rafmacalaba/data-use-mentions-extended huggingface dataset:
North Atlantic Treaty Organization (NATO).<sup>6</sup> The final sample covers EU and US investment between January 2015 and December 2022 to 156 destination markets.

A first inspection of US investment data in Figure 1 shows no indication of permanent shifts in nearshoring or friend-shoring following the trade tension with China (2018-19), the Covid-19 pandemic (2020-21), and the Russian invasion of Ukraine (2022-23).<sup>7</sup> The share of greenfield foreign direct investment in neighboring and NATO countries declined by a few percentage points when comparing the 2015-17 and 2022-23 periods (Figure 1). However, reshoring activity by US multinationals intensified following the Covid-19 pandemic, with the share of greenfield investment in the US increasing from 46 percent to 56 percent, between the 2015-17 and 2020-21 periods. This evidence corroborates the anecdotal evidence of US manufacturers starting to bring back factories to the US.<sup>8</sup> These investments are likely to have an impact on trade flows once they become operational.

> 6 List of NATO countries: Belgium (1949), Canada (1949), Denmark (1949), France (1949) Iceland (1949), Italy (1949), Luxembourg (1949), Netherlands (1949), Norway (1949), Portugal (1949), United Kingdom (1949), United states (1949), Greece (1952), Türkiye (1952), Germany (1955), Spain (1982), Czechia (1999), Hungary (1999), Poland (1999), Bulgaria (2004), Estonia (2004), Latvia (2004), Lithuania (2004), Romania (2004), Slovak Republic (2004), Slovenia (2004), Albania (2009), Croatia (2009), Montenegro (2017), and North Macedonia (2020). We assume that the EU is a NATO member even if some of the EU countries are not part of the alliance.

>
_** Net labor income gains encompass both the losses incurred from job displacements and the earnings increases resulting from higher wages for those who retained their employment. Since it is assumed that all dismissed workers move to unemployment or inactivity, this represents a lower bound of the net gains (or an upper bound of the earning losses). If workers move to formal or informal employment and have a positive labor income, net labor gains could be higher._ _*** This column presents information on the share of full-time employees who become unemployed or inactive after the minimum wage increase. 95% confidence intervals are provided in parentheses._ _Source: Own simulations based on administrative tax data._

**<mark>Overall, the simulation shows a relatively small reduction in labor income inequality due to the minimum wage increase,</mark> as it** **<mark>narrows the wage gap between low-wage and higher-wage workers, promoting a more equitable income distribution.</mark>** <mark>Table 6 presents the impact of a minimum wage (MW) increase on various income inequality indicators. The baseline values represent the conditions before the MW increase, and subsequent columns show simulations after the increase. The impact of labor inequality depends on the final labor market status of the dismissed workers, which can affect their new earnings (or lack of earnings).  When dismissed workers move to unemployment or inactivity (Table 6, column 3), we observe moderate declines in labor income inequality driven by a compression in the income distribution. The Gini coefficient, a measure of income inequality, decreases from 0.3560 to 0.3498. The Theil Index and Mean log deviation, which also gauge income inequality, follow a similar trend, indicating a reduction after the MW increase. The income shares for different population percentiles demonstrate minor shifts, with a slight increase in the bottom 50% income share and marginal decreases in the middle 40% and top 10% income shares. On
as inelastic in supply; services (electricity/water, construction, trade, transport/communications, private services and public administration) are modeled as elastic in supply.

# **6. Results: Province-level Tourism Spending, Growth Linkages and Poverty**

The aggregate economic impact of the tourism sector on the Panamanian economy is very significant—according to the data from the national accounts, expenditures by foreign tourists reached around 7 percent of the GDP in 2006. Arrivals of foreign tourists in Panama exceeded 2 million visitors in 2006, of which over 1 million were in direct transit; and total expenditures by foreign tourists totaled US$960 million (Table 3).<sup>14</sup>

> 14 Tourists in direct transit are defined as visitors that stay in the airport less than 10 hours, do not leave the airport area and proceed to other destinations.

10
that there have not been many changes in the ranking, with Mozambique and Namibia still on the top of the list.7 A rapid comparison across regions reveals that Singapore had a FDI business indicator index twice as high as the best African country in 1995/1997. However, Ireland and Hungary were ranked about the same level as Senegal and Mauritius. This result may appear surprising at first sight, but one can observe that the flows of FDI were about the same in Senegal and Ireland, when compare to their respective GDP in 1997 (about 3.8 per cent) and Ireland has, in dollars, more natural resources than Senegal. It may also reveal some of the limits of our indicator when the differences in GDP are too big across countries - the assumption that FDI is perfectly elastic to changes in GDP might not be robust across region or countries with large differences in GDP levels.

Our ranking can be compared with those obtained in some well known surveys such as the International Country Risk Guide (ICRG) and the Institutional Investors (II) ratings that are reported in the second and third columns of table 2.8 If the ranking appears quite similar for a few countries,<sup>9</sup> there exist significant differences both at the top and bottom of the table. While South Africa, Zimbabwe, Kenya and Malawi appear in the bottom half of our ranking, they are on the top of the list for the two other indicators. On the other hand, Mali and Mozambique have not been ranked very high by the ICRG and II indexes but are among the most attractive countries according to our indicator. Io possibilities; Zambia has followed a relatively aggressive privatization programme and liberalization policy. 7 The 1998 ranking is incomplete because the data on FDI inflows are still missing for a few countries.

8 Unfortunately, the Competitiveness Indicator developed by the World Economic Forum is not available for most of the countries covered in this note. However, Namibia and Mauritius were also well ranked in their
which is surrounded by water, this can have important consequences (Gendron-Carrier _et al._ , 2022). Additionally, particles that are present in the atmosphere, such as smoke or dust from long-range transport, can lead to no (or poor) correlation between satellite measures and PM from ground-monitors, which measure particles only at the surface (Kumar _et al._ , 2007).

Given this, we focus on an estimate of PM2.5 that we generate using data from the MERRA-2 assimilation. This assimilation provides processed data on individual aerosols at the surface rather than column level, helping to address the issue of water vapor and additional particles in the atmosphere. The MERRA-2 data is accessed via Google Earth Engine, and it is available hourly at the 55 x 69km2 spatial level (Gelaro _et al._ , 2017). MERRA-2 provides assimilation data on individual aerosols (dust, organic carbon, black carbon, sulfate, and sea salt). To generate a measure of PM2.5, we combine these aerosols using a formula provided by NASA (2023): The MERRA-2 data has numerous benefits. It is freely available. It provides a measure of pollution that can be compared across the world, and there is high temporal granularity, so we are able to produce PM2.5 estimates at the hourly level. It has data from the time period of 1980 to the present day. However, an important limitation is that due to the low level of spatial granularity, the MERRA-2 data in effect provides only a single value of pollution for Dakar at any given time.

> 10See Appendix Table A1 for a description of different satellite products.

10
# **Table 1. Background Characteristics**

|**Panel A. Background Information**<br>Age|28.43|||
|---|---|---|---|
||(6.98)|||
|Age Began Seeing Clients|18.67<br>|||
||(5.14)|||
|Educational Attainment|9.20<br>|||
||(2.69)|||
|Literacy|_Read Kiswahili_<br>|_Write Kiswahili_<br>||
||0.95|0.88||
|Respondent is Head Of Household|0.84|||
|Total # of Dependents|2.96<br>|||
||(2.36)|||
|Respondent is in a Peer Group|0.44|||
|Number of Regular Clients (at time of|2.24|||
|background survey)<br>|(1.07)|||
|Marital Status||||
|_Widowed_|0.23|||
|_Divorced / Separated_|0.20|||
|_Cohabitating_|0.13|||
|_Never Married / Not Cohabitating_|0.44|||
|Observations|192|||
|**Panel B. Labor Supply in the Sex Market(Daily Averag**<br>Participated in Sex Market|**es)**<br>0.76|||
|Total Income from Sex Work|686.84|||
||(749.55)|||
|Total Income from All Sources|788.26<br>|||
||(778.89)|||
|Number of Clients Seen|1.52|||
||(1.12)|||
on the cost that students have to sustain for uniforms, materials and school contributions.<sup>12</sup> This information is not reported for indigenous and community schools, since materials and uniforms are provided by the government either in the form of in-kind or cash grants to the schools. The average costs of the different items are provided by a school representative at least three years after the end of the PAL evaluation, thus ruling out the possibility of selective miss-reporting. We use information from the 2003 Formato 911 to assess whether treatment and control localities differed in terms of school supply prior to the rollout of PAL. For each student merged with the ENLACE test score, we have the unique school identifier ( _clave de centro de trabajo_ CCT) associated with each student test. Therefore, using information from the Formato 911 2007-2013, we can test whether the program affected the characteristics of the school where students attended primary education. In particular, we can potentially track the results of students whose families moved within Mexico after the follow up survey. In order to disentangle whether potential changes in distance are driven by increased willingness to spend on commuting or changes in residential location, we test the effect of PAL on 4 discrete measures: 1) attending a school within 5km from the community, 2) attending a school between 5 and 10km, 3) attending a school between 10 and 30km, 4) attending a school more than 30km from the community. Changes in the last outcome are possibly driven by changes in the household residential location.

# **3.2 ENLACE take-up**

In the follow-up survey of the experimental evaluation there were 5,444 children in PAL villages born between 1996 and 2005, and we match 69% of them write at least one ENLACE test. Thus, our final sample includes 3,773 children from 200 villages for whom we observe a total of 11,006 ENLACE tests; Table 1 shows how these observations vary across academic grades and years.

There are several potential reasons
In cross-country comparison of the trends in investment rate, we note that the Indian experience differs from that of other emerging markets in that the investment rate increased far more rapidly in India prior to the GFC than in other countries, and the decline after the crisis is steeper too. This cycle is evident both in public and private investment rates, as both increased in the few years prior to the global financial crisis and declined thereafter. While private investment has continued to remain depressed in recent years, public investment rates have increased.<sup>39</sup>

# **Figure 20: A comparative analysis of the rate of investment in India and other developing countries**

<!-- Start of picture text --> 20 A: GFCF (% GDP)<br>GFCF (%GDP)<br>40 IND EMDE (median)<br>EMDE (25-75%)<br>35<br>30<br>25<br>20<br>15<br>10<br>20 C: Public investment (% GDP)<br>Public investment (%GDP)<br>12 IND EMDE (median)<br>EMDE (25-75%)<br>10<br>8<br>6<br>4<br>2<br>0<br>1990 1992 1994 1996 1998 2000 2002 2004 2006 2008 2010 2012 2014 2016<br>1990 1992 1994 1996 1998 2000 2002 2004 2006 2008 2010 2012 2014 2016<br><!-- End of picture text --> <!-- Start of picture text --> 20 B: GFCF growth rates<br>GFCF growth rates<br>30<br>IND EMDE (median)<br>EMDE (25-75%)<br>20<br>10<br>0<br>-10<br>-20<br>20 D: Public investment growth rates<
**TABLE 1 Global growth forecast** **_uncertainty_ weights**

||**V**<br>|**AR-based weigh**<br>|**ts**<br>||**OLS-based**<br>**weights**|
|---|---|---|---|---|---|
||6-months<br>ahead|12- months<br>ahead|18-months<br>ahead|24-months<br>ahead|(All horizons)|
|S&P500 (β1)|0.25|0.22|0.14|0.11|0.23|
|Term spread (β2)|0.01|0.03|0.06|0.15|0.48|
|Oil price (β3)|0.01|0.17|0.40|0.42|0.27|
|Other factors (βε)|0.73|0.58|0.40|0.30|-|

Source: World Bank staff estimates.

Note: VAR-based results are variance decomposition weights derived from a VAR of oil prices, S&P500, term spread and global growth—they are calculated as the share of variance of global growth explained by selected risk factors and other risk factors not included in the analysis. OLS-based weights are the absolute values of the coefficients obtained from an OLSregression of global growth on oil prices, S&P500 and global term spreads. Regressions use annual data for 1982-2014.

**TABLE 2 Global growth forecast** **_skewness_ weights**

||**V**<br>6mnth|**AR-based weights**<br>12 mnth||**OLS-based**<br>**weights**|
|---|---|--
Asia, Beirut, 2020.</u>

> 3 Syrians, especially traders, have been depending on banks in Lebanon for accessing foreign currency (dollars). However, due to the financial crisis in Lebanon and with Lebanese banks limiting the release of funds, many Syrians have lost access to their savings. The heavily reduced release of cash from Lebanese banks has meant that dollars have become scarce in Syria, pushingup the informal market exchange rate. As a result, prices of food and non-food items have risen across Syria.

> 4 The Poverty and Inequality Platform <mark>(PIP) i</mark> s a publicly available dataset of World Bank’s official country-level, regional, and global estimates of poverty. Most of the data in PIP comes from the Global Monitoring Database, which is the World Bank’s repository of harmonized multitopic income and expenditure household surveys used to monitor global poverty. PIP contains more than 2,000 surveys from 168 countries covering 98% of the world’s population. The data available in PIP are standardized as far as possible but differences exist with regards to the method of data collection, and whether the welfare aggregate is based on income or consumption.

5 These surveys were used by UNDP and ESCWA to estimate national poverty estimates (see Abu-Ismail, Abdel-Ghadir and ElLaithy (2011) “Poverty and Inequality in Syria 1997-2007”, Arab Development Challenges Report, Background paper 2011/05).
# **I. Introduction**

**This paper aims to assess the level of tax compliance and under-reporting of income in Romania, including among minimum wage workers, and understand the distributional implications and its links with minimum wage policy and design.** This study reflects a collaboration between the Romanian Ministry of Public Finance and the World Bank to evaluate the extent to which taxpayers, particularly those earning minimum wages, comply with tax obligations and report their income accurately to tax authorities. It also examines the distributional implications of tax compliance and under-reporting of income in Romania by assessing how tax compliance affects the distribution of tax burdens and benefits across different income groups, including minimum wage earners. By doing so, the study can provide insights into how tax policies and design can address inequalities and promote greater equity. Furthermore, the study aims to identify the links between minimum wage policy and tax compliance. Finally, it examines the impact of the minimum wage policy on tax compliance and under-reporting of income. Additionally, the study assesses how tax policies can be designed to support minimum wage earners and promote greater tax compliance.

**Understanding the extent and distribution of tax evasion is relevant for several reasons.**

**Improving tax collection is a critical component of domestic revenue mobilization and addressing fiscal deficits.** It can help the country address its fiscal deficits and outlook; enhancing domestic tax capacity is also critical for strengthening social protection and human capital development. Among other factors, the country's response to the pandemic has required significant public expenditure, putting pressure on its fiscal resources. According to the IMF Fiscal Monitor Database of Country Fiscal Measures in Response to the COVID–19 Pandemic, the value of additional spending and foregone

2
Finally, we consider the possibility that the non-responses occur because of “reticent” behavior that signals sympathy with radical beliefs.

The Gallup World Poll includes the question (Q1) on attitudes toward extreme violence in 27 developing countries from five geographic regions around the world during the period 2006-12 (Table A1). The full sample for this set of countries consists of 245,064 observations, but once we drop the instances with nonresponses on Q1, the sample size reduces to 92,993 observations. Since we have also many instances of nonresponses to questions related to the covariates of radicalization, the baseline sample we use in the regression analysis contains only 30,787 observations. As shown in Table A1, the small country sample sizes imply that country-level regressions would have limited degrees of freedom because our main regressor variables are dummies describing the respondents’ status in terms of different characteristics. We overcome this problem by relying on pooled samples in all regressions and including country and locality fixed effects to control for country-level and locality-level unobserved heterogeneity.

# **4. Estimation results**

This section discusses first the unconditional association between radicalization and subjective wellbeing. Then, we present our baseline results obtained using UOP and BOP estimations and the issues of non-responses to the radicalization question, Q1.

# **4.1 Association between radicalization and subjective economic welfare**

The incidence of radicalization and the level of subjective economic welfare are relatively low in our sample. The self-assessed radicalization and economic welfare indexes are 1.718 and 4.751, on average, given their ranges of [1, 5] and [0, 10], respectively (Appendix A, Table A2). However, 20
domestic materials for imported ones, thereby increasing firm-level DVAR. The findings of this paper indicate that, in addition to trade and FDI liberalization—previously recognized as key drivers of China’s rising DVAR—enhancing domestic transportation infrastructure can also play a critical role in enabling countries to capture more DVA and move up the GVC.

While it is beyond the scope of the current paper, our approach of estimating DVAR and capture firm-level IO linkages are general enough to applied to other countries, due to less data requirements. Regarding the last remark, we have started a new project on measuring the DVAR for a wide range of countries, based on the matched importer-exporter customs transaction data from the World Bank’s Exporter Dynamic Database and also applied it to Indonesia to analyze the impact of export ban on Nickel on downstream industry value-added creation.

35
# **Table 1:** The ITU’s ICT Regulatory Tracker

||**Regulatory framework**|**Regulatory mandate**|**Regulatory regime**|**Competition framework**|
|---|---|---|---|---|
|**G1**|Consolidated withpolicymaker/regulator|Business as usual|Doingas we have always done|State-owned monopoly|
|G2|Separate agency|First wave of regulatoryreform|Doingmore|Liberalization|
|**G3**|Separate agency, autonomous in decision making|Advanced liberalization of ICT sector|Doingthe right things|Partial competition|
|**G4 (85/100)**|Separate agencywith enforcementpower (18/20)|Adjacent issues become core mandate (11/22)|Doingthe things right (28/30)|Full competition (28/28)|
|**G5**|Separate agencyaspart of a network ofpartner regulators|Active collaboration across the board|Doingthings together|Intra-modal competition|

Note: Number in parenthesis are scores for Peru.
61.3|690|100.0|91.6|79.6|573|100.0|88.0|72.1|57.4|
|2.A. and IMSS=0|1144|65.4|61.5|50.9|396|57.4|87.1|74.7|282|49.2|97.9|82.5|58.8|
|2.B. and IMSS>0|604|34.6|95.0|81.1|294|42.6|97.6|86.1|291|50.8|77.7|61.3|56.0|
|3. Inbusiness>3 years|5220|100.0|42.8|37.1|1365|100.0|79.1|76.3|1284|100.0|68.8|56.9|62.6|
|3.A. not in 1989 Census|3493|66.9|20.1|18.5|482|35.3|55.6|63.3|637|49.6|46.3|37.4|64.2|
|3.B. and in 1989 Census|1727|33.1|88.8|74.8|883|64.7|92.0|83.4|647|50.4|91.0|76.0|61.1|
|4. Pd wkrs>0, >3 years|1096|100.0|72.9|63.8|440|100.0|92.7|82.7|378|100.0|89.2|75.1|58.7|
|4.A. IMSS no, Census no|348
Also, δ<sup>B</sup> = (δ11, δ12, δ13); δ<sup>S</sup> = (δ21, δ22, δ23); and ZtB and ZtS are matrices of explanatory variables accompanying the δ’s of the decisions to intervene as defined in equations (4.1) and (4.2).

We use the probit ML to estimate the parameters in (4.1) and (4.2), taking into account (5) and using intra-daily data for the sol/USD between 2004 and 2009. Now, μtB and S μt are random disturbances with the following distributions:

# _4.2 Empirical specification of the model for the exchange rate_

Estimation of the model containing equations (2) and (3) requires identification and estimation of their parameters. Note that the central bank is assumed to minimize the cost of intervening, which implies minimization of K(Yt – _Y_ ), R(m), and L(ξj) > 0. Once this minimization is achieved, the exchange rate is expected to evolve as described in (2). We need to determine an econometric specification to estimated parameters of equation (2). Let us consider a possible econometric specification for the conditional mean of sol/USD: 15
# Challenges for the vocational education

The expansion in TVET enrollment may not necessarily be associated with increased educational quality and relevance. Based on a study conducted in 118 schools located in a province in central China, Yi et al. (2018) found that 90 percent of students do not show gains in vocational or general skills and 60 percent of students expressed dissatisfaction with their programs. While the skills assessment and sampling methods can always be improved, this study suggests sizable gaps in the quality and relevance of the vocational programs delivered at the upper secondary level. Specifically, the challenges can be described in the following five dimensions.

- **Link between schools and industry.** Some of the schools lack industrial guidance on the skills in need. In consequence, some skills demanded by the labor market are not offered by the schools’ education programs, for example, the lack of mechanical skills training offered for vehicle powered by new energy and the lack of training provided for the application of silicon materials. Due also to the low levels of industrial link, fragmentation and repetition are common among schools. Some of the popular training subjects are delivered in many schools and cause redundancy and low efficiency.

- **Teaching quality.** Many of the teachers are from pure educational background and lack necessary industrial experience. As a result, there is a lack of teachers with both technical and teaching qualification for the level of training they are providing.

- **School management.** Schools need to set up or update their school charters and regulations in accordance with the priorities highlighted in the 13th FYP (2016–2020). As some of the schools are recently merged and upgraded, there is also a need to review their management and personnel arrangement. Extracurricular activities need to be expanded to provide more services for students.

- **Curriculum, pedagogy, and assessment.** The curriculum, pedagogy, and assessment mechanisms are generally not competence based. This is due partially to the lack of good national