rafmacalaba/data-use-mentions-extended huggingface dataset:
of sulfur combine with oxygen to form sulfates, and,with water vapor to form aerosols of sulfurous and sulfuric acid. These acid mists can irritate the respiratory systems of humans and animals. Therefore, a high concentration of S02 can affect breathing, and may aggravate existing respiratory and cardiovascular disease. Sensitive populations include asthmatics, individuals with bronchitis or emphysema, children, and the elderly.

The second, and probably more significant, effect of S02 is traceable to the impact of fine particulates on mortality and morbidity. A review of recent evidence by the US Environmental Protection Agency suggests that fine particulates are the source of the worst health damage from air pollution. In the case of China, there is reason to believe that 30-40% of fine particulates are in the form of sulfates from S02 emissions.

While Xu's results and the available data have led us to focus on S02, we also recognize the potential contribution of total suspended particulates (TSP) to atmospheric concentrations of fine particulates (FP). For Beijing and Shenyang, we interpret Xu's weak results for TSP to mean that measured total suspended particulates in those cities do not have a substantial FP component. Nevertheless, we recognize that this may not be true in other cases. Therefore, our policy scenarios include projections for industrial TSP emissions, both for China as a whole and for our five cities.

# 4.4 **Projecting Abatement Costs**

Abatement costs are projected from our econometric analyses of Chinese factory data for several air and water pollutants. The major determinants are pollutant type; industry sector; ownership; scale of abatement activity (related to the volume of discharge); pollutant concentration in the waste stream; and degree of abatement (or percent reduction in pollutant concentration). At the city level, we use our results to project incremental costs for degrees of abatement ranging from 10% to 90%.

Data on fine particulate emissions are not available, so we have used S02 as a proxy because S02 emissions have a significant
stadiums, sport clubs, art galleries, theatres—were closed most of the time during COVID-19 before vaccination became available.

We estimate excess deaths using a simple regression of the number of deaths in each province on dummy variables for province and time (quarter of observation) and the population size for four years (16 quarters in total) before the arrival of the pandemic (2015-2019). The linear regression with quarter and province fixed effects offers a good fit (R<sup>2</sup> = 0.99), resulting in good estimates for the predicted number of deaths in each province for the remaining 12 post-pandemic quarters, 2020 through 2022, had COVID-19 not arrived. The results are presented in Table 2 and Figure 7.

For the most part, excess death estimates confirm the timing and severity of the COVID-19 pandemic as reported in the news. Most provinces experienced unusually high death rates in 2020 and 2021, but not in the same quarter (see Figure 7). For example, in the Gilan province, where the pandemic disrupted life early in the pandemic (Salehi-Isfahani, 2020a), government vital registrations data report a one-third increase in the number of deaths winter 2020 (the first quarter of the pandemic) over winters 2019 and 2018, one quarter ahead of other provinces. The variation in the arrival dates of the pandemic across provinces adds to the variation that we need to identify the causal impacts of the pandemic on women’s market work because other shocks, like sanctions, are unlikely to have impacted provinces in the same way.<sup>6</sup> Excess deaths and official lockdowns are different indications of disruptions in normal life and the labor market that affect individual decision to participate in market work. Each has its advantages and drawbacks as markers of labor market shocks. While excess deaths are less precise than lockdowns as markers of labor market disruptions, they are considered the gold standard for

> 6 Ghafari _et al._ (2021) use excess deaths in the
agent approach. The pioneering papers on multi-household models in computable general equilibrium models were done by Adelman and Robinson (1978) and Piggott and Whalley (1985); recent examples include Harrison, Rutherford and Tarr (2003) and Harrison, Rutherford, Tarr and Gurgel (2004). But this approach can mask large differences among the households within each representative household. In order to incorporate information on all available households, but given the difficulty in incorporating large household data sets as multiple agents of a computable general equilibrium (CGE) model, several authors (e.g., Bourguignon, de Melo and Morrison, 1991; Chen and Ravallion, 2004) have adopted a sequential (also called ―open loop‖ or ―top down‖) micro-simulation approach. In the first step a single representative agent computable general equilibrium model is employed to obtain the estimated price changes from a trade policy change. These price changes are then fed into a microsimulation household model for predicted household effects. But the sequential approach ignores feedback effects of the quantity changes in the micro-simulation household model on the equilibrium prices in the representative agent general equilibrium model. Moreover, although efforts are sometimes made at data reconciliation, the sequential approach does not require reconciliation of inconsistent information on household income from the national accounts (which report factor payments) and the household surveys (which report factor income).<sup>40</sup> Consequently, in summarizing the state of the literature, Bourguignon and Perreira (2003, p.343) have argued that one of the major challenges for the analysis of the impact of economic policies on poverty and income distribution is to integrate a computable general equilibrium model with ―real‖ households from the household survey rather than representative households, but they note that this is empirically difficult.

> 40 Without data reconciliation the household model and the representative agent model can produce very different results. Ianchovichina and Martin (2004) estimated that China will gain from WTO accession based on their representative agent model. But Chen and
Haddad, Lawrence, John Hoddinott, and Harold Alderman, eds. 1997. _Intrahousehold resource allocation in developing countries: models, methods, andpolicy._ Baltimore: Johns Hopkins University Press.

King, Elizabeth M. and Anne M. Hill, 1993. _Women s education in developing countries: barriers, benefits, and policies._ Baltimore: John Hopkins University Press.

Lavy, Victor. 1997. "School supply constraints and children's educational outcomes in rural Ghana," _Journal of Development Economics,_ 51: [291]-314.

Mingat, Alain, and Jee-Peng Tan, 1999. "Analyzing Problems in the Structure of Student Flow," mimeo. Institut de Recherche sur l'Economie de l'Education, Universite de Bourgogne and Human Development Network, The World Bank.

Montgomery, Mark, Kathleen Burke, Edmundo Paredes, and Salman Zaidi, 1997. "Measuring Living Standards with DHS Data: Any Reason to Worry?" mimeo, Research Division, The Population Council. New York, NY.

Nehru, Vikram, Eric Swanson and Ashutosh Dubey, 1993. "New Database on Human Capital Stock in Developing and Industrial Countries: Sources, Methodology, and Results," Journal of Development Economics, 46:379-401 Oxfam International, 1999. _Education Now._ Oxfam International: London.

Pitt, Mark, 1995. "Women's Schooling, the Selectivity of Fertility, and Child Mortality in SubSaharan Africa" LSMS working paper, 0253-4517; no. 119. The World Bank, Washington.

Rutstein, Shea, 1999. "Health Nutrition, and Population Fact Sheets." Macro International. Calverton, MD. Mimeo.

Schultz, T. Paul, 1993. "Investments in the Schooling and Health of Women and Men," _The Journal of Human Resources_ 28(4):689-734.

Stecklov, Guy, Antoine Bommier, and Ties Boerma, 1999. "Trends in Equity in Child Survival in Developing Countries: A Illustrative Example using Ugandan Data." MEASURE _Evaluation_
<!-- Start of picture text --> Name Date of Date of ae<br>map content ublicationneers Scale Source Description of the map content Map<br>Municipalities with increased Mato Grosso State Foundation of Municipalitieswoe with. targeted .increased enforcement activitya upon the .implementation. of Map 1<br>FEMA enforcement 2000-01 2001 1:250,000 the Environmental (FEMA) the FEMA environmental control system in 1999, The municipalities were identified from<br>FEMA documents, notably FEMA (2002),<br>Incremental deforestation in 1996-97, 2004 1: 250,000 Mato Grosso State Foundation of Human induced loss of any natural vegetation in Mato Grosso (unlike INPE, FEMA also Map 2<br>Mato Grosso 1998-99, the Environmental (FEMA) considers deforestation in the Cerrado biome), Until 2001, deforestation was measured bi-<br>2000-01, annually (two years), Since 2002, deforestation is measured annually.<br>2002<br>Ecoregions of Mato Groso 1977-1981 2001 1; 1,000,000 Derived from the vegetation map Vegetation map with three biomes (i.e. Amazon, Transition, Cerrado) as classified by FEMA Map 3<br>of the RadamBrasil Project by (Instrugdo Normativata No. 2/2001) identify the required legal reserve proportion for a<br>FEMA given property in Mato Grosso.<br>Permanent preservation unknown 2003 1: 250,000 Mato Grosso State Foundation of APP around waterbodies (rivers and lakes), The width of the waterbody defines the width Map 4<br>areas (APP) of Mato Grosso the Environmental (FEMA) of the APP buffer (Forest Code 1965).<br>Protected areas of Mato 2003 1: 250,000 Mato Grosso State Foundation of Indigenous territories and conservation units Map 5<br>Grosso the Environmental (FEMA)<br>Land use/land cover of Mato 1995 2002 1; 250,000 State Secretary of Planing and Actual
However, since prices are not likely to vary in a community and there is no time variation with cross sectional data, it could be argued that price effects get subsumed into ε _,_ the unobserved community effects (Alderman, Hentschel and Sabates, 2002). How the data from the Ethiopia DHS is used to construct these variables is explained next.

# _Variables_

Data from the Ethiopia DHS constitute the primary basis for the empirical analysis of the present study. The discussion here focuses on how this data has been used to construct variables used to proxy for _Y_ , household resources, and _E_ , environmental conditions, in equation (3) above.

# <u>Individual characteristics</u>

Several control variables are included to control for heterogeneity at the individual level. Individual specific controls include the child’s gender, age, and the mother’s height. The age variable is included as a dummy variable for different age groups to control for the cumulative effect of malnutrition. Mother’s height is included to proxy for the child’s genetic endowment. Previous research suggests mother’s height is associated with a child’s birth weight and has a stronger impact on a child’s height than father’s height.

# <u>Household resources</u>

No income or expenditure data is collected as a part of the DHS surveys. In the absence of such data, Filmer and Prichett (1998) have suggested the use of a wealth index, which can be constructed from information regarding housing characteristics and the possession of household durable goods. This is approach adopted in this study. The wealth index serves as a proxy for the household’s long-run wealth or economic status. Its reliability is dependent on having the appropriate weights for the assets. Using principal component analysis, factor scores are generated for each type of asset and determine the weight these assets receive in the construction of the asset based wealth index. The idea is to extract from a large number of variables the
# 3) **HOW INTEGRATED IS TRADE IN THE SADC REGION?**

Table 1 and Table 2 present the share of SADC trade in regional exports and imports from 1980 to 2003. African trade data are notoriously problematic with different datasets providing different values of trade flows (Yeats 1990). Concerns about data quality are particularly relevant for intra-African trade flows. Nonetheless, various insights emerge from the data.

Intra-SADC trade grew significantly from 1980, but has halted in recent years. For example, the share of SADC exports destined for the region more than tripled to 9.9 percent from 1990 to 1995, but then rose very gradually to 12.1 percent in 2008 (based on sample excluding Angola, DRC, Madagascar and Seychelles for which earlier data are not available) (Table 2). These trends are corroborated by the gravity model estimates (column 1 of Table 9 in Appendix 1) which imply no significant change in intra-SADC trade over the 1991 to 2005 period (see the insignificant coefficient on the intra-SADC dummy variable).

Dependence on the region for trade divides countries into two groups. Malawi, Mozambique, Zambia, and Zimbabwe depend heavily upon SADC, particularly for imports. These countries source upwards of 50 percent of their imports from other SADC countries and sell more than 20 percent of their exports to the region. The remaining countries in SADC maintain much stronger trade relationships with the rest of the world (ROW). For example, intra-regional trade makes up approximately 10 percent of Mauritian exports and imports. SACU sources only 5.6 percent of its imports from the region. SADC accounts for a much higher percentage (10.5) of SACU exports, which leads to large trade imbalances between SACU and the rest of SADC.

Substantial asymmetries in trade flows persist. SACU trade, which is predominantly made up of South African trade flows, dominates intraregional trade flows. Between 60 to 70 percent of SADC exports to the region are sold to SACU (Table 1), while 80 to
**Table 3: Decomposition of Changes** in **Growth Rates from 1986-90 to 1991-93** - **Investment Included**

|Country|Actual<br>change<br>|Predicted<br>change in<br>|Contributio|n to predicted ch<br>rates from|ange in growth|Regression<br>residuals|
|---|---|---|---|---|---|---|
||in|growth rates|||||
||growth<br>rates||Six refonn<br>variables|Time effect'|Other<br>variables||
|Argentina|7.464|5.619|6.304|-1.73|1.044|1.845|
|Bolivia|1.561|2.836|3.308|-1.73|1.258|-1.275|
|Brazil|-0.252|-1.643|-0.344|-1.73|0.43|1.392|
|Chile|0.901|-1.501|0.944|-1.73|-0.715|2.402|
|Colombia|-0.049|0.109|0.644|-1.73|1.195|-0.157|
|Costa Rica|0.923|-0.41|0.712|-1.73|0.608|1.333|
|Ecuador|1.584|0.069|0.291|-1.73|1.507|1.516|
|Guatemala|1.197|1.187|2.142|-1.73|0.775|0.01|
|Honduras|1.141|0.698|
<!-- Start of picture text --> Figure 6. Effective residential tariff for 100 kWh (cents/kWh)<br>35.0<br>30.0<br>25.0<br>20.0<br>15.0<br>10.0<br>5.0<br>-<br>Chad Mali Cape Verde Senegal Uganda Burkina Faso Congo, Rep. Kenya Rwanda Niger Benin Côte d'Ivoire Namibia Cameroon Ghana Botswana Lesotho Mozambique Tanzania Zimbabwe Ethiopia Malawi Congo South Africa Nigeria Madagascar Zambia<br><!-- End of picture text --> _Source:_ Africa Infrastructure Country Diagnostic Power Tariff Database On average, residential electricity in Africa is among the most expensive in the world. For the average consumer, residential tariffs are over 12 cents/kWh in about 60 percent of the sample countries and over 20 cents/kWh in close to 25 percent of the sample (Burkina Faso, Cape Verde, Uganda, Madagascar, Mali, and Chad) (figure 6). To compare it against other world regions, Africa’s residential electricity price averages between 50 and 150 percent higher than the 8 cents/kWh in Latin-America, Eastern Europe, and East Asia, and up to 400 percent higher than average residential tariffs in South Asia.

What is the pattern of average effective residential tariffs across levels of consumption? Tariffs that increase with consumption effectively impose a penalty on higher consumption and vice versa. About half of the sample countries have increasing average effective residential tariffs (for example, Cape Verde, Chad, Côte d’Ivoire, Ethiopia, Ghana, Kenya, Madagascar, Mozambique, Nigeria, Tanzania, and Uganda [figure 7a and b]). One-third of countries have decreasing average effective residential tariffs, not only because they have explicitly adopted DBT (for example, Senegal) but also because the size of their first blocks is large meaning that the weight of the fixed charge is spread across a
sorghum output for female-headed households in Rwanda. We do not find any further instances of significance for level of education or for female-headed households.

Turning to recall of cash crop sales, we assume that farmers will report cash crop sales more accurately than quantities, and in Table 2 we find some evidence of recall effects, although limited to tobacco sales in Malawi. The direction of the bias, however, goes against our initial intuition, with respondents reporting higher sales for longer recall periods. This finding holds even when we drop outliers in sales. Introducing the land interaction terms suggests that such bias among tobacco farmers in Malawi is driven by smaller landholders. Since we found no impact on the kilograms produced for Malawi tobacco (Table 1 results), this finding could be the result of an overestimate of either the recall of the portion of total harvest sold or it may reflect differences in the recall of prices received from sales. Regressions using the amount sold, measured in kilograms as opposed to local currency units, show similar results, therefore indicating that the source of this bias in the Malawi tobacco recall data is at least partly tied to reports of the quantity sold (results not presented). No differences from the base model are detected after introducing the other two interaction terms in Malawi. Male-headed households growing coffee in Kenya also seem to overestimate sales when interviewed further from harvest.

Turning now to inputs, using a probit specification, we explore the recall bias in reporting any hired labor for staple and cash crops (Table 3). One might expect that hiring labor would be an event of high salience, especially for low-income smallholders for whom there is some payment (cash or in-kind) necessary for this input, resulting in less recall effects. However, if little is expended on hired labor (that is, few days of hired labor particularly in relation to the household’s own labor input levels), it might be a decision of low salience and hence there will be recall bias in reporting. There is some
of origin can be computed by specifying `output(gtrade)` .

As far as import flows are concerned (i.e. `[` _output_ _~~i~~ mports_ `]` in Section 4.1), the distinction between domestic and foreign items is less relevant, as the former would refer only to the items produced, exported and then re-imported by the importing country itself. For this reason, the _default_ in this case is to compute the gross trade value (i.e. `gtrade` ). The imported value-added (gross-content) can be traced back to the country of origin specifying the option `origin(` _country_ _~~c~~ ode_ `[,` _sector_ _~~-~~ code_ `])` together with `output(va)` ( `output(gtrade)` ). Of special note is that it is possible to pin down also a specific market/sector of final demand by specifying the `destination(` _country_ _~~c~~ ode_ `[,` _sector_ _~~c~~ ode_ `])` option.

As for the standard options, (i.e. `[` _standard_ _~~o~~ ptions_ `]` in Section 4.1), the `save()` and `group()` options are available for both export and import flows (see Section 3.1 for a description of these options).

# **4.1.3 Examples: Value-added in trade flows**

In this section we provide some examples of the insights that `icio` , dealing with the break down of value-added in trade flows, can bring for the economic analysis of ICIO tables.

As running example, we select a specific trade flow, the Chinese total aggregate exports. After having loaded the year 2014 of the last release of the WIOD tables using ``` icio_load,iciot(wiodn)year(2014
verses (independent) or a stanza, identify the literary work<br>From a text, table, or list of sentences; recognize the author's vices<br>From a stanza, identify the kind of rhyme or rhythm of the verse.<br>From a text, list, or table; recognizes the characteristics of literary schools<br>From a list, identify the use of homonymous words.<br>From a text, table, or list of sentences; recognize the author's vices LARGE<br>From a list of literary works, identify its author. COGNITIVE GAPS<br>From a list / table of authors, identify the literary genre to which it belongs<br>From a case, list, or table; identify the main communication elements<br>From a text, list, or table; recognize famous authors or works<br>Recognize the subgenre to which the fragment of a literary work belongs.<br>From a list of verses (independent) or a verse, recognize the metrics.<br>From a text or list, recognize the logical connectors.<br>0.0 20.0 40.0 60.0 80.0 100.0<br>% of correct answers<br><!-- End of picture text -->

## _Source:_ Author using _Ser Maestro_ 2016 data.

_Note:_ The red, yellow, and green bars display the subjects on which teachers, on average, correctly answered less than 30 percent of the subject-related questions, between 30 and 35 percent of the subject-related questions, and between 40 to 60 percent of the subject-related questions, respectively.

_Can the results of the Ser Maestro assessments be trusted?_ Using the _Ser Maestro_ assessments to guide the content of in-service teacher training could be problematic if the knowledge areas measured by the exam are not necessarily related to the curricula imparted in the classroom. If this were the case,
typically measured by looking at consumption and dietary patterns in food groups, one can look at the dollar expenditure on food purchases as a complementary measure of food security. Food expenditure can be defined as the amount of money spent on food in any given period. This measure can be particularly useful for people who purchase most of their food rather than produce it themselves. In our review, seven studies look at the impact of humanitarian assistance programs on food expenditure, four of which compared the intervention

> 11 For detailed descriptions of different indicators, see “Data4Diets: Food Security Indicators” from Tufts University Friedman School of Nutrition Science and Policy.

14