rafmacalaba/datause-encoder-data huggingface dataset:
<u>Table 10. Effects on NAPE 2012, Parametric Estimation</u>

||OLS||<br>Left-censoring at|<br>Tobit E|<br>stimates|<br>Right-censoring a|<br>t|
|---|---|---|---|---|---|---|---|
|||5 Percentile|10 Percentile|15 Percentile|85 Percentile|90 Percentile|95 Percentile|
||Treatment-<br>Control<br>(1)|Treatment-<br>Control<br>(2)|Treatment-<br>Control<br>(3)|Treatment-<br>Control<br>(4)|Treatment-<br>Control<br>(5)|Treatment-<br>Control<br>(6)|Treatment-<br>Control<br>(8)|
|B. Test scores results||||||||
|Math (official)|0.067|0.066|0.063|0.063|0.037|0.055|0.061|
||(0.10)|(0.10)|(0.10)|(0.10)|(0.09)|(0.10)|(0.10)|
|Math (independent)|0.127*|0.123|0.139*|0.139*|0.106*|0.095|0.114|
||(0.08)|(0.08)|(0.08)|(0.08)|(0.06)|(0.07)|(0.07)|
|English|0.15|0.150*|0.152*|0.156*|0.162*|0.15|0.15|
||(0.09)|(0.09)|(0.09)|(0.09)|(0.10)|(0.10)|(0.09)|
|Biology|0.074|0.066|0.066|0.073|0.047|0.056|0.068|
||(0.12)|(0.12)|(0.12)|(0.13)|(0.11)|(0.11)|(0.11)|
|Region Fixed effects and baseline controls|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Number of obs.|2249|2249|2249|2249|2249|2249|2249|

Note. Coefficient of impact. Columns (2)-(4): Tobit estimation, with different censoring 27
Imp Imp 0 = —_ ~~+>)~~ x ~~——~~ 4+ 1] Xx Dom ( <u>Qo gimp= 24</u> ) ~~17a~~ <u>ay</u> ) QyDom Dom Imp Imp 0 ( ~~-—_, >)~~ x ~~—~~ +1) xa =| (c ~~Q~~ <u>gimp</u> ~~-Q,~~ ) <u>1-a@,ay</u> ) 1 o = ~~(at~~ KAM)(a@1/0.5) XK ~~1-a,——~~ +1) XY 1+t ay

t>0 <!-- Start of picture text --> 1<br><!-- End of picture text --> t=0 1 Dom _ <u>(21__Dom — 20Dom 1</u> Dom 1 = ( <u>qpom</u> ~~Ta~~ t DX Po Dom

> pom = (—t x A/S) x ~~1-a—~~ 0 + 1) x QUTM ~~Qi~~ Dom ~~Qo~~ Dom = it yy J(@0/0.5) yy ~~1740~~ epem ao

> Aprom ~~pom~~ —~— ~~pPom_pDom~~ pbom _~ tx ~~A6@o/0.5)~~ itu ye ~~Lao~~ ao

<u>ApimP pDom_pDom</u> ~~———~~ — ~~1+~~ <u>pimp</u> ~~__°_~-~~ ppom {¢
the peak is closer to 20 ̊C.

Figure 2 displays the estimated relationship between the probability of choosing a crop and annual precipitation measured in mm/mo. The mean annual precipitation in Latin America is 118 mm/month. The probability of choosing maize and potatoes declines precipitously as one moves from dry to wet farms. By contrast, moving from dry to wet farms increases the probability of selecting fruits, rice, and squash. Wheat and soybeans exhibit a hill-shaped pattern. They are less likely to be picked in very dry farms, more likely to be picked in moderately dry farms, and then less likely to be picked in wet farms. The peak condition for wheat and soybeans is around 70 mm/mo which is well below the average precipitation in Latin America.

# **5. Climate scenarios**

In this section, we simulate the consequences of climate change using the parameter estimates in the previous section. We examine a set of climate change scenarios predicted by AOGCMs. The climate scenarios reflect the A1 SRES scenarios from the following three models: the Canadian Climate Center (CCC) scenario (Boer et al. 2000), Centre for Climate System Research (CCSR) scenario (Emori et al. 1999), and the Parallel Climate Model (PCM) scenario (Washington et al. 2000). We use country level climate change scenarios in 2020, 2060, and 2100 from each climate scenario. The change in temperature predicted by each climate model is added to the baseline temperature in each district. The percentage change in precipitation is multiplied by the baseline precipitation in each district. This gave us a new climate for every district in Latin America for each scenario.

Table 2 summarizes the climate scenarios of the three models for the years 2020, 2060, and 2100. The models predict a broad set of scenarios consistent with the range of outcomes in the most recent IPCC (Intergovernmental Panel on Climate Change) report (Houghton et al. 2001). In 2100, PCM predicts a 2 ° C temperature increase in Latin America 8
which is a renewable resource that is rendered nonrenewable and thus may become biologically or commercially extinct over time. Actually, according to technical assessments conducted by IMROP (2006) and CECAF (2010),<sup>36</sup> several species are now under serious threat of being over-fished in Mauritania. Under the given assumptions, in 2013 the value of Mauritania’s fisheries sector is estimated at roughly US$10 billion and it is being depleted at an estimated annual rate of US$390 million.

**Table 1: Exploitation of Fishery Resources in Mauritania by Species**<sup>**37**</sup>

||**Fishing product**|**Total Catch, 2005-2010**|**Diagnosis**|
|---|---|---|---|
|Cephalopodes|Octopus|~ 30mln tons|overexploited|
||Cuttlefish|~ 7mln tons|fully exploited|
|Non-|Black Hake (cod-like)|~ 12mln tons|moderately exploited|
|cephalopodes|Sardine (stock C)|~ 5mln tons|moderately exploited|
||Sardine (round)|~ 1.5mln tons|overexploited|
||Sardine (flat)|~ 1.3mln tons|under-exploited|
||Jack mackerel (black + Atlantic)|~ 1mln tons|overexploited|
||Mackerel|~ 0.6mln tons|fully exploited|
||Thiof (white grouper)|~ 12mln tons|overexploited|

# **<u>Pastureland</u>**

**Though smaller than fisheries, pastureland for livestock also represents a significant share of natural wealth, and is a more important source of livelihoods for poor Mauritanians.** Mauritania’s pastureland is valued at US$7 billion, or roughly US$2,000 per capita, and accounts for approximately one-fifth of the country’s total natural capital. This sector is particularly important for job creation and food security, especially among the poor and members of vulnerable groups, and it is highly sensitive to rainfall fluctuations and the effects of climate change. Over half of the Mauritanian population derives its exclusive income from livestock<sup>38</sup> . It should be noted that the contribution of pastureland to the natural wealth stock is likely to be underestimated in this analysis, as the methodology sums the value of individual livestock products (milk, meat, leather, etc.), but is unable to capture the totality of production. Meanwhile, the value of Mauritania’s cropland could not be estimated due to the absence or unreliability of key data.

# **<u>Iron Ore</u>**

> 36 CECAF is the UN Food and Agricultural Organization’s Fisheries Committee for the Eastern Central Atlantic.

> 37 Source: IMROP

> 38 See ‘’ Amélioration des Interventions des projets du FIDA dans le secteur de l’élevage en Afrique Sahélienne’, International Fund for Agricultural Development (IFAD), Rome, 2007

15
Policy Research Working Paper 4954

# **Abstract**

This paper provides a snapshot of Mauritania’s labor market using data from the 2004 national household survey. The results show that the labor market is characterized by lower participation rates, lower employment-to-population rates, and relatively higher unemployment rates than in neighboring countries. The non poor fare better in the labor market than the poor. Although the labor force participation of the poor is higher than that of the non poor, the poor display a higher unemployment rate and a lower employment rate than the non poor. The data also suggest a negative correlation between wage employment and poverty. Substantial differences in labor market indicators emerge when disaggregating the analysis by gender and age-group. Female non-participation is extremely high. Women systematically earn less than men independently of their sector and type of employment and controlling for other factors, such as education. Young adults face considerable difficulties in entering the labor market: more than half of the population aged 15–24 is neither studying nor participating in the labor force. As gender disparities remain important for similar levels of education, more work is needed to understand whether cultural factors may prevent women from entering the labor market. Concerning young adults, future poverty reduction strategies need to pay more explicit attention to the promotion of employment through informed labor market policies.

This paper—a product of the Africa Technical Families , Poverty Reduction and Economic Management Network (Africa Region) and the Social Protection Team, Human Development Network—is part of a larger effort in the networks to analyze the labour markets of low-income countries. Policy Research Working Papers are also posted on the Web at http:// econ.worldbank.org. The authors may be contacted at npontara@worldbank.org and msanchezpuerta@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
Table 6: Export impacts of BRI (change relative to baseline in 2030)

||BRI|LBD|BRI|UBD|BRIU|BBD|BRI|UBBT|BRIU|BBTPD|
|---|---|---|---|---|---|---|---|---|---|---|
||%|$bn|%|$bn|%|$bn|%|$bn|%|$bn|
|World|0.7|233.1|1.7|564.8|2.4|776.6|2.9|970.1|3.6|1200.9|
|BRI Area|1.6|247.6|2.8|437.8|4.4|684.7|5.9|915.2|6.9|1070.1|
|Cambodia|6.0|2.7|5.6|2.5|10.9|4.9|10.9|4.9|12.0|5.3|
|China|1.5|84.1|2.7|147.8|3.7|206.6|4.6|253.5|6.3|348.2|
|Indonesia|-0.2|-1.3|1.4|7.5|1.4|7.5|2.2|12.0|2.5|13.7|
|Lao PDR|1.9|0.4|1.5|0.3|27.0|5.1|27.4|5.2|28.1|5.3|
|Malaysia|11.2|56.2|12.4|62.2|12.8|64.5|13.2|66.5|13.5|67.9|
|Philippines|0.6|1.1|2.5|4.9|1.8|3.7|2.4|4.8|3.0|5.9|
|Thailand|4.2|22.0|14.9|77.4|17.8|92.4|18.3|95.1|19.0|98.4|
|Vietnam|2.2|10.3|2.0|9.4|1.1|5.2|3.2|14.8|3.8|17.4|
|Rest of East Asia|-0.7|-5.1|1.1|7.8|0.8|5.4|0.9|6.0|0.7|4.7|
|Bangladesh|7.4|7.6|8.7|8.8|9.7|9.9|22.8|23.2|24.5|25.0|
|India|1.6|17.5|2.9|31.0|3.6|38.0|8.9|95.7|10.8|115.8|
|Nepal|-0.3|0.0|-0.2|0.0|30.2|3.4|43.1|4.8|43.4|4.8|
|Pakistan|8.6|6.7|9.8|7.7|12.7|9.9|20.7|16.2|22.5|17.6|
|Sri Lanka|0.4|0.2|1.0|0.5|1.1|0.5|4.5|2.0|6.1|2.8|
|Rest of S. Asia|2.4|0.3|3.2|0.4|19.3|2.2|26.9|3.0|27.4|3.1|
|EU ECA|-0.3|-2.9|-0.7|-5.6|-0.6|-5.1|0.2|1.7|0.0|0.3|
|Russian Federation|1.0|8.5|1.2|9.6|3.3|27.2|4.8|39.5|6.1|50.3|
|Poland|-0.2|-0.7|-0.4|-1.3|-0.7|-2.2|-0.1|-0.3|-0.1|-0.4|
|Rest of E. Europe|0.0|0.0|0.8|1.7|2.0|4.5|2.6|5.7|2.5|5.6|
|Kazakhstan|0.8|1.4|1.1|2.1|22.6|42.9|23.6|44.8|23.7|44.9|
|Kyrgyz Republic|5.4|0.9|5.4|0.9|36.8|5.8|37.4|5.9|36.9|5.8|
|Rest of C. Asia|2.4|3.3|2.6|3.5|8.9|11.9|10.3|13.8|10.5|14.1|
|Iran,Islamic Rep.|1.5|2.5|4.2|7.0|7.5|12.4|15.4|25.5|16.1|26.7|
|Egypt,Arab Rep.|-0.2|-0.3|-0.2|-0.4|-0.5|-0.8|1.8|3.0|2.7|4.5|
|Turkey|6.0|20.9|5.8|20.1|6.4|22.1|8.2|28.5|8.6|29.7|
|Rest of MENA|0.5|10.8|1.4|31.5|4.6|105.7|5.9|134.1|6.4|146.9|
|Kenya|0.3|0.1|0.2|0.1|1.9|0.7|8.0|3.0|9.0|3.3|
|Tanzania|2.4|0.7|2.5|0.7|2.6|0.7|8.4|2.3|9.5|2.6|
|Non-BRI Area|-0.1|-14.5|0.7|127.1|0.5|91.9|0.3|55.0|0.7|130.7|
|Ethiopia|2.4|0.4|3.9|0.7|12.2|2.1|11.9|2.1|12.2|2.1|
|Rest of SSA|0.3|2.9|0.8|8.4|1.0|11.3|0.9|10.0|1.1|12.6|
|Lat. Am. & Car.|-0.1|-2.5|-0.5|-10.5|-0.6|-14.0|-0.7|-15.9|-0.7|-14.5|
|United States|-0.6|-16.1|3.4|97.9|2.7|76.6|2.5|70.8|3.2|91.9|
|Rest of High-inc.|0.0|1.6|1.4|53.9|1.2|46.2|0.9|35.8|1.4|52.5|
|Rest of W. Eur.|0.0|-0.7|-0.3|-23.3|-0.4|-30.2|-0.6|-47.8|-0.2|-13.9|

- 28 -
Jayanthi, H. and Husak, G. 2013. A probabilistic approach to assess agricultural drought risk, background paper prepared for GAR 2013 available at <u>http://www.preventionweb.net/english/hyogo/gar/2013/en/bgdocs/Jayanti%20and%20Husak,%202 012.pdf</u> Kiratu, S., Marker, L., and Mwakolobo, A. 2011. _Food Security: The Tanzanian Case_ , International Institute for Sustainable Development, Winnipeg, Canada.

Legesse, G. and Suryabhagvan, K. V. 2014. _Remote Sensing and GIS based agricultural drought assessment in East Shewa Zone, Ethiopia_ , Tropical Ecology 55(3): 349‐363, 2104 Mahul, O. and Stutley, C. J. 2010. _Government Support to Agricultural Insurance, Challenges and Options for Developing Countries_ . The World Bank, Washington, D.C.

Mortgatt, C. P., Pane, S., Boissonnade, A. C. and Alex, B. 2012. _Developing Agricultural Risk Microinsuranc Products for Mozambique_ , 4<sup>th</sup> International Disaster and Risk Conference, Davos, Switzerland, 26‐30 August 2012 Mude, A., Barrett, C. B., Carter, M. R., Chantarat, S., Ikegami, M. and Mcpeak, J. 2009. _Index Based Livestock Insurance for Northern Kenya’s Arid and Semi‐Arid Lands: the Marsabit Pilot_ , International Livestock Research Institute, Ethiopia Oxfam America 2013. _R4 Rural Resilience Initiative_ Prakash, A. (ed) 2011. _Safeguarding Food Security in Volatile Global Markets_ , FAO 2011 Rosegrant, M.W. and the IMPACT Development Team.2012. _International Model for Policy Analysis of Agricultural Commodities and Trade (IMPACT): Model Description_ . International Food Policy Research Institute (IFPRI), Washington, D.C.

Save the Children and Oxfam 2012. _A Dangerous Delay, the cost of late response to early warnings in the 2011 drought in the Horn of Africa_ , Joint Agency Briefing Paper, Save the Children & Oxfam Seaman, J., Clarke, P., Boudreau, T. and Holt J. 2000. _The Household Economy Approach_ , Save the Children Senay, G. 2004. _Crop Water Requirement Satisfaction Index Model Description_ , available at <u>http://iridl.ldeo.columbia.edu/documentation/usgs/adds/wrsi/WRSI_readme.pdf</u> Senay G., Verdin, J. and Rowland, J. 2011. _Developing an Operational Rangeland Water Requirement Satisfaction Index, International Journal of Remote Sensing_ , Vo. 32, #21, 2011 Sivakumar, Mannava V.K., Raymond P. Motha, Donald A. Wilhite and Deborah A. Wood (Eds.). 2011. _Agricultural Drought Indices_ . Proceedings of the WMO/UNISDR Expert Group Meeting on Agricultural Drought Indices, 2‐4 June 2010, Murcia, Spain: Geneva, Switzerland: World Meteorological Organization. AGM‐11, WMO/TD No. 1572; WAOB‐2011. 197 pp.

Smith, V. H., Watts, M. (2009), _Index Based Agricultural Insurance in Developing Countries: Feasibility, Scalability and Sustainability_ , Monograph published electronically by the Bill and Melinda Gates Foundation, 45 pp.

23
# 1. Introduction

High inflation has been one of the key macroeconomic challenges facing Egypt over the past few years. Indeed, following a decade of low one-digit level, CPI inflation gathered momentum in the end of 2003 and once again in 2007 before accelerating in 2008 to peak close to 24% in August. Headline inflation is driven by inflation in food prices which have the largest weight (around 40%) in the average household consumption basket. In fact, food price inflation has been much higher than CPI inflation, remaining in the double-digit level since January 2008.

As these domestic increases have been often accompanied by spikes in international food prices, it has been claimed that the latter has been their main driving factor, especially that the food consumption basket has a large imported component, e.g. Egypt is the largest importer of wheat in the world (FAO, 2011). Naturally, more frequent rounds of international food price increases could increase Egypt‟s vulnerability to high domestic inflation.

The objective of this paper is to examine whether domestic inflation spikes in Egypt during the period 2001-2011 were primarily the result of external food price shocks. In addition, other fundamental questions addressed here are: (i) Have surging domestic food prices spilled over into core inflation or are other factors at play? (ii) Is the response of Egypt‟s domestic inflation symmetric for positive and negative international price shocks?

While a vast literature has examined the determinants of inflation in Egypt (Kheir-El-Din, 2009 and Kiguel and Okseniuk, 2009), only a few studies have focused on the role of international prices, mostly in panel and multi-country studies (World Bank, 2011; Albers and Peeters, 2011; and Crowley, 2010). Other work has focused on estimating the long-run relationship (El-Sakka and Ghali, 2005). Only one study has provided evidence on the spill-over effect of domestic food shocks to other prices in Egypt (FAO, 2009).

This paper contributes to the literature on international food price pass-through effects using two complementary methodologies. The first is a 2-step regression (IMF, 2008a and 2011) that produces longrun estimates of pass-through effects of international price inflation to domestic price inflation and was never applied to the Egyptian case; and the second is a traditional VAR approach that allows estimating the short-run effects and examining the relative importance of different sources of inflation.

Our empirical evidence confirms the widely held view that inflation is due in the short-term to external shocks whereas the effect of the shock seems to dissipate in the long-run. The empirical results can be summarized as follows: (i) in the long-run, pass-through to domestic food inflation is low and lies between 13% and 16% and the spill-over from domestic food inflation to core inflation is moderate; (ii) in the short-term, pass-through is high and is estimated around 29% after 6 months and around two-thirds 2
The decompositions suggest somewhat different stories for the rural and urban samples (see summary results in Table A-2.3).<sup>10</sup> Among rural households, increasing _returns_ over time had as strong an impact on the observed consumption growth as did changes in household and location characteristics. Among urban households, changes in _characteristics_ played a larger role than that in returns or coefficients on the aggregate.<sup>11</sup> Changes in returns to household size, other demographic variables, land ownership and geographic location contributed more to the consumption growth of rural than urban households. The fact that a rise in returns to endowments played a significant role in rural poverty reduction suggests an improvement in the economic environment in rural areas.

Among household endowments, changes in household size and education of household members contributed the most to consumption growth. The role played by reduction in household size is consistent with the finding in World Bank (2008) – that if household size had not changed between 2000 and 2005, poverty reduction would have been almost half of what it actually was. This is similar to Wodon’s (1999) finding for the 19883-1996 period that ‘ _changes in the returns to demographic variables account for the lion’s share of the change in per capita consumption over time_ ’ (page 13). The effect of an increase in education endowments was particularly strong for urban households but the returns are negative for both rural and urban areas. In other words while a shift to higher levels of education is associated with improving welfare, the overall returns to education at each grade level appears to have declined. This is in contrast to Wodon’s

> 10 More detailed results – including decomposition results for each variable – are available in an Addendum to this paper, which can be provided upon request.

> 11 These results are similar (but not identical) to those obtained by Serajuddin et al (2007), using the same datasets, but with some differences in specifications.

17
- <u>Step 3.</u> The Facility rapidly responds to pandemics on day-zero . In ‘war time,’ the Facility is activated, and its management can draw on the credit line to execute its mission to purchase a portfolio of health products for low- and middle-income countries.<sup>11</sup> Free or subsidized health products are then allocated to eligible countries according to an agreed rule. The allocation of investment across the health products would be determined based on the nature of the pathogen and guided by an independent panel of experts. For instance, if the timeline to develop a vaccine is slower than during COVID-19, early access to tests and treatments would be useful.

An (optional) feature could be that low- and middle-income countries would have the option to buy directly from the Facility at cost, like the COVAX self-financing participant (SFP) facility. Funds from these sales would be used to repay loans made by the credit line.

Table 3 summarizes the key features of the proposed Facility. The experience of ACT-A provides several lessons learned that motivate these features:

- <u>Credit line size: The credit line size could target $20 billion in 2020 dollars, comparable to</u> the amount ACT-A raised within the first two years of the COVID-19 pandemic. Donors have shown they are willing to provide this amount to benefit low- and lower-middleincome countries during a pandemic, if not before. Indexing the size of the credit line to global inflation will ensure its purchasing power does not decline over time. Low- and middle-income countries could contribute additional funds, for instance the amount they spent on health products during COVID-19 without the support of donors.

- <u>Credit risk management:</u> There are four options to manage the credit risk, either separately or in combination.

- <u>Option A: Donor countries assume risk through legally binding pledges.</u> Even if donors pledge in advance to assume the liability from the credit line, they may not

> 11 CEPI has raised over $1.5 billion to fund research and development of new vaccines at the onset of the next pandemic, a key component of the 100 days mission. The Facility would be complementary, as it would fund sizable purchases of the vaccines once they have been discovered, incentivizing firms to scale up production capacity.

15
# **II. Methodology and Data**

Empirical Model and Methodology

## **Statistical matching techniques, also known under the term data fusion, require at least one donor**

**and recipient dataset and overlapping variables.** Traditionally, in statistical matching, there is a donor dataset that contains information one wants to add to a second dataset lacking this information (here denoted as variable C). The second dataset often goes by the name recipient dataset and has information on a second variable of interest not included in the donor dataset (here denoted as variable I). Moreover, for statistical matching techniques to work, there needs to be a set of overlapping variables X, also often called matching variables. Overlapping (or matching) variables are variables that are included in both the recipient and donor datasets. Consequently, they facilitate the matching of both datasets.<sup>7</sup> Ideally, one would have a third dataset, which has information on all variables under consideration. This dataset is often called auxiliary data. If there is no such data available, then the data fusion relies on the assumption of conditional independence (Donatiello et al., 2016b). The assumption of conditional independence means that the overlapping variables X fully explain the relationship between C and I, a strong assumption that often does not hold in practice (D’Orazio et al., 2006).

## **There are several possibilities to address the assumption of conditional dependence in the absence**

**of auxiliary data (Donatiello et al., 2016b).** First, previous estimates on the relationship between C and I might be available (for example, correlation coefficients between C and I). Is it then possible to use these estimates to avoid the assumption of conditional independence. In the absence of such data, one can rely on at least one matching variable that is highly correlated with the target variables. If X and C are perfectly correlated, we can rely on the regression function in the donor dataset between both variables to predict C in the recipient dataset. While the set of matching variables that are

> 7 Examples are information on the number of household members, the age and gender of household members, or their labor market status.

8