rafmacalaba/datause-encoder-data huggingface dataset:
groups – the lowest wealth quartile, indigenous students, students whose mothers have secondary education or lower, and students without internet access - have less access to remote learning technologies, and are less likely to be doing schoolwork. For example, 22 percent of students without internet access did no schoolwork in the past day, compared to 9 percent of those with internet and a computer or tablet. Nevertheless, while these differences exist, the majority of students in the lowest wealth quartile and of those with no internet access are still engaging in remote learning.

In addition to detailed information on remote learning and time use, our surveys directly ask students questions on mental health status, and on how the COVID-19 pandemic is most affecting them. Most students had spent zero hours outside their house in the past day. The majority identify disruptions in schooling as the biggest issue they face because of COVID-19, with feelings of social isolation the next biggest concern. Despite this, more than two-thirds say they were happy most or all of the time during the past month. While the majority appear to be coping, 16 percent have mental health scores that lie below a threshold indicative of major depression. This suggest scope for psychosocial support to supplement remote learning efforts.

The information provided here is, to date, unique in providing detailed time use information and reports directly from students during the COVID-19 pandemic. Less detailed information in other developing countries is being collected from parents or other household adults as part of rapid response household surveys. An early example is provided by Le Nestour et al. (2020) for Senegal, who provide a more pessimistic view of remote learning there: most students were either doing no educational activities, studying alone, or doing exercises given by parents; with fewer than 5 percent doing exercises given by teachers and less than one percent doing online courses. Ecuador therefore provides an example of much more engaged remote learning.

The remainder of the paper is structured as follows: Section 2 provides details of the context and survey methodology; Section 3 summarizes access to and use of remote learning technologies; Section 4 examines time-use; Section 5 mental health; and Section 6 concludes.

# **2. Context and Sample**

The first case of COVID-19 was confirmed in Ecuador on February 29, 2020 (El Comercio, 2020), with the first death occurring March 13 (Reuters, 2020). On this date (March 13), the government 3
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27
and sequestration (Oli and Shrestha 2009), capacity building (Luintel et al., 2013), social and environmental safeguards and benefit sharing. In Nepal, CFs are perhaps the most important REDD+ institution and it is especially important to understand the linkages between CF collective action, deforestation and degradation.

# **4. Sample Frame and Data**

# **4.1 Sampling and Data Collection Methodologies**

This paper relies on forest and plot level data divided into legally defined community forests (CFs) and non-community forests (NCFs), which are government forests used by communities. All forests have names and these are provided in Appendix1. Forest inventory and community level data were collected in spring 2013 from 130 study sites in the middle hill (approximately 700 – 3000 meters in altitude) and _Terai_ areas of Nepal. The high mountains, which can be remote, are generally less populated and have limited carbon sequestration potential, are excluded.

The Ministry of Forest and Soil Conservation (MoFSC) in 2010 conducted an evaluation of the CF Programme (MoFSC, 2013). The evaluation randomly selected 137 CFs (no NCFs) from 47 out of 75 districts throughout the country, with the objective to obtain a nationally representative sample. To preserve this essential randomness, representativeness and take advantage of previously collected data, we sample all 15 _Terai_ CF sites and randomly select 50 CF sites from 122 hill CF sites in the sample of MoFSC (2013).

Researchers at ForestAction Nepal, based on their field knowledge, then chose 65 NCF sites in areas close to CFs that were ecologically and socially similar. NCF sites were selected so they resemble the CF sites to the extent possible in terms of ecological zone, forest type and village ethnic composition, farming system, socio-economic characteristics, etc. in all senses

**9 |** P a g e
8

# **Table 2. Achieving the MDGs in Mauritania: Total Cost, Budget and Financing Gap**

## **(US$ and MRO, unless otherwise indicated)**

|||**CO**|**ST**|**BUD**|**GET**|**FINANCI**|**NG GAP**|
|---|---|---|---|---|---|---|---|
||||**Per**||**Per**||**Per**|
|**MDGs**||**Total (M)**|**Capita**|**Total (M)**|**Capita**|**Total (M)**|**Capita**|
|**Goal 1. Eradicate extreme poverty and hunger**|US$|275.57|16.04|113.33|6.60|**_-162.24_**|-9.44|
|_Target 1, Indicator 1_|MRO|75,736.05|4,435.21|31,161.84|1,825.61|-44,574.21|-2,609.59|
|**Goal 2. Provide primary education for all**|US$|**_679.71_**|40.45|613.29|36.37|-66.43|-4.08|
|_Target 3, Indicator 7_|MRO|187,221.60|11,206.73|168,863.47|10,073.23|-18,358.13|-1,133.50|
|**Goal 3. Promote gender equality and empower women**|US$|52.61|3.00|10.79|0.65|-41.83|-2.35|
|_Target 4, Indicator 9_|MRO|14,430.03|827.43|2,973.47|178.92|-11,456.55|-648.51|
|**Goal 4. Reduce child mortality**|US$|202.50|12.12|91.60|5.56|**_-110.90_**|-6.56|
|_Target 5, Indicators13 and 14_|MRO|55,816.45|3,360.86|25,280.47|1,543.00|-30,535.98|-1,817.85|
|**Goal 5. Improve maternal health**|US$|163.26|9.97|38.86|2.41|**_-124.40_**|-7.56|
|_Target 6, Indicator 16_|MRO|45,094.16|2,770.94|10,753.74|671.41|-34,340.42|-2,099.53|
|**Goal 6. Combat HIV/AIDS, malaria and other diseases**|US$|26.61|1.49|10.05|0.61|-16.56|-1.00|
|_Target 7,8, Indicators 18, 21, and 23_|MRO|7,285.70|410.36|2,772.32|167.89|-4,513.38|-271.72|
||US$|392.37|23.58|140.51|8.58|-250.16|-15.12|
|_Subtotal Health_|MRO|108,196.30|6,542.16|38,806.53|2,382.30|-68,903.34|-4,189.11|
|**Goal 7. Ensure environmental sustainability**|US$|220.83|11.04|161.52|7.93|-59.31|-2.81|
|_Target 10, Indicator 30_|MRO|59,919.94|3,008.51|43,783.77|2,159.59|-16,136.17|-771.58|
|**TOTAL ESTIMATIONS**|US$|**1,621.10**|**94.12**|**1,039.44**|**60.12**|**-579.96**|**-33.81**|
||MRO|**445,503.92**|**26,020.04**|**285,589.08**|**16,619.66**|**-159,428.40**|**-9,352.29**|
||% GDP|**8.95**|**n/a**|**5.73**|**n/a**|**3.22**|**n/a**|

_Source:_ Authors’ calculations.
sectors in the SAM. The activities most affected by this price shock are, by far, chicken broiler, and eggs and layers, big consumers of maize as intermediate consumption. Informal food processing and other meats are also affected, again through maize consumption for intermediate demand in both cases, and through other grains in the case of informal food processing. The final impact on the Producer Price Index (PPI) would be 3.16 percent.

In terms of the increase in the cost of living, rural households are this time more affected by the increase in the prices of cereals than urban households. The main explanation for this result is a consumption share for cereals in rural households that is twice as high as the corresponding share for urban households. The total increase in cost of living for households in Ghana would be 3.65 percent as a result of this combined price shock on all key cereal products. The results are summarized in Table 8.

# **_3.5. Impact of an increase in the overall price of food_**

At the extreme, one could argue that the price of other food items might also increase if the price of cereals increases. We therefore run one last simulation, with a 34 percent increase in the price of all food products. The 2005 SAM for Ghana includes twenty five accounts for food, with imports accounting for 8.5 percent of the total food supply in Ghana. Table 9 contains the resulting price change for all the sectors in the SAM. The activities most affected by this price shock are, by a large margin, cocoa processing (27.2 percent), and formal (22.2 percent) and informal food processing (25.3 percent). Textiles (17.0), dairy products (16.6), and meat and fish processing (14.9) follow in impact size. The final impact on the Producer Price Index (PPI) would be 11.6 percent.

14
communities. Overall, energy poverty observatories serve as indispensable tools in the fight against energy poverty, aiding in informed decision-making and the design of effective interventions.

# References

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<mark>Culver, L. C. (2017, May). Energy poverty: What you measure matters. In Proceedings of the reducing energy poverty with natural gas: Changing political, business and technology paradigms symposium, Stanford, CA, USA (pp. 9-10).</mark> Day, R., Walker, G., & Simcock, N. (2016). Conceptualizing energy use and energy poverty using a capabilities framework. Energy Policy, 93, 255-264.

<mark>Energy Poverty Advisory Hub (2022). Introduction to the Energy Poverty Advisory Hub (EPAH) Handbooks: A Guide to Understanding and Addressing Energy Poverty Published by the Energy Poverty Advisory Hub</mark> 35
# **3) THE IMPORTANCE OF THE INVESTMENT CLIMATE IN POST-CONFLICT SITUATIONS**

The previous section underlined the wide range of ways in which conflict negatively impacts the investment climate, both in specific policy areas and in the broader enabling environment. This section provides an overview of why a strong investment climate is so important in post-conflict situations. The arguments for a strong investment climate in any context have been well-rehearsed elsewhere, particularly in relation to supporting economic growth and creating jobs and new livelihood opportunities.<sup>22</sup> But in addition, in post-conflict situations, the private sector can also become a mechanism to deliver vital public services in situations where government capacity is much diminished. More subtly, the web of connections that private enterprise builds are an important means of rebuilding social capital.

These three factors—jobs, public service delivery and social capital—are key to addressing some of the principal risk factors for a return to conflict. While the debate on the causes of conflict rumbles on in the academic literature,<sup>23</sup> most analysts accept that a complex (and context-specific) mix of ‘greed’ and ‘grievance’ factors explain the motives for conflict, including relapse into conflict following a peace agreement. The role of the private sector in addressing both of these factors through generating jobs, delivering public services and boosting social capital means that it can play a vital, but under-acknowledged, role in cementing the peace.

## **Employment**

Without jobs, individual livelihoods are threatened and dependants cannot be provided for. For many combatants, conflict itself is a livelihood, albeit a desperate one. In a post-conflict situation, large groups of demobilized ex-combatants who lack any means of supporting themselves are a natural constituency for those seeking to re-ignite hostilities.<sup>24</sup> The successful generation of employment opportunities gives individuals a stake in success of the peace process, helping to stabilize a volatile political environment.

Public works and infrastructure reconstruction projects can absorb some excess labor in the short to medium term. In Liberia, for example, a “jobs for guns” program persuaded fighters to lay down their arms and provide labor for infrastructure rehabilitation projects,<sup>25</sup> while in Timor Leste, the World Bank’s first post-conflict operation funded urban rehabilitation jobs.<sup>26</sup> However, the private sector is fundamental to resolving the jobs problem in a sustainable fashion over the longer term. Public works are not a sustainable or scalable source of employment

> 22 As the 2005 _World Development Report_ puts it, “a good investment climate fosters productive private investment...creates opportunities and jobs...and expands the variety of goods and services available.”

> 23 The now-classic paper that kick-started the debate on ‘greed vs. grievance’ was “Greed and Grievance in Civil War” (Collier and Hoeffler 2005).

> 24 See, for example, Human Rights Watch 2005. 25 Hoeffler 1999.

> 26 World Bank 2000.
Table A.2: Types of Nonfarm Employment: Multinomial Regression Results Marginal effects reported.

|Depend|ent variable= t<br>|ype of nonfar<br>|m occupation|
|---|---|---|---|
||Low Return<br><br>Wage em|High Return <br><br>ployment|Self<br> Employment<br>|
|Cash Crop Suitability Index|-0.0121|-0.0439|-0.0396|
||(1.84)*|(3.59)***|(2.80)***|
|Distance to towns (KM)|0.1089|-0.0640|-0.0650|
||(10.69)***|(2.57)**|(2.65)***|
|Female (yes=1)|-0.0015|-0.0023|-0.0005|
||(5.00)***|(5.82)***|(1.47)|
|Age (year)|0.0001|0.0213|0.0033|
||(0.11)|(13.12)***|(1.94)*|
|Education (year)|-0.0002|-0.0022|-0.0016|
||(0.13)|(0.82)|(0.52)|
|Household size|-0.0470|-0.2342|-0.1110|
||(1.80)*|(5.15)***|(1.80)*|
|Land owned per capita(hectare)|0.0000|-0.0001|0.0002|
||(0.11)|(2.70)***|(2.75)***|
|Total HH assets (1000 Tk.)|-0.0013|0.0679|0.0587|
||(0.09)|(4.31)***|(3.27)***|
|HH has electricity (yes=1)|0.0274|0.0103|0.0265|
||(2.22)**|(0.51)|(1.26)|
|Village has electricity (yes=1)|-0.0145|0.0533|-0.0215|
||(1.04)|(1.67)*|(0.82)|
|Village has NGO programs (yes=1)|0.0003|-0.0262|0.0672|
||(0.02)|(0.67)|(1.97)**|
|Village has Credit programs (yes=1)|0.0083|0.0161|0.0387|
||(0.77)|(0.94)|(2.19)**|
|Village has market (yes=1)|-0.0002|-0.0006|-0.0004|
||(0.84)|(1.79)*|(1.27)|
|Observations|6828|||
|loglikelihood|-7249|||

Robust z statistics in parentheses * significant at 10%; ** significant at 5%; *** significant at 1%
13 groupings is the close correlation between the TRI and NRA series. This result is driven by the dominance of the import-competing sector in each of these two aggregations. The close correlation between the two series need not always result, however. Oceania provides a counter example, where the TRI has the opposite sign to the NRA aggregates, indicating that trade policy overall in Oceania was trade expanding despite positive NRA aggregates, because there was positive assistance to Australia’s (and in some time periods New Zealand’s) dominant export sub-sector. Another example of where the correlation between the TRI and NRA breaks down is in the time period 1980–84 to 1985-89. The NRA is increasing for 34 focus countries, from around 40 to 60 percent, while the TRI falls in this period by a similar amount. Agricultural policies in the focus countries were on aggregate becoming less trade restrictive in this period (even though the NRA is increasing) because assistance was increasing for exportable products, in the form of export subsidies.

The WRI results reveal that over the period shown the aggregate NRA measure greatly understates the extent of welfare losses from agricultural and trade policies (Figure 2 and Table 4). Figure 2 shows that for EU countries the extent of understatement is greatest in the 1970s, and for the 34 focus countries the understatement is greatest in 1985–89. These large gaps coincide with world pricespikes. The 1985–89 period is when a downward price spike resulted in importcompeting products being more distorted relative to export products, and conversely for the 1975–79 period.

The fall in the WRI for EU countries is dramatic following the peak in the early 1980s, and more dramatic than the fall in the EU’s aggregate NRA over the same time period (Figure 2). From the peak in 1980–84, there is a fall in both the weighted mean and the weighted variance of producer (consumer) distortions. Thus, the two elements of the WRI are falling, resulting in a steeper decline in the WRI than the NRA. This shows one of the benefits of generating a WRI: it provides a better sense of welfare improvements from policy reforms that reduce assistance to covered farm products. It should be noted, however, that from the mid-1980s, OECD members moved towards a re-instrumentation of agricultural policy, which is not fully reflected in the WRI and TRI estimates presented in Figure 2 (see next section).

The individual country WRI results are presented in Table 4. They are necessarily always above the TRI and the average of the NRA and CTE measures, and are always positive because they are means of order two. There is considerable
based surveys. De Walque (2006) presents a comparison of the determinants of HIV infection and associated sexual behaviors using data from five Demographic and Health Surveys (Burkina Faso, Cameroon, Ghana, Kenya, Tanzania). The paper also fits in the literature related to prevention campaigns through randomized evaluation, even if we use non-experimental data. Duflo et al. (2006) conducted a randomized evaluation comparing three school-based HIV/AIDS interventions in Kenya; training teachers, encouraging students to debate the role of condoms and write essays on how to protect themselves against HIV/AIDS, and reducing the cost of education. After two years, girls in schools where teachers had been trained were more likely to be married in the event of a pregnancy and the condom debates increased practical knowledge and self-reported use of condoms without increasing self-reported sexual activity. Dupas (2006) carried on a campaign that provided Kenyan teenagers with the information that HIV prevalence was much higher among adult men than among teenage boys because of their longer exposure to the HIV virus. As a result of this campaign, she found a 65 percent decrease in the incidence of pregnancies by adult partners among teenage girls.

Analyses of HIV within a couple have mainly been published in the medical literature. Serwadda and others (1995), Quinn and others (2000) and Gray and others (2001) use data from discordant couples from the same community-based study in Rakai district in Uganda to explore the dynamics of HIV transmission, the rates of male-to-female and female-to-male transmission and the probability of HIV transmission per coital act. In the economics literature, De Walque (2007a) studied HIV infection at the level of the cohabiting couple for Burkina Faso, Cameroon, Ghana, Kenya and Tanzania using Demographic Health Surveys.

As main results of our study, we show that in Lesotho education appears to be an important aspect of preventing HIV infection and adopting less risky behaviors. We also highlight the vulnerability of married women who engage in extra-marital sex without using a condom. In addition, we show that more than 40 percent of HIV infected couples are discordant couples, i.e. couples where only one of the two partners is infected. This result suggests that there is still room for prevention inside couples.

The present paper uses data from the first Demographic and Health Survey conducted in Lesotho in 2004. The Lesotho Demographic and Health Survey (LDHS) is particularly appropriate to study the determinants of HIV/AIDS for different 3