
# **E.22 Geo-coding Procedure**

The VHF data includes information on individuals’ residences, including their district, chiefdom, and village or parish. We use this information to place observations within sections. Our geo-location protocol involves several steps. First, a human coder inspected and cleaned all district and chiefdom names that did not exactly match the conventional spelling. Of 85,410 entries in the case data, we can code the chiefdom of residence for 97% of observations.

Second, we employ fuzzy string matching to match the available village or parish names to gazetteer files of placenames from Sierra Leone. Fortunately, in the chiefdoms that include our sample, only 14 confirmed, suspected, or probable Ebola cases do not include village or parish information.<sup>A10</sup> We employ the gazetteer file from Open Street Map ( `www.openstreetmap.org/` ), which includes 9,975 entries, ranging from hamlets to cities. We prefer this list to the 2004 census data from Sierra Leone, which only provides names for around 5,000 localities. Moreover, during the Ebola epidemic, Open Street Map mounted a humanitarian effort aimed at updating and verifying information on the locations of villages and roads in Sierra Leone.<sup>A11</sup> Ten sample entries from OSM gazetteer file:

|`osm_id`|`name`|`c`|`oordinates`|
|---|---|---|---|
|`1 27565056`|`Freetown`|`(-13.26802 `|`8.479002)`|
|`2 314001434 `|`Bo`|`(-11.73665 `|`7.962065)`|
|`3 314005602 `|`Kenema`|`(-11.18639 `|`7.885936)`|
|`4 314007819 `|`Koidu`|`(-10.97163 `|`8.642281)`|
|`5 320058940 `|`Kambia`|`(-12.91934 `|`9.125073)`|
|`6 320060481 `|`Kamakwie`|`(-12.24125 `|`9.496301)`|
|`7 320060535 `|`Pujehun`|`(-11.72124 `|`7.356632)`|
|`8 320060540 `|`Zimmi`|`(-11.31032 `|`7.312338)`|
|`9 370327499 `|`Goderich`|`(-13.28887 `|`8.432966)`|
|`10 370495828 `|`Murray Town `|`(-13.26534 `|`8.491613)`|

Fuzzy string matching calculates the string distance between each village or parish name in the VHF data and each placename in the gazetteer file that falls within the exact same district and chiefdom.<sup>A12</sup> An exact match returns a distance of zero; “FREE TOWN” and “FREETOWN,” for example, would return a distance of 1. We do not match any entries with a string distance that exceeds 2.

While the geo-coding process introduces measurement error, we expect this is uncorrelated with treatment and, thus, only going to attenuate our estimates. To bolster this assumption, we look at whether placenames in the gazetteer file tend to be more numerous or longer in treated versus control sections. We see no indication that treated sections have significantly more or shorter placenames; moreover, the placenames are not more likely to contain a space between words (see Table E.27).

> A10 Of all entries in the case data that fall within the chiefdoms the include our sample, only 0.07 percent are missing an entry for village or parish of residence.

> A11 `http://wiki.openstreetmap.org/wiki/2014_West_Africa_Ebola_Response`

> A12 We use optimal string alignment distance, a variant of the Levenshtein distance, which is commonly employed in geo-coding algorithms.

A63
Haahr, M. T., and A. Hróbjartsson. 2006. “Who Is Blinded in Randomized Clinical Trials?” _The Cochrane Collaboration Methods Groups Newsletter_ 3: 14.

Heckman, J. J. 1979. “Sample Selection Bias as a Specification Error.” _Econometrica: Journal of the Econometric Society_ , 153–61.

Higgins, J. PT, S. Green, and C. Collaboration. 2008. _Cochrane Handbook for Systematic Reviews of Interventions._ Vol. 5. Wiley Online Library.

Hutton, J. L., and P. R. Williamson. 2000. “Bias in Meta-Analysis due to Outcome Variable Selection within Studies.” _Journal of the Royal Statistical Society: Series C (Applied Statistics)_ 49 (3): 359–70.

- Jüni, P., D. G. Altman, and M. Egger. 2001. “Assessing the Quality of Controlled Clinical Trials.” _BMJ_ 323 (7303): 42–46.

Jüni, P., A. Witschi, R. Bloch, and M. Egger. 1999. “The Hazards of Scoring the Quality of Clinical Trials for Meta-Analysis.” _JAMA: The Journal of the American Medical Association_ 282 (11): 1054–60.

Kaplan, R. M., and V. L. Irvin. 2015. “Likelihood of Null Effects of Large NHLBI Clinical Trials Has Increased over Time” PLoS ONE 10(8): e0132382 Kling, J. R., J. B. Liebman, and L. F. Katz. 2007. “Experimental Analysis of Neighborhood Effects.” _Econometrica_ 75 (1): 83–119.

Kodrzycki, Y. K., and P. Yu. 2006. “New Approaches to Ranking Economics Journals.” _Contributions in Economic Analysis & Policy_ 5 (1).

Leonard, K., and M. Masatu. 2006. “Outpatient Process Quality Evaluation and the Hawthorne Effect.” _Social Science and Medicine_ 63 (9): 2330–2340.

39
> themselves partly endogenous to the country’s fundamentals; and _Ft k_ is a vector of residuals that includes domestic and external stochastic shocks, as well as all remaining unidentified _k k_

> fundamentals or policy choices. Replacing _Zt_ from (16) in (15) and solving for _Xt_ : _Xtk_  ( _I_  _Q_ )  1[( _R_  _SR_ ') _Ytk_  ( _Ftk_  _SF_ ' _tk_ )] (17)

> This yields a set of policy-neutral benchmarks, _X_ ˆ _tk_ , such that:

In turn, from (18), the following set of policy-neutral gaps, , is inferred as the expected value of a country’s deviation from benchmarks: Thus, the benchmarks indicate where a country is expected to be for a given endogenous variable, taking into account its level of economic development, its non-policyrelated structural characteristics, and the average policies and policy-related institutions of its peers. The gaps reflect country specificities, which can be unidentified endowments, preferences or policy choices embedded in the _F_ term (in all cases omitted variables), or identified policy deviations from the average policies of peers embedded in the _SF’_ term. To the extent that these omitted variables primarily reflect preferences or policy choices (rather than unidentified endowments), saving gaps become the result of policy choices (that can presumably be altered), rather than irreversible facts of life. In this case, on at least a first approximation, the gaps can be interpreted as reflecting the deviation of a given country’s policies and policy-related institutions from the average of peer countries. Note also that because the gaps are linearly related through the _Q_ matrix, the cross-correlations between them reflect the elasticities embedded in the model. In particular, the saving and real exchange rate gaps should be negatively correlated if the elasticity of the exchange rate with respect to changes in the current account deficit is positive and significant.

# **4. Econometric estimates**

We first estimate each structural equation using ordinary least squares (OLS). Because cross-equation correlations between the endogenous variables are not accounted for, these estimates are biased. Moreover, they do not prove causality. To circumvent both of these problems, we subsequently use reduced-form instrumented estimates. Reflecting the high endogeneity, these estimates could be calculated only for a limited subset of elasticities. Even so, results suggest that the impact of saving on growth could be substantial.

# **_(a) Data and specification_**

We use country-level yearly data for the period 1981-2012 and a sample of 119 countries, including low, middle and high-income countries.<sup>11</sup> To better capture medium-term equilibria, we run all the regressions on three-year averages instead of on the original annual

> 11 See Appendix B for data definitions, statistics and sources.

12
reforms to reduce disparities in assets. Fourth, it should improve targeting mechanisms. Fifth, it should allocate resources to monitor poverty and evaluate the implementation of poverty reduction interventions.

The paper is organized in eight sections. Section 2 outlines briefly the geography and economy, people, and demographic development. Section 3 outlines the data and methodology applied in the paper. Section 4 addresses poverty, income inequality, and quality of life, and Section 5 constructs a poverty profile of Pará. Section 6 addresses the question of assets, including employment, education and social safety nets. Section 7 analyzes poverty correlates in Pará. Finally, Section 8 proposes a six-pronged poverty-reduction strategy for Pará.

# **2.  Geography, People, and Growth**

This section outlines what can serve as a base for a poverty reduction strategy in Pará, namely geography and economy, people, and demographics. Individual and household assets, in particular human capital and labor market association, are other important poverty reducing factors are analyzed in Sections 5, 6, and 7.

## **GEOGRAPHY AND ECONOMY**

Pará is the second largest state in Brazil. Pará covers an area of 1.24 million square kilometers in the Amazon. This is, around 45 percent of the area of the northern region and 14.6 percent of Brazil’s total area. Pará is roughly the size of Peru and Colombia and it is only smaller in size than the neighboring state of Amazonas. Pará’s area is divided into 143 municipalities (see Appendix A) and these into six meso regions, the largest being the Southeast region (see Table 2.1).

|**Table 2.1: Territoria**|**l Area per Meso region, 2000**|
|---|---|
|**Region**|**Area(Km2)**|
||**Absolute Value**<br>**Percentage**|
|**Pará**|**1,247,702.7**<br>**100.0**|
|Baixo Amazonas|340,446.9<br>27.3|
|Marajó|104,141.5<br>8.4|
|Metropolitan Belém|6,875.5<br>0.6|
|Northeast Pará|83,182.6<br>6.7|
|Southeast Pará|415,775.9<br>33.3|
|Southwest Pará|297,280.4<br>23.3|
|Source: SEOF/DIEEST/BDE.||

Pará’s coastline is 562 km long and the state contains 40 percent of the interior waters of Brazil. The majority of Pará’s vegetation (80 percent) is forest ( _Floresta de Terra Firm_ ). Another 10 percent is _Áreas alteradas por atividades Antrópica_ and the remaining is divided between cerrado, fields, and forest areas that are flooded part of 6
**Table 5. Correlates of Sudden Stops (Probit model, marginal effects, 1991-2014)**

||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
|---|---|---|---|---|---|---|---|---|
|VIX, Log|0.01***|0.0121***|0.0120***|0.0120***|0.0121***|0.0069***|0.0094***|0.0066***|
||[7.02]|[6.92]|[6.66]|[6.87]|[6.90]|[3.62]|[4.36]|[3.28]|
|US Policy Rates (%)|0.00*|0.0030**|0.0030*|0.0034**|0.0031**|0.0042***|0.0042***|0.0045***|
||[1.81]|[2.04]|[1.81]|[2.34]|[2.15]|[2.61]|[2.75]|[2.77]|
|Capital Flows/GDP|0.01***|0.0052***|0.0050***|0.0050***|0.0051***|0.0040***|0.0043***|0.0038**|
||[4.03]|[3.62]|[3.50]|[3.65]|[3.60]|[2.58]|[2.59]|[2.32]|
|Domestic Credit/GDP||0.0029**|0.0033***|0.0022*|0.0028**|0.0028**|0.0034***|0.0030***|
|||[2.49]|[2.96]|[1.71]|[2.48]|[2.48]|[2.98]|[2.68]|
|RER (% Change)|||-0.0013<br>[1.04]||||||
|Reserves/GDP||||0.0019<br>[1.21]|||||
|External Liabilities/GDP|||||0.001<br>[0.35]||||
|# of Sudden Stops<br>elsewhere in the world||||||0.0053***||0.0045***|
|||||||[4.41]||[2.86]|
|# of Sudden Stops<br>elsewhere in the Region|||||||0.0036***|0.0014|
||||||||[3.16]|[1.01]|
|Observations|2,208|2,178|2,150|2,178|2,177|2,178|2,178|2,178|
|Pseudo R-squared|0.180|0.185|0.185|0.188|0.186|0.229|0.213|0.232|

Dependent variable is a binary variable which is equal to 1 if a sudden stop occurs and 0 otherwise. The first quarter of sudden stop is included in the regressions, and all subsequent quarters dropped. Domestic variables are averages of previous eight quarters. All variables have been standardized around zero mean and standard deviation equal to 1. Capital flows, domestic credit and reserves, and international investment are in percent of GDP. Real exchange rate is in percent change; an increase denotes a depreciation. VIX is in log; sudden stop episodes elsewhere in the world or region are the number of sudden stops elsewhere in the same quarter. Regressions are estimated with robust standard errors, and observations clustered by countries. Z statistics reported in parentheses. ***,** and * indicate significance at 1, 5, and 10% levels, respectively.

22
**Table 7**

**Determinants of  Power Sector Performance (Fixed Effects and interactions with Group A)**

|**Interactions with Group A**<br>**Fixed effects**|**Residential**<br>**Access (% )**|**Energy sold**<br>**(MWh/employee)**|**Tariff**<br>**(US$c/KWh)**|**Emission index**<br>**(CO2 ton/KWh)**|
|---|---|---|---|---|
|**DEGREE OF**|**UNBUNDLING (**|**EITHER PARTIAL O**|**R FULL)**||
|**Group A**|9.13***<br>(1.99)|-61.05<br>(251.98)||0.01<br>(0.01)|
|**Vertical Unbundling**|-6.67***<br>(1.78)|47.97<br>(146.29)|2.51***<br>(0.92)|0.03**<br>(0.01)|
|**Group A*Vertical Unbundling**|7.92***<br>|-365.51<br>|-1.36<br>|-0.03**<br>|
||(2.12)|(230.38)|(1.23)|(0.02)|
|**F**|**ULL VERTICA**|**L UNBUNDLING**|||
|**Group A**|9.71***<br>(1.96)|-186.01<br>(237.65)||0.03**<br>(0.01)|
|**Vertical Unbundling (full)**|-6.75***<br>(1.83)|77.04<br>(136.79)|2.49***<br>(0.92)|0.03**<br>(0.01)|
|**Group A*Vertical Unbundling (full)**|7.81***<br>(2.27)|-63.59<br>(222.99)||-0.08***<br>(0.02)|
|**D**|**EGREE OF DIS**<br>|<br>**AGGREGATION**<br>|||
|**Group A**|-29.18***<br>(5.96)|-2247.13**<br>(48097.47)||0.15***<br>(0.41)|
|**HHI (reverse)**|-0.002***<br>(0.0006)|18***<br>(13.38)|-0.021<br>(.016)|0.00002***<br>(0.000004)|
|**Group A* HHI (reverse)**|0.002***<br>(00006)|0.66**<br>(1338)|0.019<br>(0016)|-0.00002***<br>(0000004)|
|<br>|.<br>**DEGREEOF **|.<br>**PRIVATIZATION**|.|.|
|**Group A**|12.27***<br>(2.40)|-702.76*<br>(370.87)||0.09<br>(0.01)|
|**Privatization**|2.51*<br>(1.59)|4.40<br>(3.51)|0.17<br>(0.27)|0.023***<br>(0.01)|
||1.66|21.61**|0.47|-0.009|
|**Group A*Privatization**|(2.18)|(9.14)|(0.38)|(0.01)|
||**REGUL**|**ATION**|||
|**Group A**|10.48***<br>(2.39)|-341.33<br>(283.82)||-0.0008<br>(0.01)|
|**Introduction of a regulatory agency**|3.06***<br>(1.12)|73.41<br>(120.71)|4.96***<br>(0.95)|0.04***<br>(0.01)|
|**Group A*Regulatory Agency**|5.9***<br>(1.65)|564.08***<br>(176.14)|-9.6***<br>(1.66)|-0.04***<br>(0.01)|

Note: *, **, *** indicate respectively level of significance of 10, 5 and 1 percent.

18
**Table 25. Governance-related drivers of legal and regulatory obstacles: Inter-agency coordination**

|VARIABLES|(1)<br>OLS|(2)<br>OLS|(3)<br>OLS|(4)<br>OLS|(5)<br>Ologit|(6)<br>Ologit|(7)<br>Ologit|(8)<br>Ologit|
|---|---|---|---|---|---|---|---|---|
|Services|0.0536|0.0494|||0.104|0.0712|||
||(0.0581)|(0.0554)|||(0.108)|(0.111)|||
|Export % of sales|-0.00195**|-5.32e-05|-0.00189**|-6.34e-05|-0.00366**|-0.000162|-0.00344**|-6.70e-05|
||(0.000773)|(0.000762)|(0.000776)|(0.000760)|(0.00142)|(0.00149)|(0.00148)|(0.00154)|
|Share of sourcing via imports|0.000791|-0.000165|0.000763|-0.000304|0.00161|-0.000139|0.00171|-0.000287|
||(0.000846)|(0.000827)|(0.000844)|(0.000815)|(0.00157)|(0.00164)|(0.00161)|(0.00168)|
|High-income source country|-0.147*|-0.0413|-0.159*|-0.0409|-0.278*|-0.0914|-0.316*|-0.0967|
||(0.0835)|(0.0799)|(0.0847)|(0.0796)|(0.158)|(0.164)|(0.165)|(0.168)|
|>250 employees|-0.0259|-0.0210|-0.0184|-0.00840|-0.0565|-0.0847|-0.0595|-0.0707|
||(0.0709)|(0.0703)|(0.0708)|(0.0691)|(0.128)|(0.139)|(0.133)|(0.141)|
|Cumulative inv. >US$10M|0.0604|-0.0346|0.0326|-0.0607|0.136|-0.0289|0.0878|-0.0719|
||(0.0709)|(0.0683)|(0.0700)|(0.0677)|(0.131)|(0.136)|(0.135)|(0.139)|
|Years in country|-0.00198|-0.00287|-0.00153|-0.00255|-0.00377|-0.00670|-0.00337|-0.00629|
||(0.00222)|(0.00217)|(0.00219)|(0.00217)|(0.00391)|(0.00447)|(0.00407)|(0.00463)|
|Level of foreign ownership (%)|-0.00212**|-0.000415|-0.00258**|-0.000457|-0.00358*|-0.000732|-0.00464**|-0.00105|
||(0.00108)|(0.00112)|(0.00105)|(0.00107)|(0.00192)|(0.00218)|(0.00195)|(0.00217)|
|Country fixed effects||X||X||X||X|
|Subsector fixed effects|||X|X|||X|X|
|Observations|2,424|2,424|2,424|2,424|2,424|2,424|2,424|2,424|
|R-squared|0.017|0.139|0.053|0.164|||||

Standard errors in parentheses

*** p<0.01, ** p<0.05, * p<0.1

The dependent variable in all models is an ordered categorical variable corresponding to responses to the following question: "In general, how much of an obstacle were the following factors in this country? [Coordination between public agencies]". Higher numbers denote more significant obstacles. Subsector fixed effects are at the ISIC A38 level.

Coefficients for constants (for OLS), cut points (for ordered logit), and fixed effects not shown in table.

42
# **7. Implications for Sectoral Output**

Finally, table 13 compares the impact on output of the Uruguay Round reforms from 2005 (P3) and 1992 (S3). It is immediately clear that the largest discrepancies between these two sets of estimates are for the textiles and wearing apparel sectors. This is hardly surprising in light of the increased export tax equivalents in the 2005 data base. However, the pattern of discrepancies between the two sets of estimates is not a simply monotonic transformation of the change in quota rents. For example, there is a very significant increase in quota rents from 19922005 for China, yet the production response in table 13 is little changed. In contrast, Indonesia's quota rents increase less markedly, yet their output response is stronger from the 2005 base. This is explained in part by the fact that Indonesia maintains its export share to the MFA markets (58% for wearing apparel), whereas the share of textile and wearing apparel exports to the restricted markets drops significantly in the case of China. (Textiles drops from 19 to 12% and wearing apparel falls from 33 to 20%.) (See table 6).

These strong differences in output responses in the textile and wearing apparel sectors are mirrored by changes of the opposite sign in the other sectors of these economies. This is simply a reflection of the fact that national factor endowments are fixed in the Uruguay Round experiments reported here. Therefore, the stronger output response in Indonesian textiles and wearing apparel comes at the expense of output in other activities and the decline in output in these sectors is underestimated by the 1992-based simulation. For example, whereas light manufacturing output declines by 5.4% from the 1992 base, it falls by 7% from the 2005 base, due to the stronger rate of increase in textiles and wearing apparel output in 2005.

# 8. **Assessment of the Uruguay Round in a Changing World Economy**

This paper has aimed to assess the relative importance of evaluating the Uruguay Round in the context of a changing world economy. Most applied general equilibrium analyses of the Round have asked the question: "What would be the effect on the world economy in the base year (e.g. 1992), had the full Uruguay Round been introduced in that year?" However, the Final Act of the Uruguay Round will be phased in over the period of a decade. Therefore, it becomes relevant to ask how the world economy might change over that entire period, and how this in 17
# **Food Insecurity and Public Agricultural Spending in Bolivia: Putting Money Where Your Mouth Is?**

Jose Cuesta, Svetlana Edmeades and Lucia Madrigal<sup>1</sup> The World Bank

> 1 Jose Cuesta is a Sr. Economist at the Poverty Reduction and Equity Unit, Poverty Reduction Network; Svetlana Edmeades is a Sr. Agricultural Economist at the Agriculture and Rural Development Unit, Sustainable Development Department in the Latin American and Caribbean Region; and Lucia Madrigal is a consultant at the Poverty Reduction and Equity Unit, Poverty Reduction Network.