rafmacalaba/datause-encoder-data huggingface dataset:
Also, Chiroma, Abdulkareem and Herawan (2015) found that genetic algorithm and neural network (GA–NN) methods have superior forecasting performances for monthly WTI prices than other benchmark algorithms.

An EMD-based neural network ensemble learning model has performed better than an ARIMA model and other nonlinear methods, for both WTI and Brent prices, in terms of forecast prediction and directional accuracy (Yu, Wang and Lai 2008). Similar results for EMD-based machine learning approaches were found by Xiong et al. (2013), Zhang, Zhang and Zhang (2015) and Ahmad et al. (2021).

Wavelet-based machine learning models have performed better than conventional back propagation neural network models for both level and directional predictive accuracy in WTI oil markets (Jammazi and Aloui 2012).

# **2.1.5 Conclusion from the literature on oil price forecasting**

The following general conclusions can be drawn from the above literature review. _First_ , many studies have empirically established that forecasts of WTI and Brent oil prices based on futures contracts are inferior to several model-based approaches. _Second_ , model-based approaches have generally outperformed other methods. _Third_ , several studies have found that incorporating relevant external regressors and controlling for time series properties embedded in oil prices can improve forecast accuracy. _Fourth_ , machine learning techniques have tended to yield better forecasting performance than traditional benchmarks and univariate methods, but they have been sensitive to the choice of specifications. Their comparisons with model-based approaches have been limited. The few available studies show that, in at least two cases, machine learning techniques have outperformed reduced-form VAR models, but only at the very shortest forecast horizons (Cheng et al. 2019) or up to one year (Mirmirani and Li 2004).

## **2.2 Metal commodity price forecasting**

This literature draws on 20 studies of price forecasts for aluminum (11 studies), copper (14), lead (8), nickel (8), tin (5), and zinc (8). The most evaluated methods are univariate and multivariate time series models—mostly benchmarked against no-change forecasts—although the number of studies based on machine learning techniques is also growing rapidly ( **Figure 2** ). As for oil prices, most studies use sample periods that end before the commodity price collapse of mid-2014 and most examine monthly data. In contrast to oil prices, where studies examining forecast horizons up to one year are common, the most examined forecast horizon for metals prices have exceeded one year.

# **2.2.1 Futures prices**

Like for oil prices, futures prices of metals have underperformed the no-change benchmark, but they have predictive content that can improve model forecasts. In a study of several metals (aluminum, copper, lead, nickel and tin), Chinn and Coibion (2014) showed that the no-change 8
Policy Research Working Paper 10213

# **Abstract**

Unlike many countries, the Arab Republic of Egypt did not experience significant labor market improvements following trade liberalization. This paper investigates why increased Egyptian exports did not directly increase employment. To illustrate the relationship between firm-level exporting and employment, the paper presents a simplified general equilibrium model with two sectors: one able to export and one “reserve” sector. The paper tests the implications of this theory using firm-level data from the World Bank’s Enterprise Surveys in 2013, 2016, and 2020. The firmlevel microanalysis demonstrates that although there is a positive employment response to export expansion, it does not occur at a large enough scale to be felt at the macro level. To seize the benefits of trade, Egypt requires deeper business environment reforms to incentivize large export, labor-intensive sector growth and integrate its economy into global value chains.

This paper is a product of the Poverty and Equity Global Practice. 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://www.worldbank.org/prwp. The authors may be contacted at gacevedo@ worldbank.org, cberg@worldbank.org, and robertson@tamu.edu.

_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
Another important implication of this study is that empirical findings support the usefulness of the conjoint analysis methodology. We introduced the basic methodology of conjoint analysis and how it can be used as a tool to understand consumer preferences in the water and sanitation sector. We have also identified challenges of using the methodology and provided suggestions on how these challenges can be minimized. The primary weakness of the SP approach is often cited to be its hypothetical nature. Respondents are placed in unfamiliar situations in which complete information is not available. At best, respondents give truthful answers that are limited by their unfamiliarity. At worst, respondents give trivial answers due to the hypothetical nature of the scenario. Based on the Sri Lanka study, we feel that respondents were able to process information presented in the conjoint analysis questions and provide insightful answers.

Future microeconomic studies of water and sanitation programs should give more attention to understanding multiple water policy attributes and their affect on consumer demand. While the conjoint methodology requires a more complex experimental design and analysis techniques than contingent valuation methods, the subsequent results allow for more accurate analysis of the real-life water and sanitation situation. Our example has shown one application of conjoint analysis and how surveys prior to program implementation can aid in program design, theoretically improving access and demand of WSS for both poor and non-poor households. _Ex ante_ analysis of consumer preferences would also be valuable for project design and evaluation. With concerns over access and affordability of poor households, another use of conjoint analysis is to evaluate preferences across sub-populations. This information allows service delivery programs to do two things: explicitly target user groups and improve service demand, both lowering transaction costs. The impact of different water policy tools (e.g. subsidies, provision of credit, vouchers, and targeted tariff structures) given preferences and income of customers could be estimated on a case-by-case basis.

18
several neighboring villages. This is most notably the case of the distribution of transfers in temporary and mobile outposts, located in hub localities, which serve an additional function to assist beneficiaries and disseminate information on the program. Hence, program beneficiaries from different neighboring villages can interact in a number of places.

# _Sample Description_

We combine the geo-referenced locality data mentioned above with three of the five rounds of the evaluation survey collected in October 1997 (from the baseline targeting ENCASEH survey), October 1998 (second round of the ENCEL evaluation surveys), and November 1999 (fourth round of the ENCEL surveys).<sup>8</sup> The resulting dataset contains detailed information on the outcomes of children and socioeconomic characteristics of a panel of households that reside within the evaluation localities.

The evaluation survey was intended to cover all inhabitants of the localities under study. However, a small share of the population was not interviewed at baseline, and there were some changes in the village populations so that the total number of households observed in the data is 24,077 in October 1997, 25,846 in October 1998, and 26,972 in November 1999. Some attrition occurred due, in part, to migration out of the villages and, in part, to errors in identification codes that occurred for a few enumerators: 8.4 percent of the 1997 households cannot be followed and matched in all three rounds of the survey. Yet, this is unrelated to the treatment assignment.

At baseline (October 1997), 60 percent of the households in evaluation localities were classified as eligible to receive program benefits. In this paper, we study the schooling decisions of the children of those eligible households.<sup>9</sup> Our main outcome of interest is school enrollment, for which we also use the term “school participation” interchangeably.

8. We have discarded the March 1998 and June 1999 rounds of the survey because we only have information on the roll-out of the program at the end of each year.

> 9. About 12 percent of the households were classified as non-poor at baseline but were later reclassified as eligible. To avoid arbitrary classifications, we exclude those households from our analysis.

11
The proportional change in household income is given by the share of cotton income, θ<sup>_h_</sup> _c_<sup>,</sup> multiplied by the proportional change in prices.

In section 1, we reviewed some evidence indicating that a full liberalization of agricultural markets along the lines of the Doha Round negotiations would bring about an increase in world cotton prices of around 12.7 percent. Similar results are obtained if we use the price increase predicted by Sumner (2004) of 11.6 percent. Using the data for 1998, we can estimate the average welfare effect given by equation (1). These averages, estimated again with a Fan regression, are shown in figure 6.5. We observe that the increase in prices would benefit farmers across the income distribution. The effects would range from over 0.75 to nearly 1.5 percent, at the bottom of the distribution, to roughly 0.5 percent at the top. The unconditional average gain would be around 1 percent of the initial household per capita income.

Figure 6.6 explores the regional differences in cotton gains. The solid line corresponds to the non-parametric average in the Central province; the broken line, to the non-parametric average in the Eastern province; and the dotted like, to the non-parametric averages in the Southern province. The figure shows that larger gains would take place in the Eastern province, particularly at the bottom of the distribution. In the Central province, the gains track the average gains in the Southern province at the bottom of the distribution; from the middle to the top, however, the gains in the Central province remain high, whereas the gains in the Southern province sharply decline with income.

These findings show that a 12.7 percent increase in the price of cotton would cause household income to increase, at most, by 1.5 percent (among the poorest households in the Eastern province, for instance). Although these are positive effects associated with welfare gains, 14
Table 4: Robustness check (3), using alternative services input intensity measures for financial & business services combined (fb)

||(1)|(2)|(3)|
|---|---|---|---|
||Use time-varying U.S.<br>services input intensity|Use countries’ own time-<br>varying services intensity|Use U.K.’s average<br>Services input intensity|
|**Dfb*SIIfb**|**23.941*****|**11.965*****|**30.300****|
||**(6.546)**|**(4.147)**|**(14.332)**|
|TFP|0.064**|0.067**|0.067**|
||(0.028)|(0.029)|(0.029)|
|log(emp)|0.964***|0.944***|0.969***|
||(0.099)|(0.095)|(0.099)|
|SKratio|0.800|0.730|0.746|
||(0.818)|(0.682)|(0.687)|
|K/L|0.070***|0.075***|0.070***|
||(0.026)|(0.027)|(0.025)|
|rwage|0.000|-0.001|0.001|
||(0.009)|(0.008)|(0.008)|
|GVC Participation|2.575***|2.572***|2.556***|
||(0.496)|(0.499)|(0.498)|
|Country*Year FEs|Yes|Yes|Yes|
|Sector*Year FEs|Yes|Yes|Yes|
|Observations|5,686|5,855|5,714|
|R-squared|0.597|0.591|0.593|

Notes: The dependent variable is manufacturing export RCA. Dfb refers to the share of financial & business services value added in GDP. In regression (1), SIIfb is the ratio of the U.S. embodied domestic financial & business services to U.S.’ manufacturing value added (not averaged over years). In regression (2), SIIfb measures countries’ own services input intensity (not averaged over years). In regression (3), SIIfb is the ratio of the U.K. embodied domestic financial & business services to U.K.’ manufacturing value added, averaged over 1995-2007. All WIOD manufacturing sectors 3-16 are covered (not grouped together). U.S. observations are dropped from regression (1) and the U.K. observations are dropped from regression (3). Robust standard errors in parentheses, clustered by country*sector. *** p<0.01, ** p<0.05, * p<0.1.

38
al. 2005) have provided more convincing evidence that class size and perhaps other school inputs improve learning. One policy of particular interest involves decentralizing management decisions to the level of schools rather than national, state, or local bureaucrats. The discussion focuses not only on education—other policies have decentralized provision of public services with mixed results (World Bank 2004).

We examine the impact of a public program which sheds light on both school quality and decentralization debates—Mexico’s Quality School’s program ( _Programa Escuelas de Calidad_ , or PEC). We combine school census data, population census data, and program administrative data to evaluate the impact of PEC on dropout, repetition, and failure. PEC provides US$15,000 five-year grants to about ten percent of all Mexican public primary schools.

PEC started in 2001 with the goals of expanding autonomy and improving learning in Mexican preschools, primary schools, and secondary schools. Participation in PEC entails four activities. First, the staff and parents of a school prepare a plan which outlines steps for improving the school’s quality. Second, schools receive a five-year grant to implement the activities discussed in the school plan. In the first four years, PEC requires schools to spend 80 percent of the grant on supplies, infrastructure, and other physical goods. In the final year, schools must only spend 50 percent of the grant on such goods, and much of the grant funds teacher training and development. Third, PEC involves parent associations in designing school improvement plans, purchasing supplies, and carrying out the plans. Fourth, PEC trains school principals. Mexico requires no formal training of principals, and many principals switched from being teachers to principals without formal transition.

Every Mexican primary school may participate, but PEC targets disadvantaged urban schools through direct mail, radio, and other media. To identify disadvantaged schools, PEC uses a poverty index that the Oportunidades program and Mexico’s National Population Commission (CONAPO) constructed. To identify urban schools, PEC uses 2000 2
# **Contents**

|**1**<br>**Intr**|**oduction**|**1**|
|---|---|---|
|**2**<br>**The**|**Jamaican Study**|**3**|
|2.1|The Intervention and Experimental Design . . . . . . . . . .|. . . . . . . . .<br>3|
|2.2|External Comparison Group . . . . . . . . . . . . . . . . . .|. . . . . . . . .<br>5|
|2.3|Previous Studies<br>. . . . . . . . . . . . . . . . . . . . . . . .|. . . . . . . . .<br>6|
|**3**<br>**The**|**New Survey**|**7**|
|3.1|The Experimental Sample . . . . . . . . . . . . . . . . . . .|. . . . . . . . .<br>7|
|3.2|Non-Stunted Comparison Sample . . . . . . . . . . . . . . .|. . . . . . . . .<br>8|
|**4**<br>**Me**|**thods**|**9**|
|4.1|Treatment Efect Analysis . . . . . . . . . . . . . . . . . . .|. . . . . . . . .<br>9|
||4.1.1<br>Randomization<br>. . . . . . . . . . . . . . . . . . . . .|. . . . . . . . .<br>9|
||4.1.2<br>Permutation Tests<br>. . . . . . . . . . . . . . . . . . .|. . . . . . . . .<br>10|
||4.1.3<br>Baseline Imbalance . . . . . . . . . . . . . . . . . . .|. . . . . . . . .<br>11|
||4.1.4<br>Accounting for Multiple Outcomes<br>. . . . . . . . . .|. . . . . . . . .<br>13|
|4.2|Catch-Up Analysis<br>. . . . . . . . . . . . . . . . . . . . . . .|. . . . . . . . .<br>13|
|**5**<br>**Mig**|**ration**|**14**|
|**6**<br>**Ear**|**nings Results**|**16**|
|6.1|Measurement<br>. . . . . . . . . . . . . . . . . . . . . . . . . .|. . . . . . . . .<br>16|
|6.2|Earnings Densities<br>. . . . . . . . . . . . . . . . . . . . . . .|. . . . . . . . .<br>17|
|6.3|Point Estimates . . . . . . . . . . . . . . . . . . . . . . . . .|. . . . . . . . .<br>17|
|6.4|Attrition of Migrants . . . . . . . . . . . . . . . . . . . . . .|. . . . . . . . .<br>18|
|6.5|Employment and Labor Force Participation<br>. . . . . . . . .|. . . . . . . . .<br>19|
|6.6|Catch-up in Earnings . . . . . . . . . . . . . . . . . . . . . .|. . . . . . . . .<br>19|
|**7**<br>**Pat**|**hways to Earnings**|**20**|
|7.1|Parental Investment<br>. . . . . . . . . . . . . . . . . . . . . .|. . . . . . . . .<br>20|
|7.2|Education . . . . . . . . . . . . . . . . . . . . . . . . . . . .|. . . . . . . . .<br>21|
|7.3|Cognitive and Psychosocial Skills . . . . . . . . . . . . . . .|. . . . . . . . .<br>22|
|7.4|Catch-up in Education and Skills . . . . . . . . . . . . . . .|. . . . . . . . .<br>23|
|**8**<br>**Gen**|**der Diferences**|**24**|
|**9**<br>**Con**|**clusions**|**25**|
|**Refere**|**nces**|**27**|
|**Tables **|**and Figures**|**32**|
|**Appen**|**dices**|**49**|

i
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35 expect a reversal of policies in the near future. The survey evidence provided by European Economy supports this view. If anything, Denmark suffered from a problem of over-confidence. The announcement of the plan was followed by very optimistic expectations about future output developments. Permanent income considerations can thus explain much of the demand boom. In addition, this boom might have been reinforced by effects of the "German view" type, as argued by Giavazzi and Pagano (1990). These demand side effects were reinforced on the supply side by optimism about the _pace_ of disinflation, that led to a fall in both real wages and real interest rates.

One important implication of the present paper is that it provides supporting evidence to the view that the high inflation economies are a group in themselves. In particular, disinflation in these economies is likely to face obstacles that are inherently different from those observed in most industrialized, low inflation countries.
Reducing Hunger with Payments for Ecosystem Services (PES): Experimental Evidence from Burkina Faso Guigonan Serge Adjognon<sup>a,</sup><sup>_∗_</sup> , Daan van Soest<sup>b</sup> , Jonas Guthoff<sup>a</sup>

> _aResearch Department; The World Bank, Washington D.C., USA_

> _bDepartment of Economics and Tilburg Sustainability Center, Tilburg University, Netherlands_

_Keywords:_ `P` ayments for Ecosystem Services, Food Security, REDD+, Reforestation, Burkina Faso _JEL Codes_ : I38, Q18, Q23, Q53

- _∗_ Correspondence: gadjognon@worldbank.org, Tel.: +1-202-473-0222
<!-- Start of picture text --> Figure 8: Flow of Study Participants<br>Potentially eligible participants (N=364) Excluded (N= 64)<br>Not approachable (n= 65) • Went to perform Umrah =2<br>* Wide geographical spread = 35 • Health conditions (age above 70) =<br>*Phone Switched off/did not pick 3<br>Participants contacted and assessed for<br>up=30 • Grant application terminated=2<br>eligibility (N= 299) • Talked to secretary, could not reach<br>participant = 1<br>Consent declined at initial contact=<br>Consented (n= 235) 19<br>Consent declined at baseline = 37<br>• Busy (n=9)<br>• Did not pick up the call (n=9)<br>• Not interested (n=9)<br>Baseline assessment (n= 235)<br>• Not comfortable in disclosing<br>information (n =7)<br>• Hospitalized (n=1)<br>• Death of a family member (n=1)<br>Randomized (n= 235)<br>• Consent decline<br>• Half assessment (n=1)<br>Allocation<br>Allocated to Control= 117<br>Allocated to Intervention= 118<br>Received allocated<br>Enrolment<br>Received allocated intervention n= 75 intervention n= 117<br>Did not receive allocated intervention= 43<br>Conflicting commitments= 25<br>Phone switched off/did not pick up= 5<br>Reasons not known= 3<br>Business closed=1<br>Business transferred = 1<br>Personal issues= 4<br>Death in family= 1<br>Heath issues=2<br>Expected financial assistance=1<br>Post‐Intervention Follow‐Up<br>Completed post‐intervention assessment=104<br>Completed post‐intervention assessment=106<br>Did not complete post‐intervention assessment= 14<br>Did not complete post assessment= 13<br>Did not pick up phone/Busy = 12<br>Did not pick up phone/Busy= 8<br>Consent declined= 1<br>Consent Declined=1<br>Refused = 1 Did not receive grant = 1<br>Health issues=1<br>Business is closed=1<br>Out of city = 1<br>3 months after assessment<br>Completed 3‐month follow‐up assessment = 100<br>Completed 3‐month follow‐up assessment = 102<br>Did not complete 3‐month follow‐up assessment = 16<br>Did not complete 3‐month follow‐up assessment = 16<br>Not available due to busy schedule = 11<br>Not available due to busy schedule = 11<br>Phone switched off = 1<br>Refused to give interview = 2<br>Deceased = 1<br>Phone switched off = 3<br>In jail = 1<br>Family member is hospitalized =1<br>Did not receive grant, refused to give interview = 2<br>Analyzed (n=118) Analyzed Analyzed (n=117)<br>31<br>Eligibility<br><!-- End of picture text -->
<!-- Start of picture text --> Figure 3: Adjusted Statistics: REER and CPI/PPI<br>REER (2005 = 100) CPI / PPI Ratio in Logs<br>Source: BIS, INDEC, PriceStats, and authors' calculations 5.1<br>250<br>4.9<br>200 4.7<br>4.5<br>150<br>4.3<br>Large Adj. CPI / PPI<br>100 4.1<br>Adjusted CPI / PPI<br>3.9 CPI / PPI<br>50<br>3.7<br>Inflation-adjusted REER REER<br>0 3.5<br>1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 1979 1981 1983 1985 1987 1989 1991 1993 1995 1997 1999 2001 2003 2005 2007 2009 2011 2013 2015<br><!-- End of picture text --> Figure 4: Output - Official vs. Private Sector Estimates <!-- Start of picture text --> Economic Activity Index ( 2007m1 = 100 ) 150 Industrial Production Index ( 2007m1 = 100 )<br>Source: INDEC, Elypsis, Orlando J. Ferreres & Asociados Source: INDEC, Orlando J. Ferreres & Asociados<br>210<br>130<br>190<br>170 110<br>150<br>EMAE 90 INDEC<br>130 ICAE OJF<br>IGA<br>70<br>110<br>90 50<br>Figure 5: Agriculture-Nontradables Labor Productivity Differentials<br>4.8 Agriculture/Nontradables Labor Productivity<br>4.7<br>4.6<br>4.5<br>4.4<br>Excl. utilities & transport<br>4.3 Excl. utilities<br>Baseline<br>4.2<br>1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015<br>1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000 2002 2004 2006 2008 2010 2012 2014<br><!-- End of picture text --> 15