rafmacalaba/data-use-mentions-extended huggingface dataset:
# _Indirect Taxes: VATs, Import Taxes, and Other Consumption Taxes_

Indirect taxes on consumption of goods and services are considered through their effective rate derived from each country’s social accounting matrix (SAM). This allows informality to be corrected for when assessing the effect of the various indirect taxes. In reality, because of the high level of informality in developing economies such as Mali and Niger, the statutory rates of indirect taxes cannot provide an accurate estimate of the actual indirect taxes collected because tax evasion can be significant, but the effective rates from the SAM indicate the actual taxes collected, meaning that they correct for informality. In Mali, indirect taxes are estimated by analyzing price multipliers using the 2005 SAM, calibrated and updated based on 2012 macroeconomic indicators; in Niger, indirect taxes are estimated using the effective rates derived from the 2011 SAM.

The direct and indirect effects of the VAT are considered in this assessment. The effective rates applied to each product in the SAM represent the direct effect of VATs on consumers. In an economy with exempt sectors, VATs have indirect effects on prices through VATs paid on inputs. In general, the producers can claim VAT refunds for the inputs they used. The VAT is therefore levied only on the final product, but for sectors that are exempt from the VAT, no VAT is paid directly on final goods, although the VAT has an indirect effect. A VAT paid on inputs results in higher producer prices because producers cannot claim VAT refunds for the inputs they used to produce the final (exempted) goods. In this regard, the input-output matrix is used to determine the indirect effects of VATs in exempt sectors. The total effect of the VAT is equal to the sum of the direct and indirect effects.

Although the richest individuals in Mali and Niger pay the majority of indirect taxes, the poorest individuals pay a nonnegligible amount. The richest decile is the main contributor to indirect taxes in the two countries, paying more than 50 percent of all indirect taxes in Mali
>r<br>times<br>at<br>r posts along<br>ors 1–4|

We create counterfactuals using the transport networks from each country described in Section 3. Figure A10 in the appendix shows the new corridors and investments in border posts. We assume a speed of 70 kilometers an hour for the new corridors and reduce the time by half at the border in some of the scenarios. Finally we create counterfactuals combining both road and electricity investments to study the interaction effects from combining infrastructure investments (Table 5).

21
Policy Research Working Paper 11055

# **Abstract**

The paper examines the early integration of Ukrainian refugee students into Italy’s education system following the Russia’s invasion of Ukraine in 2022. Using administrative and survey data, the study presents enrollment trends, academic performance, and barriers to educational integration. Findings from the analysis indicate that Ukrainian refugees face lower enrollment rates, higher absenteeism, and lower test scores than other students, particularly in subjects requiring language proficiency. Despite these challenges, teachers often recommend Ukrainian refugee students for advanced educational tracks, thus revealing their optimism about the potential of these students. Language barriers, mental health challenges, and uncertain futures are identified as major obstacles to integration. The study highlights the importance of tailored interventions, such as psychological support and more dedicated teaching time, to foster refugee students’ academic and social inclusion.

This paper is a product of the Development Data Group, Development Economics and the Social Protection and Labor Global Department. 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 michela_carlana@hks.harvard.edu; pcastaing@worldbank.org; mtestaverde@worldbank.org; and mtiberti@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
4 100.0 eee<br>oO 20 40 60 80 100 0.5 1.0 LS 2.0 2.5 3.0<br>RSI of EMA Percentage of MA<br>Exchange rate index (YER to USD) Drought index (Standardized Precipation Index)<br>0.030 04 — Heightened threshold<br>— Critical threshold<br>0.025<br>0.3<br>2 0.020 2<br>0.010<br>0.1<br>0.005<br>Fi 0.015 i | | 5 0.2<br>0.000 [~~ 0.0<br>20 40 60 80 100 2 oO 2 -4 -6<br>RSI ‘SPI<br><!-- End of picture text -->
<!-- Start of picture text --> A high concentration of Informal Jobs in developing<br>4.8 countries<br>4.6 India<br>Indonesia<br>4.4<br>Kenya Philippines<br>4.2<br>Sri Lanka<br>Pakistan Thailand Argentina<br>4<br>Zimbabwe Mexico<br>Brazil<br>3.8 South Africa<br>Ethiopia<br>3.6 Egypt Chile<br>3.4 Turkey<br>3.2<br>3<br>5 6 7 8 9 10<br>ln(GDP per capita), 2008<br>Data source: OECD, 2009. World Development Indicators, 2010. ILO KILM.<br>Note: 52 countries with available data shown. Chart uses latest data on informal share of employment available<br>(1995-99 or 2000-07). GDP per capita is in 2005 constant PPP international $.<br>employment)<br>ln(informal employment share of non-agricultural<br><!-- End of picture text -->

**Figure 2: Informal sectors are exceptionally Persistent** (Share of Population in Informal Employment, Earliest and Latest Data Points Available)

<!-- Start of picture text --> 90<br>80<br>70<br>60<br>50<br>40<br>30<br>20<br>10<br>0<br><!-- End of picture text --> Data source: ILO KILM Table 8, "Employment in the informal economy". India: Annual Survey of Industries/National Sample Survey (Manufacturing only).

The relationship between the size of informal sector and development in Figure 1 is downward sloping. This is the stylized cross-section relaionship, established many times in the literature, which informs the mindset that predicts a decline
Table B3: Rider audits: rides (cont.)

|**Variable**|**Defnition**|
|---|---|
||Revealed preferences rides only|
|Chose reserved space<br>Reserved space shown frst<br>Share of reserved space rider|= 1 if rider takes up the reserved space during ride<br>= 1 if option to ride reserved space is on the top of the app screen<br>s who are men<br>Rider report from the platform before check-in<br>_Opportunity cost of riding the reserved space (USD)_|
|0 cent opportunity cost|= 1 if there is no diference in pay of for riding the public and<br>the reserved space|
|5 cents opportunity cost|= 1 if diference between pay of for riding the public and the<br>reserved space is $0.05|
|10 cents opportunity cost|= 1 if diference between pay of for riding the public and the<br>reserved space is $0.10|
|20 cents opportunity cost|= 1 if diference between pay of for riding the public and the<br>reserved space is $0.20|
|Positive opportunity cost|= 1 if opportunitycost of ridingthepublic space larger than zero<br>Randomized car assignment rides only|
|Assigned to reserved space|= 1 if rider is assigned the reserved space for a given ride|
|Assigned to public space<br>Switched spaces|= 1 if rider is assigned the public space for a given ride<br>= 1 if rider moved from reserved to public space, of vice-versa,<br>during a ride<br>_Riders experiences of harassment during ride_|
|Any harassment<br>Physical harassment|= 1 if any of the three following variables is 1<br>= 1 if rider reports being touched intentionally by a stranger<br>during the ride in a way
>**Territory**|0.15|11,241|79,615|174|298|3|0.42|0.8|
|**Khyber Pakhtunkhwa**|3.54|693,349|453,363|2,216|2,216|3|0.46|0.21|
|**Punjab**|9.1|796,756|1,776,028|19,895|18,560|3|0.54|0.6|
|**Sindh**|4.57|822,675|822,675|6,264|16,784|2.5|0.42|1.6|

Notes: Public enrollment statistics are from the Annual School Census of each province. Private enrollment statistics are computed based on the share of the population that reports being enrolled in a private school. The number of ECE teachers and classrooms is estimated based on the figures reported for the primary level in each province's Annual School Census. The cost of classroom construction, teacher salary and the cost of providing a WASH facility are provided by provincial education departments. A WASH facility is comprised of one enclosed flush toilet and one sink connected to piped water.

7
This indicates a certain level of positive reallocation among workers who had found other formal jobs.

The EIS data do not allow us to observe what happened to workers who were employed by firms that wound up existing, and who were no longer formally employed after the minimum wage increased. We do not know whether these workers transitioned into informal employment, became unemployed, or dropped out of the labor force. To provide indicative evidence on this question, we complement the EIS data with microdata from the Turkish Household Labor Force Survey (HLFS) published by the Turkish Statistical Institute, a nationally representative household survey that is available annually from 1998 to 2018. The survey asks all employed individuals whether they are registered with social security institutions through their primary employment. This allows 26
our results differ from those in Austria when it comes to hate crimes. There we find significant effects of the refugee inflow. A potential factor driving the difference in results is the economic and social composition of Germany versus Upper Austria. The effects in Germany are concentrated in areas with high deprivation, mainly in East Germany, which do not compare economically and socially to Upper Austria.

# **5.4 Relation to Other Recent Findings on Forced Displacement and Social Cohesion**

Our findings complement those of several recent studies on Forced Displacement and Social Cohesion. Aksoy and Ginn (2022) carry out an analysis that is similar to ours. Based on survey data from 14 countries, they find virtually no effect of the local presence of refugees on the attitudes of natives. This is consistent with our findings based on high-quality panel data in Germany. This study suggests that refugee inflows of the magnitude we have seen in recent years do not fundamentally change the social fabric of host countries. Complementary evidence is also provided for three East African countries in Betts et al. (2022). The authors show that the interaction between natives and refugees has no significant impact on attitudes towards refugees in the host population. However, there is a positive correlation between the ethnolinguistic proximity between the refugee and host population. This result highlights a gap in the literature: not only is the evidence on migration and social cohesion mixed, but we also lack knowledge of the underlying transmission mechanisms. Does immigration affect social cohesion through the local presence of migrants, through the media, or through other channels?

Our evidence on anti-immigrant violence suggests that the effect of refugee inflows on social cohesion is not uniformly zero in the population. It is rather the case that refugee inflows affect the behavior of certain groups. This evidence is consistent with the results for Sub-Saharan Africa in Coniglio et al. (2022). The authors show that the establishment of a refugee camp increases the likelihood of an armed conflict and the frequency of protests in an area. Similar
framework. (For a comprehensive review, see the World Bank’s Bank Regulation and Supervision Survey, presented in Čihák, Demirgüç-Kunt, Martínez Pería, and MohseniCheraghlou 2012.) How far have these reforms gone in addressing the underlying incentive failures in the financial sector? Outside reviews (e.g., Geneva Report 2009; the LSE Report on the Future of Finance 2010; the Squam Lake Working Group 2010; and the CEPR Future of Banking report 2010) suggest that much more is needed at the global level to address the underlying incentive breakdowns that led to the global financial crisis. Empirical analysis of the regulatory steps taken at the national level in a large sample of countries since the onset of the crisis also makes it clear that there is still a major scope for improvement to address the incentive problems in the financial sector (Čihák, Demirgüç-Kunt, Martínez Pería, and Mohseni-Cheraghlou 2012).

The motivation for this paper is the need for incentive issues to be more fully reflected in the design of regulatory systems. Reflecting the importance of incentives in the financial sector, several authors have argued for the need to make incentives more central to regulatory frameworks, or to embark on regulatory reforms that are incentive-robust
declines in nighttime lights in China (Elvidge et al., 2020), India (Beyer, Franco-Bedoya and Galdo, 2021), and Morocco (Roberts, 2021). By the same token, some scholars have taken advantage of real-time information on air pollution. Improvements in air quality during the onset of the COVID-19 pandemic were particularly noticeable in China and the Republic of Korea, two countries with generally poor air quality (Liu et al., 2020; Koo et al., 2020; Seo et al., 2020; Zhang et al., 2020; Ju, Oh, and Choi, 2021), but were also apparent in many cities in Sub-Saharan Africa (Masaki et al, 2021).

Finally, several studies investigate the ability of Google data and food prices to track the COVID19 crisis. For example, Woloszko (2020) uses Google Trends data to track economic activity in 46 OECD and G20 countries. Abay et al. (2020) utilize Google search terms to investigate changes in demand for selected services across 182 countries during the COVID-19 pandemic. Sampi and Jooste (2020) employ Google mobility data to nowcast monthly industrial production dynamics in Latin America and the Caribbean in roughly the same period. Dietrich et al. (2021) document increases in food prices following the introduction of more stringent social distancing policies.

In this paper, we contribute to the latter strand of studies, seeking to answer the following question: how well did real-time indicators track the COVID-19 crisis across a broad range of countries? To answer this question, we collected information on nitrogen dioxide (NO2) emissions and nighttime light intensity at the subnational level for 142 countries with a population of more than 500,000 people. Our findings reveal substantial heterogeneity in the ability of remote sensing indicators to track the COVID-19 crisis, across regions of the world and country income groups. Following the beginning of the crisis in the first quarter of 2020, NO2 emissions showed the steepest drops in the Middle East and North Africa (when
Figure 7: Correlation between Innovation Score and IO Coefficient of ICT‐services (2015), Malaysia <!-- Start of picture text --> Innovation Score and IO Coefficient<br>Computer & Electr.<br>Coke & Petroleum<br>Chemicals & Pharm.<br>Rubber & PlasticsMotor vehicles<br>Other transport Electrical equipment<br>Basic metals Machinery and equip.<br>Non-metallic mineral<br>Food & Beverages<br>Wood products<br>Paper & Printing<br>Metal products Other manuf. & Repair<br>Textiles & Wearing<br>0 .001 .002 .003 .004 .005<br>IO Coefficients (Domestic)<br>.1<br>.08<br>.06<br>Innovation Score<br>.04<br>.02<br><!-- End of picture text --> Source: World Bank. Note: the IO Coefficients are computed as the fraction of ICT‐services usage in total input use for each industry in Malaysia using IO tables. Innovation represents a composite indicator varying between 0‐1 of all innovation variables from the Malaysian firm‐level dataset (see text for further explanations). The fitted values line is plotted on the basis of excluding Coke & Petroleum and Other manuf. & Repair.

Figure 8: Malaysia’s level of data policy restrictiveness (2010‐2015) <!-- Start of picture text --> Level of data innovation restrictiveness<br>2010 2015<br>.4<br>.3<br>.2<br>Data innovation restrictiveness .1<br>0<br><!-- End of picture text --> Source: ECIPE 25
in freight rates across destinations. It stems from the fact that trade in commodities is highly imbalanced. As a result, countries with large net imports face much higher ‘incoming’ than ‘outcoming’ freight rates, with many ‘outcoming’ vessels traveling without a cargo, i.e., ballast traveling (Brancaccio et al., 2021).<sup>10</sup> Leveraging the Clarksons SIN spot freight rates, we demonstrate this fact with Table 3 (Column 5) and summarize it as follows.

**Stylized Fact 8.** _There is a pronounced dispersion in the normalized dry-bulk freight rates across destinations._

Using Clarksons Research data from 2010 to 2016, Brancaccio et al. (2020) demonstrated that the asymmetry and endogeneity in dry-bulk freight rates have important implications for international trade and beyond. First, it weakens the strength of comparative advantage and reallocates production from exporters to importers. Second, there is a network effect which can be demonstrated as follows. If one of the large importers experiences a slow-down in its imports, some ships reallocate to other countries and regions, thus reducing their freight rates and increasing trade in those regions.

Over time, the freight rates are affected by the demand volatility and uncertainty and by the sluggish response in supply due to time to build and convex operating costs (Kalouptsidi, 2014).<sup>11</sup> To construct the trend of dry-bulk freight rates, we utilize Jacks and Stuermer (2021) data and estimates, who draw on a bevy of historical sources to construct an impressive 170 year-long time series (see Appendix A in their paper for specifics). We present the resulting trend in Figure 8. When explaining the long-run trend in real dry-bulk freight rates, the authors appeal to Harley (1988), Mohammed and Williamson (2004), and Tenold (2019) who suggested the long-run productivity gains as the major contributing factors.<sup>12</sup> Instead, for the short-run variation,
. It is probably biased toward low income - low productivity informals. The bias was intentional, designed to provide contrast to the high income - high productivity taxicabs and kiosks. The composition of the sample is presented in Table B4.

> Table **B4.** Generalized Informal Sector Survey - Activities Represented in the Sample Number of observations by location, type of activity, and category of product or service.

|Location||Categories of Products or Services||
|---|---|---|---|
|"Black" Market|57|Prepared foods_(buuz, hushuur),_baked goods, etc.|17|
|Harhorin Market|40|Dairy, meat, pine nuts, etc.|7|
|Technical Market|40|Art|3|
|Street|80|Individual cigarettes, soft drinks, candy, bread, etc.|37|
||217|Newspapers|14|
|||Retailing food and carton cigarettes|20|
|||Ice cream|9|
|||Clothing|3|
|||Household goods|15|
|**Type of Activity**||Religious souvenirs|I|
|Selling own-produced goods|27|Oil and fuel (breaking bulk)|10|
|Selling new goods|109|Automobile parts and tools|17|
|Selling used goods|32|Books|15|
|Providing services|49|Boot and shoe repair, painting leather|29|
||217|Shoe shine|9|
|||Measuring weight|3|
|||Photography|3|
|||Billiards (outdoor)|I|
|||Porter|4|
||||217|

_Source:_ Informal sector surveys conducted by the author.
absorb FDI spillovers. Overall, studies confirm that FDI spillovers are larger in countries that are more open towards trade (Meyer and Sinani 2009; Du et al. 2011, Havranek and Irsova 2011).

Weak _institutions_ – including corruption, red tape, or intellectual property rights – are linked to protectionism with regard to local firms, network-driven business practices, and inefficient markets. Foreign investors may, in such cases, be constrained from exploiting fully their competitive advantages. This may influence the type of FDI attracted in the first place, as well as the domestic firms’ absorptive capacities. Empirical evidence is mixed. Using firm-level data for 17 emerging countries over the period 2002-2005, Gorodnichenko, Svejnar, and Terrell (2007) find no evidence that the extent of FDI spillovers is affected by the degree of corruption (measured as bribes) or red tape (measured as manager’s time spent with officials). Measuring transparency with a corruption perception index, Meyer and Sinani (2009) find evidence that a country’s level of transparency has a U-shaped effect on FDI spillovers, i.e. countries with a medium level of transparency benefit the least from FDI, while countries with a low and high level of transparency benefit show stronger FDI spillovers.

Finally, competitive pressures from multinational firms might be lower if the local firm already faces a high level of _competition_ at the sectoral level. As in the case of exports, local firms in competitive sectors might have a lower incentive to improve, resulting in lower benefits from FDI spillovers. Sinani and Meyer (2004) confirm that more sectoral competition both of foreign firms and of domestic firms have a positive impact on the growth of sales of local firms in Estonia, but do not control for the interaction of competition with FDI. On the other hand, local firms could be better equipped to benefit from positive demonstration effects. Barrios and Strobl (2002) find that less sectoral competition, captured by a higher Hirschman-Herfindahl index (HHI), increases the productivity gains from FDI
# **Supporting Information**

## **S1 Hurricane Windstorm Model**

The local wind speeds experienced due to a hurricane are derived from a spatial hurricane windstorm model developed by the World Bank Group Latin America and the Caribbean Disaster Risk Management team (Pita et al., 2015). The model estimates maximum wind speeds at the height of 10 meters with a spatial resolution of 1 km<sup>2</sup> for observed hurricanes and tropical storms in Central America during our sample period. The structure of this wind field model is based on the well-known equation for cyclostrophic wind and sustained wind velocity by Holland (1980) and is calibrated for the Central America region. The model’s accuracy of the simulated wind speeds has been tested and validated with observed data. The maximum sustained wind speed experienced in a particular location due to a hurricane is given by: where _B_ is the Holland shape parameter, ∆ _p_ is the deficit of central and outer peripheral pressure (Pa), _ρ_ is the air density at the gradient height (kg/m<sup>3</sup> ), _RMW_ is the radius of maximum winds (m), _r_ is the radius of any location to the center of the storm (m), _VS_ is the hurricane translation speed measured (m/s), _f_ is the Coriolis parameter (1/sec), and _φ_ is the angle between the north and the storm heading.

The data source for the terrain roughness is the Global Land Cover Dataset 2000 and for the topography characteristics is the Shuttle Radar Topography Mission database. The method to estimate speed-up occurring in escarpments and ridges comes from the American Society of Civil Engineers in 1994 and to retrieve the wind gusts factors comes from Vickery and Skerlj (2005). For more detailed information about the hurricane windstorm model, see Pita et al. (2015).

## **S2 Implied monetary losses**

To convert the benefit of mangrove protection (mitigation of hurricane