rafmacalaba/datause-encoder-data huggingface dataset:
For each simulation, the model<sup>was run 60 times over the period 1985</sup> to 2004 with different shocks<sup>representing random elements affecting produc-</sup> tion (such as weather) being<sup>applied in each year and in each simulation. The</sup> results presented are the averages<sup>of the 60 simulations for each year. They</sup> therefore represent the average<sup>impact under a wide range of potential market</sup> conditions. Accordingly, the results should<sup>be regarded as illustrative of</sup> the potential impact under a<sup>variety of market conditions rather than forecast</sup> effects based on current market<sup>conditions.</sup> The forward simulations are performed based on market information<sup>up until 1985. The simulations therefore</sup> do not take into account the<sup>recent, and most unexpected, decline in Brazilian</sup> exports. The prices presented<sup>for the baseline simulation are not World Bank</sup> forecasts. They do, however,<sup>represent the type of price behavior that can</sup> generally be expected in the<sup>world market--although to some extent the true</sup> variability of price is masked<sup>by the averaging of the 60 simulations.</sup> The estimated effects of sustained<sup>increases in production and</sup> exports from 1985 onwards are<sup>presented in Table 1. Over the entire 20-year</sup> period simulated, the 0.5<sup>million ton increase in Brazilian production</sup> translates into an average 8.3%<sup>increase in exports.</sup> Because the increase is model is run. To overcome this difficulty<sup>a simpler procedure was used</sup> which achieves a similar result. The expected world price was set<sup>equal</sup> to the stochastic (or actual<sup>model-generated) world price plus a random</sup> error with a mean of zero and<sup>a standard deviation equal to that generated</sup> for each year from all 60 stochastic<sup>runs for the baseline. The assumed</sup> producer forecasting error was<sup>therefore assumed to be random with a</sup> distribution reflecting the<sup>expected distribution of prices in any</sup> particular year, given the typical<sup>sorts of random shocks in production</sup> and their effect on the uncertainty<sup>of prices. The standard forecasting</sup> error is over 40% of the long-run<sup>average world price, and is therefore</sup> quite large.
**Table 1a: Correlations of Aid Interventions Among Major Donors**

||**US**|**Japan**|**Germany**|**UK**|**Norway**|
|---|---|---|---|---|---|
|**US**|1.00|||||
|**Japan**|0.31|1.00||||
|**Germany**|0.27|0.22|1.00|||
|**UK**|0.25|0.43|0.32|1.00||
|**Norway**|0.26|0.27|0.23|0.29|1.00|

**Table 1b: Correlations of Aid Amounts Among Major Donors**

||**US**|**Japan**|**Germany**|**UK**|**Norway**|
|---|---|---|---|---|---|
|**US**|1.00|||||
|**Japan**|0.53|1.00||||
|**Germany**|0.52|0.59|1.00|||
|**UK**|0.29|0.36|0.29|1.00||
|**Norway**|0.18|0.53|0.35|0.25|1.00|

24
11 Table 5

# Eamings Functions. Brazilian Vocational Leavers Full Time Private Sector Males (dependent variable Ln monthly earnings)

|Independent<br>variables|Matchl<br>coeff.|Model<br>t|Match<br>coeff.|2 Model<br> <br>t|
|---|---|---|---|---|
||(1)||(|2)|
|Intercept|6.055*|8.56|6.007*|8.61|
|EXPER|0.078*|6.25|0.080*|6.47|
|EXPERSQ|-0.001*|3.69|-0.001|*4.01|
|YRSCHL|0.223*|3.67|0.220*|3.67|
|MATCH1|0.164*|2.02|||
|MATCH2|||0.272*|3.67|
|SERVICE|40.676*|3.94|-0.631*|3.71|
|FARMW|-0.339*|1.62|-0.291 *|1.41|
|SALES|-0.103|0.94|-0.029|0.26|
|CLERICAL|-0.306*|3.86|-0.226*|2.74|
|NONDEF|0.290*|1.62|0.352*|1.98|
|REGIN|-0.281*|1.07|-0.288*|1.10|
|REG2N|-0.251*|2.80|-0.253*|2.86|
|REG4N|-0.035|0.40|-0.046|0.54|
|REG5N|-0.285*|2.42|-0.307*|2.65|
|SOCSEC|0.570*|3.22|0.545*|3.12|
|MIGRANT|0.130*|1.98|0.123*|1.90|
|R2|0.393||0.407||
|Number observations|406||406||

* Significant at the .001 level or better.

The results presented in Table 5 support the findings of other studies with regard to expected eamings outcomes of vocational school leavers when matched in relevant fields of employment. The coefficients of both the strict and the wider match indicate that vocational leavers who work in fields related to their studies do have higher eamings than those who do not.

# EamInas Functions Results for Academic and Vocational Leavers

Eamings functions are estimated for the overall sample of secondary school leavers. The purpose of the analysis is to examine the comparative returns to vocational and academic study, and to examine whether work in a study related field makes a significant contribution to the eamings of the
Finally, the Area under the Curve (AUC) is a measure of the efficacy of a targeting method in identifying the poor population at different targeting thresholds (Wodon, 1997, Olken and Hanna 2018). The curve in question is a receiver operator characteristic (ROC) curve, which indicates the trade-off between true and false positives at different poverty lines. We plot the ROC curve using each percentile of the benchmark village welfare predicted from the census. In other words, for poverty lines defined at each percentile of the benchmark distribution, we plot the true positive rates (TPR) on the Y axis and the false positive rate (FPR) on the X axis. The former is defined as the proportion of poor villages that are correctly predicted to be poor when using predicted village welfare from each candidate prediction method, while the latter is defined as the proportion of non-poor that are incorrectly predicted to be non-poor. Because the true positive rate is on the Y axis, a higher AUC score represents an improvement in true positives for a given level of false positives, or a better targeting method. The 45-degree line, which is what one would expect if villages were ranked randomly, corresponds to an AUC score of 0.5, while a perfectly accurate ranking that correctly identifies poor households under all poverty lines would receive an AUC score of 1.

# 3.3 Candidate Targeting Methods for Identifying Poor Villages

Proxy Mean Test scores in the UBR The administrative data from Malawi’s UBR provides information on households’ characteristics to assess their prospective eligibility for social programs. The data set contains an extensive range of variables such as geographic location, households’ assets, food security questions, and economic characteristics.

This information was used by the Malawian government and the World Bank to calculate ProxyMeans Test (PMT) scores to identify poor and vulnerable households. It is used to create a proxy score of weighted variables that are highly correlated with household consumption. The PMT, like our benchmark welfare measure, is a measure of chronic poverty. This is because it uses variables that are less responsive to economic shocks than household consumption, such as assets and household composition (Lindert et al., 2018). Because the PMT variable is available in the data and was used for the UBR, it is useful to evaluate it against welfare predicted into the census extract.

## Relative Wealth Index

The Relative Wealth Index predicts the relative standard of living within countries using nontraditional data sources such as satellite imagery, cellular network data, topographic maps, and proprietary connectivity data from Meta. Using supervised machine learning models, the team predicts the relative wealth for grid cells of 2.4 km<sup>2</sup> . The estimates of wealth are relatively accurate. Depending on the method used to assess the model’s performance, the model explains 56 to 70 percent of the actual variation in household-level wealth in 56 low- and middle-income countries (Chi, et al, 2022). However, the model is trained on a wealth index, which may 13
both the groups of affected and non-affected countries for the analysis among any pair or group of countries that appear in the sample subject to data availability; and (ii) because it allows for the researcher to explicitly control for all observable bilateral determinants of trade. Third, the approach is _comprehensive_ because, by construction, the border estimates will account for all trade costs that have not been captured explicitly by other control variables that are observable to the researcher. Note that, in addition to the border variables, our method allows for the inclusion of tariffs, FTAs, and all other variables for which data are available. Finally, the method is implemented within the standard empirical structural gravity equation and, therefore, the partial equilibrium estimates that it delivers can be integrated within a wide class of _new quantitative trade models_ , c.f., Arkolakis, Costinot and Rodriguez-Clare (2012); Costinot and Rodriguez-Clare (2014), or even complex computational general equilibrium models, such as the standard GTAP model. We demonstrate that in Section 4.2, where we obtain general equilibrium (GE) welfare effects corresponding to our partial equilibrium estimates.

To demonstrate the effectiveness of our methods we study the integration between the countries from the Central European Free Trade Agreement (CEFTA) and the European Union (EU). The integration of the CEFTA countries has been an extremely important goal for the governments of those nations but also from the perspective of the European Union, especially after the EU released a new strategy for the Western Balkans countries.<sup>3</sup> We offer further details on the background on CEFTA and EU accession process and status in Section 3.1. Data limitations have been the main obstacle to study the impact of the potential CEFTA integration with the EU. We overcome this challenge by being the first to utilize the new International Trade and Production Database for Estimation (ITPD-E), which was construced by Borchert et al. (2021 _a_ ) for the U.S. International Trade Commission. The two main advantages of ITPD-E for our purposes are that (i) it covers all

> 3The economic integration process between EU and the CEFTA countries has been slowly progressing for the last 15 years. Four CEFTA countries (Albania, Montenegro, North Macedonia and Serbia) are EU candidate countries, while Bosnia and Herzegovina and Kosovo are potential candidate countries, and Moldova recently signed a bilateral free trade agreement with the EU. The recent EU strategy in 2018 provided a target accession date of 2025 for Serbia and Montenegro, with the possibility for the other Western Balkans countries to join them, but this target date may be delayed given the 2020 COVID-19 pandemic. A review of the status of accession process and challenges (Grieveson, Grubler and Holzner, 2018) finds that target date is ambitious and may be established more as an incentive for reforms in the countries. Nonetheless, there is still a possibility that in five years or so, Serbia and Montenegro will be new EU members, with the other countries to follow in the next 10 years.

3
Policy Research Working Paper Series

||Title|Author|Date|Contact<br>for paper|
|---|---|---|---|---|
|WPS92|0 Economic Incentives and Point<br>Source Emissions: Choice c'<br>Modeling Platform|Raymond J.Kopp|June 1992|C. Jones<br>37754|
|WPS92|1 Road Infrastructure and Economic<br>Development: Some Diagnostic<br>Indicators|Cesar Quelroz<br>Surhid Gautam|June 1992|M. Laygo<br>31261|
|WPS922|Central America at a Crossroads|Sylvia Saborio<br>Constantine Michalopoulo|June 1992<br>s|M. Colinet<br>37044|
|WPS923|Listening to Firms: How to Use Firm-<br>Level Surveys to Assess Constraint<br>on Private Sector Development|Andrew H. W. Stone<br>s|June 1992|P. Infante<br>37642|
|WPS924|How Reduced Demand for Children<br>and Access to Family Planning<br>Accelerated the Fertiiity Decline in<br>Colombia|Rafael Rofman|June 1992|0. Nadora<br>31091|
|WPS925|A General-Equilibrium-Based<br>Social<br>Policy Model for C6te d'lvoire|Ngee-Choon Chia<br>Sadek Wahba<br>John Whalley|June 1992|A. Cox<br>34778|
|WPS926|Options for Reshaping the Railway|Neil E. Moyer<br>Louis S. Thompson|June 1 9q2|B. Gregory<br>33744|
|WPS927|General Equilibrium Effects of<br>Investment Incentives in Mexico|Andrew Feftenstein<br>Anwar Shah|June 1991|C. Jones<br>37699|
|WPS928|Pesticide Externalhites, Comparative<br>Advantage, and Commodity Trade:<br>Cotton in Andhra Pradesh, India|Nalin M. Kishor|July 1992|D. Ballantyne<br>38004|
|WPS929|Managing Pollution Control in Brazil:<br>The Potential Use of Taxes and<br>Fines by Federal and State Governm|Antonio Estache<br>Kangbin Zheng<br>ents|July 1992|A. Estache<br>81442|
|WPS930|Participatory Development: Myths<br>and Dilemmas|Robert Picciotto|July 1992|A. Muhtasib<br>84573|
|WPS931|How Much to Commit to an Exchange<br>Rate Rule: Balancing Credibility and<br>Flexibility|Alex Cukierman<br>Miguel A. Kiguel<br>Nissan Liviatan|July 1992|R. Luz<br>34303|
|WPS932|Interest Rates, Official Lending,<br>and the Debt Crisis: A Reassessmen|Asli DemirgOg-Kunt<br>t Enrica Detragiache|July 1992|K. Waelti<br>37664|
|WPS933|Developing Country Capital<br>Structures and Emerging Stock Mark|Asli DemirgOg-Kunt<br>ets|July 1992|K. Waelti<br>37664|
Policy Research Working Paper 7826

# **Abstract**

A spatial econometric model is used to link road upgrading to forest clearing and biodiversity loss in the moist tropical forests of Bolivia, Cameroon, and Myanmar. Using 250meter cells, the model estimates the relationship between the rate of forest clearing in a cell and its distance to the urban market, with explicit attention given to road quality and simultaneity, terrain elevation and slope, the agricultural opportunity value of the land, and its legal protection status. Forest clearing is found to be most responsive to the distance to the nearest urban market, especially with secondary roads with lower typical speeds. Using the estimated forest-clearing response elasticities and a composite biodiversity indicator, an index of expected biodiversity loss from upgrading secondary roads to primary status is computed in each cell. The results identify areas in the three countries where high expected biodiversity losses may warrant additional protection as road upgrading continues. In addition, the results provide ecological risk ratings for individual road corridors that can inform environmentally sensitive infrastructure investment programs.

This paper is a product of the Environment and Energy Team, Development Research Group. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The authors may be contacted at sdasgupta@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
of finance pumped into an environment of solid institutions and social infrastructure. We would argue that this one case disproves hypothesis #3, that aid is always money down the rat hole. There are quite a few case studies of aid to developing countries. Many of these support the view that money channeled to a highly corrupt government with distorted economic policies provides no lasting benefit. On the other hand, studies of successful aid typically emphasize that the recipient government had a good set of policies to enhance growth and directed assistance to useful investments in roads, schools, and the like.

A third type of evidence that is relevant comes from data on individual projects financed by aid. In a variety of sectors, projects are more likely to be successful in countries with growth-enhancing institutions and policies [Isham and Kaufmann (1999)]. When South Korea was a low-income country with a large amount of aid in the 1960s, most projects, of many different types, were successful. In Kenya and Zimbabwe in recent decades, on the other hand, many projects, of all types, have failed, in the sense that they have not provided the services or benefits anticipated from the investment. If aid were not fungible, this project level evidence would settle the debate. However, it is possible that all of the good projects in Korea would have been financed by private capital in the absence of aid, so that project-level evidence alone cannot settle the debate about aid effectiveness. Once we combine the evidence from case studies and projects with the cross-country correlations, however, we feel more confident that aid effectiveness depends on institutions and policies.

We were also interested to see the results of a global poll commissioned by the World Bank from a private survey company [PSRA (2003)]. The poll focused on “opinion makers” in a wide range of developing and developed countries (that is, government officials, academics, the media, trade union leaders, NGOs, etc.). In SubSaharan Africa, 84% of opinion makers agreed with the statement that, “Because of corruption, foreign assistance to developing countries is mostly wasted.” In other regions of the developing world, similarly large majorities agreed with the statement. Opinion makers in the rich countries were the least skeptical (only 58% agreed with the statement). So, while first-world academics may find some specifications in which aid 20
sums those anomalies weighted by the average contribution of each month to the annual total, according to the following formula: In (1) _Pi_ and<sup>_P_</sup> _i_<sup>are the observed precipitation in the</sup><sup>_i_th month and the long term average precipitation</sup> for the _i_ th month,  _i_ is the standard deviation of monthly precipitation for the _i_ th month and _PA_ is the mean annual precipitation. The number of months over which the index is calculated is indicated by _N_ . We use _N_ = 12 to capture annual precipitation anomalies. The WASP is designed such that rainfall anomalies are measured relative to the typical rainfall for a given month. Next, in order to produce a national level value from the gridded values, a threshold level is designated (for example, 1 standard deviation) and the total number of cells above and below those thresholds is counted.

This produces a measure of the portion of a country that is experiencing anomalously dry (WASP(-1)) or wet (WASP(+1)) over the time period measured. The result for WASP(-1) is well correlated with drought indices, such as the Palmer Drought Severity Index which uses precipitation and temperature to measure drought (Alley, 1984). The WASP(+1) index indicates a period of anomalously wet conditions but may not indicate flood events. Floods can occur on short time spans that are not captured by this index which is calculated with monthly data. The WASP(+1) may capture flooding events caused by longer periods of rainy conditions that saturate soils and lead to intense flooding events over shorter time periods. Further details of the creation of the WASP index and its use in climate analyses can be found in Lyons and Barnston (2006).

# **_Fixed effects regressions_**

To conduct an assessment of countries’ historical sensitivities to climate variation, we use the data described above in regressions within several different specifications, including, regressions with country 9
Table 8: Origin Frauds: First Stage Instrumental Variable Regressions

|Dependent variables|AV|E<br>|AVE+Tari¤||AVE|AVE+Tari¤|
|---|---|---|---|---|---|---|
||(3)|(4)|(5)|(6)|(7)|(8)|
|Average AVE of exporter|0.597***|0.571***||0.613***|0.591***||
||(0.028)|(0.029)||(0.028)|(0.029)||
|Tari¤||0.021***|||0.019***||
|||(0.001)|||(0.001)||
|Average AVE of exporter|||0.899***|||0.900***|
|+Tari¤|||(0.067)|||(0.017)|
|SPS/TBT NTMs|||4.601***|||4.300***|
||||(0.941)|||(0.940)|
|Other NTMs|||1.466|||1.464|
||||(1.034)|||(0.992)|
|Exporter FE|Yes|Yes|Yes|Yes|Yes|Yes|
|Importer-Product FE|Yes|Yes|Yes|Yes|Yes|Yes|
|F-Statistics|444.17|388.29|2422.37|465.06|409.57|2871.97|
|Observations|251,184|244,689|146,609|246,373|239,854|144,111|

Notes: Robust standard errors in parentheses are clustered by importer-product; *, ** and *** indicate that coe¢ cients are signi...cant at 90%, 95% and 99%, respectively.

Table 9: Product Classi...cation Frauds: Downward Bias Least Squares Regressions

|Dependent variables|Quan|tity Discrep|ancies|Valu|e Discrepa|ncies|
|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|
|AVEborder|0.342***|0.381***||0.320***|0.360***||
||(0.0654)|(0.0670)||(0.0633)|(0.0649)||
|Tari¤||0.131***|||-0.0141||
|||(0.0459)|||(0.0476)||
|AVEborder+Tari¤|||0.215***|||0.103***|
||||(0.0363)|||(0.0371)|
|SPS/TBT NTMs|||-2.048**|||-1.303|
||||(0.973)|||(0.939)|
|Other NTMs|||-3.620***|||-3.550***|
||||(0.937)|||(0.925)|
|Constant|95.90***|94.70***|97.01***|98.73***|98.69***|100.4***|
||(0.318)|(0.478)|(0.756)|(0.309)|(0.482)|(0.741)|
|Observations|256,565|250,285|250,285|251,785|245,466|245,466|
|R-squared|0.227|0.226|0.226|0.233|0.232|0.232|
|Product FE|Yes|Yes|Yes|Yes|Yes|Yes|
|Importer-Exporter FE|Yes|Yes|Yes|Yes|Yes|Yes|

Notes: Robust standard errors in parentheses are clustered by importer-product;

*, ** and *** indicate that coe¢ cients are signi...cant at 90%, 95% and 99%, respectively.

44
tallied reports of smoke (from forest and land fires, excluding urban pollution) and fire (from forest fires). Map 3 depicts the spatial distribution of smoke and fire problem perception.

<u>Education:</u> The data for 2000, at municipio level, obtained from the Brazilian Research Institute of Applied Economics (IPEA) and derived from the Census, presents _average years of adult education (above 25 years)_ as depicted in Map 4.

<u>Income:</u> This data for 2000, at municipio level, from the Brazilian Research Institute of Applied Economics (IPEA) but ultimately derived from the Demographic Census, refers to _mean per capita income_ in Brazilian Reais for 2000 and is visualized in Map 5.

<u>Population:</u> At municipio level, this data for 2000, and classified into rural and urban population share, originated from the Brazilian Institute of Geography and Statistics (IBGE). We use the municipio’s rural population share to compute _rural proportion_ of municipal population (Map 6).

<u>Fire Spots (heat sources):</u> This data for 2000 and 2001 originates from the Brazilian Institute for Space Research (INPE). The heat sources were measured by the NOAA-12 satellite during the passage at 6-7 p.m. Brasilia local time; the spatial resolution is 1.1 km. For our analysis, we calculate _fire spot density 2000-01_ based on a spatial moving average in order to account for fires in neighboring municipio. We calculate for each 1 km grid cell the mean number of fire spots within a 25x25 cell neighborhood to then calculate the mean of this derived variable over the cells within the MCA. (Map 7).

<u>Biome:</u> This data originates from the Brazilian Institute for the Environment and Natural Resources (IBAMA) and World Wildlife Fund (WWF) based on Dinerstein et al. (1995). We grouped the originally 10 biome classes into 5 biome categories, notably _Amazonia_ (including the biome classes _Costeiro_ and _Ecotono Cerrado-Amazonia_ ), _Caatinga_ , _Mata Atlantica_ , 6
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