rafmacalaba/data-use-mentions-extended huggingface dataset:
not a trivial task.** This is more straightforward on education spending as they directly benefit children of different cohorts. However, many of the other benefits (health, social assistance) combine benefits aiming to reach children and benefit children indirectly.  On the revenue side (i.e., taxes), it is even more difficult as we do not have expenditure patterns at the individual level. Additionally, in our analysis, we are interested in child-specific budgets and fiscal measures (either transfers or taxes) that can indirectly affect children or interact with families with children. In-kind transfers, especially education, are critical for children. However, the household survey does not allow to identify children attending the public school system, so some assumptions were made for this differentiation between public and private schooling. Nevertheless, we attempt to identify child-relevant budgets (see Table 1).

**As for the child poverty indicator, we use AROP among children, as this is the official poverty measure used in the country for monitoring and targeting purposes.** This is also used for comparability issues, that is, to have comparable metrics with the previous CEQ, which uses AROP as the primary measure. Moreover, to identify the changes in welfare post-fiscal policy, we fix the AROP poverty line. Therefore, the welfare distribution changes due to fiscal policy but is compared against a fixed threshold or benchmark. As mentioned above, we abstract from multidimensional poverty measures among children and literacy, stunting, and other non-monetary measures.

**Given the nature of the HBS and EU-SILC data, which collects household income and expenditure, this analysis is first done at the individual level, assuming adult equivalence scales, and then at the household level; with the latter approach, we identify the subgroups of households, rather than the subgroup of children that bear the burden of fiscal policy and deserve more policy attention** . Understanding the underlying child/adult relationships requires information on individual expenditure

16
. While the OECD patented 5581 inventions per year during the period 2000-2004, SubSaharan African countries patented an average of 2.5 patents and Latin America and the Caribbean 258.4. Furthermore, the developed economies more than doubled their number of patents per year since the 1960s. The estimations are conducted with a panel of five-year periods, and thus the estimations cover data from 1963-2002.

For the estimations of inter-firm spillovers we employ data from the Enterprise Surveys to estimate the magnitude (and direction) of intra-industry domestic and international knowledge spillovers at the firm level. The surveys were conducted by the World Bank, and they are designed to capture business perceptions about the biggest obstacles to enterprise growth, the relative importance of various constraints to increase employment and productivity, and the effects of a country’s business environment on its international competitiveness. Each survey was completed by managing directors, accountants, human resource managers and other company staff. The core survey is organized into two parts. The first part seeks managers’ opinions on the main constraints in the business environment. The second part focuses on productivity measures. The Enterprise Surveys sample from the universe of registered businesses and follow a stratified random sampling methodology. A small number of sectoral sub-samples are included to provide measures of productivity that can be compared across countries (within industries). Because the distribution of establishments in most countries is overwhelmingly populated by small and medium enterprises, surveys generally 14
# **4 Data and methodology**

This section describes the nine data sets that we analyze, and the methodology that we use to measure targeting performance, informed by the framework laid out in section 2.

# _4.1 Data_

We analyze nine data sets from six countries, presented in Table 1. Two main criteria were used in choosing these data sets. First, we looked for data sets from Sahelian environments, to compare across poor, homogeneous settings. Second, the data had to include information on implemented targeting schemes of social safety nets programs. Third, data had to have been collected prior to the intervention. The data sets obtained included information on programs that employed CBT, PMT or both. The reports from the original studies for which the data were collected (Table A1) indicate several issues in the implementation of the targeting operations (e.g., CBT thresholds deviating from the original target in Cameroon). These implementation gaps are inherent to targeting and to CBT selection in particular (de Sardan & Piccoli, 2018). Since we study actual targeting outcomes, the results reflect these implementation issues. On the other hand, studies suggest that data quality is high.

In all data sets, information was collected on households in the areas of intervention, after a geographical targeting process. This geographic selection process varied across data sets: it was very narrow in Cameroon (15 villages in the poorest _commune_ of the country) but not as focused on the poorest areas in Senegal 2 and Burkina Faso 2.<sup>19</sup> Most data sets include information on per-capita consumption and on the food consumption score (FCS). These well-being measures have the advantage of being quasi-continuous. However, the Household Dietary Diversity Score (HDDS) is available in two data sets only, and in Chad, no household consumption is available (Table 1). CBT selection thresholds vary from 21% to 68%, reflecting the wide variation in coverage rates within program areas. As
Levinsohn, 2002).<sup>5</sup> They also have the advantage of being easier to understand than general equilibrium modeling. As seen above, partial-equilibrium/cost-of-living analysis often requires a researcher to abstract from the income side of the issue, or limit the analysis to consideration of a single factor (typically labor). The historical focus on commodity markets is due in part to lack of good data on household earnings, since in household surveys, income information tends to be less complete and less reliable than expenditure information (Cockburn, 2001; Lipton and Ravallion, 1995). However, as noted above, recent studies provide evidence that – regardless of the data limitations – this abstraction is not innocuous.

General equilibrium (GE) models, by contrast, can assess the impact of economic shocks that reverberate across sectors and, in some cases, regions of a country or the world. They are capable of producing disaggregated results at the microeconomic level, while providing a consistency check on macroeconomic accounts. GE models are typically benchmarked to a Social Accounting Matrix and based on neoclassical theories of firm and household behavior. While most are comparative static in nature, dynamic versions have also been developed, and a few of these will be discussed below in the section on economic growth.

One caveat with GE models is that they can become quite complex, thereby making it harder to distinguish the extent to which results are driven by particular modeling assumptions, or whether they are robust to model specification and largely data-driven. While this type of criticism can be leveled at almost any empirical modeling exercise, the fact is the work of GE modelers is very assumption-intensive, and they therefore bear a greater burden in ensuring that their results are robust to variation in these assumptions. Of course, the payoff from GE analysis is that it offers the prospect of being able to examine a wider range of issues that are critical for policy-making.

GE analysis of income distribution and poverty issues in developing countries has only recently become widespread. However
_The Global Information and Communications Technology Industry: Where Vietnam Fits in GVCs_ that the party has decided that ICT is a propulsive sector with the potential to transform Vietnam’s economy. Below we offer a number of recommendations that aim to support Vietnam’s ICT push in the coming years.

# **Part III: Policy Recommendations**

## **Develop Local Suppliers to Support Global Hardware Exporters**

As the trade, production and FDI data indicate, Vietnam has become a production base for a number of lead ICT hardware firms, platform leaders and contract manufacturers looking to leverage the country’s low wages and favorable institutional profile to improve profit margins on global sales. While lead firms like Samsung and Nokia (Microsoft), platform leaders like chip maker Intel, and global contract manufacturers such as Foxconn and Jabil have large manufacturing facilities in Vietnam, linkages to the local supply base remain extremely weak across the board. Local suppliers mainly supply paper and plastic packaging and in many cases, even these very basic products are provided by foreign-owned suppliers. Because these firms have global suppliers, it is difficult for local firms to participate in the GVC in any meaningful way. This is not unique to Vietnam; it is true in many countries that serve as production platforms in GVCs, including China, where Foxconn’s 11 subsidiaries accounted for an astonishing 17.3% (USD 79.3bn) of the aggregate export value of China's top 200 exporters in 2012 (Grimes, 2015).

However, given the scale of recent and planned investments, these global firms have indicated a willingness and desire to develop the local support industry. **We see a short but very significant window of opportunity for supplier development, inclusion, and upgrading. This opportunity will exist only while Vietnam remains a favorable manufacturing location for these global firms. Interviews with key lead firms indicate that government efforts to foster development of supporting industries for ICT hardware have been ineffective so far.** We believe that efforts by
14 raise the issue, non-central government expenditures are probably omitted from IFS data just as they are in the WDR central government expenditure figures.

With these qualifications in mind, consider Pluta's results (Table 8). He finds a median value in the mid-1970's for Ge as a share of GDP of 17.4%. This is below half the median value of 43.2% reported by Tanzi(1986) (Table 4) for OECD economies in 1975. Furthermore, by the mid-1970's only two out of the twenty countries in Pluta's sample, Cyprus (27.8%) and Sri Lanka (30.0%), have Ge shares greater than the lowest value reported for any OECD country in 1975, Spain (24.7%). Even if state and local government expenditures were added to the developing country data, it is unlikely that the reported differences with OECD nations would change substantially.

Pluta also presents a decomposition of E-e suggesting some further differences between OECD and developing economies. Not surprisingly, transfer payments are a much smaller percentage of developing nation government outlays. In Pluta's sample the ratio, Gt/GDP, has a median value of 6.3% in the mid-1970's as compared to a mean value of 24.3% in OECD countries in 1982. The results suggest that the level and composition of government expenditures for developing economies differ substantially from those of industrialized economies.

When considering growth in government expenditures, a somewhat different result is obtained. Pluta estimates the elasticity of Ge, as well as of Gc and Gt, with respect to GDP in both current and constant prices for each nation (Table 9). The elasticities based on current prices can be compared
# **The link between unreliable electricity and output losses varies between countries and industries**

Aside from econometric estimation, losses can also be quantified using specifically designed surveys. In Bangladesh, about 14% of industrial sector electricity demand could not be met between 2001 and 2003, with 94% of outages being unplanned according to a survey conducted by Wijayatunga and Jayaleth (2008). In 2001, this is estimated to have translated into damages equivalent to 1.72% of Bangladesh’s GDP. A survey conducted with Pakistani industrial firms in 2008 finds that, on average, firms suffer losses of 23% of value added as a result of unserved electricity (Siddiqui _et al._ , 2008). A newer study of Pakistani manufacturing firms finds that an increase in average daily power outages by one hour causes a reduction in value added of 1.26% (Zhang, 2019). In Cameroon, about 80% of industrial firms experience output losses of between 16% and 50% due to power outages, according to one survey. Costs of outages are found to increase with outage duration and decrease if advance notice is given (Diboma and Tamo Tatietse, 2013).

In Ethiopia, power supply disruptions result in productivity losses of about 4% to 10% among manufacturing firms, according to census data. Disaggregating the impact shows that firms with very low and very high power usage intensities suffer the most productivity losses from electricity disruptions. Intuitively, firms with low electricity intensity adopt costly coping mechanisms to a lesser degree and are therefore hit harder by disruptions, whereas firms with high power intensity do invest in adaptation measures, but are also most affected by the high costs of self‐generation, thus incurring greater losses (Gurara and Tessema, 2018).

<!-- Start of picture text --> Productivity loss due to power outages by<br>energy intensity of industry in Ethiopia<br>Energy intensity<br>Low Medium High<br>0<br>‐
Policy Research Working Paper 9387

# **Abstract**

This paper presents new estimates of the returns to education in the Russian Federation using data from 1994 to 2018. Although the returns to schooling increased for a time, they are now much lower than the global average. Private returns to education are three times greater for higher education compared with vocational education, and the returns to education for females are higher than for males. Returns for females show an inverse U-shaped curve over the past two decades. Female education is a policy priority and there is a need to investigate the labor market relevance of vocational education. Higher education may have reached an expansion limit, and it may be necessary to investigate options for increasing the productivity of schooling.

This paper is a product of the Education Global Practice. 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 hpatrinos@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
and<br>retail (differing by region). Government removed VAT on<br>petroleum products in the 2006/07 budget. Excise duties for diesel<br>and kerosene were adjusted in Jul 2011 to narrow and even<br>eliminate the price difference between the two fuels, which<br>previously favored kerosene by a wide margin and led to large-scale<br>adulteration of diesel with kerosene. In mid-Oct 2011, government<br>reported that kerosene imports had fallen by two-thirds and diesel<br>imports rose by 50% as a result. Oil marketing companies<br>complained in Aug 2011 that the new ceilings on Aug 3 forced them<br>to operate at a loss, said they would operate at a loss for only 24<br>hours,and stopped sellingfor a week. After threateningto revoke|<br> <br>**Supply**: Tanzania stopped government<br>involvement in fuel imports in 2000 and<br>shut down its sole refinery in the early<br>2000s. An order issued in Oct 2007 requires<br>all product importation to be carried out<br>using a bulk importation system, and<br>government issued Petroleum (Bulk<br>Procurement) Regulations in Jun 2011.<br>Aside from exploiting economies of scale,<br>bulk importation was also considered to<br>help reduce tax evasion by collecting oil<br>importation data. By Mar 2012, there were<br>complaints about lack of transparency by<br>Petroleum Importation Coordinator, which<br>awarded the contract to the same company<br>three times in succession and which was<br>accused of rejecting too many bids from<br>established international oil companies on<br>minor technicalities. In Apr 2012, oil<br>companies issued a letter complaining<br>about the non-compliance of the imported<br>fuel with fuelquality,damagingvehicles.|
-0.1294***|0.8573***|
||(0.0260)|(0.0110)|(0.0163)|(0.1575)|(0.0994)|(0.0976)|(0.0259)|(0.1159)|
|Region-specific time trend|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|District fixed effects|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Year fixed effects|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Constant|0.2182***|0.0400***|-0.0979***|-0.6343***|0.6536***|0.5768***|0.2783***|-1.3268***|
||(0.0359)|(0.0104)|(0.0188)|(0.1919)|(0.0829)|(0.0804)|(0.0537)|(0.2875)|
|Observations|128,446|128,446|128,446|128,446|86,980|86,980|50,645|50,645|
|R-squared|0.193|0.066|0.178|0.156|0.097|0.092|0.196|0.337|

Note: This table reports district fixed-effect RDD-DD regressions of individual-level outcomes. The additional control variables include education levels of individuals (dummy variables), education levels (dummy variables), household size, the proportion of children in households, the proportion of older members in households.

Robust standard errors in parentheses. Standard errors are clustered by district and village-year levels. *** p<0
EMDEs EAP ECA LAC MNA SAR SSA<br><!-- End of picture text -->

# F. Regional trade agreement participation

<!-- Start of picture text --> Number of RTAs 2000 2020<br>AEs<br>EMDEs<br>EAP<br>ECA<br>LAC<br>MNA<br>SAR<br>SSA<br>0 20 40 60 80 100<br><!-- End of picture text --> Sources: CEPII (Gravity database); Gurevich and Herman (2018); World Bank; World Trade Organization: UNCTAD.

Note: AEs = advanced economies; EMDEs = emerging market and developing economies. EAP = East Asia and Pacific, ECA = Europe and Central Asia, LAC = Latin America and the Caribbean, MNA = Middle East and North Africa, SAR = South Asia, SSA = Sub-Saharan Africa. RTA = regional trade agreement.

A. B. Average tariff rates are computed as unweighted cross-country averages of applied weighted tariff rates. Sample includes up to 35 advanced economies and 123 EMDEs. Primary tariffs are used as a proxy for agriculture tariffs.

C. Proxy for trade uncertainty is the difference between the bound and applied tariff rates, as defined by the WTO. Data through 2020. Sample includes up to 27 advanced economies and 97 EMDEs.

D. World Bank’s logistics performance index is a summary indicator of logistics sector performance, combining data on six core performance components into a single aggregate measure. The indicator is available for a sample of 160 countries. Sample includes 36 advanced economies and 123 EMDEs.

E. UNCTAD’s liner shipping connectivity index is an average of five components and captures how well countries are connected to global shipping networks. The index value 100 refers to the country with the highest average index in 2004. Sample includes up to 30 advanced economies and 118 EMDEs.

F. Regional trade agreements are reciprocal agreements between two or more partners and include both
applies and the list of countries within that group. In the case where a country gains or loses eligibility for a specific trade regime, the country enters or exits the group. We observe variation over time in the list of countries that benefit under each of the trade regimes.

Product codes for tariffs are defined at a 10-digit level in the WITS database. This is a further breakdown of the 8-digit Combined Nomenclature (CN) used by the EU, of which the first six digits are taken from the Harmonized System (HS) nomenclature. Both the HS and CN nomenclature are regularly updated, eliminating some product codes, and introducing, amending or merging others. To make sure we track the same products over time, we converted and aggregated the product codes in the tariff data to a uniform HS1992 6-digit level of classification (using conversion tables from the WITS database). This is the same nomenclature as used for the import data and the most detailed classification that is shared by all countries. To aggregate tariff rates, we take simple averages across all 10-digit product codes within a 6-digit product category.

The construction of the discrete measure of market access is based on different data sources for the EU and the United States. For the EU, there is no public available information at this level of disaggregation. Therefore, we infer whether the exporter-product pair is granted preferential access from the list of products and countries affected by the various trading regimes as reported in WITS.<sup>18</sup> For the United States, the list of products and countries that are eligible for AGOA treatment is publicly available and published on the US Trade Representative, US International Trade Administration and AGOA websites.<sup>19</sup>

# **5.2 Sample**

After cleaning and merging the trade flows data with information on market access, we are left with a dataset covering 186 countries that report trade flows directed to the United States and/or the EU. For consistency, we define the EU
## 2.6 Summary of Theory Developments.

A summary of the main thrusts of the theory can be put as follows. The older theory o¤ered two stories: Trade a¤ects inequality either by a¤ecting the skill premium (in the StolperSamuelson theorem), or by a¤ecting industry premia (in the speci...c-factors model). It was hard to rationalize how trade could raise inequality everywhere in the world at the same time, or inequality within any group of similarly-skilled workers all doing the same job. It was also hard to see how North-North trade could a¤ect inequality at all. But now, we have stories that predict rising skill premia across countries as a result of North-South trade in tasks, and even as a result of North-North trade in goods due to R&D e¤ects or the skill bias of the transport sector. We have high-dimensional models that go beyond the skill premium to analyze the e¤ect of trade on the ‘middle class,’and distinguish between wage inequality and inequality in lifetime consumption through explicitly dynamic models of labor adjustment. We are also able to analyze the e¤ects of trade on inequality among observationally identical workers doing the same job in the same industry, through heterogeneous-...rms models or implicit contracts models. This rich set of stories helps in describing the e¤ects of trade on income distribution in the real world.

# 3 Empirical Work.

An immense empirical literature exists on the possible linkages between trade and inequality. Most of this literature predates the ‘new’trade models and focuses on testing the implications of the Heckscher-Ohlin framework for trade-inequality linkages. A number of literature surveys also review this work. See for example, Feenstra and Hanson (2001), and Goldberg and Pavcnik (2004, 2007). As in Section 2, we begin in Section 3 with only a brief review of the empirical literature associated with the ‘older’trade models, and then move on to