rafmacalaba/data-use-mentions-extended huggingface dataset:
the _Plus_ modality ( _p_ -values = 0.746, 0.183, 0.442, 0.517, 0.539, and 0.575). In spite of quite noisy estimates due to the sample attrition and the reduced sample size—parents in the control group cannot be part of this analysis by design—the evidence does show a systematic pattern. Over a two-month period, mentors in the _Plus_ modality met one time more with parents at school and 0.7 times more at home compared to those in the _Original_ modality (sample means in the _Original_ group are five and three, respectively). The GLS-weighted index shown in the third column documents that the quantity of parent-mentor interactions increased by 0.36 standard deviations under the _Plus_ modality, which is significant at the 10 percent level. Columns 4 and 5 of Panel A show marginally significant estimates on two measures of the quality of the interactions between parents and the mentors: (i) an indicator variable for whether the mentors have informed parents about their children’s learning difficulties, (ii) and whether the mentors provide concrete advice to the parent on how to tackle these difficulties. The effect sizes are large for both outcomes, implying a 14 percent increase in the probability of informing parents relative to the respective sample means in the _Original_ group (70 percent). The estimated coefficient for the GLS-weighted quality index is 0.25 standard deviations, which is significant at the 90–95 percent level depending on the inference procedure.

Panel B in Table 4 shows the effect of the _Plus_ modality on different competencies, or “parenting style,” that the mentors report to have promoted during their encounters with parents. This information was collected during a follow-up interview at the end of the field experiment. Of a total of 126 mentors between the _Original_ and _Plus_ modalities, enumerators were able to interview 107 of them. The attrition of survey participation of mentors is unrelated to the treatment assignment ( _p_ -value = 0.514
<br>states states states<br>D. Composition of tax revenue by population<br>size, 2011-22<br>Percent Consumption Trade<br>Income Property<br>100<br>80<br>60<br>40<br>20<br>0<br>Micro states Other small states<br><!-- End of picture text --> _Sources_ : GFS (database); International Monetary Fund; World Bank.

_Note_ : EAP = East Asia and Pacific; EMDEs = emerging market and developing economies; LAC = Latin America and the Caribbean.

A.-D. Bars show average consumption, trade, income, and property taxes as share of total tax revenues for period 2011-22.

A. Sample includes 28 small states and 95 other EMDEs.

- B. EAP small states include 11 economies, LAC small states include 8 economies, and other small states include 9 economies.

C. Small states commodity exporters include 9 economies, small states commodity importers include 19 economies, and tourism-reliant small states include 21 economies.

D. Micro states have a population of less than 200,000. Micro states include 12 economies, and other small states include 16 economies.

39
|68|_18_|89|87|80|72|_17_|
|_Aggregate HOIs by group_|||||||||||
|IOHa9-15-Housing Services|95|43|17|56|78|95|75|64|79|32|
|IOHa0-4-Housing Services|94|64|28|65|66|97|88|74|86|23|
|IOHa0-4-Nutrition & ECD|19|17|9|11|10|18|16|10|10|8|

_Notes_ : (*) The gap is computed as the difference between the maximum and the minimum HOI for each opportunity. _Source_ : HIECS 2000 and 2009 surveys, EDHS 2000 and 2008, and authors’ calculations.

_Improvements across Regions Benefit Upper Egypt in Housing Services, but Not in Nutrition_ . Figures 4 and 5 show aggregate HOIs for housing services (9-15) and nutrition & ECD (0-4), by region in 2000 and 2009. The fact that all points in Figure 4 are above the 45 degree line indicates that all four regions had better aggregate HOIs in 2009 than in 2000. Nevertheless, regions that made larger progress were those further from the 45-degree line, the Upper and Lower Egypt regions. Although for those two regions the aggregate HOIs advanced at a much faster pace (45 and 32 points, respectively), Frontier Governorates made moderate progress by 23 points, and the Metropolitan regions remained unchanged.<sup>26</sup> In contrast, the regional dynamic of aggregate opportunities in nutrition & ECD 0-4 (Figure 5) show the stagnation of opportunities for early childhood across all regions, except Upper Egypt that only “advanced” by one point. Thus, expanding opportunities for early childhood remain a crucial challenge across all Egyptian regions.

> 26 The pattern of progress across regions was nearly identical for the aggregate HOI of Housing Services for children 0-4 years of age.

16
longitudinal surveys in the United States. Her results indicate that differences in these noncognitive traits explain about 8 percent of the wage gap. Similar results have been reported for the Netherlands, where adding the factor of personality was found to reduce the gender wage gap from 75 percent to 63 percent (Nyhus and Pons, 2012); and for Russia, where the authors found that the noncognitive skills used (locus of control and challenge-affiliation) explain about 8 percent of the gender wage gap (Semykina and Linz, 2007). Sakellariou (2012) used data from the International Adult Literacy Survey (IALS) for Norway, Denmark, Finland, Hungary, and the Czech Republic to examine whether cognitive skills matter in gender wage decomposition analysis. His results indicate that using the IALS score underestimates the effect of cognitive skills on earnings, but when cognitive skills are specified in terms of origin (i.e., acquired at school or outside of school), the size of the unexplained component changes substantially in Finland, Hungary, and the Czech Republic, with differing patterns in the different countries.

# **III. Methodological Approach**

As a first step toward decomposing earnings differentials, we express earnings as a function of observed characteristics. In our first set of empirical examinations, we use the approach popularized by Mincer (1974); the standard wage function includes experience and schooling. We add a dummy variable for type of employment to this standard formulation in order to control for differences among workers: In the equation above, �� indicates (log) hourly earnings and _Observed Characteristics_ represents a vector of variables that includes years of potential experience calculated as ( _Age_ – _Years of education_ – 6), completed years of education, and a dummy variable for self-employed workers (wage workers serve as the reference group). The above equation can be estimated separately for men and women to get estimates of returns to schooling for men and women, respectively.

We add skills measures to the model above to estimate the association between
’s objective. Leaving this question to future research, our concern remains with bilateral trade; MR is only relevant for our efforts to calculate the bilateral comparative static effects correctly.

# **4. Data and estimation**

We use the same data as Baier & Bergstrand (2007),<sup>11</sup> which come from various sources: nominal bilateral trade flows for 96 trading partners and at 5 year intervals from 1960 till 2000 come from the International Monetary Fund's Direction of Trade Statistics; nominal GDPs are from the World Bank's World Development Indicators (2003); bilateral distances, language and adjacency dummy variables were compiled from the CIA Factbook; and the FTA dummy variable was calculated using appendices in Lawrence (1996) and Frankel (1997) as well as various websites detailed in the Data Appendix. It includes full FTAs and customs unions but not partial agreements. 10% of them are between Northern countries, 31% between Northern and Southern countries and 49% between countries from the South. A list of the trade agreements analyzed, including a classification of them into North-North, North-South and SouthSouth FTAs is detailed in the Data Appendix together with a table containing the 96 potential trading partners.<sup>12</sup> Our estimation approach draws on that of Baier & Bergstrand (2007) but instead of having only one dummy for the FTA, we split agreements into those between two Northern countries, between two Southern countries and between a Northern country and a Southern country. The criteria to classify

> 10 For example, countries 1 and 2 can be Chile and Peru respectively such that the equation is for exports from Peru to Chile. Changes in exports from Uruguay to Chile can be found by assigning Uruguay the subscript 3. In general, .

> 11 We thank Scott Baier and Jeff Bergstrand for facilitating our use of the data.

> 12The analyses covers the period from 1960 till 2000 and a total of 119 new agreements have been implemented since 2000,
|-|-|-|-|-0.650**|-0.667**|-112.1|-161.1**|
||-|-|-|-|(0.271)|(0.272)|(70.48)|(68.83)|
|Observations|330,330|330,330|341,165|341,165|372,204|372,203|371,814|371,813|
|Observation level|Commune|Commune|Commune|Commune|Individual|Individual|Individual|Individual|
|Individual controls|-|-|-|-|No|Yes|No|Yes|
|Month fxed efects|No|Yes|No|Yes|No|Yes|No|Yes|
|Commune fxed efects|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|

Note: Panel A presents the difference-in-difference estimates of the aggregate effect of COVID-19 on household business performance, following Equation 3. Panel B presents the estimated effect for each month after January 2020, following Equation 4. Data on household business suspension and closure filings are obtained from the Household Business Tax Census (HBTC). Data on individual work hours and income are obtained from the Vietnamese Labor Force Surveys. Data on the face-to-face (F2F) index is derived from Avdiu and Nayyar (2020), constructed using the 2019 HBTC industrial composition in each commune. Monthly income is detrended from pre-COVID linear trend and residualized from individual observables including age, gender, educational level, and occupation level. Standard errors are clustered at the commune level and presented in parentheses: *** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1.

20
expenditures and revenues, and the resulting budget deficit to each of the structural shocks using the variance-covariance matrices of reduced form errors derived from these coefficients.<sup>5</sup> The confidence bands for the IRF come from parametric bootstrapping on the model assuming normally distributed reduced form errors.

# **3. Data**

To conduct the analysis we collected data on the incidence of disasters and several measures of macroeconomic and fiscal performance for middle and high-income countries. Low-income countries are not included because their fiscal expenditures, revenues, and overall debt are typically related to official and multilateral aid support. Therefore, the fiscal responses to shocks are likely to differ qualitatively from those of other countries and depend on exogenous aid allocation.

Data for natural disasters were obtained from the Emergency Disasters Database (EMDAT) maintained by the Center for Research on the Epidemiology of Disasters (2008) (CRED). This is a comprehensive database that includes data on the occurrence and effects of over 12,800 mass-disasters in the world since 1900, and is compiled from a diversity of sources. As a general principle, to enter into the database an event has to meet any of the following conditions: there are ten or more people reported killed; there are 100 or more people reported affected; a state of emergency is declared; or there is a call for international assistance.

The data contain information on various types of disasters that following Skidmore and Toya (2002), we classify in three broad categories. Geological disasters include earthquakes, landslides, volcano eruptions, and tidal waves. An important characteristic of this type of events is their unpredictability and relatively fast onset. The second category is climatic disasters. This category includes floods, droughts, extreme temperatures, and windstorms (e.g. hurricanes).

> 5 The use of SURE is standard for the estimation of the reduced form equation. It is equivalent to estimating the model equation by equation by OLS, but is more efficient because it takes into account contemporaneous correlations among
# **<mark>6.4 Transmission Mechanisms</mark>**

<mark>While the association between the refugee shock and nutrition outcomes for children of mothers who lived under the shock in early age has proven to be robust, the mechanisms that turned the shock into these results remain unexplored. These mechanisms can be many, are difficult to capture with retrospective data and would require a separate study. However, our data contains information on grandmothers of the children we found in 2015-2016 and this allows us to explore the hypothesis that these are children of mothers who, because of the refugee shock, may have received reduced home care during early childhood. Studies on early childhood development have largely documented the importance of the first five years of life for health and nutrition, cognitive behavior, earnings, motherhood and other outcomes in adulthood and it is possible that the refugee shock has altered the behavior of grandmothers in a way that affected mothers later in life.</mark> <mark>What we know from previous studies on refugees in Tanzania is that household well-being increased for households living close to the refugee camps. Most importantly, the sudden arrival of refugees created temporary secondary employment opportunities for mothers and fathers who, as a consequence, should have been expected to dedicate less time to intrahousehold domestic chores including child care. Our hypothesis is that an increase in female labor market participation and the decrease in health and educational services already documented has led to a decrease in child care. In Table 6, we first use the treatment group</mark> _<mark>q</mark>_ <mark>=1 to see whether grandmothers</mark><sup>21</sup> <mark>who lived under the refugee crisis in 1996 (during the crisis) had a higher labor market participation than those in 1991(before the crisis). We find that grandmothers who lived under the refugee shock in 1996 had a higher labor market participation than grandmothers in 1991 whereas
88***|***|0.89***|0.89***|***|
|N||11706|||11706||

_Exponentiated coefficients. * p<0.10;** p<0.05;*** p<0.01. Source: Own calculations based on Lao PDR customs data. The time dependence of the hazard is introduced as log(time) in all models_ One exception to the “trap” effect is the prior experience with destination: having already sold a product on a certain market in the past increases the odds of experiencing an upgrade by ~40%. Firms which know well a destination and whose reputation is established with a particular partner are more likely to be awarded an upgraded contract, as hypothesized (a "springboard" effect). The same effect is seen for RCA, but only in model (2) and with a lower level of significance.

Another such effect, although weaker, is the impact of familiarity with the destination in both models. In this case, selling another product on the same market not only increases the odds of survival (as was found in the previous section) but also slightly increases the odds of signing up to export a more sophisticated or a more expensive one (or at least it leaves the odds of an upgrade unchanged because the coefficient is not significant). This type of experience can be an accurate proxy for reliability from the point of view of the importer – if the two partners interact repeatedly in many goods they will be more likely to trust each other and extend this interaction to superior products.

26
Private markets function effectively if they provide the pattern of goods and services that consumers prefer, given their levels of income. Private education thrives in a market environment where competition, efficiency and profits are guiding factors. The production of education is considered to be efficient if there is no opportunity in the economy by which more or better education could be produced to make everyone better off. If the efficiency and effectiveness properties do not hold, markets do not function effectively. An important mechanism for promoting efficiency is to increase the scope of competition in providing education. Mechanisms such as demand-side financing (money follows students) are being designed to promote choice in education for reasons of equity in access. Vouchers are increasingly cited as a possible mechanism for this purpose. Students are given vouchers funded by public tax dollars but redeemable at any school, private or public. Parents have the liberty to choose the school their children will attend. This in turn is expected to induce schools to compete for students. For country examples of voucher schemes, see Patrinos and Ariasingam (1997).

_Willingness to pay._ The other aspect which is important to note is that people are willing to pay for the desired service and cost recovery through fees as a feasible financing method. Empirical evidence from 1990 and 1992 household surveys data indicate a remarkable willingness of households in Bolivia to pay for private education for their children (Psacharopoulos, Arieira and Mattson 1997) (Box 1). Other examples include the Philippines, Colombia, Brazil and Tanzania.

Box. **1. Bolivia: Private Education in a Poor Country** Bolivia is among the poorest developing countries in South America, with a GNP per capita income of US$800 (1995). High illiteracy and repetition rates are some of the pervasive characteristics of the country. Private education is found at all levels of schooling in Bolivia, and comprises approximately 20 percent of school types (78 percent being public and 2 percent, mixed). In Bolivia, mean household expenditure on private education
# **E. Share of economies with sovereign wealth funds, 2022**

# **F. Quality of governance, 2000-22**

<!-- Start of picture text --> Percent EMDEs excl. small states Score Max score<br>60 Advanced economies Advanced economies<br>3 EMDEs excl. small states<br>50<br>40 2<br>30<br>1<br>20<br>10 0<br>0<br>-1<br>Small states Small states: Small states:<br>Small states Small states: Small states:<br>Commodity Commodity<br>Commodity Commodity<br>exporters importers exporters importers<br><!-- End of picture text --> _Sources_ : Davoodi et al. (2022a); Global SWF; International Monetary Fund; WDI (database); WEO (database); World Bank.

_Note_ : EMDEs = emerging market and developing economies.

A. Annual growth rate of real revenue and real expenditure in 35 small states.

B. Average public debt to GDP ratio and primary balance to GDP ratio in 32 small states for the period 2020-23.

C.-E. Sample includes 35 small states, 13 small states commodity exporters, 22 small states commodity importers, 119 EMDEs excluding small states, and 38 advanced economies.

C. Percent of economies that have fiscal rules as of 2021.

- D. Percent of economies that have fiscal councils as of 2021.

- E. Percent of economies that have sovereign wealth funds as of 2022.

F. Average of Worldwide Governance Indicators for the period 2000-22. Sample includes 35 small states, 13 small states commodity exporters, 22 small states commodity importers, 119 EMDEs excluding small states, and 38 advanced economies. Dotted red line denotes maximum score of 2.5 for governance indicators.

47
: where X��� indicates the monthly value of NDVI in community _j_ in year _y_ ; X��� is the mean value over each month for the years 2000 to 2016; and SD�X��� is the standard deviation over the period.<sup>15</sup> Negative z-scores mean that the vegetation during the drought period was worse than the long-term average. Thus, households in the communities with z-scores below zero are identified as exposed to drought shock, accounting for 37.5 percent of households in the treated communities and 41.0 percent in the control group.

# **4. Empirical strategy**

The primary approach of the econometric analysis in this study is a DID matching approach (Smith and Todd 2005), which combines DID and propensity score matching. In the impact analysis of rural road development, endogeneity problems arise when initial conditions influence project placement and the subsequent growth paths and prospects of the communities. While either DID or household fixed-effects-panel regression models can statistically control for time-invariant factors,

> 13 Official poverty lines are developed for HICES, which is a different household expenditure survey from the ESS. Thus, poverty estimates in this paper are not comparable with the official poverty statistics. 14 In the ESS data, a lot of households reported that they had been affected by drought during the last 12 months. In the study area, 29 percent of the households were affected by drought in 2015/16. However, such measure of drought exposure reported by households—and even by communities—can be susceptible to measurement errors, and the errors can be correlated with the respondents’ income levels if lower-income households are more likely to overstate the drought impact and/or higher-income households tend to understate the impact.

> 15 NDVI was calculated based on the MODIS Terra satellite data.

7
,_ 1] interval to ensure the penalization effect is not driven by differences in variance of the different variables. We scale back the estimation results for easier interpretation.

## **3.1 Forest cover data**

We use data from (Hansen et al., 2013), which contains estimates of global tree cover extent (2000) and annual tree cover loss (2001-2014) at a spatial resolution of 30 meters.<sup>9</sup> Hansen et al. (2013) analyzed satellite images from Landsat 5, 7, and 8 to identify tree cover extent, defined as vegetation taller than 5 meters in height, and loss, defined as complete

> 8Degrees of freedom is a parametric concept whose translation to the non-parametric setting is complex. One can approximate the degrees of freedom empirically, which may result in numbers that are real-valued.

> 9The data can be found at https://earthenginepartners.appspot.com/science-2013-globalforest/download ~~v~~ 1.2.html.

12
>g</mark>_ <mark>. Demographic groups include female workers, male workers, high-skilled and low-skilled workers.</mark>

# **B) Skills measures using the U.S. O*Net database**

To check the sensibility of our results using the Philippines STEP survey, we construct skills measures using information from the U.S. 2004 O*Net database, i.e., a year previous to our period of analysis, to avoid the skills measures to be affected by the technological advancement taking place after that year.

<mark>We work with a set of 41 work activities describing 914 detailed occupations. We classify the 41 work activities into five skills categories: n</mark> on-routine analytical, non-routine interpersonal, routine cognitive, routine manual, and non-routine manual. Table A1 provides details on the classification we use.

We map the occupations available in the 2004 U.S. O*Net database that follows the SOC 2000 classification (Standard Occupational Classification 2000) to the ISCO 2008 used in the STEP survey, and then to <mark>the Indonesian Classification of Occupation 1982 that is used in the Sakernas.</mark> In this process we mapped 899 occupations from the 914 occupation that were originally in the O*Net database. Using the ISCO 2008 we identify the same six occupational groups we identify in the STEP survey and that can be match to the Sakernas survey: <mark>administrative and managerial workers; professionals and technicians; clerical workers; sales and services workers; agriculture, animal husbandry, forestry workers, fishermen and hunters; and production workers, transport and equipment operators, and laborers.</mark> 55