rafmacalaba/datause-encoder-data huggingface dataset:
SSA, and for the latter, grazing intensities are fairly stable after 2030—cattle numbers have peaked by 2040 and there are fewer in 2050 than in 2030 (see Table 3), small ruminant numbers by 2050 are only somewhat above those for 2030, while at the same time the model indicates some loss of grazing land in SSA to necessarily marginal mixed rainfed systems. Grazing intensities change relatively little in NAE (North America and Europe). Again, given typical stocking rates of 10-15 ha per animal in the arid and semiarid grazing systems, these results of the reference run imply considerable intensification of livestock production in the humid and subhumid grazing systems of the world, but particularly in LAC.

Table 4. Grazing intensities in rangeland systems to 2030 and 2050 for the reference run, by region (TLU per ha). Rosegrant et al., 2009.

||**2000**|**2030**|**2050**|
|---|---|---|---|
|**CWANA**|0.052|0.077|0.083|
|**ESAP**|0.044|0.067|0.067|
|**LAC**|0.188|0.293|0.318|
|**NAE**|0.052|0.063|0.060|
|**SSA**|0.062|0.090|0.090|
|**Globe**|0.064|0.094|0.098|

CWANA Central and West Asia and North Africa ESAP East and South Asia and the Pacific LAC Latin America and the Caribbean NAE North America and Europe SSA Sub-Saharan Africa This analysis does not take into account the impact of infrastructural development, so projected changes in grazing intensities may be under-estimated. The analysis also makes implicit assumptions about the relative share of production that is projected to come from the rangeland versus the mixed systems in the future, in terms of relative animal numbers. Even so, given the fragility of semiarid and arid rangelands, particularly in SSA, and the uncertainties concerning technological change and the institutional landscape that will affect these livestock systems in the future (Freeman et al., 2006), the shifts in production to the wetter and mixed systems that are implied are likely to have considerable potential environmental impacts in the reference run.

20
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40
# **Appendix 1: Modeling the Effect of the Global Plan to Stop TB**

<!-- Start of picture text --> births<br>uninfected, U<br>progressive progressive<br>primary TB latent TB primary TB<br>transmission<br>infected: latent, L<br>exogenous+ exogenous+<br>endogenous endogenous<br>TB deaths infectious non-infectious TB deaths<br>TB, I TB, N<br> cure relapse relapse cure<br>recovered, recovered,<br>Ri Rn<br><!-- End of picture text -->

**Figure A.1** . Flow diagram of the compartmental model for tuberculosis. Refer to equations below for a formal description of the model, and to Table 3 for definitions and values of parameters.

The potential impact of scenarios 2 and 3, as compared with scenario 1, has been evaluated with a mathematical transmission model describing, as in previous models (Blower, McLean et al. 1995; Dye and Williams 2000; Dye and Espinal 2001), how the planned interventions can be expected to reduce TB incidence, prevalence, and death rates through time. The structure of the core TB model is based on literature describing the natural history of TB, and fitted quantitatively to data defining the course of TB epidemics in seven regions of the world (Dye, Scheele et al. 1999; Corbett, Watt et al. 2003; World Health Organization 2007). The core model is sketched as a flow chart in Figure A.1. We previously have shown how models of this kind can replicate the 26
WPS3784 double that presented in table 6. There is substantial potential for lower transport costs on the main trade routes. It is estimated that the cost per ton of transport between Mombasa and Kigali can be more than 70 percent higher than the cost of transport between Mombasa and Kampala (World Bank 2005).

Furthermore, reducing rural transport costs has a positive impact on the distribution of income – the poor benefit (those in the first four deciles) proportionally more than their wealthier counterparts (table 7). The effect on the distribution of income (ranked in deciles) mirrors the distribution of the share coffee income in total income. Coffee farmers in the 4<sup>th</sup> decile, for whom coffee provides 14 percent of total income, would see a 3 percent rise in their income. In contrast, coffee farmers at the top of the income distribution would only enjoy a 0.36 percent increase in income. In terms of the different regions, the largest welfare gain accrues to Cyangugu (+2.7 percent), Kigali Ngali (+1.8 percent) and Kibuye (+1.8 percent) whilst in regions where coffee is less important as a source of income, Umutara, Ruhengeri and Buyamba, there is little increase in income (table 7).

# _Potential second-round effects_ .

While the direct effect of changes in coffee producer prices falls on the welfare of coffee farmers, other farmers and non-farmers may reap significant indirect benefits from higher coffee incomes. The two traditional channels through which spillover effects work are through the labor market and the markets for food and other local non-farm goods. In Rwanda, only 8 percent of coffee farmers employ paid labor, implying that greater returns to coffee farming may have little employment impact in the short run.

Furthermore, since farmers use sub-optimal levels of fertilizer, they may be more inclined to use some of their extra income to apply more fertilizer and boost productivity, rather than increasing employment. Finally, given Rwanda’s very high labor to land ratio (with a population density of 350/ km2), a land constraint may prevent high incremental employment while at the same time, a large pool of landless people may suppress the potential increase in wage rates (in other words, it is unlikely that a neoclassical closure rule --full employment and flexible wage rate- applies in rural Rwanda).

9
statuses in February 2020, right before the pandemic. We deemed the SYPJ data to be more suited to our question since it allows to look at the trend of employment outcomes before the pandemic as well as right after the shock in the summer of 2020 and therefore allows investigating the effect of the pandemic on the two populations. The CMM data would be better suited to capture the recovery from the pandemic (Krafft et al., Forthcoming). The overlapping portion of the CMM data with the SYPJ data (February 2020 to February 2021) did not allow for a comparable analysis to the one we did in this paper. The overlap is not perfect. CMM data was collected a few months after the bulk of the SYPJ data only allowing for a comparison between the first half of 2021 (CMM) to the second half of 2020 rates (SYPJ). This is not a reasonable comparison especially in a period with rapidly changing events like the pandemic time. In addition, the time intervals are different as CMM data allows for examining annual change (February 2020 to February 2021) as opposed to semi-annual, and that is only one period which does not allow to compare the trend.

In our analysis we focus on young males aged 15 and above who ever worked. Since those who never worked do not have job finding and job separation outcomes we believe including them in the sample will not add much information. Moreover, given low participation rates among women for both populations, the number of women who ever worked in the SYPJ sample is only 229 (75 of whom are Syrians). It is therefore difficult to conduct the analysis by sex. Given the difference in the experiences of women and men in the labor market, we limit the sample to male respondents for whom we have information on their first job.<sup>8</sup> Furthermore, since our analysis spans the period from 2016 to 2020, we limit the sample to those who were 15 or above in 2016. This results in a sample of 824 young males, among whom 314 are Syrians.

> 8 We show the results of the full sample, men and women, in the appendix.

10
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38
Table 3 - Night lights data for several periods

||**1992-2010**|**1990s**|**2000s**|**1992**|**2000**|**2010**|
|---|---|---|---|---|---|---|
|**DN 0 (Unlit)**|92.90%|94.07%|91.82%|96.88%|91.34%|89.62%|
|**DN 1-5**|5.19%|4.22%|6.07%|1.91%|6.61%|5.79%|
|**DN 6-10**|1.04%|0.92%|1.15%|0.65%|1.14%|2.88%|
|**DN 11-20**|0.40%|0.36%|0.44%|0.26%|0.42%|0.87%|
|**DN 20-62**|0.46%|0.41%|0.50%|0.30%|0.48%|0.76%|
|**DN 63**|0.01%|0.01%|0.01%|0.01%|0.01%|0.07%|
|**Average DN**|0.5654|0.4633|0.5867|0.2983|0.5903|0.9728|

Table 4: Distribution of gold production by distance bins

||0-10km|10-20km|20-30km|30-40km|40-50km|50-60km|60-70km|70-80km|80-90km|90-100km >|100km|
|---|---|---|---|---|---|---|---|---|---|---|---|
|**2000**||||||||||||
|Mean|0.13|0.29|0.45|0.67|0.95|0.97|1.17|1.22|1.35|1.48|41.09|
|SD|1.49|2.28|2.81|3.43|4.12|4.02|4.48|4.45|4.76|4.99|14.39|
|Max|24.44|26.04|27.07|27.22|32.02|34.48|36.42|34.89|37.35|40.83|49.78|
|% Zero|97.3%|93.7%|89.7%|84.0%|80.4%|76.6%|75.1%|74.6%|74.8%|72.7%|0.0%|
|**2000**||||||||||||
|Mean|0.15|0.31|0.47|0.69|1.02|1.28|1.34|1.51|1.64|1.80|55.42|
|SD|1.45|1.99|2.38|2.74|3.75|4.40|4.12|4.26|4.45|4.82|16.55|
|Max|25.65|34.39|36.28|30.04|36.28|38.31|43.48|40.63|40.14|42.99|65.62|
|% Zero|97.7%|94.9%|92.7%|88.9%|86.0%|82.8%|81.0%|0.784876|77.2%|76.1%|0.0%|

Note: Gold production [in t] within distance intervals from EA, distribution across all EA. % Zero refers to percentage of EA for whom gold production in the respective distance bin is zero.
than 180 countries. For each round of the survey in the country, we gather government policy stringency data a day before the day that the survey was started. The final sample used in this study includes 16,038 firm-observations from 41 countries.

# 3.2. Variables

## 3.2.1. Firm Performance and Channel Variables

Firm performance during the COVID-19 pandemic is captured by two variables: firm closure and growth rate. Firm closure, _Survival,_ is a dummy variable that equals one for firms that had not temporarily or permanently closed and zero otherwise. Firm growth is measured by the changes in monthly sales between the latest month (survey month) and the same month a year ago, denoted as _Sales growth_ .

We rely on four variables to gauge strategic responses for explaining firm performance during COVID-19. They are four channels through which firms implement their strategies: stable demand, stable input supply, stable liquidity, and innovation. We use them as dependent variables to see how firm organizational resources and national characteristics affect strategic adjustments by firms, and how such adjustments explain firm performance. Specifically, _Stable demand_ is a dummy variable that equals one for firms that had not experienced decreased demand for production or service compared to a year ago and zero otherwise. _Stable input_ is a dummy variable that equals one for firms that had not experienced decreased supply of inputs compared to a year ago and zero otherwise. _Stable liquidity_ is a dummy variable that equals one for firms that had not experienced decreased liquidity or cash flow availability since the COVID-19 began and zero otherwise. _New products_ is a dummy variable that equals one for firms that have introduced new production or service in response to the COVID-19 and zero otherwise. The original question corresponding to _New Products_ has been added into the questionnaire since November 19, 2020, thus it is available mostly in second-round surveys. Since the pandemic tends to reduce product demand, disrupt input supply, and reduce liquidity access, firms’ stability in demand, input supply, and liquidity represents positive and vigorous strategic changes to keep them prosperous at turbulent times. Introducing new products during the pandemic also clearly represents proactive 10
18 ff countrv-specific information on Yr. and ?i the algorithm actually used is extended from (9) above to compute modified bank-failure prok-' :lities conditional upon-pavment of the ith country.

# 3.3 Si;.iulation Results

The method just described is employed to **simulate the model.** In **all** the simulations, default probabilities of countries are assumed to **be equal** to focus on the impact of exposure distribution. First, a **benchmark case** (for 1985) is simulated. Second, the **following alternative scenarios are** considered: an increase in the default probability of **countries, a decrease** in banks' capital, an increase in miniimum **capital requirements, and an** increase in the share of insured deposits.

The results of the benchmark simulation and the parameter **values are** in Table 5 colomn (1). Secondary market prices are positively **related** to the extent of the country's bank indebtedness as is consistent **with our** empirical findings (prices range from about 49.9 cents for the smallest debtors to 54.4 cents for Brazil). The probability of the **bank's survival** is 88.9%, the expected value of the bank is roughly $19.9 billion, and the expected loss of the deposit insurance agency loss is about $3.5 **billion.** d An increase of the default probability, n , from 0.50 to 0.70 significantly decreases the bank's chance of survival to 55.7% (column 2). The bank's expected value is reduced, the deposit agency's **loss is** augmented, and secondary market prices decrease.

Second, we consider a reduction of the amount of bank **capital the bank** can loose before it is closed, as presented in coulumn 3. This **yields a** lower survival probability for the bank, lower expected future value of the bank, and higher expected losses for the deposit agency. Lower bank capitalization engenders higher secondary market prices, reflecting the
**Table 4: Summary Statistics derived from classroom observation instruments (Level 1 variables)**

|||Al|l|||Ru|ral||Ur|ban and|Semi-Urb|an|
|---|---|---|---|---|---|---|---|---|---|---|---|---|
||Mean|SD|25 ptile|75 ptile|Mean|SD|25 ptile|75 ptile|Mean|SD|25 ptile|75 ptile|
|**SDI**|||||||||||||
|Share of time teaching|0.85|(0.25)|0.81|1.00|0.8378|(0.25)|0.79|1.00|0.90|(0.20)|0.91|0.99|
|Share of time teaching and learnin|0.75|(0.28)|0.61|0.98|0.74|(0.28)|0.60|0.98|0.78|(0.28)|0.62|0.98|
|Good teacher demeanor|0.67|(0.23)|0.57|0.86|0.68|(0.23)|0.57|0.86|0.63|(0.20)|0.57|0.75|
|Good pedagogical practice|0.56|(0.18)|0.49|0.69|0.56|(0.17)|0.49|0.69|0.55|(0.19)|0.48|0.68|
|Classroom environment|0.30|(0.37)|0.00|0.50|0.28|(0.36)|0.00|0.50|0.40|(0.42)|0.00|0.88|
|Materials and infrastructure|0.68|(0.11)|0.57|0.71|0.68|(0.10)|0.57|0.71|0.70|(0.11)|0.57|0.71|
|Use of materials|0.76|(0.26)|0.67|1.00|0.76|(0.26)|0.67|1.00|0.80|(0.27)|0.67|1.00|
|**Stallings**|||||||||||||
|Share of time teaching|0.73|(0.25)|0.60|0.93|0.72|(0.26)|0.60|0.92|0.78|(0.23)|0.63|0.98|
|Share of time teaching and learnin|0.61|(0.29)|0.41|0.87|0.60|(0.30)|0.40|0.87|0.65|(0.28)|0.50|0.86|
|Classroom environment|0.28|(0.10)|0.40|0.47|0.42|(0.10)|0.40|0.47|0.44|(0.12)|0.40|0.53|
|Materials and infrastructure|0.43|(0.35)|0.00|0.50|0.28|(0.36)|0.00|0.50|0.29|(0.32)|0.00|0.50|
|**CLASS (7-point scales)**|||||||||||||
|Emotional support|2.92|(0.97)|2.33|3.67|2.93|(0.98)|2.33|3.67|2.84|(0.96)|2.08|3.50|
|Classroom organisation|5.75|(0.63)|5.42|6.17|5.78|(0.61)|5.50|6.17|5.58|(0.72)|5.33|6.08|
|Instructional support|2.66|(0.95)|2.00|3.20|2.67|(0.94)|2.00|3.18|2.58|(1.05)|1.80|3.38|
|Overall CLASS score|3.78|(0.73)|3.27|4.24|3.79|(0.72)|3.29|4.26|3.67|(0.80)|3.13|4.14|
|Student engagement|4.02|(1.13)|3.50|5.00|4.06|(1.13)|3.50|5.00|3.80|(1.14)|3.00|4.75|
|**Teach (5-point scales)**|||||||||||||
|Share of time teaching|0.84|(0.17)|0.67|1.00|0.83|(0.18)|0.67|1.00|0.89|(0.16)|0.83|1.00|
|Share of time teaching and learnin|0.81|(0.21)|0.67|1.00|0.80|(0.21)|0.67|1.00|0.88|(0.17)|0.83|1.00|
|Classroom culture|3.65|(0.31)|3.50|4.00|3.64|(0.31)|3.50|4.00|3.68|(0.30)|3.50|4.00|
|Instruction|2.43|(0.38)|2.25|2.75|2.44|(0.37)|2.25|2.75|2.40|(0.41)|2.19|2.75|
|Socioemotional skills|2.07|(0.37)|1.83|2.33|2.06|(0.36)|1.83|2.17|2.15|(0.41)|1.83|2.33|
|Overall Teach score|2.46|(0.24)|2.30|2.64|2.46|(0.24)|2.28|2.61|2.51|(0.28)|2.33|2.74|

41
Policy Research Working Paper 6894

# **Abstract**

This paper looks at differential progress on the health Millennium Development Goals between the poor and better-off within countries. The findings are based on original analysis of 235 Demographic and Health Surveys and Multiple Indicator Cluster Surveys, spanning 64 developing countries over the period 1990–2011. Five health status indicators and seven intervention indicators are tracked for all the health Millennium Development Goals. In most countries, the poorest 40 percent have made faster progress than the richest 60 percent. On average, relative inequality in the Millennium Development Goal indicators has been falling. However, the opposite is true in a sizable minority of countries, especially on child health status indicators (40–50 percent in the cases of child malnutrition and mortality), and on some intervention indicators (almost 40 percent in the case of immunizations). Absolute inequality has been rising in a larger fraction of countries and in around one-quarter of countries, the poorest 40 percent have been slipping backward in absolute terms. Despite reductions in most countries, relative inequalities in the Millennium Development Goal health indicators are still appreciable, with the poor facing higher risks of malnutrition and death in childhood and lower odds of receiving key health interventions.

This paper is a product of the Human Development and Public Services Team, Development Research Group. 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://econ.worldbank.org. The authors may be contacted at awagstaff@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