rafmacalaba/datause-encoder-data huggingface dataset:
**Table 3: Unconditional Correlations between Labor Provisions and HS2 Mean Tariff Preference Margins**

|||1. Aspirational|2. Substance-|3. Investment|4. Cooperation|5. Institutions|
|---|---|---|---|---|---|---|
|HS2|Description|goals/objectives|related||||
|N.A.|Mean|-0.1250*|-0.0945*|-0.1123*|-0.0227*|-0.0349*|
|01-05|Animal Products|-0.0124*|-0.0299*|-0.0593*|0.0037|-0.0069*|
|06-15|Vegetable Products|-0.0861*|-0.0317*|-0.0462*|0.0263*|-0.0116*|
|16-24|Foodstuffs|-0.0926*|-0.0396*|-0.0548*|-0.0178*|-0.0215*|
|25-27|Mineral Products|-0.1069*|-0.0843*|-0.0935*|-0.0379*|-0.0638*|
|28-38|Chemicals|-0.1244*|-0.0846*|-0.0985*|-0.0044|-0.0411*|
|39-40|Plastics/Rubbers|-0.1464*|-0.0781*|-0.0904*|0.0054|-0.0346*|
|41-43|Leather et al.|-0.0672*|-0.0818*|-0.0941*|-0.0442*|-0.0062|
|44-49|Wood Products|-0.1444*|-0.1318*|-0.1323*|-0.0616*|-0.0684*|
|50-60, 6|3 Fabrics|-0.0847*|-0.0799*|-0.0996*|0.0017|-0.0006|
|61-62|Apparel|-0.0516*|-0.0437*|-0.0666*|-0.0273*|0.0189*|
|64-67|Footwear/Headgear|-0.0861*|-0.0701*|-0.0872*|-0.0202*|-0.0173*|
|68-71|Stone/Glass|-0.1137*|-0.0936*|-0.1079*|-0.0364*|-0.0431*|
|72-83|Metals|-0.1557*|-0.1119*|-0.1174*|-0.0003|-0.0616*|
|84-85|Machinery/Electrical|-0.1758*|-0.1121*|-0.1100*|-0.0061|-0.0736*|
|86-89|Transportation|-0.1137*|-0.0895*|-0.1011*|-0.0535*|-0.0616*|
|90-97|Miscellaneous|-0.1497*|-0.1263*|-0.1283*|-0.0125*|-0.0615*|

**Notes:** Column headings correspond to categories shown in bold in Table 1. Animal Products includes animals. Fabrics includes other raw materials. Wood products includes Wood.  The unconditional correlation coefficients are calculated across all country pairs with a trade agreement.  The * indicates statistical significance at the 5 per cent level.

pg. 26
# **4. Conclusion**

In most developing countries, including Malawi, estimates of HIV prevalence are obtained through sentinel surveys of women visiting antenatal clinics (ANCs). Sentinel surveys are often the only source of information on HIV prevalence in a country, and as such are extremely useful. However, they are subject to numerous limitations. These surveys are not based upon a probability sample and are not representative of the population as a whole. Moreover, they do not provide reliable information on prevalence at the sub-national level.

In recent years, several countries in Africa have implemented Demographic and Health Surveys (DHS) that include HIV testing of adult population.<sup>26</sup> These surveys are generally large in size and thus usually permit the calculation of prevalence rates at some sub-national level. Although these surveys are known to suffer from non-response bias, they unquestionably represent an improvement over sentinel surveys. However, such population-based surveys of prevalence rates are still very rare. There is thus an interest to search for improved methods for small-area estimation (SAE) of HIV prevalence which would make it possible to produce more accurate (compared to those extrapolated directly from the sentinel surveys) sub-national estimates of HIV prevalence when population-based surveys of prevalence are not available.

This paper proposes a new approach to the estimation of HIV prevalence for relatively small geographic areas. The proposed approach is believed to overcome some of the difficulties/biases involved in currently existing methods of deriving HIV prevalence (at both national and sub-national levels) directly from sentinel surveys. The paper provides justifications why a new method could be useful, and also describes the limitations of the proposed approach.

The methodology proposed in this paper is similar in spirit to the poverty mapping methodology introduced by Elbers et al.(2003) **.** The method essentially involves 3 stages. In the first stage the information from a DHS (or similar survey) on individual characteristics (such as age and education) of women (age 15-49) is used to weight the respective individual information from the sentinel antenatal clinics. In the second stage,

> 26 The full list of those countries is provided in the UNAIDS Report (UNAIDS, 2006).

17
- 24 -

Short-term stability depends on actions contingent on expected future conditions. Medium-term possibilities depend on the path taken in reaching the future. While there is no cross-conditionality between Fund facility agreements and Bank adjustment lending, the implicit consistency of stabilization and adjustment programs implies a linkage to the extent that analyses of policies required to reach an equilibrium position are correct.

Coordination with other lending sources also plays an important role in the dialogue process. The Fund and Bank program analyses often provide the framework within which coordinated official action is predicated. External commercial banks also depend on such program analyses. The dialogue on debt restructuring and Issuance of new capital is not necessarily centered on the Bank-member dialogue, but to the extent that creditworthiness is linked to economic viability the adjustment dialogue and program are critical. Relating policy conditionality to these players indicates additional roles which macroeconomic-based development aid plays. In particular, the conditionality in adjustment lending may provide an assurance of ongoing participation by the external agencies in evaluating the evolution of economic conditions and assisting in the design of policy to meet those conditions.

The rhanges that have been taking place in the Bank with respect to policy-based lending reflect more than a change in such lending from 10 percent to over 20 percent of total lending. The conceptual change that has been taking place may have wider relevance to other agencies, and it seems to have three logs. The first of these is
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below the national poverty line of IQD 110,000 and eligible for support. Despite having the same qualification cutoffs (IQD 110,000), the difference in the estimated eligibility rates between the SEVAT and MoLSA PMT can be attributed to: (i) difference in targeting model - while the MoLSA PMT is devised to assess households’ monetary poverty, the SEVAT model evaluates vulnerability, (ii) difference in the survey methodologies between the GOI’s SWIFT and the CLCI’s MPCA surveys – the SWIFT collected detailed household food and non-food expenditures from a nationally representative sample to estimate monetary poverty in Iraq, whereas the MPCA asked only about the total expenditure in a single question; research shows that these measures can be significantly different (Beegle et al., 2012).

_Table 2: Share of households eligible in each targeting (n = 10818)._

|Eligible criteria|Cutoff|IDQ (Monthly per capita<br>consumption)|Number<br>eligible|Share of the total<br>sample(%)|
|---|---|---|---|---|
|SEVAT|Log10||||
|Extreme vulnerable|<4.84|<70,000|2,921|27%|
|Extreme - Mid vulnerable|<4.96|<92,000|5,029|46%|
|All vulnerable group|<5.04|<110000|6,815|63%|
|Non<br>vulnerable<br>/<br>not<br>qualified|>5.04|> 110,000|4,003|37%|
|Legacy-VA|||||
|Extreme vulnerable|from 17 -<br>26|Host or displaced > 3<br>months|4,868|45%|
|Extreme - Mid vulnerable|from 17 -<br>26|Host or displaced|6,239|58%|
|All vulnerable group|9<br>and<br>above|Host or displaced|6,491|60%|
|Non<br>vulnerable<br>/<br>not<br>qualified|below 9|Host or displaced|4,327|40%|
|MoLSA-PMT|Ln||||
|Bottom 15% nationally|4.6|100000|2,124|20%|
|Bottom 18% nationally|4.7|110000|2,325|21%|
|Bottom 25% nationally|4.9|135000|4,356|40%|
|Bottom 30% nationally|4.9|140000|4,644|43%|
|Bottom 35% nationally|5.0|150000|5,375|50%|

Note: The unit of PMT score of SEVAT is Log10, the unit for legacy VA score is an index between 0 and 50, and the unit of MolSA is the natural log.

# 4.3 The referral rate using the Full PMT

The share of households referred (i.e., cross-eligible households) was analyzed using Equation 1 described in the methodology section. We are interested in calculating the proportion of households eligible (or 15
A Second Look at the Pesticides Initiative Program: Evidence from Senegal<sup>_∗_</sup> Melise Jaud<sup>_†_</sup> Olivier Cadot<sup>_‡_</sup> <mark>May 2011</mark> Keywords: Senegal, EU, Agriculture, Export Promotion, Technical Assistance, Impact Evaluation.

JEL classification codes: F13, F14, L15, L25, O17, O24, C23.

> _∗_ This paper is part of a joint program of Switzerland’s NCCR and the World Bank on the application of impact-evaluation methods to trade-related interventions. Without implicating them, we would like to express our thanks to C ́eline Carr`ere, Ana Fernandes, Julien Gourdon, Daniel Lederman, Aaditya Mattoo, Jaime de Melo, Marcelo Olarreaga, Alberto Portugal and Akiko Suwa for useful comments. We are also grateful to the European Commission and to Senegal’s _Direction G ́en ́erale des Douanes_ and _Direction Nationale des Statistiques_ for access to their data.

> _†_ The World Bank and Paris School of Economics; mjaud@worldbank.org

> _‡_ The World Bank, University of Lausanne, and CEPR; ocadot@worldbank.org
simply compare γ1 _k_ and γ2 _k_ , the advantage of using the single regression is that we can more easily compute general statistics by industry.

(10) _tradeijk_ , _t_ − _tradeijk_ , _t_ −1 = (α _itk_ −α _itk_ −1 ) + (α _kjt_ −α _kjt_ −1 ) + (γ _tk_ −γ _tk_ −1 ) _dis_ tan _ceij_ + (ε _ijtk_ − ε _ijtk_ −1 ) . This yields a straightforward estimating equation: _k k k k k_ (11) _gtradeij_ , _t_ = β _it_ + β _jt_ +η _t dis_ tan _ceij_ + μ _ijt_ , where _gtrade_ is trade growth. The _η_ s gives an estimate of _d_ γ in equation (8), which represents whether trade growth is biased toward more distant countries. It is the percentage point change in annual trade growth for a percentage change in distance. For example, if η is -.1, it would imply that 10 percent increase in distance between two countries leads to 1 percentage point slower trade growth.

In order to provide a benchmark, we also estimate equations (7) and (11) using the aggregate bilateral trade data. This allows us to compare the average industry distance elasticity with the distance elasticity from aggregate data.

# **V. Regression Results**

We use data on the value of bilateral trade for 766 SITC2 industries.<sup>8</sup> Within the industries, we calculate average bilateral trade for three periods, 1985-89, 1990-1994, 1995-2000. Using averaged data should reduce problems due to idiosyncratic shocks to an industry in any given year or exchange rate fluctuations. We restrict the sample to be a balanced sample. This has an advantage in making the data directly comparable across periods, but a disadvantage because we are excluding new trade that may be more or less

> 8 Here we dropped two industries that did not have enough observations to estimate a distance coefficient.

14
|P075399|2003|2004|5/26/2004|3 Closed|Vietnam|
|---|---|---|---|---|---|
|P075399|2003|2005|6/15/2005|5 Closed|Vietnam|
|P075399|2003|2006|12/28/2005|6 Closed|Vietnam|
|P075399|2003|2007|7/25/2006|7 Closed|Vietnam|
|P075399|2003|2007|3/29/2007|8 Closed|Vietnam|
|P075399|2003|2008|2/20/2008|9 Closed|Vietnam|
|P075399|2003|2008|4/23/2008|10 Closed|Vietnam|
|P075399|2003|2009|12/17/2008|11 Closed|Vietnam|
|P075399|2003|2010|1/30/2010|12 Closed|Vietnam|
|P075399|2003|2011|10/25/2010|13 Closed|Vietnam|
|P075399|2003|2011|1/16/2011|14 Closed|Vietnam|
|P075399|2003|2012|4/1/2012|15 Closed|Vietnam|
|P075399|2003|2013|2/13/2013|16 Closed|Vietnam|
|P075399|2003|2014|10/16/2013|17 Closed|Vietnam|
|P075399|2003|2014|10/30/2013|18 Closed|Vietnam|
|P075407|2006|2006|5/10/2006|1 Closed|Vietnam|
|P075407|2006|2007|6/24/2007|2 Closed|Vietnam|
|P075407|2006|2008|6/2/2008|3 Closed|Vietnam|
|P075407|2006|2009|6/24/2009|4 Closed|Vietnam|
|P075407|2006|2010|1/15/2010|5 Closed|Vietnam|
|P075407|2006|2011|2/16/2011|6 Closed|Vietnam|
|P075407|2006|2012|12/23/2011|7 Closed|Vietnam|
|P075407|2006|2013|12/5/2012|8 Closed|Vietnam|
|P075407|2006|2013|6/26/2013|9 Closed|Vietnam|
|P075407|2006|2014|2/8/2014|10 Closed|Vietnam|
|P075407|2006|2014|6/24/2014|11 Closed|Vietnam|
|P075435|2002|2008||0 Closed|Europe and Central As|
|P075435|2002|2009||0 Closed|Europe and Central As|
|P075435|2002|2010||0 Closed|Europe and Central As|
|P075435|2002|2011||0 Closed|Europe and Central As|
|P075435|2002|2012||0 Closed|Europe and Central As|
|P075435|2002|2013||0 Closed|Europe and Central As|
|P075464|2006|2006||0 Closed|Philippines|
|P075464|2006|2007|11/28/2006|1 Closed|Philippines|
|P075464|2006|2008|10/23/2007|2 Closed|Philippines|
|P075464|2006|2008|4/18/2008|3 Closed|Philippines|
|P075464|2006|2009|9/29/2008|4 Closed|Philippines|
|P075464|2006|2010|7/8/2009|5 Closed|Philippines|
|P075464|2006|2010|6/9/2010|6 Closed|Philippines|
|P075464|2006|2011|4/23/2011|7 Closed|Philippines|
|P075464|2006|2012|4/23/2011|7 Closed|Philippines|
|P075464|2006|2021|7/10/2020|8 Closed|Philippines|
|P075502|2005|2006|6/19/2006|1 Closed|Venezuela,Republica|
|P075502|2005|2007|12/30/2006|2 Closed|Venezuela,Republica|
|P075502|2005|2007|6/25/2007|3 Closed|Venezuela,Republica|
|P075502|2005|2008|12/27/2007|4 Closed|Venezuela,Republica|
|P075502|2005|2008|6/12/2008|5 Closed|Venezuela,Republica|
# **Acknowledgments**

The authors wish to thank Patrick McEwan for contributing his advice and knowledge throughout the project’s duration, and to Hank Levin, John Strand, and Howard White who provided views and comments on an earlier draft.

Aliza Marcus and Joost de Laat graciously facilitated access to the contact list of Strategic Impact Evaluation Fund (SIEF) subscribers. We are grateful to the 400+ survey respondents from that list. The authors are grateful for feedback from Joy Behrens, Diana Epstein, David Evans, and Emmett Keeler in developing that policymaker survey, and for the assistance of Holly Blagrave in its implementation.

The authors also thank those who met with them for telephone, in-person, or email interviews, including Juan Belt, Logan Brenzel, Annette Brown, Laura Chioda, Joost de Laat, Shanta Devarajan, Markus Goldstein, Penny Hawkins, Sarah Lane, Ariana Legovini, Ruth Levine, Gideon Lukens, Manny Jimenez, Temina Madon, Meghan Mahoney, David McKenzie, Jack Molyneaux, Owen Ozier, Jyotsna Puri, Dan Rosenbaum, Adam Ross, Justin Sandefur, Lyn Squire, Miguel Szekely, Caitlyn Tulloch, Edit Velenyi, Damian Walker, Howard White, and Keith Wood.

The authors also wish to thank editors of the top seven journals that publish impact evaluations in international development who participated in the Journal Editor Survey— _American Economic Journal: Applied Economics; Economic Development and Cultural Change; Journal of Development Economics; Journal of Development Effectiveness; World Development; Quarterly Journal of Economics;_ and _World Bank Economic Review_ . Maria MacDicken assisted in implementing the survey of journal editors.

Erik Bloom, Marie Gaarder, Richard Scobey, Mark Sundberg, and Nicholas York provided insights and encouragement throughout. Yunsun Li and Karol Acon Monge provided excellent research assistance. Yezena Yimer gave unflagging administrative and clerical support.

This work was originally used as an unpublished manuscript developed under the auspices of the Independent Evaluation Group (IEG) with financial support of the government of Sweden, for which we are especially grateful.

The findings, interpretations, and conclusions are the authors’ own and should not be attributed to the World Bank, its Executive Board of Directors, or any of its member countries.

vi
16 Using household and individual panel data, the authors further explore the relationship between agricultural commercialization, welfare and nutritional outcomes. Conventional wisdom suggests that the more farmers commercialize their operations through increased product‐market orientation, the better off they can become. Greater market‐orientation of agriculture would therefore be expected raise incomes, improve consumption, and enhance nutritional outcomes in rural households (von Braun and Kennedy, 1994).

The authors find little evidence of this in the three countries studied, underscoring that many factors other than the degree of agricultural commercialization intervene in shaping nutritional outcomes, including other agriculture related factors. The articles in the 2015 Journal of Development Studies Special Issue Vol 51, Issue 8 guest‐edited by Carletto et al. (2015), explore some of them, such as the role of diversity in crop production and livestock ownership. Livestock seems to emerge as particularly important and positively linked to nutrition, with crop production and diversity of production positively associated in certain contexts. Bio‐fortification also pays off. Given the current emphasis on nutrition‐sensitive agriculture, a better understanding of the transmission channels between crop choice, agricultural market engagement and nutritional outcomes continues to be a research priority.

_There is substantial excess seasonality in food prices._ Although it is commonly accepted that seasonality permeates African livelihoods, surprisingly little attention is paid to it. Because of the seasonal nature of agricultural production, one area where seasonality manifests itself is in food prices. But there is also very little systematic evidence on the extent of food price seasonality, and what is available is largely dated. Better integration of domestic food markets today may explain part of the neglect. Gilbert, Christiaensen and Kaminski (2017) conclude that “while we all know about seasonality, it is very unclear precisely what it is we know.” The authors show that seasonality in staple crop prices can be substantial. This is achieved using trigonometric and sawtooth models to overcome some of the systematic upward bias in seasonal gap estimates from the common monthly dummy variable approach, which arises especially in shorter samples of 10‐15 years and which has so far gone unnoticed.<sup>14</sup> Maize prices in the 193 markets from the seven African countries studied are on average 33 percent higher during the peak months than during the troughs. For rice the price gap is on average 16.5. These seasonal markups are two and a half times larger than in the international reference markets. Seasonality varies substantially across markets, but in virtually none of them is it lower than in the reference markets. Seasonality does not explain much of overall price volatility over the year.

> 14 Unlike most of the studies reported here, the main data sources for this study are not the LSMS‐ISA household surveys, but rather price data obtained in key markets across the seven countries covered.
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14