rafmacalaba/datause-encoder-data huggingface dataset:
which improves transparency, and the use of fitted distributions allows us to project forward (and back) in time the entire distribution.

Using the 2011 PPP data with the urban-rural adjustments removed from China, India and Indonesia, along with the re-estimated $1.82 poverty line, we estimate a global poverty rate of 14.9 percent in 2011. This is 0.4 percentage point greater than the World Bank (2014a, 2014b) estimate for 2011 based on the $1.25 line using 2005 PPPs. Using the $1.92 line gives us a poverty rate of 16.7 percent in 2011. These estimates differ markedly from public perception, largely informed by Chandy and Kharas and Dykstra, Kenny and Sandefur, primarily because of the removal of the 2005 urban-bias adjustments in the 2011 PPP analysis. When we maintain the poverty line at $1.82 but use the adjusted 2011 PPP data, the estimated poverty rate falls to 11.1 percent. This is equivalent to 300 million people being re-classified as not poor, similar in magnitude to the analysis of Chandy and Kharas. Similarly, if we use the $1.44 line used by Dykstra, Kenny and Sandefur, the adjusted 2011 PPP analysis results in a decline by more than a half as compared to the face-value 2011 PPP analysis, a decline that is similar in magnitude to that found by Dykstra, Kenny and Sandefur.

The analysis based on the 2011 PPP data and the $1.82 line presents a global count of poor people that is very similar to the global count based on the $1.25 line and the 2005 PPPs.

However, some of the regional changes are larger. For example, in the East Asia and the Pacific region, poverty increases from 8.0 percent under the $1.25 line to 13.2 percent under the $1.82 line. This increase is largely driven by an upward adjustment to the poverty headcount in China. Under both the 2005 and 2011 PPPs, the largest number of poor people in 2011 is in Sub21
To overcome discrepancies in temporal coverage and currency terms (constant and current), and to keep the data consistent and comparable for countries across the world, shares from sub-national statistics are calculated and then applied to a national total to derive a calibrated number at the sub-national level. The national totals are obtained from the World Development Indicators (World Bank, 2019) and averaged over three years around 2010. For a few countries, which do not report their national agricultural GDP in the WDI database, sums of all agricultural GDP components are used as proxies.

The calibrated statistics are then linked to grids through a shapefile of the Global Administrative Unit Layers (GAUL) that maintains global geographic layers with a consistent and comprehensively unified coding system (FAO, 2015). Then, we overlay the GAUL administrative boundaries on the grid network to assign the corresponding codes of the administrative units to each grid. For areas where sub-national AgGDPs have different administrative areas than GAUL, the GAUL areas are merged or split to match the sub-national AgGDP area.

# **2.3 Spatial Allocation Model**

After constructing all the components, we define a spatial allocation model in a crossentropy framework following (You, Wood, et al., 2014) to allocate administrative statistics to 5 arc-minute pixels.<sup>8</sup> National and sub-national AgGDP values are used as a constraint, while the distribution of crop, livestock, fishery, and forestry production (hunting is included in non-timber products of forestry) is used to create priors for estimating pixel-level AgGDP. Measurement units are unified using deflators and exchange rates.<sup>9</sup> The first step is to transform all real-value parameters into corresponding probabilities. Let _Si_ be the share of the total agricultural GDP allocated to pixel _i_ within a country x. _AgGDPi,x_ is the agricultural GDP allocated to pixel _i_ in country _x_ and X is a set including all pixels that fall within the boundary of a nation. Therefore: Let _PreAgGDPi_ be the pre-prior allocation of AgGDP share from our best estimate. The first approximation can be done by summing all five calculated pixel level components of

> 8A comprehensive presentation of the cross-entropy method is in Rubinstein and Kroese (2004)

> 9The currency varies by source. Crops are in local currency. Livestock are in International USD 20042006. Fish are USD 2009. Non-timber forest products are in USD 2012 and Timber (forest) are in USD 2011.

13
|__________<br>Country<br>__________|___________________<br>Unit of analysis<br>___________________|____________________________________________<br>Coverage<br>____________________________________________|_________________<br>Years<br>_________________|____________________________________<br>Contact<br>______________________________________|
|---|---|---|---|---|
|Republic of<br>Ireland|Establishment|Census of Industrial Production includes all plants<br>with 3 or more employees in NACE Rev 1.1 manu-<br>facturing sectors 10-41. Plants are not necessarily<br>dropped if they fall below 3 employees.|1996-2004|Stefanie Haller<br>stefanie.haller@esri.ie|
|Slovenia|Establishment|All establishments, including firms with less than<br>10 employees|1994 – 2002|Črt Kostevc<br>crt.kostevc@ef.uni-lj.si|
|Spain|Firm|All firms with more than 200 employees<br>plus a sample of firms employing between 10 and<br>200 employees selected according to a stratified<br>random sampling procedure.|1990 – 1999|Jose C. Fariñas<br>farinas@ccee.ucm.es|
|Sweden|Firm|All firms|1997 – 2004|Martin Andersson<br>martin.andersson@ihh.hj.se|
|United<br>Kingdom|Firm|All firms operating in the UK; over representation<br>of large firms because of missing value problems|1995 -- 2004|Mauro Pisu<br>mauro.pisu@nbb.be|

________________________________________________________________________________________________________________________________ 28
**Table S4.3.** Balance between treatment groups in FREG sample, composed of actual FREG members in treatment communities and their matched controls.

||T1<br>(extension)|T2<br>(extension<br>+ video)|C<br>(control)|T1 vs. T2|T1 vs. C|T2 vs. C|
|---|---|---|---|---|---|---|
|Gender HH head (1=male)|0.90|0.91|0.91|0.00|-0.01|0.00|
||(0.04)|(0.03)|(0.02)|(0.05)|(0.05)|(0.04)|
|Age HH head (in years)|42.02|45.79|43.18|-3.77*|-1.16|2.61|
||(1.43)|(1.34)|(0.98)|(1.95)|(1.72)|(1.64)|
|No. of months HH head away|0.05|0.12|0.08|-0.07|-0.03|0.04|
||(0.03)|(0.09)|(0.05)|(0.09)|(0.06)|(0.10)|
|HH head married (1=yes)|0.87|0.93|0.90|-0.06|-0.03|0.03|
||(0.06)|(0.02)|(0.02)|(0.06)|(0.06)|(0.03)|
|Education HH head (completed grades)|3.49|3.57|3.48|-0.08|0.01|0.09|
||(0.59)|(0.47)|(0.31)|(0.75)|(0.66)|(0.56)|
|HH head has off-farm wage employment (1=yes)|0.15|0.16|0.14|-0.01|0.00|0.02|
||(0.03)|(0.04)|(0.02)|(0.05)|(0.04)|(0.05)|
|No. of HH members|5.73|5.83|5.91|-0.10|-0.17|-0.07|
||(0.27)|(0.21)|(0.13)|(0.34)|(0.30)|(0.25)|
|No. of organisations involved (0-12)|5.51|5.00|5.39|0.51|0.12|-0.40|
||(0.35)|(0.24)|(0.19)|(0.42)|(0.39)|(0.30)|
|Father of HH head important in community (1=yes)|0.78|0.73|0.78|0.05|0.00|-0.05|
||(0.05)|(0.05)|(0.03)|(0.07)|(0.06)|(0.06)|
|Walking dist. from homestead to nearest FTC (min)|31.11|30.75|33.44|0.35|-2.34|-2.69|
||(3.54)|(4.06)|(2.12)|(5.35)|(4.10)|(4.53)|
|No. of times talked to extension agent in the last 12 months|9.85|11.75|9.23|-1.90|0.62|2.52|
||(2.24)|(1.69)|(1.37)|(2.79)|(2.61)|(2.16)|
|Attended agric. training in the last 12 months (1=yes)|0.63|0.61|0.60|0.01|0.03|0.01|
||(0.07)|(0.05)|(0.04)|(0.08)|(0.08)|(0.06)|
|Basic assets score (0-4)|1.97|2.21|2.10|-0.24*|-0.13|0.11|
||(0.10)|(0.09)|(0.08)|(0.13)|(0.12)|(0.11)|
|Radio owned (1=yes)|0.30|0.43|0.34|-0.14**|-0.05|0.09|
||(0.05)|(0.05)|(0.04)|(0.07)|(0.06)|(0.06)|

9
<!-- Start of picture text --> 3000 140<br>c<br>’ a<br>an 2500 GDP per capita a - 120 3<br>3 3000 —i- Electricity consumption 4 — | g<br>uo<br>@ 1500 25<br>- 60 o=<br>22<br>a1000 w<br>ram<br>=I RR REE<br>oO 500 EB ine9 «CS<br>a)<br>a) EEEEEEESE,:<br>2000 2001 2002 2003 2004 2005 2006 # 2007<br>Year<br><!-- End of picture text -->
|||**PRkE**Wrklg<br>Poor<br>Seri|s|Contact|
|---|---|---|---|---|
||DII~~~~~~~~~ALAe||am|foapger|
|WPS402|The GATT**as**International<br>Disciplin<br>Over Trade Restrictions:APublic<br>Choice Approach|e J. Michael Finger|March 1990|N. Artis<br>38010|
|WPS403|Innovetive<br>Agriultural Extension<br>for Women:ACase Study of<br>Cameroon|S. Tjip Walker|||
|WPS404|Chie's LUbor Markets in an Era of<br>Adjustment|Luis A. Riveros|April 1990|R. Luz<br>34303|
|WPS406|Invetments in Solid Waste<br>Management: Opportunites for<br>E-vironmental<br>Improvement|Cart Bartone<br>Janis ermstein<br>Frederick Wright|Apnl 1990|S. Cumine<br>33735|
|WPS406|Township, Village, and Private<br>Industry in China's Economic<br>Reform|William Byrd<br>Aban Geib|April 1990|K. Chen<br>38966|
|WPS407|Public Enterprise<br>Reform:<br>AChallenge for the World Bank|Ahmed Gabal|April 1990|G. Orraca-Tetteh<br>37646|
|WPS408|Methodolgical Issues in Evaluating<br>Debt-Reducing<br>Deals|Stijn<sup>Claessens</sup><br>ishac Diwan|May 1990|S. King-Watson<br>33730|
|WPS409|Financial Policy and Corporate<br>Investment<br>in Imperfect Capital<br>Markets: The Case of Korea|Mansoor Dailami|April 1990|M. Raggambi<br>37657|
|WPS41<br>0|The Cost of Capital and Investmen<br>in Developing Countries|t Alan Auerbach|April 1990|A. Bhalla<br>37699|
|WPS411|Institutional Dimensions<br>of Poverty<br>Reduction|Lawrence<br>F. Salmen|May 1990|E. Madrona<br>37496|
|WPS412|Exchange Rate Policy in Developin<br>Countries|g W. Max<sup>Corden</sup>|May 1990|M. Corden<br>39175|
|WPS413|Supporting Safe Motherhood:<br>A Review of Financial Trends<br>Full Report|L. M. Howard|May 1990|S.Ainsworth<br>31091|
|WPS414|Supporting Safe Motherhood:<br>A Review of Financial Trends<br>Summary|L. M. Howard|May 1990|S. Ainsworth<br>31091|
|WPS415|How**(;ood**(or Bad) are Country<br>Projctins?|Norman Hicks<br>Michel Vaugeois|April 1990|M. Berg<br>31058|
A third issue concerns aid disbursed as technical cooperation. It usually does not go through the recipient country’s budget, and as such does not have a real effect on the recipient’s GNI. We have netted it out in two of the scenarios presented below.

Finally, an important issue that arises in measuring the redistributive effect of aid is whether aid flows should be subtracted from countries’ gross national incomes (expressed in PPP dollars) in dollar terms or in PPP dollars. If one converts the aid flows into PPP dollars, then the redistribution of aid is no longer zero-sum.<sup>15</sup> Yet, it is shown in appendix 2 that this is a satisfactory way of proceeding if indeed welfare is measured by PPP-corrected GNI. General equilibrium effects of aid actually lead to a corrective term but that term is likely to be small.

In summary, there are three measurement issues for which we calculated various different scenarios. The first issue is the grant equivalence of imputed multilateral lending, the second issue is the inclusion of debt relief in the aid figures, and the third issue is whether technical cooperation should be included or excluded from this analysis.

To arrive at a range of values for the redistributive impact, we then took as a _maximum value scenario_ as follows: 0.8 grant equivalent for concessional lending, aid flows measured in PPP dollars relative to PPP-corrected GNI per capita, zero discounting of debt relief so that the stock is counted as is reported to OECD, and technical cooperation not netted out. For a _minimum distributive impact_ calculation, we lowered the grant equivalent of lending to 0.5, and completely removed both debt relief and technical cooperation from the aid flows.

Finally, for an _intermediate calculation_ – and our preferred estimate— we used the 0.5 grant

> 15 To convert aid in dollars to aid in PPP terms, we multiply the former by a ratio of GDP, PPP to GDP in dollars, all taken from WDI 2004.

11
to an increase in the term premium and a resulting decrease in the maturity structure of debt, generating a negative comovement.

In this section, we study whether the behavior of the quantity data are consistent with demand or supply type of shocks. We concentrate on two sets of variables, the amount issued at different maturities and the average bond maturity. We study the time-varying patterns of these variables. In particular, we analyze how their behavior is affected by crises, by different measures of spreads, and by the term premium.

To study the amount issued at different maturities, we run a separate regression of issuance at each maturity as a function of each conditioning variable. We use a Tobit model, estimated by maximum likelihood, pooling all observations. These estimations take into account the fact that observations are left censored at zero. The dependent variable is the amount issued at each maturity in any given week, normalized by the average weekly issues for each country. This normalization takes into consideration that the average amount issued varies across countries. We use four different bond maturities: up to 3-year maturity (short term), more than 3-year maturity and up to 6-year maturity (medium-short term), more than 6-year maturity and up to 9-year maturity (medium-long term), and more than 9-year maturity (long term). As explanatory variables we use, alternatively, the crisis dummy de fi ned above, the 3-year spread, the 9-year spread, the emerging market bond index (EMBI) spread for each country, and the predicted term premium for each country. For spreads, we use log-spreads, de fi ned as log(1+ st,τ ). The EMBI spreads, calculated by J.P. Morgan, are well-known measures of long-term spreads in emerging markets. They not only provide an alternative estimate of long-term spreads, but also extend the sample for Mexico to cover the Mexican crisis (though they exclude Uruguay). We use the predicted term premium because it most directly captures the cost of issuing long-term debt relative to short-term debt. This variable is computed by regressing ex-post excess returns on 3-year and 9-year spreads and then obtaining the predicted value. As before, we compute robust standard errors using the country and crisis indicators as clusters.

The Tobit estimations are reported in Table 7. The estimations show that short-term issues are hardly affected by any of the conditioning variables; only the 3-year spread is statistically signi fi cant at the 10 percent level. However, medium- and long-term issues do show clear cyclical patterns. The conditioning variables become more statistically signi fi cant and the point estimates negative 27
about 70 years is higher than that of many countries with far higher average incomes; in Brazil, for example, life expectancy is 65 years for a country with roughly four times Sri Lanka's GNP per capita.

HPA's optimism on the scope for direct support-led security is shared by many other recent writings on the subject, including World Bank (1986), FAQ (1987), Berg (1987), UNDP (1990), Brown University Faculty (1990) and a series of public declarations in recent years.<sup>2</sup> There is a subtle but potentially important difference of emphasis between these writings and the 1990 M (World Bank, 1990). In the latter document one finds a more qualified, but still fairly positive, view of the prospects for certain sorts of direct public support, particularly in the social sectors and in the provision of income "safety nets". However, in overall tone at least, the 1990 WDR clearly sees direct support as less important instrument for poverty alleviation in the longer-term than achieving the right sort of economic growth.

In identifying their success stories of support-led security, Dreze and Sen look for large deviations below the line of best fit between under-five mortality (on the vertical axis) and GNP per capita (horizontal axis). This approach follows Sen (1981b). The aim is not to model mortality, but rather to control for income variation in assessing performance. The authors dwell little on the fact that their results suggest that GNP per capita is generally an excellent predictor of mortality; their concern is more with the cases where it is a weak predictor, i.e., the "outliers".

Again, one can question the methods of investigation used here. The authors appear to have used an ordinary regression of mortality against GNP per capita (both in logarithms) to find the outliers. (The regression equation is not given, nor is there a single summary statistic or diagnostic 24
Aflatoxins have acute and chronic toxicity in animals, however, their toxicity in humans has been encountered only rarely. One of the most important cases of aflatoxin contamination occurred in 150 villages in Northwest India in the fall of 1974. According to one report of this outbreak, 397 persons were affected and 108 persons died. Contaminated corn was the major cause. A second outbreak was reported in Kenya in 1982 where aflatoxin intake was estimated at 38 ,ug/kg body weight for an undetermined number of days. In developed countries, aflatoxin contamination rarely occurs at levels that cause acute carcinogens in humans, therefore studies on human toxicity from ingestion of aflatoxins have focused on their carcinogenic potentials.

A number of studies have revealed an association between liver cancer incidence and the aflatoxin content of the diet. These studies have not established a cause and effect relationship but rather suggest an association. A 1997 report by the joint FAO/WHO Expert Committee on Food Additives (JECFA) concluded that "aflatoxins should be treated as carcinogenic food contaminants, the intake of which should be reduced to levels as low as reasonably achievable" (FAO-WHO, 1997). JECFA analyzed potential human health impact of aflatoxin for two hypothetical levels (10 part per billion (ppb) and 20 ppb). It estimated that reducing the standard from 20 ppb to 10 ppb in countries where percentage of carriers of hepatitis B 1 is around one percent (e.g. members of the European community) would result in a drop in the population risk of approximately 2 cancer deaths a year per billion people.<sup>5</sup> _5_
- **Ivanic, Maros, Will Martin, and Hassan Zaman.** 2011. “Estimating the Short-Run Poverty Impacts of the 2010-11 Surge in Food Prices.” Policy Research Working Paper 5633, The World Bank.

- **Karali, Berna, and Gabriel J. Power.** 2013. “Short- and Long-Run Determinants of Commodity Price Volatility.” _American Journal of Agricultural Economics_ , 95(3): 724–738.

- **Ng, Serena.** 2006. “Testing Cross-Section Correlation in Panel Data Using Spacings.” _Journal of Business & Economic Statistics_ , 24(1): 12–23.

- **Pindyck, Robert, and Julio Rotemberg.** 1990. “The Excess Co-Movement of Commodity Prices.” _Economic Journal_ , 100(403): 1173–1189.

- **Qu, Zhongjun, and Pierre Perron.** 2007. “Estimating and Testing Structural Changes in Multivariate Regressions.” _Econometrica_ , 75(2): 459–502.

- **Rajagopal, Deepak, and David Silverman.** 2007. “Review of Environmental, Economics and Policy Aspects of Biofuels.” Policy Research Working Paper 4341, The World Bank.

- **Saadi, Hadj.** 2011. “Price Co-movements in International Markets and Their Impacts on Price Dynamics.” In _Methods to Analyze Agricultural Commodity Price_ . , ed. Isabelle Piot-Lepetit and Robert M’Barek. Springer New York.

- **Sheppard, Kevin, and Robert F. Engle.** 2001. “Theoretical and Empirical Properties of Dynamic Conditional Correlation Multivariate GARCH.” _NBER Working Paper w8554_ .

- **Svensson, Leo.** 2008. “The Effect of Monetary Policy on Real Commodity Prices: Comment.” In _Asset Prices and Monetary Policy_ . 327–333. University of Chicago Press.

- **Tangermann, Stefan.** 2011. “Policy solutions to Agricultural Market Volatility: A Synthesis.” ICTSD Programme on Agricultural Trade and Sustainable Development, Issue Paper No. 33.

- **Tang, Ke, and Wei Xiong.** 2012. “Index Investment and the Financialization of Commodities.” _Financial Analysts Journal_ , 68(6): 54–74.

- **Zhao, J, and B Goodwin.** 2011. “Volatility Spillovers in Agricultural Commodity Markets: An Application Involving Implied Volatilities from Options Markets.” Selected Paper prepared for presentation at the AAEA & NAREA Joint Annual Meeting, Pittsburgh, Pennsylvania, July 24-26.

14
Reifschneider, D. and Stevenson, R. (1991) “Systematic Departures from the Frontier: a Framework for the Analysis of Firm Inefficiency”, _International Economic Review_ , 32 (3): 715-723.

Ritter, C. and Simar, L. (1997) “Pitfalls of Normal Gamma Stochastic Frontier Models”, _Journal of Productivity Analysis_ , 8 (2): 121-129.

Roll, Y. and Hayuth, Y. (1993) “Port Performance Comparison Applying Data Envelopment Analysis (DEA)”, _Maritime Policy and Management_ , 20 (2): 153-161.

Shephard, R.W. (1953) _Cost and Production Functions_ , Princeton University Press, Princeton.

Shephard, R.W. (1970) _Theory of Cost and Production Functions_ , Princeton University Press, Princeton.

Suykens, F. (1986) “Ports should be Efficient (even when this Means that some of them are Subsidized), _Maritime Policy and Management_ , 13 (2): 105-126.

Suykens, F. and Van de Voorde, E. (1998) “A Quarter of a Century of Port Management in Europe: Objectives and Tools”, _Maritime Policy and Management,_ 25 (3): 251-261.

Talley, W.K. (1994) “Performance Indicators and Port Performance Evaluation”, _Logistics and Transportation Review_ , 30 (14): 339-352.

Tongzon, J.L. (1995a) “Systematizing International Benchmarking for Ports”, _Maritime Policy and Management_ , 22 (12): 171-177.

Tongzon, J.L. (1995b) “Determinants of Port Performance and Efficiency”, _Transportation Research_ , Part A, 29 (3): 245-252.

Tongzon, J.L. (2001) “Efficiency Measurement of Selected Australian and Other International Ports Using Data Envelopment Analysis”, _Transportation Research_ , Part A, 35: 113-128.

Valentine, V.F. and Gray, R. (2001) “The Measurement of Port Efficiency Using Data Envelopment Analysis”, 9<sup>th</sup> World Conference on Transport Research, Seoul, Korea.

21