rafmacalaba/data-use-mentions-extended huggingface dataset:
Table 3: Effect of Extended Provider Training and Patient Information on Malaria Testing at the Clinic: Patient Survey

||(1)<br>Any<br>Malaria<br>Test|(2)<br>RDT Only|(3)<br>Microscopy<br>Only|(4)<br>Multiple<br>Tests|
|---|---|---|---|---|
|Extended Training|-0.0257|0.116<sup>_∗∗_</sup>|-0.0919|-0.0475<sup>_∗∗_</sup>|
||(0.0547)|(0.0486)|(0.0729)|(0.0198)|
|Patient Information|-0.0613<sup>_∗∗∗_</sup>|-0.0432<sup>_∗∗_</sup>|-0.0171|0.00632|
||(0.0177)|(0.0189)|(0.0245)|(0.00837)|
|Mean (Basic Training, No Pat. Info.)|0.58|0.21|0.25|0.07|
|N|1973|1973|1973|1973|

_Notes_ : Robust standard errors clustered at the clinic-pair level in parentheses. All regressions include strata and survey date fixed effects. Additional covariates are selected using double lasso, from the set of all covariates listed in Table 1 and all individual-level characteristics in Table A4 (excluding predicted malaria risk and characteristics only measured at the home survey) as well as the square of patient age and illness duration. We also include pairwise interactions of the aforementioned characteristics. Missing values for all characteristics are dummied out and recoded to the mean prior to forming interactions. Missing dummies are also included in the
from DHS. Section 4 contains our results and Section 5 concludes.

# 2. Related literature

A large literature relates air pollution to a host of negative health outcomes (see Manisalidis et al., 2020 for a review). However, identifying causal impacts of air pollution is complicated due to the presence of many confounding factors. Air pollution is not randomly assigned, so unobserved factors may be correlated with both air pollution and health outcomes. People may undertake endogenous risk-avoidance behaviours such that richer people or those who place greater weight on health sort into locations with better air quality so observational data may overstate effects of pollution (Neidell, 2009; Moretti & Neidell, 2011; Sun et al., 2017). Conversely, polluting industries, such as oil and gas, may attract younger, healthier in-migrants in search of employment, so observational studies of polluted areas may underestimate the effects of air pollution. It is also difficult to assign pollution exposure to individuals, so measurement errors may bias estimates of the impact of pollution on health. Notwithstanding these identification difficulties, there is causal evidence that air pollution increases child hospitalisations for respiratory problems (Neidell, 2004; Tzivian, 2011), increases infant mortality (Currie and Neidell, 2005; Knittel, et al., 2016), affects birth outcomes (Currie et al, 2009; 4
|0.29|0.22||
|Post-harvest round|||||||
|Only/mainly intended for sale<sup>(a)</sup>|0.41|0.34|0.07**|0.48|0.39|0.09***|
||[0.49]|[0.47]||[0.50]|[0.49]||
|Mainly intended for HH|0.40|0.44|-0.04**|0.36|0.40|-0.04*|
|use/consumption, some for sale|[0.49]|[0.49]||[0.48]|[0.48]||
|Only for HH use/consumption|0.19|0.22|-0.03|0.17|0.21|-0.04**|
||[0.40]|[0.42]||[0.37]|[0.41]||
|Number of individuals in own-farm work|1157|474||796|692||
|Share of total own-reporting sample|0.53|0.28||0.34|0.27||

Notes: Standard deviations in brackets. T-tests of equality of means were conducted across men and women within each set of columns; *** p<0.01, ** p<0.05, * p<0.1. Sampling weights used. (a) Mainly intended for sale = either all, or at least half of output intended to be sold (b) In Malawi, households were compared above and below the poverty line. In the Nigeria data, the poverty line variable was not available, so households in the bottom two quintiles of consumption expenditure
# specifically for such sectors as manufacturing and agriculture.

Such trade cost estimates refer to bilateral trade. To obtain country and regional measures of multilateral trade costs, bilateral trade costs from the UNESCAP-World Bank database are aggregated using 2018 bilateral country export shares from the UNCTAD database. Regional and sectoral aggregates are obtained as unweighted averages of individual country measures.

# IV.2 Literature view

Trade costs and trade. A growing literature has documented evidence that lower trade costs raise trade growth (Anderson and van Wincoop 2003). A study of data for the period 1870-2000 found that declines in trade costs explain roughly 60 percent of the growth in global trade in the pre-World-War 1 period and around 30 percent of trade growth in the period after World War II (Jacks, Meissner, and Novy 2011). Studies of firm-level data have found that lower trade costs have encouraged firms to locate abroad (Amiti and Javorcik 2008), and to choose out-sourcing over in-sourcing and intra-firm rather than arm’s-length trade (s).

Trade costs and productivity. A link between lower trade costs and higher productivity has also been substantiated. For advanced economies, one study found that a 1 percentage point lower tariff rate was associated with a 2 percent gain in total factor productivity during 1997-2007 (Ahn et al. 2019). Analyses of firm-level and sector-level data have shown similar results. Industries with larger declines in trade costs had stronger productivity growth; lower-productivity plants in industries with falling trade costs were more likely to close; and non-exporters were more likely to start exporting in response to falling trade costs (Bernard et al. 2007).

# IV.3 Patterns across regions and sectors

Despite a sharp decline in the past two and a half decades, recent data show that trade costs in EMDEs raise the prices of goods traded internationally to more than double the prices of goods traded domestically and that they remain about one-half higher than in advanced economies (figure 5). Among EMDE regions, average trade costs range from tariff equivalents of 96 percent in ECA
US$480.0 billion in 1990–2018. As a share of GDP, the rise was from 18.7 to 39.2 percent. This analysis focuses on 2004–14 because the municipal-level export data set only covers those years. While this excludes the initial period of NAFTA, it still captures a significant increase in exports, from about 28.4 percent to about 31.9 percent of GDP.

According to various measures, poverty declined almost every year, with the main exception of the 1994–95 tequila crisis (figure 3). This is particularly true of the US$3.20-a-day and

> 5 Even though the 2015 population count is from a survey, the sample size is sufficiently large to provide statistics representative at the municipality level. See Enamorado et al. (2016).

> 6 These include the following sectors: agriculture, mining, food products, textiles, wood, paper, printing, chemicals, rubber and plastics, metal, electronics, machinery, automotive, other manufacturing, utilities, construction, education, and other nonmanufacturing.

7
both emergency assistance and food consumption scores. We find no significant impact of this variable, neither on assistance nor on food consumption score. This is an important result since it proves that households seem to succeed in coping with this shock. Besides, food assistance appears to have no effect on household food consumption score in non-directly affected areas. Female headed families have lower food consumption score, while agricultural producers are less food insecure.

# **6. Cash and Food-for-Work Programs**

Before the earthquake, the labor market in Haiti was already a major issue. According to CNSA (2008), in rural Haiti, half of the population was inactive (with a higher concentration in the North) and the dependency rate (i.e. the number of inactive persons over active ones) appears to be particularly high with about 103 inactive persons for 100 employed persons.

In response to these very restrictive conditions on the labor market, and in addition to food assistance programs, cash-for-work (CfW) programs provide immediate cash support to the unemployed. In general, CfW are short-term interventions (normally 2-3 months) which provide temporary employment in public projects (such as repairing roads, cleansing irrigation channels or re-building infrastructure) to the most vulnerable segments of the population or in areas directly affected by natural disasters. After the earthquake, CNSA (2010a) reported that many people interviewed in focus groups expressed a preference for activities where their skills and capabilities could be used, such as the cash and food-for-work (C/FfW) programs which allow people living in directly affected areas to keep their dignity and self-esteem.

# _(a) Main results_

Tables 14a and 14b present descriptive statistics by pre-earthquake wealth. First, Tables present variables describing household vulnerability such as employment status, death of workers, housing damage and asset losses after the earthquake. Second, variables such as the household’s main income source, participation in cash and food-for-work programs as well as the benefice derived from other transfers from relatives or friends are presented before
was estimated with low precision. Therefore, <mark>in the next section, we rely on dry route trajectories to build a representative wet network, but adjust travel speeds to wet conditions using estimates provided here. This was an important part of the analysis as few papers previously inferred the impact of flooding and rain on travel speeds in developing countries such as the DRC. Former research focused on developed countries where an abundance of public transit data facilitates the estimation. Further, the estimation of travel speeds under wet conditions is crucial in our simulation study that provides accessibility and economic loss estimates due to floods (sections 4.2 and 4.4).</mark>

# _<mark>Matching procedure</mark>_

<mark>The following lays out in detail the different steps involved in the matching procedure, illustrated in Figure 9. It also explains how significance tests were constructed, relying on the bootstrap methodology. We denote the set of all route segments: R = {R</mark> d <mark>, R</mark> w <mark>}. A route segment r is defined as part of a route extending from one stop point to the next. Data on route segments can directly be obtained from the GTFS</mark> dd ww <mark>feeds. First, each route segment is transformed into a set of support points by resampling points every d meters along the route (Figure 9, panel B). Denote the resulting set of support points P r ∈ P has coordinates sdi r. Hence the assumed travel speed at</mark> dd<sup><mark>. Each point</mark></sup> ii dd <mark>point r vd</mark><sup><mark>and inhabits all other attributes from route segment</mark></sup> <mark>i r, denoted v</mark> ii<sup><mark>, denoted,
# **Figure 8. Kernel Density Function of the change in ln Employment a. 2016-2020 b. 2013-2020**

# **b. 2013-2020**

<!-- Start of picture text -->
-4 -2 0 2 4 -5 0 5<br>ln Employment ln Employment<br>Exporter Non-exporter Exporter Non-exporter<br>c. 2013-2016<br>-4 -2 0 2 4 6<br>ln Employment<br>Exporter Non-exporter<br>.5<br>.6<br>.4<br>.4<br>.3<br>.2<br>Kernel Density Kernel Density<br>.2<br>.1<br>0 0<br>.5<br>.4<br>.3<br>.2<br>Kernel Density<br>.1<br>0<br><!-- End of picture text -->

Source: Authors’ elaboration using data from the Enterprise Survey for Egypt, 2013, 2016, and 2020.

40
Lastly, the paper investigates how green job holders fare on the labor market in terms of labor income. Overall, green occupations are linked to significantly higher wages, even when controlling for other individual characteristics (see Table 8): green occupations are linked to hourly wages that are about 20 percent higher (as a benchmark, this magnitude is similar to the gender wage gap).<sup>20</sup> When disaggregated by type, the wage premium is significantly higher for new and emerging green occupations (see second column in Table 8). In line with the results above, the most vulnerable workers (i.e., those in non-green occupations and non-green sectors) display lower wages compared to the other groups (see the third column in Table 8, in which this group is entered as the omitted category). It is important to mention that the wage premium associated with green jobs may be upward-biased because workers with green jobs are significantly different from the rest in terms of observable characteristics, which raises concerns about significant differences in unobservable factors that could introduce omitted variable bias. Within these caveats, the results above suggest the green transition in general and bottlenecks in the supply of green occupations (at least in the short/medium term) could lead to higher wage and income inequality.

> 20 Granata and Posadas (2022) use a different method to classify green jobs (based on text-analysis of the ISCO08 task database) and find that they are linked with higher earnings in Indonesia even when controlling for a host of other individual characteristics. IMF (2022) also finds that green jobs are associated with higher earnings.

29
# 3.2.The Global Income Distribution Dynamics Model

**The first step in the microsimulation exercise is to implement a set of changes in the household surveys’ demographic structure** . The population growth adjustment is particularly important in countries with rapid demographic changes. In practical terms, the adjustment for population growth allows the analysis to explicitly take into account the changes in the size of the working-age population.<sup>13</sup> We perform population and education projections during the first stage of the microsimulation model and in creating the baseline scenario for the CGE model. For each country, we construct the demographic profile in two steps. First, the age and gender composition are exogenously determined following medium variant estimates from the World Population Prospects (UN DESA 2019). In the second step, following Bourguignon and Bussolo (2013), country-specific educational profiles are constructed using initial educational achievement levels observed in the household surveys with some conservative yet simple assumptions about educational progress.

**The microsimulations’ second step adjusts individual factor returns by skill and sector in accordance with the results of the CGE model** . The GIDD imposes an entirely new vector of earnings on each worker, conditional on each worker’s individual characteristics. In a basic setup, the CGE and GIDD can be linked with two sectors (agricultural and non-agricultural) and two types of workers (skilled and unskilled) but depending on the quality of the data and the scope of the study, this restriction can be relaxed.<sup>14</sup> The GIDD reallocates workers moving them out from shrinking into expanding sectors. The sectoral reallocation process estimates the probability of each worker to be reallocated into new sectors, based on individual characteristics. Once workers are reallocated, a new vector of earnings is generated using estimates from a set of Mincer equations. To account for unobserved characteristics, each individual residual is brought into the new sectors and scaled accordingly. This latter process assures that an individual switching sectors can carry his unobservable personal
Sudan and Sudan, while the opposite is true for Somalia, where people living in male-headed households have a higher MPI. In Nigeria, there is no statistically significant difference between the poverty levels of the two groups. These differences also hold for incidence of multidimensional poverty, where female-headed households are 39 percentage points poorer in Ethiopia, and 17.6 and 10.3 percentage points poorer in South Sudan and Sudan, respectively.

_Figure 3. MPI for male- and female-headed households by country_ <!-- Start of picture text --> 0.450<br>0.400 58.5%<br>0.350 61.7%<br>0.300<br>41.5%<br>0.250<br>42.1%<br>0.200 35.7%<br>65.9%<br>0.150<br>38.2% 64.3%<br>34.1% 58.0%<br>0.100<br>0.050<br>0.000<br>Ethiopia Nigeria Somalia South Sudan Sudan<br>MPI Male-headed household Female-headed household<br><!-- End of picture text --> Source: Authors’ calculations based on data from the High Frequency Surveys of Ethiopia (2017), Nigeria (2018), Somalia (2017), South Sudan (2017) and Sudan (2017). Note: Population shares of headship included above each bar.

With the above results, it is important to consider the gender differences by displacement status as the gender of the household head may be correlated with disruption in family structures that occurred after displacement. Looking at the sample composition by headship at the country level, 45% of the population in Ethiopia, 34.3% in Nigeria, 49.8% in Somalia, 44.7% in South Sudan, and 41.6% in Sudan live
10 levels although older cohorts in inner towns appear to be slightly less educated. The two groups of towns are similar also in terms of employment rates. In this respect, the census allows us to compare the populations also strictly before the quake, exploiting a recall question on employment status five years earlier, in 1976. Figure (4) displays the estimated differences by age and the corresponding 95 percent confidence interval, showing that before the quake employment rates were not significantly One concern with this evidence is that the census was run one year after the quake. In principle, there are at least two reasons why this could impair the relevant comparisons. First, populations from inner towns might have migrated elsewhere because of the quake so that those observed in 1981 might already be a selected subsample of those truly exposed to the quake and related interventions. Second, even if there is no migration the comparison might already reflect the presence of different effects of the quake. Geographic proximity should guarantee that both channels worked, if at all, in the same direction in both groups of towns.

To address empirically the selection effect induced by differential outward migration we examine yearly gross outflow rates from the sampled towns between 1974 and 1987. These are on average around 2 percent and the difference between towns just inside and just outside the quake region is an order of magnitude smaller, around 0.2 percent. To establish whether the quake led to a larger outflow from inner towns we regressed yearly outflow rates between 1974-1987 on a set of year dummies and their interactions with an indicator for towns inside the quake region. None of the interaction terms was statistically significant at customary levels: p-values are always well above 0.15 except for three years (1977, 1978 and 1981) for which they are still larger than 0.05<sup>11</sup> . Moreover, an F-test of the joint significance of the interacted terms does not reject the null with a p-value of 0.3. Overall, it seems that outflows
In this context, IFC investments can be procyclical and profitable in countries experiencing expansions, whereas IFC investment can be countercyclical and riskier in countries facing recessions with the asset quality and profitability of the IFC overall portfolio still healthy. The same reasoning applies to IFC investments at the industry level within a country, especially in large EMDEs which are strongly diversified. It is perfectly plausible that even if a country faces an economic slowdown, there could be sectors experiencing expansions and the IFC portfolio in this country is still healthy ad profitable. Of course, the quality of projects in terms of individual profitability and development impact is critical in ensuring that global and country portfolios are resilient to short term economic fluctuations.

This paper empirically assesses the cyclical patterns of IFC investment activity in the last two decades. Using quarterly data on IFC investments and GDP in 35 client countries from 2000 to 2019, the paper examines how the cyclicality of IFC investment has evolved over time and across countries, regions and industries. To determine how these dynamics interact with the IFC’s financial performance objectives, the analysis compares the average ex post portfolio performance, as measured by nonperforming loan (NPL) ratios and risk-adjusted return on capital (RAROC),<sup>2</sup> of countries in which IFC investment has been procyclical with those in which it has been countercyclical. Finally, the paper highlights IFC’s recent corporate initiatives which may contribute to reconciling the objectives of providing countercyclical support to its clients, when they need it most, while preserving financial sustainability.

The paper is organized as follows. Section 2 briefly reviews the existing research on the cyclicality of capital flows to EMDEs and situates this report in the context of existing literature. Section 3 presents the empirical approach used in the analysis, including the definition of key variables and comments on the data treatment. Section 4 presents the stylized facts for IFC investment cyclicality observed over the last two decades, including disaggregated results by subperiod, region, and industry sector. Section 5 discusses the empirical findings
_CHAPTER 3. BASIC INDIRECT ESTIMATORS_ 13 direct estimators at the cost of slightly increasing their design bias. It is worth noting that averaging different predictors is one of the main ideas behind modern machine learning procedures.

In composite SAE estimators, optimal weights are sought from a designbased standpoint. Unfortunately, the optimal weight depends on the true design MSE estimates of the two estimators involved, encountering once again the problem of estimating the design MSE for synthetic estimators. Griffiths (1996) studied composite estimators and applied them to the estimation of labor force characteristics for US congressional districts.

Purcell and Kish (1979) considered a common weight for all the areas and obtained the optimal weight that minimized the total design MSE for all the _D_ small areas. The resulting composite estimators have good overall efficiency for the _D_ areas, but not necessarily for each small area. In SAE, it is desirable to reduce the largest MSEs, which typically correspond to the areas with the smaller sample sizes, and this is not ensured by these composite estimators.

Actually, in SAE, it is much more appealing to consider composite estimators with area-specific weights, such that the weight attached to the synthetic estimator grows for areas with small sample sizes and decreases for areas with large sample sizes, giving more weight to the direct estimator. Following this idea, Drew, Singh and Choudhry (1982) proposed the sample-size-dependent (SSD) estimators. These are composite estimators defined with simple weights that depend on the area sample size. They applied these estimators to produce estimates for Census Divisions from the Canadian Labor Force Survey. In practice, as observed in the application by Drew, Singh and Choudhry (1982), SSD estimators borrow little or no strength, because the weights attached to the direct estimators frequently turn out to be either equal or close to one.

Chapter 4 describes the first SAE model, which also leads to a composite estimator, but with optimal properties under model assumptions made for the study variable. These model-based estimators outperform the