rafmacalaba/data-use-mentions-extended huggingface dataset:
Table A1

|Time Period|Tarif Defnition|
|---|---|
|1974-1991|Tarif + Statistics rate (1)|
||Extra-MERCOSUR tarif +|
|1992-2001|Statistics rate +|
||ad-valorem equivalent of DIEM (2) +|
||Convergence factor (in 2001) (3)|

Notes: (1) The Statistics rate is an additional ad-valorem levy which the government adds to certain goods. Its purpose is to finance the collection of statistical data. It was originally instituted by Executive Decree 6123/61. (2) DIEM (Derechos de Importación Mínimos Específicos, minimum specific import taxes) originated as anti-dumping measures for certain import categories (including textiles). An “average import price” was calculated for each category, and the corresponding tariff was applied to each price. The actual tariff paid was the higher of this “minimum import levy” and the ad-valorem tariff applied to the actual price. (3) The convergence factor was established by law in June 2001. It was calculated as cf = 1 −<sup><u>1+</u></sup> 2<sup><u>e</u>,whereeisthedollarpriceof1Euro.</sup> ISIC classification is coarser, we constructed an aggregate indicator for NADI categories corresponding to a single ISIC code. This resulted in two series - one for 1974-1991, another for 1992-2001. At a second stage, we produced a uniform classification for the whole period. Each stage is described in detail below.

Official conversion tables between 8—10 digit NADI classification and 5-digit ISIC (Revision 2) are available starting from 1980 (when 1000 new categories were added to the NADI classification). Backward compatibility with data from 1974-1979 is also possible, but at the 5-digit level of aggregation of the NADI classification. Starting in 1991, we used official conversion tables between NADI and ISIC Revision 3. Table A2 describes the sources used in this process.

Given that 5-digit ISIC
Policy Research Working Paper 10314

# **Abstract**

This study examines the relationship between food insecurity and trust using the 2014–17 waves of the Gallup World Poll and the Food and Agriculture Organization’s Food Insecurity Experience Scale. Trust improves public institutions, social capital, public health interventions, and economic development. Vertical trust is represented as an index of trust in national institutions, while horizontal trust is represented as a measure of trust in friends and family.

The findings show that food insecurity is associated with a decrease in both measures of trust. The study further document heterogeneous effects of food insecurity across economic development rankings. The results suggest a need for governments to increase food security to bolster public trust, strengthen the social contract, and enhance the effectiveness of development efforts.

This paper is a product of the Office of the Chief Economist, Africa Region. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at wkassa1@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
unique global dataset provides comprehensive coverage for more than 50 market indicators grouped into five main thematic areas - government and corporate bonds, equity, institutional investors, and sustainability over the period 2015-2020. The construction methodology of the IFC capital markets database includes data collection from both primary and secondary sources. Primary data was collected through desk research using reports of local stock exchanges, stock market regulators, central banks, and other reports on country specific stock markets published by independent bodies. Most of the secondary data collection came from a commercial data provider – Refinitiv – as well as from other prominent sources such as the World Federation of Exchanges (WFE), Organization for Economic Cooperation and Development (OECD), African Development Bank (AfDB), the World Bank Group (WBG), and Asian Development Bank (ADB).

Our sample covers over 150 countries across the globe in the period 2015-2020. In addition to the IFC’s capital markets database, the paper leverages the World Bank’s World Development Indicators (WDI), the World Governance Indicators (WGI) and the International Country Risk Guide (ICRG) databases.

# **_Capturing issuer composition_**

The study relies on the following variables from the IFC capital market database that capture changes in issuer composition:

- _Total number of listed firms_ – measured by the total number of companies listed on a stock market. This captures the market depth, indicative of the barriers to entry for public stock markets.

- _Share of listed domestic firms_ – measured by the share of domestic companies listed on a local stock market as a portion of the total firms listed. A higher share of listed domestic companies suggests that markets are more accessible for local firms.

- _Sectors_ – data are also classified across seven sectors: financials, agriculture, extractives, manufacturing, construction, utilities, others.

- _Share of market capitalization of the top 10 largest domestic companies_ – captures the level of market concentration. A higher value indicates that the stock market is highly concentrated with a few large
17 overall health spending. Additional resources for targeting the uninsured poor can come from an extension in the coverage of social insurance (GIS and LIS), the passage of a basic package in social as well as future private insurance policies, and selective increases in user fees for the uninsured wealthy. The discretionary clinical package can be financed from user fees and insurance and public funds should be used only for truly poor and indigent patients.

_Redeploying_ **_Medical Personnel._** Alternative mixes of labor inputs are influenced by the allocation of public spending which in turn will influence future health outcomes. In China, indices of labor input for western medical treatments show an inegalitarian allocation across provinces. Poor provinces are deprived of more qualified medical staff. Table 4 showed the distribution of more qualified health personnel (doctors, assistant doctors) and less qualified health personnel (specialized nurses, nurses, and midwives). Computed on the basis of relative personnel ratios (RPR) the richest quintile of provinces-- Beijing, Shanghai, Guangdong, Zhejiang, Tianjin and Liaoning clearly have an oversupply of all medical staff but particularly of more qualified staff, as the value of RPR far exceeds unity. Conversely, the poorest quintile of provinces--Guizhou, Xinjiang, Anhui, Henan, Guangxi and Sichuan (Annex Table 9)--have an undersupply of all medical staff including more qualified personnel. Other less disadvantaged provinces in the second quintile--Jiangxi, Yunan, Shannxi, Hunan, and Tibet--also have an undersupply of more qualified medical personnel especially doctors.

Besides their impacts on health outcomes, the different mixes of health personnel will have important underpinnings on cost efficiency. In provinces with high morbidity and mortality rates from infectious and communicable diseases, redeploying the personnel mix toward more preventive health services staff like nurses and midwives can generate further cost savings. To attract and retain more qualified staff in poorer provinces, the government should also provide such incentives as housing, bonuses, education for dependents, and better posting after
, . . . , P_<sup>_d−_1</sup> _, P_<sup>_d_+1</sup> _, P_<sup>_D_</sup> ) may be correlated with one another, the relationship between _P_<sup>_d_</sup> and _P_<sup>_−d_</sup> could be complex, and _P_<sup>_d_</sup> may also depend on _h_ other available regressors _X_ = ( _X_ 1 _, . . . , Xh_ ). Assume that the hypothetically complete price data _P_ is a partially observed random sample from the _d_ - variate multivariate distribution _P_ ( _P |_ **_θ_** ) and that the multivariate distribution of _P_ is completely specified by the unknown parameter vector **_θ_** . Thus, the objective is to obtain estimates for **_θ_** . The algorithm estimates a posterior distribution of **_θ_** by iteratively sampling from the conditional distributions

The parameters **_θ_** 1 _, . . . ,_ **_θ_** _d_ are specific to the respective conditional densities. Starting from a draw from the marginal distributions, in the current application modeled using univariate time series methods, the _i_ -th iteration of chained equations is a Gibbs sampler that successively draws (B2) where _Pd_<sup>(</sup><sup>_i_)</sup> = ( _Pd,_ obs _, P_<sup>_∗_(</sup> _d_<sup>_i_))isthe</sup><sup>_i_-thimputationofprice</sup><sup>_d_atiteration</sup><sup>_i_.Theimputationsofthe</sup> previous iteration _P_<sup>_∗_(</sup> _d_<sup>_i−_1)</sup> enter the next imputation _P_<sup>_∗_(</sup> _d_<sup
||**All **|**frms**|> **Med**|**ian leverage**|
|---|---|---|---|---|
||(1)|(2)|(3)|(4)|
|∆_pityit_/_kit_|0.208<sup>_a_</sup>|0.211<sup>_a_</sup>|0.303<sup>_a_</sup>|0.292<sup>_a_</sup>|
||(0.001)|(0.001)|(0.001)<br>|(0.001)<br>|
|∆_pigtyigt_/_kit_|0.182<sup>_a_</sup>|0.161<sup>_a_</sup>|0.229<sup>_b_</sup>|0.191<sup>_b_</sup>|
||(0.055)|(0.052)|(0.100)|(0.089)|
|Observations|61,445|62,442|29,528|30,037|
|R-squared|0.238|0.084|0.296|0.118|
|Sector×year FE|Yes|Yes|Yes|Yes|
|Firm FE|Yes|No|Yes|No|

**Notes:** This table presents results from estimating the relationship between the change in firm’s leverage and the change in its average product of capital and change in its earnings coming from selling to the government divided by the firm’s total stock of capital. Regression (16) is estimated with firms obtaining at least one procurement project over 2000-13 using annual data. Standard errors are clustered at the firm level;<sup>_a_<
Research on the employment effects of PMR has repeatedly found that the institutional and economic context in which pro-competitive reforms are enacted matter greatly for their effect size and direction (Vitale et al., 2020). Bordon et al. (2016) show that the employment effect of product and LMR varies dependent on the macroeconomic context of the reform implementation. Using local projection methods and reform shocks based on OECD PMR indicators, they find that product market deregulation leads to positive effects on employment (significant after 2 years), which becomes larger and more significant until year 5. In a second step, they include an interaction of the reform variable crossed with a measure of the business cycle at the time of the introduction of the reform. Product market deregulation launched during “good times” has an immediate positive and significant effect on the employment rate. They do not have statistically significant effects on employment in “bad times.” This reassuringly implies that the additional supply capacity created by structural reforms will be absorbed even when aggregate demand is weak and does not necessarily lead to deflationary pressures. The authors also find more positive effects on employment when reforms coincide with non-restrictive monetary policy.

Again relying on recent OECD PMR indicators data, Piton and Rycx (2019) find that an average product market reform over 1998–2013 is associated with a one percentage point reduction in the unemployment rate. Interestingly, highly educated individuals seem less impacted than low- and middle-educated workers. In contrast, men and women are equally affected by deregulation. Such heterogeneous effects on different worker types are also addressed in De Serres et al. (2012), who find more pronounced effects for women and youth, two groups considered vulnerable population subgroups at times of crisis. Their paper emphasizes how decreased regulation of product markets helps make economies more resilient to shocks. They show that more competition-friendly PMR can reduce structural unemployment while raising turnover. Such regulations also seem to decrease the persistence of unemployment.

A common element of all research presented above is that more pro-competitive PMR
by US multinationals in Mexico in recent years, particularly in the automobile and electronics sectors. However, these studies only provide anecdotal evidence of reshoring.

The recent Infrastructure Investment and Jobs Act (IIJA) and the Inflation Reduction Act (IRA) have also increased the incentives to relocate production to the US through a combination of tax credits and grants requiring domestic or North American content. For instance, the IRA Section 45X MPTC Advanced Manufacturing Tax Credit applies to components for wind, solar, and battery projects manufactured in the US. To be eligible for the domestic content bonus credit, energy projects and facilities are subject to a domestic content requirement of 40 percent until 2025, which increases to 55 for facilities for which construction begins after 2026.<sup>3</sup> According to Secretary of Energy Jennifer M. Granholm, the rationale for these local content requirements is driven by the aim of “[...] strengthening American manufacturing and enhancing our national security with products stamped ‘Made in the USA.’”<sup>4</sup> To our knowledge, this is the first paper to formally analyze the extent of reshoring by US multinationals, complementing the recent trade literature on the consequences of supply chain disruptions. The paper offers novel insights about the future of US supply chains using information on announcements of greenfield investment projects from the Financial Times fDi Markets database, including projects within the US. The database provides realtime information on cross-border greenfield investments gathered from various sources, including the Financial Times newswire, industry organizations, investment agencies, and company reports. As a result, unlike other data on investments, this information is forwardlooking as it captures announcements about new projects and expansions. Furthermore, unlike other studies, the data allow us to leverage information on domestic investment projects between US states to assess reshoring activities.

> 3 <u>https://www.irs.gov/pub/irs-drop/n-23-38.pdf</u>

> 4 - <u>https:
|Number of employed, 15+|International Labour Organization|
|---|---|
|Physical capital stock|Penn World Table 9.0 (Feenstra et al 2015)|
|GDP|World Bank|

# _Resource rents_

A major challenge to including natural resources in TFP has been the lack of information on natural resources rents, their share in GDP and growth over time. The introduction of data on rents derived from natural resource use and extraction, as appears in the _Wealth of Nations_ database, makes it possible to partition value added into the sum of wages, resource rents and profits derived from fixed capital. Because unit resource rents are measured as price minus the economic cost of extraction, this measure excludes both wages and the opportunity cost of capital. It is therefore possible to neatly divide profits into a natural resource rent component and a profit on fixed capital component. Since the three components (including wages) sum to total value added, a defensible measure of TFP growth can be derived as the growth rate of GDP minus the weighted average growth rate of the three factors of production, where the weights are the shares of each factor in value added.

Considering the agriculture sector, the natural resource in question is the land used for crop and livestock production. As a practical matter, however, the quantity of agricultural land in any given country is more or less fixed – with the exception of countries where land is still being converted from forest or grassland or wetlands to agricultural uses. In what follows we therefore opt to treat agricultural produce, both food and non-food, as the natural resource in question, and the associated land rents on agricultural production as part of the natural resource share of value added. To measure the growth in agricultural output as a factor of production we use the FAO’s index of agricultural production (both food and non-food) measured in real terms.

While this approach to agriculture may appear artificial, it exactly parallels how timber production is treated in TFP growth
<!-- Start of picture text --> level, suggesting a need for refined area-specific calibration to improve relevance.<br>The analysis highlights a complex scenario where the utility of each indicator is defined by its balance<br>between sensitivity and specificity. Proper threshold calibration is essential, with price signals showing<br>optimized thresholds they do provide additional predictive power. Specifically, when combining<br>indicators in a single model, results show that inflation data and drought data have area-specific impacts<br>The findings from historical validation underline the efficacy of the chosen indicators, with a notable<br>precision in forecasting emergencies five months ahead of time. This lead time is critical for mobilizing<br>The availability of nutrition outcome indicators in particular presents significant challenges, primarily<br>significantly enhance the accuracy and effectiveness of food insecurity predictions. Integrating these<br>The overall findings suggest that data-driven indicators successfully signal prevailing food security risks,<br>significant contribution to the field of food security assessment, offering a scalable and transparent<br>human and financial costs of food crises.<br>dependency on the quality and availability of data and the necessity for continuous refinement of model<br>In conclusion, while significant challenges remain, the promising results of this research highlight the<br><!-- End of picture text -->
The Central American sub-region is of interest not only because of its high levels of income inequality, but also because of its heterogeneity and the fact that several countries have only recently emerged from civil war. The sub-region comprises two upper middle income countries (Costa Rica and Panama), two lower middle income countries (El Salvador and Guatemala) and two low income countries (Honduras and Nicaragua). Further, Guatemala and Honduras have a sizeable proportion of indigenous population, while Honduras and Nicaragua also have people of Afro-Caribbean origin. Both these groups have traditionally had limited access to tertiary education. Since the early 1990s, the focus in almost all countries (with the possible exception of Panama), especially those affected by civil war (El Salvador, Guatemala and Nicaragua) has been on promoting basic education, often using innovative models to expand coverage. The situation regarding tertiary education is different: on the one hand, there have been few policy changes with respect to public tertiary education while, on the other, the private sector has expanded rapidly. There has been no systematic analysis of the progress made in tertiary education, particularly for lower income and indigenous groups.

Our study relies heavily on household surveys but also draws on analysis of public spending data and qualitative studies of the participation of indigenous people in tertiary education in Nicaragua and Guatemala.<sup>6</sup> We begin by examining the growth in average education attainment and different measures of education inequality, as well as inter-generational and intragenerational inequality. Next, we present the progress of tertiary education attainment over time, enrollment and completion rates by income quintiles and the participation of indigenous people. This is followed by an examination of labor market outcomes for tertiary education graduates. The following section analyses the key obstacles to reaching tertiary education for students from different backgrounds across the education cycle. Finally, we evaluate the level and role of public funding for tertiary education, including the incidence of public spending on tertiary education and assess the public policy options for addressing these obstacles.
According to the 2011 National Population Census, conducted by the Bangladesh Bureau of Statistics, the total population of the country was 144 million with 76% of the total population live in rural areas in Bangladesh. The spatial distribution of poor population shows that the north-central (Mymensingh to Rangpur), southwestern (Barisal to Khulna) and some parts of southeastern (Comilla) Bangladesh are generally poor.

Upazila-level, percentage of households with access to tubewell and tapwater supplies in Bangladesh are shown in Figure S2. The data on access to water supply come from the 2011 Census of Population and Housing. The national average of households with an access to tubewell and tapwater supply (town or municipal water supply via piped network) is 82% and 10% respectively. Tapwater supply is limited to towns and large metropolitan cities such as Dhaka, Chittagong, Rajshahi, Sylhet, Barisal and Rangpur (Figure 1). The absence of tubewell-based water supply in these cities suggests that drinking water and domestic water come from municipal water supplies managed by the city authorities such as the Dhaka Water Supply and Sewerage Authority (DWASA) in Dhaka city.

Access to groundwater-fed water supplies for irrigation is an important indicator for measuring food security in Bangladesh. Currently groundwater meets 80% of all irrigation water supplies and has been sustaining the dry-season “Boro” rice cultivation since the 1970s that has made the country self-sufficient in food production and has led to major economic development.<sup>16</sup> Upazila-level, groundwater use for irrigation for the year of 2006–07 Boro rice season is shown in Figure S2c. Groundwater irrigation has been estimated using reported information on irrigated area and the number of irrigation pumps surveyed under the minor irrigation campaign by the Bangladesh Agricultural Development Corporation (BADC). Additional information on irrigation requirement for dry-season rice cultivation under various soil types and their infiltration capacity<sup>17</sup> has been used to estimate groundwater irrigation.

The social vulnerability is defined here by the lack of nutrition in children under the age of