rafmacalaba/data-use-mentions-extended huggingface dataset:
text --> <!-- Start of picture text --> Percent of regional total<br>Foreign direct investment<br>70<br>Portfolio investment<br>60 Cross border bank lending<br>50<br>40<br>30<br>20<br>10<br>0<br>EAP ECA LAC MENA SA SSA<br><!-- End of picture text --> <!-- Start of picture text --> Percent<br>US$<br>100<br>Euro<br>80 Yen<br>60<br>40<br>20<br>0<br>Europe Asia<br>Middle East<br>Latin America Africa and the<br><!-- End of picture text --> Sources: World Bank, Dealogic, Bloomberg, Bank for International Settlements, Adjiev and Takats (2016). A.B. Bond issuance expressed in current U.S. dollar. Last observation is June 2016. C. EAP is East Asia and the Pacific, ECA is Eastern Europe and Central Asia, LAC is Latin America and the Caribbean, MENA is the Middle East and North Africa, SA is South Asia, and SSA is Sub-Saharan Africa. Total portfolio investment data are as of June 2015. Foreign direct investment data are as of December 2014. Bank claims are as of December 2015. D. Currency positions of cross-border bank lending in 2014Q4.

37
Table 2: Descriptive Statistics Enterprise Survey

|**Characteristic**|**Overall**, N = 1,802<sup>_1_</sup>|**Small**, N = 814<sup>_1_</sup>|**Medium**, N = 738<sup>_1_</sup>|**Large**, N = 250<sup>_1_</sup>|
|---|---|---|---|---|
|Experienced Power Outages|||||
|Don’t know|0.3%|0.1%|0.1%|7.0%|
|Yes|45.6%|45.2%|48.8%|40.7%|
|No|54.1%|54.7%|51.2%|52.3%|
|Obstacle: Electricity|||||
|Don’t know|0.0%|0.0%|0.0%|0.0%|
|Does not apply|0.3%|0.0%|1.5%|0.2%|
|No Obstacle|30.0%|30.9%|26.0%|25.2%|
|Minor Obstacle|10.5%|10.2%|13.5%|4.6%|
|Moderate obstacle|13.3%|12.7%|17.3%|10.1%|
|Major obstacle|21.5%|23.0%|13.0%|23.4%|
|Very Severe Obstacle|24.4%|23.2%|28.6%|36.5%|
|Obstacle: Transport|||||
|Don’t
# **III.  Approaches to Estimating Informal Remittances**

# _Direct Approach_

The most accurate estimates of the informal share of remittances are likely to come from household surveys that ask respondents both how much they have received and by what means. This allows direct estimation of what proportion was received through informal channels. An alternative methodology is to scale up data on total remittances received from household surveys and compare aggregates to national accounts. However, owing to noise in reporting, this method is not viable.

There are two problems with using household survey data on remittances. First, Individuals receiving remittances, and especially those receiving large amounts, are likely to be under-sampled: concerns about confidentiality may induce them not to answer survey questions. Second, respondents tend to underreport income (Ravallion, 2003). These problems make it especially problematic to compare income from surveys to national accounts. Indeed, studies that have worked with household remittance data find that they under-estimate total flows, however, because of the biases noted this does not imply that informal flows are not significant.

Still, provided there is information about the channel of transmission, the direct approach may be more accurate for remittances than other types of informal income. Amounts sent are often round numbers that are well known to family members—for instance, a household may receive $200 every month from a relative abroad. In addition, receiving remittances through informal channels is legal in most countries, so unlike activities in the underground economy, where participants seek to evade taxes, there is no direct incentive for concealment. Assuming the extent of underreporting is uncorrelated 6
Recently, the advent of monthly and annual remote sensing databases has led to the first spatial estimation exercises that explicitly incorporate economic dynamics (Wheeler et al., 2011; Dasgupta et al., 2014). Direct impact studies in Latin America using the new databases have included high-resolution work on new road construction and deforestation in Brazil, where satellite monitoring has been available for a longer period (Laurance et al., 2009) and Bolivia, Panama, Paraguay and Peru (Reymondin et al., 2013).

More recently, Li et al. (2015) have investigated the potential impact of decreased travel time from improvement of road links in the Democratic Republic of Congo, but their crosssectional analysis employs land use information (JRC 2003) that predates the new highresolution satellite data. Damania and Wheeler (2015) have advanced the state of the art by estimating the potential impact of road upgrading on forest clearing and biodiversity in eight Congo Basin countries. Their deforestation model incorporates the latest high-resolution satellite data on forest cover changes, as well as quality estimates for specific links in the regional road system. They also create a biodiversity threat index that combines and synthesizes several measures of potential biodiversity loss.

This paper builds on the approach of Damania and Wheeler, while extending it in several ways. First, we broaden the research domain to include Latin America and Asia. Second, we gain precision by operating at higher spatial resolution. Third, we generalize the methodology by substituting globally-available road quality estimates for the Africa-specific estimates employed in the previous work. Finally, we expand the previously-developed biodiversity indices for the Congo Basin countries into a global database that includes a larger set of biodiversity measures.

4
(2017) and uses the average foreign-currency long-term sovereign debt rating by Fitch Ratings, Moody’s, and Standard and Poor’s. A dummy variable approach is used, where is set equal to one if the average rating at time _t_ is below investment grade and 0 otherwise. Consequently, the tt state-dependent impulse response function becomes a function of the dummy variable and the II endogenous variables: �αA,i,h + xi,tδA,h + shocki,tβA,h�+ yyrr,tt+h)�α= IIB,i,htt + xB,i,tδh + shocki,tβB,h�+ μ tt rr,tt+h **2.3 Modeling financial crisis probability** (1 −II Finally, the analysis explores how different U.S. interest rate shocks shape the probability of financial crises in EMDEs. To that end, it uses a logit model as in Kose et al. (2021) to assess the impact of different underlying shocks on the probability of crisis in EMDEs over the past 50 years. This is estimated using annual data from 1985 to 2018. Crisis events are based on Laeven and Valencia (2020) codified to 2017, and extended in Kose et al. (2021), and encompass sovereign debt, banking, and currency crises.

The model is estimated as: = β<sup>′</sup> + μ + ε where is a binary variable of banking, currency, or sovereign debt crises for country YYrr,tt XXrr,tt−1 rr rr,tt _i_ in year _t_ taking the value of 1 if a crisis occurred; is a vector of determinants of crisis including the rr,tt yy real, inflation, and reaction shocks as well as other control variables; μ captures unobserved rr,tt−1 country heterogeneity; and ε are the residuals. The baseline specification is a panel logit model XX rr with random effects, as the Hausmann test suggests that the random
countries, across markets of different sizes, or across countries and markets at different stages of development. Electricity generation, for example, may be a natural monopoly in a small economy, but when the economy grows, the sector may be able to profitably sustain more than one generator. Similarly, the economic rationale for state ownership in mobile telecommunications is stronger in countries with a wholesale access network because such a network is a natural monopoly.

Furthermore, the taxonomy may not capture differences in market features among some subsectors within sectors disaggregated to the four-digit level. For instance, the economic rationale for SOE presence is weaker in the generation of electricity from wind energy, but stronger in the generation of electricity from nuclear energy. To accommodate this reality, the taxonomy classifies sectors into the three categories in a generally conservative way—that is, it considers an economic activity to be a natural monopoly even when in some (more advanced or larger) economies it may already be competitive. As a result, the sector categorization is indicative only; the economic rationale for SOE presence may still vary among countries and should be analyzed in detail in the context of any country-level diagnostic.

Technological innovations that affect the fundamentals of certain sectors, including their cost structures and barriers to entry, represent another challenge to industry classification. New technologies may disrupt previous natural monopolies and make those sectors contestable, for example by lowering fixed costs. Innovations in the field of financial technology, for example, could potentially transform the market structure of traditional insurance markets through the analysis of real-time data on risk profiles, dynamic underwriting, and personalized premium setting (Ricci and Battaglia 2021). As a result, the categorization

> 36 EFI SOE Global Database Project, led by Andrea Dall’Olio (TTL), Tanja Goodwin (co-TTL), and Mariem Malouche (co-TTL).

> 37 The CPSD Knowledge note (Sanchez-Navarro, Goodwin, & Kikeri, 2021) and the forthcoming toolkit on state footprint and private sector development offer a good set of tools for practitioners
3 The paper first shows the relationship between the OECD’s PSEs on the one hand, and on the other hand the representation of the various agricultural interventions in the database used by global modelers. It then provides a back-of-the-envelope assessment of the welfare implications of global trade liberalization, before reporting in Section 3 the results from the full-blown computable general equilibrium (CGE) model, building on earlier work by Hertel and Keeney (2006). Those CGE results show the estimated effects of policies not only on developing country and global economic welfare but also on global agricultural trade and on agricultural incomes in both developed and developing countries.

# **1. OECD vs GTAP measures of the extent of agricultural subsidies and trade distortions**

The OECD’s PSE is intended to provide a summary measure of the producer subsidy that would be equivalent to all the forms of support provided to farmers, including direct farm subsidies that may or may not encourage production domestically, as well as market price support provided by import tariffs and export subsidies. All three of those components of government assistance to farmers are disciplined under the Uruguay Round Agreement on Agriculture, and have become known as the “three pillars”.

One element of the OECD’s PSE is that provided by market price support (MPS) measures. It is calculated by comparing domestic and border prices of like products so as to capture the total domestic market price effect of all trade distortions, including tariff and non-tariff import barriers as well as export subsidies. The OECD also uses the price comparison method to calculate a CSE: the consumer subsidy equivalent of those measures. The CSE is negative in countries that raise domestic food prices via restricting imports and subsidizing exports, and would only be positive if a country had direct subsidies to food consumption significant enough to offset the effect of those trade measures in raising consumer prices.<sup>2</sup>

> 2 In principle the PSE and CSE would also capture the effects of import subsidies and taxes or other restrictions on exports
26 3D PRINTING AND TRADE As a second exercise, we exploit product level characteristics to identify heterogenous effects of 3D printing on trade. This allows to study the extent to which the impact of 3D printing on trade in the previous sections depends on the product characteristics of hearing aids. Specifically, we investigate if product weight plays a role in the decision of localizing production closer to consumers versus concentrating the production in one location. Intuitively, for products like hearing aids that are light and hence have lower transport costs, incentives to localize production closer to consumer should be lower than for heavier products. Ideally, we would also like to identify how the impact of 3D printing on trade varies based on the interplay between economies of scale intensity and product weight. Unfortunately, data on scale elasticities is available only for a limited number of 2-digit sectors (see Bartelme, Costinot, Donaldson and Rodriguez-Clare, 2018).

To formally test for differential impacts of 3D printing on trade in heavy and light goods, we augment equation (1) by an interaction term between Ikt(3Dprinting 2007) and product k’s log of weight. The weight of product k is defined as the median unit weight of HS 6-digit products corresponding to a 4-digit SITC product.<sup>18</sup> The unit weight measure for printable sectors varies from 52 grams for spectacles (SITC 8842) to 98 kilograms for machine tools for deburring (SITC 7316). Before describing the results from this specification, it is important to stress some caveats. First there may be an aggregation bias, if some products in a category are light and others are heavy. In addition, the effects could be misidentified if weight is a determinant of technology adoption. Moreover, it is possible that the technology could affect product weight as it may allow to design new structures that are lighter and use less material.

With these caveats in mind, Figure 11 presents the results on the impact of 3D printing on exports and imports as a function of product weight.
1997|1997|1997|1997|2004|2005 (own survey)|2010|2010|2010|
|Rice|-0.84|-0.95|-0.83|-1.01|-0.89|-0.79|-0.38|-0.06|.|-0.27|-0.83|-0.60|
|Other cereals|.|.|.|.|.|.|.|.|.|.|.|.|
|Roots and tubers|.|.|.|.|.|.|.|.|.|.|.|.|
|Starch crops|.|.|.|.|.|.|-0.61|.|.|.|.|.|
|Protein crops|.|.|.|.|.|.|-0.74|.|.|.|.|.|
|Fruits|-0.92|-0.79|-0.86|-0.92|-0.82|-1.17|-0.91|.|-0.64|.|.|.|
|Vegetables|-1.07|-0.92|-0.90|-1.11|-0.94|-1.03|-0.91|.|-0.64|.|.|.|
|Pork|.|.|.|.|.|.|.|.|.|-0.26|-0.87|-0.68|
|Beef|.|.|.|.|.|.|.|.|.|-0.19|-0.93|-0.69|
|Chicken|.|.|.|
for human capital, labor market distortions, intergenerational bequests, etc.) would complement existing micro tools (such as gender-disaggregated expenditure incidence analysis) and help to quantify the gender and growth effects of public policies in general, and gender-based interventions in particular. As an input in these models, there is also a need to quantify the possibly _nonlinear_ effects of access to infrastructure on female labor supply to the market.<sup>48</sup>

> 48For instance, using U.S. data, Kimmel and Connelly (2006) estimate a structural time use model (a simultaneous four-equation system) in which the dependent variables are the number of minutes in a mother's diary day that she devotes to home production, leisure, market work, and care giving. They are therefore able to estimate explicit wage and child care price elasticities for these variables.  With the exception of Koolwal and Van de Walle (2010), cited earlier, studies of this type are clearly lacking for developing countries.

37
allowance for changes in other non-crisis determinants of living standards.

7There were some studies done before the APIS data became available in 1999. For instance, Reyes and Mandap ( 1999) used an existing CGE model to simulate the likely impact of the crisis on incomes. They found that the crisis would lead to a fall in average incomes of all deciles and an increase in the Gini ratio. No attempt was made to separate the El Nifno from the financial crisis effects, nor were there any simulated effects on consumption. There were also studies undertaken by the World Bank and UNDP, and by Lim (June 1998). These studies were done shortly after the financial crisis and in the midst of the El Niflo drought, and had to rely on secondary data to explore anticipated rather than actual effects.

4
To study this issue, we take advantage of microdata from Senegal’s representative 201819 Harmonized Survey of Household Living Conditions ( _Enquête Harmonisée sur les Conditions de Vie des Ménages_ , EHCVM). The EHCVM covers 7,156 households and 36,746 individuals above the age of 15. An advantage of using this survey is that it includes an in-depth labor module that covers social security contributions at the individual level and non-agricultural enterprise characteristics. Then, following Alcaraz, et al. (2015), we estimate a model by maximum-likelihood to measure the size of involuntary informality and its underlying drivers.

The results consistently suggest that the desire of being formal is greater for workers with formal education, married and with a lower proportion of children under the age of 5 living in the household, while the individual's preference for the formal sector grows with age at a decreasing rate. Furthermore, our results also show that labor informality is mainly a voluntary phenomenon in Senegal. Our model estimates that the probability of obtaining a formal job for a worker who prefers a job in the formal sector is slightly below 0.50, which results in 30 percent of informal workers being involuntarily displaced into the informal sector. The magnitude of involuntary informality varies significantly across different types of workers, and it is the lowest for wage earners with positive incomes at 11 percent.

The main findings of the analysis are robust to different models restricting the sample to individuals working either 30 or more, or 35 or more hours per week. Results are also robust to models excluding from the sample 10.8 percent of salaried urban workers reporting zero earnings, as well as other groups such as unpaid workers. As an extra robustness check, we incorporate two alternative definitions of informality. The first is a more stringent legal definition that considers whether firms have a formal accounting system transmitting to the Tax Authority. The second, a more relaxed definition, merely requiring firm registration with the Commercial Registry of the Tax Authority, or just a formal accounting system.

This
# **6. Tables**

_Table 1._ Main characteristics of selected household surveys in India

|**Survey name**|**Administering**<br>**agency**|**Number of**<br>**questions**<br>**about**<br>**consumption**|**Welfare**<br>**aggregate**|**Survey time**|**Description**|
|---|---|---|---|---|---|
|Consumption<br>Expenditure survey<br>(CES)2017/18|NSSO,<br>Government of<br>India|~ 400|MMRP|July 2017 to<br>June 2018|Withheld due to data quality concerns|
|Consumption<br>Expenditure survey<br>(CES)2011/12|NSSO,<br>Government of<br>India|~ 400|MMRP, MRP,<br>URP|July 2011 to<br>June 2012|Last available official consumption<br>survey round|
|Survey on social<br>consumption<br>(Social<br>Consumption<br>2017/18)|NSSO,<br>Government of<br>India|1|Usual monthly<br>consumption<br>expenditure of<br>the household|July 2017 to<br>June 2018|Education and health specific survey.<br>Sample: 113,823 households|
|Survey on social<br>consumption<br>(Social<br>Consumption<br>2014)|NSSO,<br>Government of<br>India|1|Usual monthly<br>consumption<br>expenditure of<br>the household|January to<br>July 2014|Education and health specific survey.
746|29|46,968|50|
|Kyrgyz Republic|0.580|73|11,142|108|
|Moldova|0.580|72|18,549|89|
|Russian Federation|0.729|32|49,663|44|
|Tajikistan|0.533|83|13,786|99|
|Ukraine|0.647|47|18,734|88|

The total number of countries is 141 Sources: HCI, world Bank, GDP PPP per worker, Penn World Tables and WDI The World Bank has recently published a Human Capital Index (HCI) which is a composite of indicators of the educational attainment and health status of the population, for more than 150 countries. In general, a higher HCI correlates with higher productivity and thus higher GDP per worker. The cross-country correlation coefficient of the HCI and GDP at Purchasing Power Parity per worker is 0.72. A labor force with more human capital will, ceteris paribus, be more productive, but the relationship also runs in the opposite direction, in that a more productive labor force generates more resources for investment in human capital, whether by the state or households.

6
|0.1|0.15|0.1|
|Caregiver is literate|0.13|0.1|0.11|0.1|0.03|0.09|0.23**|0.1|0.18*|0.1|0.19*|0.1|
|Caregiver working<br>memory (out of 20)|0.08***|0.03|0.04|0.02|0.03|0.02|0.06**|0.03|0.05*|0.03|0.07**|0.03|
|Caregiver depressive<br>symptoms (out of 26)|.|0.01|.|0.01|–0.01|0.008|0.01|0.01|–0.01|0.01|0.01|0.01|
|_Household_|||||||||||||
|_characteristics:_|||||||||||||
|Household size|–0.11|0.05|–0.08|0.05|–0.15***|0.05|–0.06|0.05|–0.07|0.06|–0.03|0.05|
|Household wealth index|0.03|0.02|0.04**|0.02|–0.01|0.02|0.04**|0.02|0.05**|0.02|0.02|0.02|
|Family care indicators<br>|0.03*|0.01|0.02|0.01|0.01|0.01|
Dataset (SARMD). India’s consumption aggregates reported are based on the Uniform Recall Period (URP).

The standardization of the consumption aggregates would reduce the international poverty rate in South Asia by 1.3 percentage points or almost 18.5 million extreme poor, with India witnessing a reduction in the headcount by 40 million. Other countries, like Bangladesh and Pakistan would see a large increase in the poverty numbers if consumption aggregates were standardized. Pakistan would increase the number of extreme poor from 10.3 million to 12 million, and Bangladesh would see its number of extreme poor go from 27.5 million to 28.9 million, mainly driven by the rural poor. India would increase the number of extreme poor by over 22 million nationwide. Most of 17
a different facet of healthcare delivery (for instance, the Kenya Medical Practitioners and Dentists Council licenses most health facilities, while the Kenya Medical Laboratory Technicians and Technologists Board addresses lab safety). Prior to the intervention, facilities were visited by inspection teams on an ad hoc basis based on the quota for the inspection period or by individual boards and councils, usually following a complaint or a serious adverse event. Four percent of facilities were inspected annually and the likelihood of two inspections in one year was zero.<sup>12</sup> Section 2 of the Supplemental Material provides details of the old inspection system.

Concerns around patient safety were raised after a national survey in 2012 reported that 2% of health facilities were compliant with minimum patient safety standards and systems. A subsequent study that used clinical observations thankfully suggested a more nuanced situation with variation across specific tasks. For instance, compliance was 87% with safe injections and blood draw practices but 2% for hand-hygiene. Even then, outpatients faced an average of 5.1 violations of infection, prevention and control (IPC) safety practices out of 7.5 observed indications where a safety action should have been taken (Bedoya et al., 2017). Despite these deficits, the quality of care in Kenyan

> 10Faith-Based and Non-Government Organizations account for 11% of facilities and 9% of patients in our baseline survey. These operate similarly to private facilities, except location decisions may be taken at a higher level.

> 11Differences between the data collected in the study counties and administrative data in other counties could also reflect under-counts of unlicensed providers in the latter (Table S1 in the Supplemental Material).

> 12Private communication with the Kenya Medical Practitioners and Dentists Council.

7