rafmacalaba/data-use-mentions-extended huggingface dataset:
R<sup>2</sup> , defined as: Where i is the target area, yi is the reference measure of poverty or welfare for target area i , ŷi is the predicted value for target area i, and y̅i is the mean across target areas. Some studies instead report the Pearson correlation between the predicted and reference measure, which can be squared to obtain R<sup>2</sup> . Table 1 lists the actual or implied R<sup>2</sup> values reported by several studies.

An important early paper on how geospatial data predict poverty and wealth was Jean et al (2016). This paper used “deep learning” in the form of a convolutional neural network (CNN) to predict welfare, using daytime imagery taken from Google Earth and the luminosity of night-time lights in several countries in Sub-Saharan Africa. Each layer of the CNN successively filters the original image into more and more condensed abstract features, until the final layer represents the predicted luminosity value. Jean et al. (2016) transfer the features from the penultimate layer of the CNN to a ridge regression that estimates the value of an asset index or per capita consumption in withheld villages.

In Jean et al (2016), the target areas are survey clusters, the reference measure yi is the predicted average values of a wealth index or per capita consumption for cluster i taken from the household survey and withheld from the training sample, and ŷi are predictions generated from convolutional neural network models. Out-of-sample R<sup>2</sup> is assessed through survey cross-validation and varied from 0.37 to 0.55 for per capita consumption, and from 0.55 to 0.75 for asset wealth. However, because the 3
part (but not always) does not discriminate between domestic and foreign service suppliers, and can help reduce trade costs in services. As stated in Hoekman (2017), trade facilitating measures in services relate to costs that stem from (a) (asymmetric) information on the rules and requirements that apply to the delivery of the services; (b) the associated certification / conformity assessment process of the service, and (c) the uncertainty / variability in the administration (governance) of (a) and (b), such as related to procedures. What needs to be made clear in this context is that measures that fall into the area of facilitating trade in services (TFiS) are largely non-discriminatory in nature, but not always.

In short, regulatory policies that relate to facilitating trade in services center around how regulatory and administrative procedures have been implemented and conducted in countries’ jurisdictions. If compliance to regulatory rules and procedures is considered costly by service suppliers because of inadequate information, non-transparency, uncertainty and unclear documentation, this category of trade cost potentially forms a significant share of the overall trade cost in services. In some instances, TFiS measures can also entail discriminatory barriers such as visa measures which are inherently applied in a discriminatory manner. However, depending on how visa requirements are implemented, this policy area may therefore fall under the scope of TFiS. This paper conceptualizes and discusses the various aspects of TFiS-related measures in detail to provide clarity on what is covered by the TFiS agenda.

To date, domestic regulatory policies going beyond up-front market access barriers to facilitate trade in services are largely unknown. Contrary to the case in goods where over the years a clear trade 1 This lower share of services in trade compared to output and employment in services remains even when taking account of the measurement issues that plague services trade data particularly in developing countries.

2
controls for parental education are added to the regressions (Table 1, Col. 4).

> 15 I also used a specification where in the regressions with the 2007 data I used a dummy (“school closure” for schools in the raion being closed by the government decree or school holidays extended due to instability or other negative events related to schools, e.g. attempts to take students as hostages. The regression results suggest that school closure had a significant and negative effect on the completion of basic education by women who were of school age during the war (significant at 1% level). The estimated coefficient is very small though suggesting that on an average women from the war-cohort in the raions with “school closures” were 0.24 percentage points less likely to complete basic schooling. The estimated coefficient in the regressions for men with a DV: “completed secondary schooling or more” is borderline significant at 11% level and is also very small, suggesting 0.47 percentage points decrease in chances of completion of this school level. All regressions include a full set of ethnicity and parental education controls and are estimated with fixed effects at the raion level (results not reported). The difference in the estimated coefficients on the interactions between “school closure” and “RCA” measure of conflict as reported above possibly indicate that “school closures” was only a temporary measure limited to several months in 1992-1993, while RCA measures conflict activity and instability that was occurring throughout the conflict period.

18
0.033)|(0.046)|(0.044)|(0.022)|(0.035)|(0.033)|(0.075)|(0.093)|(0.048)|
|**Constant**|<br>9.448***|<br>8.812***|<br>9.196***|<br>9.338***|<br>8.837***|<br>8.499***|<br>11.348***|<br>11.267***|<br>8.875***|
||(0.928)|(1.322)|(1.250)|(0.625)|(1.004)|(0.940)|(2.122)|(2.612)|(1.359)|
|**N (Observations)**|8,966|8,919|8,604|8,977|9,002|8,608|8,979|8,966|8,598|

_Source:_ Authors’ calculations based on ONDH impact evaluation panel survey. _Note:_ *p < 0.1; **p < 0.05; ***p < 0.01.

Standard errors (in parentheses) clustered on commune level. Quantile regression standard errors are bootstrapped with re-sampling at the commune and household levels.

18
the growth rate of employment declines by 1 percentage point. Further, as predicted above, this negative relationship is much larger (more negative) in countries where the cost of operating formally is higher and benefits lower as captured by higher corruption, more burdensome regulations, higher profit tax rate, and weaker rule of law. We also find no significant impact of informal competition on the growth rate of employment for large firms.

The plan of the remaining sections is as follows. In section 2, we describe the data and the variables used in the regressions. Section 3 contains the regression results for the baseline specification. Section 4 contains some robustness checks while the various endogeneity checks are provided in Section 5. The concluding section summarizes the main findings and suggests scope for future work.

# **2. Data and Main Variables**

The main data source we use is firm-level survey data collected by the World Bank’s Enterprise Surveys (ES). We complement the ES with other data sets such as the World Development Indictors (WDI, World Bank), Worldwide Governance Indicators (WGI, World Bank) and so on.

The ES are nationally representative surveys of the non-agricultural and non-financial private economy. The survey includes only the formal or registered firms with 5 or more employees. Thus, informal or unregistered firms and micro firms (fewer than 5 employees) are not covered by the survey. A common sampling methodology – stratified random sampling – is followed in all the surveys along with a common questionnaire.<sup>2</sup> The sample for each country is stratified by industry, firm-size, and location within the country. Weights are provided in the survey and used in all our regressions so that the sample is representative of the target population.

> 2 Details of the sampling methodology and other related information are available at www.enterprisesurveys.org.

7
Together, this information allows obtaining a reliable estimate of taxes and contributions to FONASA paid by workers, capital earners and pensioners using the schedular rates. In this estimation we assume that workers who do not contribute to the social security system do not pay direct taxes and do not contribute to the Health System Fund. Besides, we assume that workers who contribute do not evade taxes and contributions. Finally, we assume no evasion in the case of capital income and pensions. We estimate the individual’s pre-fiscal income adding taxes and contributions to reported income.<sup>14,15</sup> Pre-fiscal income also contains contributions to the health system paid by employees, private transfers and imputed rent (see Figure 1).

Figure 2 provides a schematic of the different income concepts. It shows pre-fiscal income (market income plus pensions), disposable income, consumable income (although it is not used in this analysis), and adjusted income.

We define Net Market Income as Pre-Fiscal Income less direct taxes and contributions to the health system. Next, we estimate Disposable Income as the sum of Net Market Income and public direct transfers. Information of the ECH on social programs receipt and household composition allows the direct identification of public transfers’ beneficiaries. Besides, the survey also provides the amount of the benefit. Finally, we define Adjusted Income as the sum of Disposable Income and public educational and health transfers. As for in-kind benefits, school attendance, type of school and access to health services allow for the imputation of monetary benefits of public spending into these services. Specifically, administrative data provide total costs by level (for education), and by provider (for health), are first scaled-down using the ratio between disposable income from aggregate sources<sup>16</sup> and the disposable income constructed from the ECH microdata. Finally, the scaled-down costs are divided between the users of the in-kind benefits.

In addition, administrative data and indicators from public programs were publicly available and used as sources to
database, if two countries sign more than one PTA with each other, the numbers of provisions in each policy chapter are cumulated across all PTAs. For example if country A and country B signed an agreement in 2000 with provisions 1 and 2 in the SPS chapter and signed another agreement in 2005 with provisions 1 and 3, between 2000 and 2004, 2 SPS provisions would be in force while from 2005 onward 3 SPS provisions would be in force between countries A and B.

> 18Export values are Freight on Board (FOB) figures measured in USD converted from local currency to USD using exchange rates taken from the IMF’s International Financial Statistics. The quality of the customs data is assessed as in Fernandes et al. (2016) by a comparison of total exports obtained from aggregating the transaction-level customs data at the country level with the total exports obtained at the country level from COMTRADE/WITS (World Integrated Trade Solution). The ratios for Chile, Colombia and Peru average close to 100% over the sample period.

10
by the aggregate value of the output of the same crops valued in reference prices. The same procedure was followed in the other aggregations. That is: NPCc = yi (GVPDi) / Xi (GVPDi/NPCi) where GVPD1 = (Gross value of production of crop i at domestic prices NPCi = Nominal protection coefficient of crop i NPCc = Nominal protection coefficient for cereals i = Rice, wheat, maize, and sorghum.

# _Prices of commodity baskets_

The aggregate nominal protection series provide an indication of the level of and trends in the nominal protection of the groups of crops to which they refer. In order to distinguish the sources of changes in nominal protection of these aggregates over time, we calculated the weighted average domestic reference and world prices of the commodity baskets included in the aggregates. The weights change from year to year and are calculated by dividing the gross value of the output of each crop by its domestic price. That is: pi i(wi~i wi (GVP i/p i) / (E; (GVP i/P i)) where, PJ = Price at j level. j = D (domestic), R (reference) and W (world) As discussed in background paper no.6, the commodity basket price indices make it possible to distinguish how the nominal protection of Indian agriculture has been affected by changes in the xxi
explicitly model interactions between predictors. Use of these models should therefore mitigate any benefit from estimating multiple models in disaggregated geographic regions or areas.

e. Out-of-sample predictions are significantly less accurate than in-sample predictions Informative sampling occurs when sampling probabilities for Primary Sampling Units vary in a way that is correlated with the outcome of interest, such as welfare. This is typically the case in two-stage samples in which primary sampling units are sampled with probability proportional to size, since population size is correlated with most outcomes of interest. Informative samples, if not appropriately adjusted, produce biased estimates. The standard approach to adjust for informative sampling is to weight observations by the inverse probability of selection. This is usually straightforward to do when estimating EBP models with household surveys containing sample weights, for example when using the R EMDI and Stata SAE software packages. This protects against bias from informative sampling within sampled areas, but not against bias in predictions for areas that are not included in the sample (Pfefferman and Sverchkov, 2009). The bias in estimates for non-sampled areas can be severe. Table 2 shows evidence on the extent to which informative sampling leads to less accurate predictions in nonsampled areas. The extent of this bias appears to depend on the country context and the nature of the sample. In the examples reported in Table 2, the estimates in Burkina Faso appear as a particular outlier, with an out-of-sample R<sup>2</sup> of 0.21. It is difficult to know exactly why the out-of-sample estimates for Burkina Faso are so inaccurate, but this likely reflects large differences in the relationship between covariates and predictors between sampled and non-sampled areas. In addition, the source of validation 10
Policy Research Working Paper 10353

# **Abstract**

Universal health coverage initiatives in Guinea have been hampered by insufficient budget allocations and inefficiencies. Nevertheless, data on the extent of catastrophic and impoverishing health expenditures in Guinea are scarce and outdated. The objectives of this study were to (1) identify the drivers of total health expenditures, (2) estimate the prevalence of catastrophic and impoverishing health expenditures, and (3) estimate the drivers of and potential financial risk protection against catastrophic and impoverishing health expenditures through subsidies in Guinea. A retrospective cohort study was conducted using the 2018–19 Guinea Living Standards Measurement Study. All 41,449 individuals in the Study were eligible, but 15 individuals who lacked consumption expenditure data were excluded. Expenditure data were converted to 2019 international dollars. Based on means and medians, the primary drivers of total health expenditures were hospitalizations ($78 and $51, respectively) and medications ($72 and $61, respectively). Based on the distribution of total health expenditures by expenditure categories, the primary driver was medications (75 percent). The main driver of hospitalization expenditures was fever and malaria (21 percent of hospitalization expenditures). The prevalences of catastrophic and health expenditures (equal to or greater than 10 percent threshold) and impoverishing health expenditures were 13 and 4 percent, respectively. Subsidizing medications would prevent 46 percent of the cases of catastrophic health expenditures and 73 percent of the cases of impoverishing health expenditures. It is recommended that the Guinean government (1) strengthen the country’s pharmaceutical sector by reinforcing existing laws and regulations and the operational aspects of the sector; (2) implement subsidy programs for rational use of medicines (notwithstanding the Bamako Initiative); (3) strengthen the National Malaria Control Program; and (4) establish an evidence-based operational financing strategy for universal health coverage.

This paper is a product of the Health, Nutrition and Population Global Practice. It is part of a larger effort by the World Bank to provide open access to its
Metal commodity price forecasting**

This literature draws on 20 studies of price forecasts for aluminum (11 studies), copper (14), lead (8), nickel (8), tin (5), and zinc (8). The most evaluated methods are univariate and multivariate time series models—mostly benchmarked against no-change forecasts—although the number of studies based on machine learning techniques is also growing rapidly ( **Figure 2** ). As for oil prices, most studies use sample periods that end before the commodity price collapse of mid-2014 and most examine monthly data. In contrast to oil prices, where studies examining forecast horizons up to one year are common, the most examined forecast horizon for metals prices have exceeded one year.

# **2.2.1 Futures prices**

Like for oil prices, futures prices of metals have underperformed the no-change benchmark, but they have predictive content that can improve model forecasts. In a study of several metals (aluminum, copper, lead, nickel and tin), Chinn and Coibion (2014) showed that the no-change 8
both Saddam Hussein and the subsequent period of instability and insurgency was the country's informal economy. In the Afghanistan context, Ciarli et al. (2015) show that the probability that a household engages in self-employment activities with low capital intensity, or in subsistence agriculture, increases with surges in conflict.

The impact of conflict could even be positive in the case of illicit activities. Stepanova (2012) argues that the war of the US-led coalition on the Taliban did not significantly reduce Afghanistan’s opium economy. Lind et al. (2014) note that conflict increases the perception of lawlessness while destroying infrastructure that is crucial to the production of alternative crops. In interviews conducted by Keohler and Zuercher (2007), farmers confirm that opium poppy is one of the few available cash crops. And Goodhand (2008) even argues that in parts of Afghanistan drugs have contributed to some degree of political order.

The goal of this paper is to offer an integrated assessment of the change in formal, informal and illicit economic activity in response to surges in conflict. The analysis is for Afghanistan, a country that has been repeatedly at war. The focus is on the period starting in 2012, at a time when the intensity of conflict increased dramatically, after a decade of relative stability and rapid economic growth.

The analysis relies on high-frequency data from non-traditional sources of information to measure economic activity. Satellite imagery allows us to assess nighttime light intensity for “tiles” measuring just one square kilometer. Remote sensing by satellites is also informative on the land surface devoted to the cultivation of poppies in every season. And anonymized cell phone traffic data from several thousand registered firms have been made available by a major telephone operator.

These non-traditional sources of information make it possible to conduct rigorous empirical analyses despite the obvious shortcomings of official statistical systems in conflict-affected countries. It has been shown that nighttime light intensity is highly correlated with GDP in developing countries (Donaldson and Storeygard 2016, Henderson et al. 2012). In South Asia,
assets either because membership give them access to additional resources or because being a member increases the maternal social network enhancing the likelihood of getting external support from other community members, the _support pathway_ .<sup>6</sup> Finally, social dynamics might play an important role in changing and shape mothers’ aspirations. As discussed by Appadurai (2004) and Ray (2006), poverty is often strongly associated with low aspirations. Escaping poverty might not be considered a feasible option because of their own experiences and the experiences of those that are close to them. An active participation to community organizations and their initiatives might constitute a unique opportunity to learn about the positive experiences of other members. Social interactions might make mothers feel like they are able to control their own lifes and plan their own future and the future of their children, the _empowerment pathway_ (Macours and Vakis, 2008).

Past studies generally fail to estimate a causal relationship between social capital and health and provide descriptive evidence on this relationship. A considerable number of studies point to an association between individual social capital and various aspects of own physical and psychological health (Hyyppa and Maki, 2001; Lindstrom, 2004; Mohseni and Lindstrom, 2007; Pevalin and Rose, 2003; Rose, 2000; Kawachi et al., 1997; Veenstra, 2000; Lindstrom et al., 2001; Hurtado et al., 2011). But just few of them look at maternal social capital and the child health.

Nobles and Frankenberg (2009) using data from Indonesia find a positive association between maternal participation and child health only for children from relatively disadvantaged backgrounds. Carter and Maluccio (2003) explore the role of household social capital as a protective factor against idiosyncratic economic shocks in South Africa. Those households with stronger ties in the community are more likely to be able to cope with economic/financial losses and this reflects on children nutritional status. Kanaiaupuni et al. (2005) look at mothers’ social networks in Northern Mexico and find that social
relationships for firms operating in industries with severe contracting problems. However, the existing firm-level studies have generally focused either on institutional differences within a single exporting country or across destination markets. We make use of the Exporter Dynamics Database, the first and the only database that provides detailed comparable information on the micro-structure of trade flows between countries across a large number of countries, to explore the effects of institutional differences across countries. This represents an advance with regard to the existing firm-level studies, which rely on a single exporting country.

# **3 Empirical Specification**

In this section, we describe the empirical models and estimation procedures. In this regard, we closely follow Manova (2013). The basic model we use to estimate the effects of institutions on export is given by: where _Xjist_ is a measures of trade flow from country _j_ to country _i_ in sector/industry _s_ in year _t_ ; _Qjt_ is the institutional quality of country _j_ in year _t_ , and _zs_ is the contract-intensity of industry _s_ ; _αj, αi, αs_ and _αt_ are the exporter, importer, industry and time fixed effects; _α0_ is the constant term and _εjist_ is the idiosyncratic error term; and

**_Z_** is a vector of additional control variables including traditional sources of comparative advantage (such as physical capital, human capital and natural resource endowments – _Kjt_ , _Hjt_ and _NRjt_ – along with their interactions with the corresponding factor intensities – _ks_ , _hs_ and _ns_ ) as well as those variables at the heart of the gravity equation (exporter GDP, importer GDP and the distance between them), and **_γ_** the vector of corresponding coefficients.

In the baseline specification where we are interested in measuring the total effect of institutional quality on trade, captured by _∂X∂Q_ <u>jjtist</u><sup>=</sup><sup>_β_0+</sup><sup>_β_1</sup><sup>_zs_, the left-hand-side (LHS