rafmacalaba/data-use-mentions-extended huggingface dataset:
convention for measuring indoor air pollution, we rely on them for our cross-sectional analysis.

However, the PDRam readings in Figure 2 provide a very useful perspective on the timing of pollution. They show that the largest source of variation in PM10 concentrations is the 24-hour cycle in individual households. Peaks occur during morning and evening cooking periods, and houses with three cooking periods have an additional peak. Typically, concentration levels plunge in the afternoon in houses where kitchens are aired out, and they fall in the evening as well (although often not as far, since windows and doors may be closed at night). Most houses have relatively low concentrations for significant parts of the day.

Inter-household differences in pollution exposure are largely attributable to two factors: the level of peak concentrations during cooking, and the rate at which concentrations decline after cooking. As Figure 2 suggests, these factors differ substantially from house to house. Their significance emerges strikingly when we control for fuel use. Table 5 presents MiniVol PM10 statistics for sample households in the three most common fuel-use categories: firewood, dung and jute. None of these households reports using an improved stove or a chimney. Across households, PM10 concentrations vary from 84 to 1165 ug/m<sup>3</sup> for firewood, 60 to 755 for dung, and 72 to 727 for jute. As Figure 2 indicates, many houses are relatively “clean” during parts of the night and afternoon, when indoor readings resemble ambient readings. However, differences in cooking practices, structural arrangements and ventilation behavior generate very large differences in overall concentrations. As a result, some households 18
peak still expected large sales losses a year later in mid-2021 (Bloom et al., 2021). These results are aligned with previous studies showing negative short-term impacts on employment (Barrero and Bloom, 2020) and business activity (Fairlie and Fossen, 2021). Another study using high-frequency data from the United States Census Bureau’s Small Business Pulse Survey (SBPS) suggests that small firms experienced very sharp declines in activity, business sentiment, and expectations in the initial phase of the pandemic (Buffington et al., 2021). While there has been some recovery since then, multiple indicators of performance remained substantially in the negative range early in 2021.

From the perspective of firm dynamics, there are two relevant dimensions that can potentially explain differences in how countries, on average, are recovering from the COVID-19 shock: i) The performance of incumbent firms; and ii) the entry and exit of firms. Evidence, mainly from advanced economies, suggest that both dimensions have been playing an important role in the recovery process. In terms of the entry-exit margins, the literature suggests enhanced rates of entrepreneurship in advanced economies such as the United States, France, Germany, Japan, and the United Kingdom in contrast to a decline in new business formation in countries such as Portugal and the Russian Federation (Buffington et al., 2021; Haltiwanger, 2021; Djankov et al., 2021; Romei, 2020; OECD, 2021). For instance, Haltiwanger (2021) notes that the pace of applications since mid-2020 in the United States is the highest on record, although this surge in new business applications has been uneven across sectors and has mostly pertained to non-employer firms.

The COVID-19 pandemic may also have contributed to the exit of many firms. Miyakawa et al. (2021) estimated an increased firm exits in Japan of about 20% compared to the pre-pandemic year. OECD (2021) shows that among all surveyed countries, monthly bankruptcies dropped year-on-year by more than 30% on average since March 2020
> one would expect that mutual funds provide a way for individual investors, with long-run horizons, to invest in bonds and equity. (Long horizons being relevant here because, over the years, emerging countries--expected to converge to industrial countries-would need to grow at a faster rate, and in the process provide higher returns than industrial countries.) In this paper, we show the stability of mutual fund flows to emerging countries **by** comparing the flows over time and, particularly, **by** focusing on crises.

There are two key advantages-beyond their growing importance-to studying mutual funds vis-i-vis other investor types. The first is data quality. Mutual funds report holdings to the **SEC** semi-annually. In addition, private companies compile mutual fund data at higher frequencies, typically quarterly, **by** conducting surveys. These data enable analysis in both the cross-sectional and time-series dimensions. In contrast, other institutional investors, like pension funds and hedge funds, are not required to disclose holdings. Nor have we found sources that compile voluntary disclosures for these investor types.<sup>5</sup> The second key advantage, beyond size, to studying mutual funds is that their emerging market investment has grown considerably in scope and size. There now exist quite specialized sub-categories within the broader mutual fund category. For example, some funds specialize in a particular country; others specialize within a region; others invest specifically in emerging markets; and others simply invest in emerging markets as part of a global strategy.

The paper is organized as follows. Section II briefly describes the evolution of capital flows to emerging markets. Section III covers the mutual fund investment in

> Thus, to study the behavior of pension or hedge funds one would need estimates of portfolio changes. Brown, Goetzmann, and Park **(1998)** provide such estimates for hedge funds during the Asian crisis.

**3**
1.5|1.6|60%|58%|26%|
|**84, 91-92**|**Mechanical Machinery (including Clocks and Music**<br>**Instruments)**|870|252|8|72%|1.9|1.6|62%|61%|24%|
|**85, 90**|**Electrical Machinery (including Optical, Medical,**<br>**Photographic Instruments)**|1183|737|8|76%|2.4|1.8|57%|54%|30%|
|**86-89**|**Transportation Vehicles**|326|1275|41|71%|1.4|1.6|65%|63%|24%|
|**93**|**Arms and Ammunitions**|25|523|39|69%|1.4|1.7|59%|61%|19%|

Note: the figures shown in the table for each group of sectors are based on measures in the country-year-product (HS 2-digit) level dataset (file CYH2.dta) averaged across HS 2-digit sectors, countries, and the 2006-2008 period.

> 35 Kuwait also exhibits a high entry rate but it covers a different sample period.

21
3|
|Male|**_52.5_**|_45.6_|**_59.0_**|**_52.6_**|
|**Unemploymentrates**<br>**_%_**_oflaborforce_<br>Female|6.4|**7.5**|**16.3**|**...**|
|Male|**5.7**|**7.0**|15.4|**...**|
|**Wage gap**|||||
|**_%_**_ofmale wage_|||||
|Female|**72.3**|**81.6**|**79.9**|...|

_Source:_ ILO; **1996** Kyrgyz **LSMS; OECD-CEET 1997** database; **E.** Barry and M. Reilly, **1999;** World Bank data.

**3.9** Comprehensive information is not available on how these differences translate in total years of contribution for men and women. For the purposes of our analysis, we generated average lengths of service based on each country's labor force participation **by** age and

**14**
Ivoire, the context of this study, only 16% of women in higher education study life sciences, mathematics or statistics (UNESCO, 2017). Similar imbalances can be observed in the TVET system. Despite achieving gender parity in TVET enrollment, including women in high-paying, male-dominated sectors (MDSs) remains a challenge in Cˆote d’Ivoire. Women’s enrolment is predominantly in services streams (89.9%), with limited representation in the secondary sector (10.2%), and almost no presence in agricultural streams (0.1%). These decisions have far-reaching consequences, as MDSs are often the most lucrative sectors.<sup>2</sup> In many countries, women are concentrated in the least profitable sectors (World Bank (2022), Goldstein et al. (2019), Jonathan et al. (2015), Bardasi et al. (2011), Das and Kotikula (2019), (Alibhai et al., 2017)). Consequently, there is growing interest in supporting women to enter better-

> 1The role of TVET in secondary education is still limited in Cˆote d’Ivoire. Despite an increase in enrolments in recent years, only 2 percent of the 15/24 years old participated in a TVET in 2018, according to UNESCO TVET Country Profile and UNESCO Institute for Statistics (UIS).

> 2Our analysis of two nationally representative datasets, the EHCVM 2018 (WAEMU Commission, 2018) and the ENV 2015 (Institut National de la Statistique, 2015), reveals that MDSs offer higher average compensation compared to other sectors. Results are available upon request.

1
# **6 Conclusions**

In this paper, I use household-level panel data from Ethiopia to show that exogenous shocks to international coffee prices have significant effect on household consumption and investment in child human capital. For a household that produces only coffee, a 1% change in international coffee price would lead to 0.8% change in household consumption. This strong pass-through of coffee price shocks to household consumption implies that households do not have meaningful consumption smoothing options during periods of price shocks. I find that borrowing increases slightly during period of low coffee price, but it has limited effect on household consumption smoothing, perhaps because the size of the loans are too small or because direct loan for consumption is limited.

Moreover, I show that households (particularly male members) adjust their on-farm labor supply in response to coffee price changes. Households reallocate more fraction of their on-farm labor supply to non-coffee fields when coffee prices drop. While coffee price movement has no significant effect child labor (on-or off-farm labor), I find that periods of high coffee prices are associated decrease in enrollment of girls but not boys. A plausible explanation for this is that young girls might have to engage in more domestic work to allow their parents work more on- and off-farm. I also show that children who are in utero in coffee producing households during low coffee price periods have lower measures of weight-to-age and weight-to-height z-scores. These results on the effect of coffee price change on school enrollment of girls and child malnutrition imply that commodity price shocks not only affect current household welfare but it may also hamper future economic growth through its effect on human capital accumulation.

A major policy implication is that governments and NGOs should design effective means to shield producers from erratic change in commodity prices. For instance, innovative insurance schemes, such as commodity price indexed insurances (similar to weather indexed insurance schemes), may have a positive effect on welfare. Future studies that seek experimental evidence of the feasibility and effectiveness of these insurance schemes would be a step forward towards
report the consumption of Tubers, the 7- and 14-day long list recall designs (modules 1 and 2) report a significantly lower consumption incidence of 58–59 percent. The only food group reported at the same frequency as the benchmark is Vegetables; two food groups, Oils/Fats and Beverages, are actually reported at a significantly higher incidence of 5–6 percentage points. These two exceptions are true only for the long list recall modules. The 7-day subset list and collapsed list (modules 3 and 4) underreport the consumption of all food groups. Indeed, the downward bias in incidence is even larger in magnitude for the 7-day subset and collapsed lists. For example, Tuber consumption incidence is estimated at 52 percent–54 percent. While the consumption incidence of the 7-day short list may be expected to be lower than the 7-day long list because the former module design asks about a fewer number of individual food items, there is no prior expectation that the 7-day collapsed list will record lower consumption incidence. The fact that the collapsed list does record a lower incidence for all food items suggests that important consumption items are excluded because of a lack of cognitive prodding that the longer list explicitly incorporates in the design.

By contrast with the other recall modules, the usual month approach to recall survey design reports significantly higher consumption incidence among almost all food groups, with the sole exception of Cereals, which are consumed by 96 percent of the benchmark households. This difference most likely derives from the different cognitive demand of considering a usual month, which apparently prompts respondents to report significantly higher consumption incidence relative to the actual consumption recorded in the benchmark. Finally, the two household diary modules (modules 6 and 7) tend to report lower consumption incidence among various food groups such as Fruits (9 percent lower) and Meals Outside the Home (7 percent–9 percent lower). While the frequency of the household diary supervision does not appear to influence the accuracy of consumption incidence measurement because the rates are equal for the
confounds the interpretation of the estimate. This requires data that matches speci fi c students and teachers, a requirement that is hard to ful fi ll in most countries; Ehrenberg and others (1991) for instance use district level data, which could partially explain the lack of a relationship between teacher absence and student learning in their study.

# 3 Theory

The model is based on Das and others (2004a). It is extended here to explicitly focus on the implications of uncertainty in teacher inputs on household decisions about educational inputs and cognitive achievement, as measured by test scores. The model assumes a household (with a single child attending school) that derives (instantaneous) utility from the cognitive achievement of the child TS and the consumption of other goods X. The household maximizes an intertemporal utility function U(.), additive over time and states of the world with discount rate β(< 1) subject to an intertemporal budget constraint (IBC) relating assets in the current period to assets in the previous period, current expenditure and current income. Finally, cognitive achievement is determined by a production function relating current achievement (TSt) to past achievement (TSt−1), household educational inputs (zt), teacher inputs (mt), non-time varying child characteristics (μ) and non time-varying teacher and school characteristics (η). We impose the following structure on preferences and the production function for cognitive achievement:

[A1] Household utility is additively separable, increasing, and concave in cognitive achievement and other goods.

[A2] The production function for cognitive achievement is given by TSt = F (TSt−1, mt, zt, μ, η) where F (.) is concave in its arguments.

Under [A1] and [A2] the household problem is Here u and v are concave in each of its arguments. The intertemporal budget constraint (2) links asset levels At+1 at t +1 with initial assets At, private spending on educational inputs zt,
were more likely to receive aid, as were less wealthy households. However, looking at the probability of receiving aid under a weather shock, we showed that wealth no longer had a significant negative impact. The latter results suggest that applying wealth criteria at the community level was not effective under weather shocks, which likely reduced access to aid by poorer households.

We then evaluated the impact of weather shocks and aid receipt on six consumption outcomes, non-food consumption per capita, value of food consumed per capita, food purchases per capita, calories per capita, the food consumption score, and school participation rates for households with school-aged children. We documented consistent negative impacts of weather shocks on a range of consumption outcomes, particularly for those households subject to sequential weather shocks. Interestingly, the ALNAP (2003) document discusses sequential and concurrent shocks in the context of targeting vulnerable households, but it does not yet seem to be operationalized as a targeting criterion.

We also showed some impacts of aid receipt, again particularly for those households subject to sequential shocks. For the FIAS sample, aid led to 20 percent greater non-food expenditures and enabled households to maintain food purchases under sequential shocks. For the IHPS sample, aid increased the food consumption score and school participation rates under sequential shocks. However, we found limited impacts on the value of food consumed or calories per capita. When we restricted the samples to only include relatively poor households or to only those interviewed from September to March (months when most weather-shock based WFP distributions were made), we documented positive impacts of aid under sequential shocks on value of food consumed and calories per capita.

On the whole, our results suggest that those involved in determining which geographic locations require aid distribution should revisit the weather variables, data sources and model parameters used as part of the larger decision-making process. Are more household survey data continue to become available, further research can improve the ability of governments and their aid relief partners in 43
of nightlight is indicative of dire economic situations and disruptions in public services (S ̈anger et al., 2022). Nightlight provides a rough indicator. However, given the lack of granular metrics at the applicant and/or district levels in Afghanistan, nightlight data provides a plausible indicator of economic development at the district level. Therefore, I cannot differentiate between applicants’ socioeconomic statuses at the individual level and instead rely on district classifications. I used the two-year pre-treatment average luminosity by district as a binary indicator to identify low-SES applicants, operationalized as districts with luminosity lower than the mean (0.2678 nanoWatts/cm2/sr) in the pre-treatment period. I used this indicator to examine the impact of quotas on the share of female students from low SES. To achieve this objective, I successfully matched 95.4%<sup>19</sup> of all matriculated applicants (336,151) to their districts of origin. I accomplished this by merging student records at the district level based on school and province names (i.e., the province from which they participated in the Kankor exam) with the applicant’s high school districts using the comprehensive school database from the MoE.

# **5 Empirical Strategy**

The gender quota implementation started in 2016 and expanded in the following years. The quota distributed the available seats at some concentrations based on gender but maintained open competition at other concentrations. Thus, I exploited the variation across concentrations and over time to study the causal impact of the gender quota on the share of matriculated female students and score-related outcomes in treated units using the following DD model shown in Equation 1:

> 19The reasons for the 4.6% of unmatched applicants include multiple schools located in different districts within the same province. However, labels for these schools are entered identically without any unique district information, making it difficult to identify the exact school from which an applicant graduated in the matriculation dataset. Additionally, applicant school names are missing, either because
2

**CONTENTS Pipeline System Operation** Costs in System Operation System Operation under the Network Code **Natural Gas Markets** Physical Gas Market Financial Gas Market **Capacity Markets** Primary Capacity Market Secondary Capacity Market **Conclusion Appendix** Structure and Regulation of the U.K. Natural Gas Industry Notes References **Tables**

**1.** Size of the U.K. Gas Market, 1994-95

2. Spot Market Prices and Volumes at the Bacton Terminal, June-July 1996 3. On-System Trading, July 22-28, 1996 4. Trading and Prices in the Flexibility Market, July 19-28, 1996 Table A. 1 U.K. Gas Price Index, Current Prices Table A.2 U.K. Gas Price Index, Real Prices Table A.3 Natural Gas Consumption, 1986-95 Table A.4 Natural Gas Production and Imports, 1986-95

# **Boxes**

1. Participants in the U.K. Natural Gas Industry

2. Transition Costs of Opening Natural Gas Markets to Competition: The Case of British Gas Energy

**Figures**

1. Network Code Processes

2. Mechanisms for Natural Gas Trading in the U.K.

3. Argus Monthly Sell-Buy Index, October 1995-September 1996 4. Balancing under the Soft Landing Regime of the Network Code 5. Price Determination in the Flexibility Market with the Source of Imbalance a Change in Demand 6. Price Determination in the Flexibility Market with the Source of Imbalance a Change in Supply 7. Pipeline Capacity Booking and Trading 8. Capacity Resale by Auction Figure A.1 Structure of the U.K. Natural Gas Industry Figure A.2 Index of Current Gas Prices, 1990 = 100 Figure A.3 Index of Real Gas Prices, 1990 = 100 Figure A.4 Natural Gas Consumption, 1986-95 Figure A.5 Natural Gas Production and Imports, 1986-95
. 7Two points are worth noting here. First, many social programs in Peru are involved with the construction of small-scale infrastructure. The wages paid to laborers in these projects are only a fraction of the total cost, and other benefits--say, of having an additional classroom--are unlikely to have a short-term impact on household income. We can easily relax the assumption of a one-for-one equivalence between changes in program expenditures and changes in household income, however, if we simply model a smaller budget. For example, if only 50% of the expenditures on poverty alleviation programs translate into short-term increases in income, the "relevant" budget would be only half the actual budget. Second, households typically do not spend all of an increase in income. Assuming that all households spend a fixed fraction of additional income is not entirely satisfactory either, because the marginal propensity to save is likely to differ _systematically_ across households. For example, if the fraction of income that is saved is higher in rich districts than in poor districts, simulations based on a constant marginal propensity to save might under-estimate the short-term impact of program investments in poor districts relative to rich districts, and under-estimate the relative performance of targeting indicators which assign a higher share of their resources to the poorest districts. One potential solution would be to estimate marginal propensities to save for households from the LSMS itself. The simplest way to do this would be to convert measures of income and expenditures into current prices, and then take the difference between them as a measure of savings (see especially Paxson 1992). By this measure, however, almost two-thirds (61%) of households in the 1994 LSMS _dissaved._ This seems unreasonable and suggests that income in these surveys is seriously underestimated vis-a-vis expenditures.

8