rafmacalaba/datause-encoder-data huggingface dataset:
Decarbonization in MENA Countries: An Empirical Analysis of Policy Impacts <!-- Start of picture text --> Impacts<br><!-- End of picture text -->

# **1. Introduction**

<mark>The MENA countries collectively produced about only 5% of global greenhouse gas (GHG) emissions in 2019, a relatively small contribution compared, for example, to East Asia (27%) (L</mark> ienar <mark>d, Brussels International Center, 2022)</mark><sup>1</sup> <mark>(see the caveat at the end of this paragraph). Despite this modest contribution, there are several reasons to consider addressing GHG emissions in the Middle East and North Africa (MENA) region, of high priority. First, MENA’s GHG emissions of 13 tons of CO2</mark> _<mark>per capita</mark>_ <mark>per year are just below North America’s 19 tons and above Europe’s 7.8 tons per capita per year (ibid.). Second, GHG production has been increasing in this region at a breathtaking pace (World Resources Institute, Insights, May 8, 2023). The case is even more dramatic among the Persian Gulf states. Qatar, for example, had the highest GHG emissions per capita in the world, rising by 30% over only 5 years from 2010 to 2015 (L</mark> ienar <mark>d, Brussels International Center, 2022). Third, we will show in this paper that removal of carbon subsidies can be growth enhancing in the long run, even if not in the short run. Fourth, if mitigation strategies involve removal of subsidies, there may be</mark> _<mark>local</mark>_ <mark>externality benefits (besides global ones) such as better health outcomes. This is because industrial processes that use or produce fossil fuels often involve health hazards with attendant impacts on fatalities and worker productivity (e.g., black lung disease in coal mining, and multiple diseases due to air pollution). Air pollution</mark> <mark>has been shown to result in large impacts, “causing more deaths globally than malnutrition, AIDS,</mark>

> 1 Clémentine Lienard “Mitigating climate change in the MENA: shifting to a new paradigm” Brussels International Center, March 2022.

2
Additionally, figure 5 below shows that being further away from the marketplace is associated with lower maize prices. It charts maize prices by distance to markets and shows that the further the household is from the market, the lower the farm-gate price. This suggests that a significant share of the difference between farm-gate prices in a given period and area for a homogenous good such as maize (henceforth referred to as price dispersion) can be accounted for by the distance of the household to the local marketplace. Additionally, figure 6 presents the coefficient of variation (a measure of price dispersion being the ratio of the standard deviation to the mean) by distance to market quartiles and by season. It shows that price dispersion increases with distance from the marketplace. So households that are further away from the market experience larger price fluctuations than those near to the market in both the harvest and lean seasons. It also shows that price dispersion is lower during the harvest season irrespective of distance to market.

_Figure 5 Figure 6_ <!-- Start of picture text --> Coefficient of Variation by<br>400<br>Distance to Market and Season<br>350 Lean Season<br>Harvest Season<br>300 25%<br>250 20%<br>200 15%<br>150 10%<br>5%<br>100<br>0 50 100 0%<br>Distance to market (in km.) 1st 2nd 3rd 4th<br>jan-feb mar-apr may-jun Quartile Quartile Quartile Quartile<br>jul-aug sep-oct nov-dec Distance to Market<br>Price of maize grain (in Tsh.)<br>Coefficient of Variation<br><!-- End of picture text --> Source: Author‟s computation using Tanzania Household Budget Survey 2007 To further explore the role of search costs and distances to markets and test our first hypothesis, we test a model of the determinants of maize prices using OLS regression. We commence with a simple univariate model: 13
- RPCPBR - Ratio of corn price to the price of broilers (computed from PAFC and PBR)

- RPCPP - Ratio of corn price to the price of pork (computed from PAFC and PPORK)

- RPF - PF/GNPD

- RPMEAT - Real price of meat (computed from PMEAT and GNPD) RPPORK - Real price of pork (computed from PPORK and GNPD)

- RPYD - Real disposable income per capita (computed from YD, GNPD, and POP)

- RRELFORC = Real release price for corn (computed from RELFORC and GNPD)

- RRS - Real interest rate (computed from RS and GNPD)

- RS - Three month U.S. Treasury-bill rate (percentage points) from the Fair model

- RSGP - Real U.S. federal deficit (computed from SGP and GNPD) RSPC - Real corn support price (computed from SPRC and GNPD)

- RSPCHIGH - Qualitative strength of corn support price (computed from RSPC, RPAFC, and COMPFG)

- RSPFORC - Real farmer owned reserve support price (computed from SPFORC and GNPD)

- RSPFORPC - Strength of corn government support (computed from RSPFPC and KFORFGE)

- RSPFPC - Ratio of support price for corn to the market price (computed from SPRC and PAFC)

- RWPRDFG - Feed grain production in the rest of the world (computed from WPRDFG and PRDFGA)

- SGP - U.S. federal deficit from the Fair model

- SPFORC - Support price for farmer owned reserve corn in dollars per bushel (Feed: _Outlook 6 Situation Report)_

- SPRC - Regular CCC support price of corn in dollars per bushel (Feed: _Outlook C Situation Report)_

- TCOWKE - Cows & heifers that have calved (cow inventory) in the U.S., 1,000 head (Livestock _& Poultry: Outlook & Situation Report,_ Aug & Mar) Ql - Average, Q2 - July 1, Q3 - Average, Q4 = January 1 of following year

- TIME - Quarterly time indey. with first quarter of 1980 = 320, second -

- quarter of 1980 321, etc.

- TPBEEF - Real border price of beef (computed from RPBEEF and EXR)

- TPBR - Real border price of broilers (computed from RPBR and EXR)

- TPPORK = Real border price of pork (computed from RPPORK and EXR)

- WPRDFG - World production of feed grains (corn, sorghum, oats, barley), million metric tons _(Corn: Background for 1985 Farm Legislation,_ Bulletin 471)

- XBR - Net U.S. exports of broilers (computed from PRDBR, PCDBR, and N) XFG = U.S. exports of feed grains (corn, sorghum, oats, barley), million metric tons (Feed: Outlook and Situation Report)

- XSB - U.S. exports of soybeans, million metric tons

- YD - U.S. disposable income in billion dollars from the Fair model

- YEAR - Two digit year

- LYR - LOG(YEAR)

YRYR - YEARWYEAR The explicit transformations made from basic data to obtain the computed variables used in econumetric estimation are summarized explicitly in Table 2 15
year in all sub-national areas of all five countries (Northern Tanzania is an exception). This simplifies the analysis. Fifth, the countries border each other and are sufficiently similar so that some pooled analysis — i.e. analysis that combines data from all five countries — is reasonable. Sixth, unlike, for example Kenya and Ethiopia, drought-contingent targeting and adaptive safety nets had not been systematically implemented during the period in which the surveys were administered.

The rest of the as follows. In addition to the main vari- paper proceeds describing ables, the next section describes the methodology we use to estimate harvest cycles and droughts. Section 3 provides estimates of the average impacts of droughts across the five countries we study, and then shows that these impacts are more pronounced for the poorest households. Section 4 describes our targeting framework and provides estimates for inclusion and exclusion errors for logistic regressions, decision trees, and decision tree ensembles (random forests and gradient boosted trees). We conclude by summarizing our results and discussing future directions.

# **2 Measuring Shocks and Outcomes in Agrarian Contexts**

## **2.1 Child Anthropometrics as a Proxy for Child Well-Being**

The first two Sustainable Development Goals (SDGs) focus on ending poverty and eliminating hunger across the world.<sup>7</sup> Cognizant of the limitations of defining poverty exclusively in monetary terms (Sen (1999), Deaton (2016)), the first goal also targets poverty in a broader sense — including non-monetary and dynamic measures. While goal 1.5 specifically emphasizes the need to " _build the resilience of the poor and those in vulnerable situations and reduce their exposure and vulnerability to climate-related extreme events_ ", precisely how this target may be measured in practice remains unclear.

Closely related, the second goal concerns ending hunger and malnutrition. Objective measures of hunger rely on caloric intake which may not be strongly correlated with nutrition. Therefore, Deaton (2010, pg. 41) recommends measuring anthropometrics, but highlights a key data limitation.

The obvious alternative is to use the anthropometric measures directly. Here there has been enormous progress, through the spread of the Demographic and Health Surveys...These surveys are as close to a gold standard as we are going to get in this area, although the irregularity of the DHS surveys makes it difficult to use them for monitoring, for example for assessing the effects of the food price crisis on the heights and weights of children.

Our study may be viewed as an attempt to address this limitation by combining existing Demographic and Health Survey (DHS) data with remote-sensed information. The

> 7https://www.un.org/sustainabledevelopment/sustainable-development-goals/

5
Tables and Figures

# **Table 1: Key words used in the searching process**

|Behavior|Forced-choice|Personality|
|---|---|---|
|Big Five|Future skills needs|Personality traits|
|Cognitive abilities/skills|Heterogeneous ability|Rate of return|
|Competences|Human capital|Situational strength|
|Demand for schooling|Intelligence|Social skills|
|Demand for skills|Labor demand|Soft competencies|
|Emerging competencies|Locus of Control|Soft skills|
|Employability|Management|Test scores|
|Employer survey|Non-cognitive abilities/skills|Training and education|
|Entrepreneurship|Occupational choice|Transferable skills|
|Five-Factor model|Performance|Work performance|

Table 2: Summary statistics of countries in the sample, 2008

|Country Name|GDP per<br>capita<br>(constant<br>2005 US$)|GDP (millions<br>of constant<br>2005 US$)|Population<br>total<br>(millions)|Exports<br>of goods<br>and<br>services<br>(% of<br>GDP)|Research &<br>development<br>expenditure<br>(% of GDP)|Employment<br>in industry<br>(% of total<br>employment)|Labor<br>force with<br>secondary<br>education<br>(% of total)|Labor<br>force with<br>tertiary<br>education<br>(% of<br>total)|
|---|---|---|---|---|---|---|---|---|
|East Asia & Pacific|4905|10,517,444|2,144|35|2.50|24|..|..|
|Europe & Central Asia|19047|16,691,987|876|39|1.73|27|46|29|
|European Union|28626|14,288,628|499|39|1.84|27|49|25|
|Latin America & Caribbean|5202|2,956,473|568|27|0.63|22|30|16|
|Middle East & North Africa|4359|1,528,350|350|52|..|25|..|..|
|Sub-Saharan Africa|889|690,904|776|35|..|..|..|..|

31
Table B12: Summary of absolute variance contributions, alternative choices of _π_ , children attending school only

|||Ke|nya<br>|Tan|zania<br>|Uga|nda<br>|
|---|---|---|---|---|---|---|---|
||π|σ.|(s.e.)|σ.|(s.e.)|σ.|(s.e.)|
|Individual|0|0.05|(0.000)|-0.04|(0.000)|0.10|(0.001)|
||0.5|0.04|(0.000)|-0.05|(0.000)|0.09|(0.001)|
||1|0.04|(0.000)|-0.05|(0.000)|0.09|(0.001)|
|Household|0|0.48|(0.002)|0.56|(0.002)|0.52|(0.002)|
||0.5|0.36|(0.001)|0.39|(0.002)|0.39|(0.002)|
||1|0.32|(0.001)|0.34|(0.001)|0.35|(0.002)|
|School|0|0.29|(0.002)|0.25|(0.002)|0.31|(0.002)|
||0.5|0.37|(0.002)|0.36|(0.002)|0.40|(0.003)|
||1|0.51|(0.003)|0.55|(0.004)|0.56|(0.004)|
|Sorting|0|-0.07|(0.000)|-0.11|(0.001)|0.10|(0.001)|
||0.5|0.20|(0.001)|0.24|(0.001)|0.25|(0.001)|
||1|-0.12|(0.001)|-0.16|(0.001)|-0.07|(0.000)|
|Residual|.|0.68|(0.002)|0.67|(0.003)|0.73|(0.003)|
|Total|.|0.88|(0.002)|0.90|(0.002)|0.97|(0.002)|

Note: the table sets out the absolute variance contribution (in standard deviation units) attributable to each component of the test score, where ‘sorting’ is the contribution of the between-factor covariance; different initializations of the partitioned iterative algorithm are indicated by column _π_ ; standard errors are reported in parentheses, calculated using the asymptotic approximation due to Ahn and Fessler (2003). Source: own calculations.

57
Table A13: Registered for social security

||(1)<br>Ever Had<br>Formal Job|
|---|---|
|Offered Training|0.02|
||(0.03)|
|Control Mean|0.62|
|Observations|900|

Notes: Offered Training is the coefficient from a regression of an indicator for whether the respondent is registered for social security (meaning that she had a formal job at some point in her life) 17 months after the training on an indicator for being offered the training, controlling for stratification dummies.

Table A14: Results: Mechanisms - Intrapersonal Socio-Emotional Skills

||(1)<br>Emotional<br>Awareness|(2)<br>Self<br>Awareness|(3)<br>Emotional<br>Regulation|(4)<br>Self<br>Control|(5)<br>Perseverance|(6)<br>Personal<br>Initiative|(7)<br>Problem<br>Solving|
|---|---|---|---|---|---|---|---|
|Offered Training|0.06|0.05|0.06|0.02|0.03|0.04|0.03|
||(0.05)|(0.04)|(0.04)|(0.05)|(0.04)|(0.04)|(0.04)|
|Emotional Awareness|0.33<sup>_∗∗∗_</sup><br>(0.04)|||||||
|Self Awareness||0.36<sup>_∗∗∗_</sup><br>(0.04)||||||
|Emotional Regulation|||0.31<sup>_∗∗∗_</sup><br>(0.04)|||||
|Self Control||||0.35<sup>_∗∗∗_</sup><br>(0.04)||||
|Perseverance|||||0.37<sup>_∗∗∗_</sup><br>(0.04)|||
|Personal Initiative||||||0.35<sup>_∗∗∗_</sup><br>(0.05)||
|Problem Solving|||||||0.34<sup>_∗∗∗_</sup><br>(0.05)|
|Control Mean|-0.03|-0.02|-0.03|-0.02|-0.01|-0.02|-0.02|
|Observations|849|849|849|849|849|849|849|

Notes: OLS regressions controlling for strata dummies used in the randomization and baseline value of the outcome. Soft skills were only measured at baseline and endline. Robust standard errors shown in parenthesis. The controls presented in the table are the baseline measures of each index.

36
of a cash crop, and the utility from spending the required time and effort throughout the cycle. More optimistic farmers tend to perceive the odds to be in their favor, and thus might be more willing to take the increased risk and uncertainty associated with a cash crop. These character traits can also provide the psychological aid women farmers need to challenge possible traditional gender role norms in agriculture.

We study this issue in the context of Malawi, one of the poorest countries in the world. We look specifically at the adoption of tobacco, a crop exclusively grown for selling in export markets, which accounts for 60 percent of the country’s export revenue, 70 percent of which is cultivated by smallholder farmers. Despite decreasing smoking rates in rich economies and tightening tobacco regulations in developing countries, the production and marketing of tobacco remains an important source of income for rural households in Malawi, and in fact one of the major entrepreneurial options available to them.

We use the _Malawi Integrated Household Panel Survey, 2013_ (IHPS) data set. For farm households, the survey included a questionnaire with items eliciting the non-cognitive skills of the managers of the farm. We use exploratory factor analysis to construct an index of non-cognitive skills capturing perseverance, combined with passion and optimism. Our working sample comprises nearly 500 farm households with valid non-cognitive skills data for both spouses and a rich set of socioeconomic and farm characteristics, which allows us to investigate the role of the non-cognitive skills of the women farmers holding constant other individual, spousal, household, farm, and community characteristics.

We show that farms managed by higher non-cognitive ability women are significantly more likely to produce tobacco. A one standard deviation increase in female non-cognitive skills is associated with a 5.4 percentage point increase in tobacco adoption, which corresponds to a one-third increase relative to the sample mean of 16 percent. This result is not explained by differences across women in education and cognitive skills. It is also not explained by the fact that higher non-cognitive ability women tend to be married to husbands of higher non-cognitive ability and education. This result holds across neighbor 3
# **Figure 2.1: Tanzania Labor Force Pyramid, 2006 (percent)**

<!-- Start of picture text --> Figure 2.1a Tanzania Pyramid including Agriculture<br>employer 2.0 2.2<br>wage with secure contract 0.9 4.8<br>wage without secure contract 2.7 6.7<br>HE without employees outside of household 9.7 11.9<br>non-agri family worker 7.7 2.6<br>agri. 77.1 71.7<br>Female Male 80 60 40 20 0 20 40 60 80<br><!-- End of picture text --> <!-- Start of picture text --> Figure 2.1b Tanzania Non-agriculture Labor Force Pyramid<br>employer 3.9 7.9<br>wage with secure contract 8.8 17.1<br>wage without secure contract 12.1 23.5<br>HE without employees outside of household 43.8 42.2<br>non-agri family worker 31.4 9.3<br>50 40 30 20 10 0 10 20 30 40 50<br><!-- End of picture text --> _Source: Calculations based on the ILFS 2006 data_ 7
Islands, Tonga, and Vanuatu) purchase catastrophe risk insurance against tropical cyclones and/or earthquakes/tsunamis with support from the Government of Japan. Tonga was the first country to receive a pay-out from the insurance program and received US$1.27 million, which is equivalent to more than the 2013 contingency budget or half of the current reserves of the Tonga National Reserve Fund, demonstrating how effective sovereign DRFI can be. A similar scheme operates in the Caribbean; the Caribbean Catastrophe Risk Insurance Facility (CCRIF), and since its inception in 2007 has made a total of eight pay-outs totaling US$32.1 million (see Table 2). The most cited pay-out was for Haiti following the earthquake in 2010, the pay-out of US$8 million enabled the Haitian public sector and emergency services to continue to function despite the loss of many staff and the vast destruction to many central government buildings.

In order to demonstrate what the injection of liquidity is worth to countries in terms of additional budget Table 2 shows what the payouts from CCRIF are worth in terms of the total budget and the contingency budget. Data limitations were such that actual contingency budgets could not be ascertained so proxy of 1.5 percent of the total expenditures has been used. Looking at St Lucia, it can be seen that their payouts range from being equivalent to 6.5 percent to 49.3 percent of the contingency budget, providing a much needed boost to the limited resources for response and relief. For example, in Barbados following the aftermath of Hurricane Tomas on October 31st, 2010, the CCRIF payout of US$8.5 million, equivalent to almost 30 percent of the contingency budget, was used to fund immediate infrastructure repairs which removed the need to gain parliamentary approval to receive the required parliamentary approval for supplementary expenditures thus removing the associated time lag enabling repairs to commence immediately (TI-UP Resource Centre, 2012).

**Table 2: Liquidity Injections from CCRIF**

|**Country**|**Event**|**Year**|**Payout (UD$**<br>**millions)**|**Payout as a**<br>**% of Total**<br>**Expenditure**|**Payout as a**<br>**% of**<br>**Contingency**<br>**Budget**<sup>**9**</sup>|
|---|---|---|---|---|---|
|Dominica|Earthquake|2007|0.52|0.4|29.9|
|St. Lucia|Earthquake|2007|0.42|0.1|6.5|
|Turks and<br>Caicos Islands|Tropical<br>Cyclone|2008|6.3|-|-|
|Haiti|Earthquake|2010|7.8|-|-|
|Anguilla|Tropical<br>Cyclone|2010|4.3|6.7|>200|
|Barbados|Tropical<br>Cyclone|2010|8.6|0.4|29.4|
|St. Lucia|Tropical<br>Cyclone|2010|3.2|0.7|49.3|
|St. Vincent and<br>the Grenadines|Tropical<br>Cyclone|2010|1.1|0.3|24.9|
|Total Payout||2007-2011|32.1|||

**Source: CCRIF, Government of Anguila, Government of Barbados, Government of the Commonwealth of Dominica, Government of St Lucia and Government of St Vincent and the Grenadines.**

> 9 Using 1.5% of total budget as a proxy.

21
SUDAN’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE

# Water supply and sanitation

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- ———. ―Draft Water Policy.‖ Juba, September 2007.

Gulyani, Sumila, Debabrata Talukdar, and Darby Jack. 2009. ―Poverty, Living Conditions, and

- Infrastructure Access: A Comparison of Slums in Dakar, Johannesburg, and Nairobi.‖ AICD Working Paper 10, Africa Region, World Bank, Washington, DC.

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Keener, Sarah, Manuel Luengo, and Sudeshna Banerjee. 2009. ―Provision of Water to the Poor in Africa: Experience with Water Standposts and the Informal Water Sector.‖ AICD Working Paper 13, Africa Region, World Bank, Washington, DC.

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> <sup>4</sup>