rafmacalaba/datause-encoder-data huggingface dataset:
mitigation as its primary objective in India, the first real-time BC concentration measurements from cookstoves carried out revealing significant amount of BC emissions (Kar et al., 2012). In China, Cao et al. (2006) estimate that 1500 Gg (giga gram) of BC emissions in 2000 mainly due to the burning of coal and biofuels. Based on an assessment of benefit cost ratios of reducing BC emissions, Kandlikar et al. (2009) estimate that for every dollar spent switching to an improved stove, the benefit is between $100 and $880 of CO2e and that improved stoves have a costeffectiveness of about $4 per ton CO2e.

# 5.3. Cooking, deforestation and climate change

In the 1970s and 1980s, deforestation due to unsustainable extraction of biomass used for cooking is considered a major environmental concern particularly in developing countries. Over the past two decades, more extensive analysis has demonstrated that biomass, mainly fuelwood, used for cooking is not the major cause of deforestation worldwide, though there may be few cases in specific parts of the world (McGranahan, 1991; Arnold et al., 2003). For example, EAC (2006) finds that heavy dependence on biomass contributed to annual deforestation rate of 3-4% in Kenya, 2% in Tanzania and 2% in Uganda. Forest research in the Chalaco District in Peru, as well as in adjacent areas, indicates that fuelwood collection in preparation for the rainy season is strongly related to cutting down trees from cloud forest areas (Córdoba-Aguilar, 1992; Ektvedt, 2011). The study also indicates that in cloud forests with high gradients (where agriculture is hardly feasible) fuelwood extraction may constitute the main cause of forest degradation and deforestation (Córdoba-Aguilar, 1992; Sánchez and Grados, 2007). However, by examining relationships among urbanization, household energy source and forest cover in India, DeFries and Pandey (2010) find that fuelwood demand may lead to local degradation but not large-scale deforestation. The study also finds that at the state level, increases in percent forest cover are positively associated with percent of total households that are urban but not related to changes in fuelwood demand. There is also growing concern of charcoal use for cooking and its environmental consequences including deforestation in many SSA countries (Mwampamba, 2007; Chidumayo and Gumbo, 2013). Unlike traditional wood-based charcoal, cooking with "green charcoal" -- charcoal cooking briquettes made from charred agricultural waste-- is helping to reduce deforestation in Haiti (USAID, 2014).

22
**Table 5: Land market restrictions and wages: Further Robustness Checks** IV Regression Results

||Real<br>Year|Annual wag<br>of Birth Afte|e<br>r|
|---|---|---|---|
||1947|1950|1960|
|% Area Under LDO|-1.841|-1.740|-1.939|
||(2.66)***|(2.51)**|(2.41)**|
|Area LDO*Travel Time|0.305|0.283|0.307|
||(2.56)**|(2.47)**|(2.58)***|
|Marginal Effects (at median)||||
|% Area Under LDO|**-1.27**|**-1.21**|**-1.36**|
|Area controls|Yes|Yes|Yes|
|District Fixed Effect|Yes|Yes|Yes|
|Individual controls|Yes|Yes|Yes|
|Sectoral Dummies|Yes|Yes|Yes|
|no. of observations|11416|10658|7542|
|**IV Diagnostics**||||
|Weak Identification Test||||
|Kleibergen-Paap wald F|70.85|62.71|31.47|
|Stock-Yogo 5% maximal IV rel. bias|9.53|9.53|9.53|
|Hansen's J Statistics (Overidentication test)|0.84|1.40|0.15|
|P-Value|0.36|0.24|0.70|

Robust t statistics in parentheses. Standard errors corrected for clustering * significant at 10%; ** significant at 5%; *** significant at 1% Instruments: Malaria Endemicity* % area under inland water, Malaria Endemicity*% of Area above 1000 feet of elevation, difference in slope, difference in slope*malaria Endemicity*inland water

**Table 6: The Importance of Interaction Effect**

|Travel Time|Travel|Marginal|Elasticity|
|---|---|---|---|
|Percentile|time(hour)|Effect||
|p20|0.57|-1.84|-8.8%|
|p30|0.93|-1.72|-8.3%|
|p40|1.40|-1.57|-7.5%|
|p50|1.88|-1.41|-6.8%|
|p60|2.32|-1.27|-6.1%|
|p70|3.01|-1.04|-5.0%|
|p80|3.58|-0.86|-4.1%|
|p90|5.89|-0.10|-0.5%|
**Table 4: Prices: Stationarity and Engle-Granger test of cointegration**<sup>_a_</sup>

||Stat|ionarity||Cointegr|ation|
|---|---|---|---|---|---|
||Levels|Diferen|ces|||
||ADF|A|DF|ADF|R<sup>2</sup>|
|World price|-1.74|-4.56|<sup>_⋆⋆⋆_</sup>|||
|Distrito Federal|-2.17|-5.35|<sup>_⋆⋆⋆_</sup>|-3.64 <sup>_⋆⋆⋆_</sup>|0.49|
|Goias|-1.93|-5.4|<sup>_⋆⋆⋆_</sup>|-3.71 <sup>_⋆⋆⋆_</sup>|0.53|
|Ceara|-1.76|-5.53|<sup>_⋆⋆⋆_</sup>|-3.93 <sup>_⋆⋆⋆_</sup>|0.64|
|Pernambuco|-2.06|-5.1|<sup>_⋆⋆⋆_</sup>|-2.81 <sup>_⋆_</sup>|0.43|
|Bahia|-2.11|-4.95|<sup>_⋆⋆⋆_</sup>|-3.5 <sup>_⋆⋆⋆_</sup>|0.47|
|Minas Gerais|-2.27|-5.68|<sup>_⋆⋆⋆_</sup>|-3.98 <sup>_⋆⋆⋆_</sup>|0.56|
|Parana|-2.4|-5.75|<sup>_⋆⋆⋆_</sup>|-4.2 <sup>_⋆⋆⋆_</sup>|0.56|
|S ̃ao Paulo|-2|-5.22|<sup>_⋆⋆⋆_</sup>|-3.61 <sup>_⋆⋆⋆_</sup>|0.56|
|Rio de Janeiro|-2.15|-5.19|<sup>_⋆⋆⋆_</sup>|-3.66 <sup>_⋆⋆⋆_</sup>|0.53|
|Rio Grande do Sul|-2.4|-5.03|<sup>_⋆⋆⋆_</sup>|-4.11 <sup>_⋆⋆⋆_</sup>|0.52|
|Para|-2.06|-4.5|<sup>_⋆⋆⋆_</sup>|-2.55|0.47|

> _a_ A _⋆⋆⋆_ implies that the null hypothesis of unit root and/or no cointegration is rejected at the 1 percent significance level; _⋆⋆_ for rejection at the 5 percent level and _⋆_ for rejection at the 10 percent level. The critical values of ADF are -2.58 at 10 percent, -2.89 at 5 percent and -3.49 at 1 percent. Six months lag length is used and all prices are in logs. World price is Contract number 5 of the London Daily Price for refined sugar, f.o.b. Europe, spot. Local prices are consumer prices.

34
|causepov2|0.03|‐0.02|**0.33**|0.26|0.08|0.04||
|---|---|---|---|---|---|---|---|
|LOC7|0.06|‐0.01|**0.32**|0.18|0.18|‐0.07||
|BF_A3|‐0.08|0.27|**0.32**|0.25|0.00|‐0.04||
|optim3|‐0.03|‐0.08|**0.30**|0.04|0.19|0.17||
|BF_O5|0.02|0.05|**0.28**|‐0.24|‐0.07|0.03||
|tenac6|‐0.07|‐0.08|**0.27**|‐0.04|0.07|0.01||
|LOC5|‐0.06|0.04|**0.26**|0.01|0.10|0.11||
|BF_N3|‐0.07|0.18|**0.24**|0.07|0.12|0.12||
|LOC6|0.13|‐0.15|**0.15**|0.10|0.11|0.13||
|causepov6|‐0.04|0.03|0.03|**0.62**|0.10|‐0.05||
|causepov5|0.06|0.06|‐0.07|**0.56**|‐0.06|‐0.01||
|causepov7|0.09|0.02|0.08|**0.55**|0.07|‐0.12|Causes of poverty|
|causepov9|0.06|0.00|0.07|**0.55**|0.09|‐0.02|(all reversed items)|
|causepov8|‐0.02|‐0.01|0.18|**0.51**|0.07|0.00||
|optim1|‐0.03|0.13|‐0.41|**0.21**|0.08|0.06||
|att_change4|0.03|‐0.02|‐0.04|0.08|**0.56**|0.10||
|att_change5|‐0.01|0.04|0.01|0.07|**0.54**|0.12||
|BF_E1|0.09|0.10|‐0.02|‐0.11|**0.45**|‐0.23||
|att_change2|0.06|‐0.16|0.09|0.13|**0.43**|0.02||
|BF_C2|0.08|0.14|0.09|‐0.11|**0.43**|‐0.28||
|BF_E3|0.05|0.17|‐0.17|‐0.17|**0.41**|‐0.09||
|att_change3|‐0.04|‐0.20|0.19|0.15|**0.40**|0.03|Attitude toward<br>|
|selfesteem4|‐0.02|0.21|‐0.02|‐0.01|**0.39**|0.20|Change|
|BF_N4|0.08|0.11|0.04|‐0.15|**0.32**|0.07||
|BF_O3|‐0.04|‐0.33|0.22|‐0.09|**0.31**|0.01|Locus of Control<br>with visual aid|
|causepov4|‐0.04|0.01|0.11|‐0.36|**0.20**|0.03||
|LOC_va2|0.07|‐0.03|0.07|0.14|**0.17**|0.02||
|LOC_va1|0.13|‐0.09|0.07|0.13|**0.16**|‐0.02||
|causepov3|0.05|‐0.04|0.08|‐0.55|**0.15**|‐0.03||
|LOC_va3|0.07|‐0.15|0.03|0.07|**0.13**|‐0.02||
|causepov1|‐0.07|0.03|0.01|‐0.57|**0.07**|‐0.10||
|cesd4|0.15|‐0.03|0.10|‐0.13|0.02|**0.40**||
|selfesteem2|‐0.05|0.24|‐0.26|‐0.02|0.15|**0.39**||
|cesd21|0.12|‐0.05|0.10|‐0.09|0.02|**0.38**||
|selfesteem3|0.10|0.07|‐0.14|0.21|0.04|**0.38**|CESD positive|
|cesd16|0.22|‐0.09|0.06|‐0.09|0.05|**0.38**||
|att_change1|‐0.04|0.00|0.10|‐0.18|‐0.06|**0.37**|Self‐esteem|
|cesd8|0.06|‐0.05|0.17|0.01|‐0.02|**0.36**||
|optim2|‐0.03|0.18|‐0.01|‐0.02|0.12|**0.33**|Risk aversion|
|tenac5|0.02|0.25|0.07|‐0.09|‐0.04|**0.32**||
|riskav2|‐0.08|‐0.06|‐0.03|‐0.06|‐0.13|**0.20**||
|riskav1|‐0.09|‐0.15|0.07|‐0.01|0.04|**0.13**||

Note: items with possible acquiescence bias are demeaned by subtracting the person‐specific acquiescence score.

42
ERV calculated for the years between 1992/93 and 2000/01 are reported in Table 14. Again, to examine the sensitivity of the results to parameter assumptions, the model was simulated for the same producer price change with the price and income elasticities of demand and GDP growth rate set at half their base simulation levels. Under base simulation parameters, the ERV falls from 0.98 in 1993/94 to 0.93 in 1994/95, and thereafter slowly declines to 0.90 by the end of the decade. Assuming demand is the more inelastic with respect to price causes the ERV to fall significantly below unity. Over the simulation period, the ERV averages 0.33, reaching as low as 0.12 in 1998/99. The results are less sensitive to changes in the income elasticity of demand and the GDP growth rate. In simulations with these parameters set at half their base simulation levels, the ERVs average 0.86 over the analysis period, thus remaining above the level at which an adding-up problem would exist. Therefore, as in the case of expanded exports under a real devaluation, an adding-up problem would not exist for a 20% producer price increase, except if the price elasticity of demand was substantially less than its estimated value.

# _6._ Conclusions

The purpose of this paper was to assess whether an adding-up problem exists in the case of cotton export expansion by countries in SSA. This issue has arisen following the fast growth in SSA cotton exports which averaged almost 8% in the 1980s. Part of the concern derives from questions about the impact of the structural adjustment programs which have increased incentives to produce cotton in some countries in SSA. The hypothesis tested in the paper was that export expansion has led or will lead to a decline in the terms of trade in cotton which offsets any benefits from export expansion.

Results from the analysis riesented in the paper rejected this hypothesis. Using comparative static analysis, a comparison of SSA's export share with estimated world demand elasticities showed that its share of world exports (14%) is too small relative to the estimated price elasticity of demand (ranging between -0.2 and -0.3) for an adding-up problem to exist. Using an econometric model of the world fibers market, it was shown that maintaining the growth rate of cotton exports in the 1990s at the same high rate as in the 1980s caused SSA export revenues to be increased by more than 50% by the end of the decade, as compared with the base case projection. Also, except when the price elasticity of world cotton demand was lowered substantially below its estimated value, estimates of the ERVs were sufficiently close to unity to conclude that an adding-up problem does not exist for expanded cotton exports by SSA.

The analysis also showed that the structural adjustment programs implemented in the 1980s were unlikely to have had a significant adverse impact on the world cotton market. For example, a 20% real 23
# **Table A2. Impact of living in the GMA and, once in the GMA, of participating in the** **<u>VAG/CRB</u>**

|||Treatment Regres<br>capita co|sion mod<br>nsumptio|els for the impact<br>nexpenditure of<br>Participating|on per<br>in|
|---|---|---|---|---|---|
|Variable|Variable description|LivinginGM|A<sup>_a_</sup>|VAG/CRB|<sup>_b_</sup>|
|||(1)||(2)||
|cons|Intercept|13.020|***|13.580|***|
|||(0.209)||(0.086)||
|park2|Kafue dummy variable|0.400|***|0.192|**|
|||(0.143)||(0.088)||
|park3|Lower Zambezi dummy variable|0.416|***|0.217|***|
|||(0.127)||(0.063)||
|park4|Luangwa dummy variable|0.434|***|0.537|***|
|||(0.100)||(0.096)||
|hage|Age of the household head|-0.001||-0.001||
|||(0.001)||(0.002)||
|fhhh|Female-headed dummy|-0.186|***|-0.150|***|
|||(0.047)||(0.054)||
|maxedu|Education of the most educated member|0.043|***|0.022|**|
||(years)|(0.010)||(0.010)||
|c0to14|Number of children less than 15 years old|-0.132|***|-0.145|***|
|||(0.011)||(0.011)||
|f15to60|Number of female members 15-60 years|-0.063<br>(0.025)|**|-0.091<br>(0.029)|***|
|m15to60|Number of male members 15-60 years|-0.100<br>(0.022)|***|-0.090<br>(0.022)|***|
|m61plus|Number of members older than 60 years|-0.140<br>(0.037)|***|-0.186<br>(0.051)|***|
|kroad|Distance to the nearest main road in km|-0.005|***|-0.006|***|
|||(0.002)||(0.002)||
|kbsch|Distance to the nearest basic school in km|0.001||0.010|***|
|||(0.003)||(0.003)||
|kheal|Distance to the nearest health centre in km|-0.002<br>(0.002)||-0.004<br>(0.002)|***|
|vacast|Value of consumer durable assets in ZMK|0.045<br>(0.014)|***|0.107<br>(0.024)|***|
|dcoop|Participation in cooperatives dummy|0.357|***|0.237|***|
|||(0.051)||(0.066)||
|gma or|Participation dummy variable<sup>_a,b_</sup>|0.665|***|0.438|**|
|dvcrb||(0.237)||(0.205)||
|Number of|observations|2,209||1,112||
|Log-likelih|ood value|-3,405||-1,333||
|Goodness|of fit Chi-Square|297.73|***|407.06|***|
|Rho||-0.583|***|-0.301|*|

> _a_ Participation dummy variable refers to the GMA dummy, equal to 1 if the household is located in a GMA

> _b_ The participation dummy variable refers to the CRB/VAG dummy, equal to 1 if the household participates in the VAG/CRB. This model uses a sub-sample of households that are located in the GMA Significance: *=Significant at 10%; **=Significant at 5%; ***=Significant at 1% Values in parentheses are standard errors

17
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be infected by malicious software or by the attacker constantly sending packets to AMS server ports to jam needed communications for normal AV functions such as routing, warning, and spatial sensing.

_<u>Interrupting communications (active attack):</u>_ A cyber attacker interferes with communications between the control components of an AMS. By issuing a data request that causes the control systems to obstruct the normal processing sequence of the systems to respond to the request, this interference undermines the stability of routine communications and therefore operations of AMS (Kisner et al., 2010; Kisner, 2009). Such disruptions, once detected, will prompt the AMS operator to reduce or even cease provisions of mobility service in order to recover or switch to backup communication networks.

_<u>Generating incorrect output values or commands (active attack):</u>_ This type of attacks generates erroneous output values or commands and sends them to devices in AMS that enable interaction between cyber and physical components. Consequently, an AMS operator can be misled to take AV routing and dispatching decisions that are not the best for the current operational status, or even disrupt AMS operations and traffic on the roads.

_<u>Collecting AMS operational information (passive attack):</u>_ This type of attack attempts to gain access to AMS operation data through eavesdropping AMS servers. Although not disrupting normal functioning of AMS, the information collected can be used to estimate the operational parameters and states of AMS such as distribution, speed, and fueling of AVs, and to help forge more targeted active cyber attacks in the future.

# **4 Enhancing the cyber resilience of AMS**

Given the variety of plausible cyber attacks, making AMS resilient to cyber attacks is critically important. The premise of doing so is to understand what cyber resilience means for AMS. Answering this question will build on the understanding of cyber resilience and resilience in general, as is in subsection 4.1. In subsection 4.2, we will examine current practices in strengthening AV cybersecurity, a closely related issue to AMS cyber resilience. Further strategies at both vehicle and system levels to enhance AMS cyber resilience are proposed in subsection 4.3.

## **4.1 Resilience and cyber resilience**

### **4.1.1 Resilience**

We start our discussion with the most general definition of resilience. Following the 2019 version of the Merriam-Webster dictionary, resilience is described as the “ability to recover from or adjust easily to misfortune or change”. In the academic literature, the concept of resilience is initialized in the 9
A Skills-Based Human Capital Framework to Understand the Phenomenon of Youth Economic Disengagement Diego F. Angel-Urdinola and Renata Mayer Gukovas The World Bank<sup>1</sup> JEL Classification : I20, I25, J23, J24, J31

**Keywords** : Youth economic disengagement, human capital accumulation, skills development, wage determination, NEETs

**Corresponding Author:** Diego F. Angel-Urdinola (dangelurdinola@worldbank.org)

> 1 This paper was prepared as a background paper for the World Bank’s Brazil Skills and Jobs Program (P156683). The authors acknowledge technical support and strategic guidance from Truman Packard, Rita Almeida, Eduardo Rios-Nieto, and Mattias K. Lundberg. The authors also acknowledge valuable comments and contributions from participants during a seminar held at the World Bank in May 2017 as part the World Bank’s Human Development Learning Week.
Those results can be understood as complementary to the results of Gerard _et al._ (2021). BF acts as both a social safety net and an economic stimulus that creates pathways to formal employment. The resulting labor market improvements facilitate beneficial exits that potentially free up resources for new beneficiaries.

# **5.2 Robustness Checks**

**Municipality level** As a first robustness check, we replicated the analysis at the municipality level. Instead of using the individual probability of leaving the program, we here use _exit rates_ as our outcome of interest.<sup>28</sup> Table A.3 shows that when replicating the analysis at the municipality level, we can see a similar effect compared to the individual level. Column (2) shows the outcome of the 2nd stage IV approach. If employment grows by 10%, the exit rate at the municipality increases by 0.245 percentage points.

**Beneficiaries receiving the basic benefit** As a second robustness check, we restrict our sample to those beneficiaries that receive either only the basic benefit or the basic and variable benefit (based on predictions derived from family composition and the total benefit amount). We thus exclude all families that _only_ receive the variable benefit which is conditioned on having children. By doing so we are aiming to capture potential mechanical reasons – such as children approaching adulthood. The results shown in Panel A of Table A.4 are similar in significance level and magnitude as in our main specification.

**Fraud** In addition, we introduce a variable to identify individuals who secure formal dependent employment subsequently exiting the CadUnico, while their family remains enrolled. This approach is designed to address the possibility of individuals being incentivized to remove themselves from the registry once they secure formal employment, thereby enabling them to access both labor income and the BF benefit concurrently. Including such a variable does not change our point estimates, as shown in Panel B of Table A.4.

**CadUnico actualization** Further, we control for the share of CadUnico registries that have been updated.<sup>29</sup> We do so by including a variable that indicates the average value of updates by municipality. As seen in Panel C of Table A.4 including the actualization score does not alter the effect of employment growth on the probability of leaving BF.

> 28This approach allows us to also consider entries in the program. Thus, we calculate exit rates by looking at the number of exits per semester divided by the total number of families in BF.

> 29The MDS provides information on the actualization score (MDS, 2020).

19
# **The Burden of Water Shortages on Informal Firms**

Asif Islam<sup>1</sup> Enterprise Analysis Unit Developing Economics Indicators Group The World Bank aislam@worldbank.org JEL codes: Q25, O17, L25 Keywords: Water infrastructure, informal firms, firm productivity

> 1 The study was developed as a background paper for the World Bank Water Global Practice flagship report titled “Uncharted Waters: The New Economics of Water Scarcity and Variability.” The author is indebted to Richard Damania, Sébastien Desbureaux, Marie Hyland, Aude-Sophie Rodella, Jason Russ, and Esha Zaveri for invaluable comments. The findings of the were also presented at an Oxford University Workshop  and the author thanks the participants for their comments - Felix Pretis, Jacquelyn Pless, Michael Gilmont, Emily Barbour, Johanna Koehler, and Jesper Svensson. The author is also grateful for the support and comments from Jorge Rodriguez Meza.
# **1. Introduction**

Deposit insurance can be explicit or merely implicit. Explicit insurance coverages are contractual obligations; implicit coverages are only conjectural. Implicit insurance exists to the extent political incentives that influence a government’s reaction to large or widespread banking problems make taxpayer bailouts of insolvent banks seem inevitable. Every country offers implicit insurance because, during banking crises, the pressure on government officials to rescue at least some bank stakeholders becomes difficult to resist. While still far from universal, explicit deposit insurance (DI) systems are multiplying rapidly. The number of countries offering explicit deposit guarantees surged from 20 in 1980 to 87 by the end of 2003 (see Figure 1).

One reason for this surge is that having an explicit deposit insurance scheme has come to be seen as one of the pillars of modern financial safety nets. Establishing explicit deposit insurance has become a principal feature of policy advice on financial architecture that outside experts give to countries undergoing reform. Starting in the 1990s, IMF crisis-management advice recommended erecting DI as a way of either containing a crisis or winding down crisis-generated blanket guarantees (Folkerts-Landau and Lindgren, 1998; Garcia, 1999). The World Bank has also actively supported the adoption of DI and provided adjustment loans for initial capital of deposit insurance funds in a number of countries.<sup>1</sup> Table 1 lists countries that have adopted DI since 1995. Although many recent adopters were transition countries of Eastern Europe seeking to comply with the European Union (EU) Directive on Deposit Insurance, adopters can be found in every

> 1 A World Bank report (OED) found that, during the period 1993-2004, the World Bank concerned itself in a total of 60 instances with reforms in the deposit-insurance schemes of 35 countries.