rafmacalaba/datause-encoder-data huggingface dataset:
This yields the change in the educational enrollment rate that will neutralize the change in risk introduced by deviation of rainfall from the baseline case: For each education level, we calculate ∆ Ei for persons killed by floods, persons affected by floods, and persons affected by droughts. Adopting a conservative approach, we only consider positive ∆ Ei.<sup>10</sup> We compute the 50-year sum of positive ∆ Ei for each of the three risks and, in keeping with our conservative approach, we choose the risk for which the sum is largest.

# **7.3  The Cost of Climate Change Neutralization**

Now we are ready to compute the cost of neutralizing the risk impact of more extreme weather events via increased schooling for young women. Once we have chosen the appropriate ∆ Ei for each schooling level in each country, computing the associated incremental cost involves two steps. First, we obtain the number of new students by multiplying ∆ Ei (as a percent) by the number of females in the appropriate age cohort.<sup>11</sup> Then we multiply by projected expenditure per pupil. To compute projected unit expenditures, we have drawn on the World Bank’s World Development Indicators to estimate panel regressions for primary and secondary expenditures per student as a proportion of gdp per capita. After extensive experimentation with available and plausible righthand variables (e.g., per capita income, size of student population, time trend), we find significance only for country, subregional and regional fixed effects. We use all three sets of fixed effects to get the most accurate estimates for countries that have

> 10  In a wetter climate regime the number of people killed and affected by floods will rise, but the number affected by droughts will fall.  The converse is true for a dryer regime.  A complete accounting would therefore involve calculation of net impacts (losses from flooding vs. losses from droughts)  But in the case of floods, such an exercise would require assigning relative weights to being killed and being affected (injured, rendered homeless, or requiring temporary assistance).  It would also require the assignment of relative weights to the affects of floods and droughts.  Rather than adopt arbitrary weights, we take the conservative approach and use the greatest change in enrollment ratio across the three risk categories. 11 Our primary-school cohort is young women in age group 5-9, plus half of young women 10-14.  Our secondary cohort is half the age group 10-14, plus the age group 15-19.

19
studies’ failure to distinguish the effects of family planning by education level would lead to a lower average estimated effect.

The available evidence further supports the conclusion that the reduction in fertility is attributable to the availability of family planning, and not the indirect result of the presence of health services per se. First, if health facilities were responsible, one would expect a larger effect on child mortality than we find. Second, the age pattern of the effects on fertility, with large reductions for women younger than 20 and women older than 30, is evidence of a strong compression of the timing of births. This is consistent with access to family planning, but not with the notion that the health facilities are primarily responsible. Without access to modern contraceptives the main African way to reduce fertility is to ensure that the space between births is as long as possible. Only with additional control over the timing of births and completed fertility would it be optimal for a woman to have children more closely spaced. The closer spacing of births also points to an important benefit of family planning: it allows women to spend more time in the labor market thereby increasing household income.

Despite the relatively large estimated effect of family planning, skeptics will rightly argue that it will by no means suffice to reduce fertility in Ethiopia to near replacement levels. Nevertheless, it does suggest a low cost and complementary entry point to reduce fertility and speed up the development process in such a setting. This is especially important in poor areas where low schooling level and high fertility rates prevail. As simulated in World Bank (2007), in addition to improving women’s health and overall empowerment, the long-term and self-reinforcing consequences of initiating such a process can be substantial. With the total fertility rate still exceeding 5 children per woman in more than 20 countries, the opportunities are clearly substantial.

23
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Of these six eligible proposals, three were randomly allocated to the treatment arm of the study, while the remainder served as controls. Each of the resulting 153 treatment groups then received about Br 3,000 (USD 220).<sup>6</sup> In addition, the treatment groups received technical support from the DAs while implementing their project. The members of the treatment FIF group were expected to bear 5 percent of the nonlabor costs of implementing their ideas. Control groups did not receive any funding or technical support for their proposal.

As mentioned above, FIF had mostly male participants. The Government of Ethiopia and the World Bank Gender Innovation Lab therefore later designed a second component to this agricultural intervention that specifically targeted women. The Women Innovation Fund (WIF) was created not only to increase women’s participation but also to address some of the identified drivers behind women’s low productivity in agriculture. By increasing women’s access to extension services, and easing associated financial constraints, the WIF intended to generate empirical evidence on the impact this had on agricultural productivity, as well as off-farm incomegenerating activities and household welfare.

The WIF was implemented in the FIF kebeles. Of the 51 FIF kebeles, 20 were randomly allocated to receive the WIF. The WIF randomization used the household list from the FIF baseline survey as its sampling frame. Within each of the 20 treatment kebeles, 10 to 15 women were randomly selected from FIF eligible households to form a WIF group.<sup>7</sup> As previously for the FIF, the DA helped each group develop a proposal for its own income-generating activity. The 20 treatment groups each received roughly Br 10,000 (USD 730) in addition to the technical support from DAs while implementing their projects.<sup>8</sup> The money was used to finance the group’s income-generating activity. It should be noted that the WIF funding was more than three times the amount of funding that FIF groups received, and we have 3 FIF groups per kebele while it is only one WIF group. In addition, the FIF group is formed by self-selected farmers while the WIF members are randomly selected from FIF eligible households.

> 6 Thus, for an FIF group of 12 farmers, this means USD 18.33 on average per member. 7 These WIF households were from either the treatment or control arms of FIF. 8 For a WIF group of 12 women, this averaged to USD 60.83 per member.

9
**Table 2: Reported Profits Vs Reported Revenue Minus Expenses**

|||KM|S||<br>SL|MS||
|---|---|---|---|---|---|---|---|
|||July|Aug|round 1|round 2|retail|manufacturing|
||Mean|5126|5012|3705|4690|5611|3743|
|Reported Profits|S.D.|4313|3560|3602|4435|4707|3925|
||Median|3875|4000|3000|3500|4500|2550|
||% negative|0|0|0|0|0|0|
|Reorted Revenue|Mean|1091|3246|1354|1958|3006|903|
|p<br>Min Exn|S.D.|12347|12006|11516|9738|11367|7636|
|us peses|Median|125|3183|1000|1661|2595|1130|
||% negative|48|30|29|28|28|27|
||Pearson|0.150|0.296|0.209|0.285|0.308|0.207|
|Correlations:|Spearman|0.021|0.143|0.329|0.422|0.436|0.411|
||p-value|0.784|0.061|0.000|0.000|0.000|0.000|

Note: SLMS retail and manufacturing breakdown is for round 2

**Table 3: Adjusting for Unreported Categories**

|||KM|S||SLMS||
|---|---|---|---|---|---|---|
|||July|Aug|round 2|retail|manufacturing|
|_Percentage of firms re_|_porting that:_||||||
|Goods used for home|consumption|75.9|90.8|39.2|63.5|14.7|
|Firm inputs given as g|ifts but||||||
|reported as a busines|s expense|2.9|2.3|2.3|2.3|2.3|
|Firm owners pay them|selves a salary||||||
|but fail to include this|as profits:|2.9|2.3|1.2|0.7|1.7|
|Business revenue is u|sed to pay||||||
|household expenses,|but is not||||||
|counted as profit||37.4|50.6|13.7|13.3|14.1|
|_Adjusting Expenses a_|_nd Profits_||||||
||Mean|6465|6060|5268|6180|4332|
|Adjusted Profits|S.D.|5251|3947|5060|5384|4526|
||Median|5000|5000|4000|5000|3000|
||% negative|0|0|0|0|0|
|Adtd Rvn|Mean|3845|6242|3399|5085|1666|
|juse eeue<br>Mins Exenses|S.D.|12741|12750|11008|12312|9188|
|u p|Median|2500|5900|2150|3875|1500|
||% negative|34|25|23|23|26|
||Pearson|0.136|0.320|0.295|0.312|0.216|
|Correlations:|Spearman|0.110|0.214|0.456|0.482|0.409|
||p-value|0.149|0.005|0.000|0.000|0.000|

Notes: adjustments were not asked in SLMS round 1 SLMS retail and manufacturing breakdown is for round 2 25
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22
15 contracts of different duration, building a contract portfolio that minimizes supply and price risks in both the long and the short run.

By generating price signals about the market value of natural gas, spot markets promote efficiency in the natural gas industry. A spot market usually develops in an area with a high concentration of buyers and sellers, such as at a pipeline interconnection near a large metropolitan area or at a terminal in a gas-producing region. The aggregation of supply and demand increases the liquidity of the market and boosts competition among market participants.

If there is competition among market participants, short-term (spot) prices follow the short-run marginal cost of natural gas. This means that the spot prices of natural gas reflect its economic value at a particular time and location (at the spot market), and market participants, facing efficient prices, can make optimal decisions about their trading strategies. But spot prices tend to be volatile because they change in response to changes in underlying factors of supply and demand, such as weather, available pipeline capacity, or consumption pattern. Market participants become exposed to this price risk because they are unable to predict the future price of natural gas. Their demand for tools to minimize price risk leads to the development of a financial gas market.

# _Financial gas market_

The financial gas market is the marketplace where financial gas contracts are traded. A financial gas contract is used primarily for managing price risk and is not necessarily for physical delivery. Participants in the financial gas market come from all segments of the gas industry. Because transactions in this market involve the transfer of risks among these participants, intermediation plays an important role. The main intermediaries are traders and financial institutions, such as banks and organized exchanges.

Financial gas contracts are highly variable because of the heterogeneity of needs of market participants. The most common types of contract are forward contracts, swaps, futures contracts, and options.<sup>7</sup>

- _Afrrward contract_ is a supply contract between a buyer and seller that obligates the buyer to take delivery, and the seller to provide delivery, of a fixed amount of a commodity at a predetermined price at a specified date. Payment in full is due at the time of or following delivery. (By contrast, for a futures contract settlement is made daily, resulting in partial payment over the life of the contract.)

- a _A swap_ is custom-tailored, individually negotiated transaction designed to manage financial risk, usually over a period of 1 to 12 years. Swaps can be conducted directly by two counterparties or through a third party such as a bank or brokerage house. The writer of the

The definitions of financial gas contracts draw on the U.S. experience. They are from U.S. Department of Energy 1995 and NYMEX 119961.
In practice, we also include interactions of one of the instruments (crop shocks) with a range of baseline characteristics (including age and sex) to increase predictive power in the first stage.

We impose several restrictions on the sample we examine. Following our previous work, we consider children between the ages of 7 and 15 in the baseline survey. Note that the prevalence of work among younger children is low. Likewise, by most definitions, working at age 16 and above would not be viewed as a particularly serious form of child labor. We also have information on whether children have ever been to school by wave 4. Tabulation of this variable shows that only 32 percent of 7-8 year olds had attended school at some point in time, which is consistent with the widespread tendency to delay enrollment, while among children age 13 and above only 12 percent had never been to school. It is unlikely that these older children who have not yet enrolled will enroll in the future. At the same time, the data suggest that, in response to a shock, households are more likely to employ the labor of the older, more productive children. Because of this, if we include these children in our sample, we would be likely to find a strong negative correlation between years of schooling and child labor. As a result, our main sample includes all 7-15 year olds who were in school at the relevant wave and those children who had not yet entered school but were still young enough to have a chance to enroll (7-9 year olds). We also present results for the full sample of 7-15 year old children.

# _IV.2 First-stage results and the plausibility of the instruments_

In this section, we discuss whether crop and rainfall shocks plausibly satisfy the requirements for a valid instrumental variable.

10
# **Introduction**

An important question for understanding the global impact of the COVID-19 pandemic is the disease’s age pattern of mortality across countries. Early in the pandemic, case data from COVID-19 patients in mainland China showed that COVID-19 mortality risk increases rapidly with age.<sup>1</sup> This risk profile would tend to limit the death toll in developing countries, which generally have younger populations. But this prediction only holds if the age profile of COVID19 mortality remains similar across countries.

Our study examines this question by comparing the age gradient of COVID-19 mortality in a broad set of countries using both official COVID-19 death counts and excess mortality estimates for 2020. Vaccine availability was very limited before 2021 and therefore is unlikely to affect the mortality patterns identified in this analysis.

Nearly all studies of the pandemic’s age mortality patterns have used only official COVID-19 death data.<sup>2–8</sup> However, excess deaths at the aggregate population level during the pandemic exceed officially recognized COVID-19 deaths in most countries.<sup>9</sup> This is to our knowledge the first study that estimates COVID-19 excess mortality by age for a wide swath of countries.

Previous studies have found a pattern of flatter COVID-19 age-mortality curves among low- and middle-income countries (LMICs) using only official COVID-19 death counts.<sup>4,8</sup> It was unclear to what extent that finding may have been driven by data reporting issues. In particular, it is possible that deaths among older people in developing countries have been more likely to be under-attributed to the disease in official COVID-19 death counts. This paper addresses that concern by showing that the same pattern is found using both official COVID-19 death counts and excess mortality estimates.

# **Methods**

_Data sources_ The analysis employs two main sources of data:

1. Official reported COVID-19 deaths drawn from the COVerAGE database, with death counts aggregated to 5-year age-groups.<sup>10</sup>

1
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- Hensel, Paul, Sara Mitchell, and Thomas Sowers. 2006. ―Conflict Management of Riparian Disputes,‖ _Political Geography_ , 25(4): 383-411.

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27
**2**

prevailing under free trade, alternative conversioi. factors are estimated that are deemed to reflect the actual rate at which foreign transactions take place. Such estimates seek to take into account the nature and restrictiveness of trading regime, information on relative prices, and the evolution of real exchange rates." **2** Estimating alternative conversion factors is a difficult process and, in part judgmental.In those **highly** egregious cases, the Bank attempts to estimate alternative conversion factors that permit meaningful calculations for intercountry comparisons.

**3.** The repertoire of exchange rates available has expanded. In addition to the official exchange rate' shown in the Fund's International **Financial Statistics (IFS),** secondary and tertiary rates are also available in the **IFS.** In addition, IEC has created an internal file on parallel or black exchange rates from various sources. These are weekly or monthly quotations covering the period **1960-89** from Pick's Currency Yearbook, International Report: Statistical Market Letter, and Africa Analysis. Other rates (e.g., purchasing-powerparities, rates accounting for trade taxes and subsidies) can also be estimated, as explained later in the paper. With the increasing availability of country-specific multiple exchange rate data, the paper suggests that these alternate conversion factors be included in a system so that time series of alternate conversion factors can be made **by** choosing a preference order among different sets of conversion possibilities. Obviously the choice **'f** preference order varies from country to country and also from year to year within each country.

> **2 Ibid; pp. 2-3.**

> **'** In general, the official exchange rate is that reported as line rf in IMF,International Financial Statistics **(IFS)** (Washington **D.C.;** International Monetary Fund).