rafmacalaba/datause-encoder-data huggingface dataset:
The enforcement policy had important distributional implications. Since offshore tax evasion was predominantly concentrated among the wealthiest individuals, the reported wealth of the top 0.1% of adults experienced substantial growth. These high-net-worth individuals, who tend to own most of their assets offshore, reported more than twice as many assets five years later. This had the added effect of increasing the amount of income taxes paid by the top 0.1% of income earners by 60% between 2015 and 2021.

Having broadened the wealth tax base, Argentina sought to maximize its potential by implementing higher tax rates on offshore wealth. In 2019, the government increased the top marginal tax rate for foreign assets to 2.25%, the highest rate observed in the past three decades, while the rate for domestic assets remained at 1.25%. In the final part of the paper, we explore the outcomes of this tax rate hike, specifically focusing on the progressivity of the tax system, revenue generation, and the repatriation of foreign capital. We find that the combination of improved reporting of foreign assets and the higher tax rates on these assets enhanced tax progressivity, resulting in a substantial rise in the effective tax rate for the wealthiest 0.1% of adults. As a result, Argentina’s wealth tax generated significant revenue, amounting to nearly 0.8% of GDP in 2019, establishing Argentina’s wealth tax as one of the most successful globally in terms of revenue generation.

Interestingly, the higher tax rates on foreign assets influenced the behavior of individuals in the top 0.1%. Some of them opted to decrease their reported assets and bring back a portion of their offshore holdings. However, for the majority of Argentines outside this wealthiest 0.1%, it seems they chose to keep their assets abroad. This implies that taxation was not their primary motivation for holding offshore assets. Instead, it is likely that their reasons encompass factors such as seeking a form of financial security against economic volatility, fluctuations in exchange rates, risks associated with inflation, currency controls, and the pursuit of potentially higher investment returns.

Our research indicates that policy packages can make a meaningful difference in combating tax evasion, offering valuable insights for policymakers in countries adopting similar combinations of international enforcement measures. the 2016 en- Specifically, forcement policy appears to have been more effective than previous initiatives because it was introduced alongside Argentina’s announcements of international tax coordination efforts, including automatic TIEAs with critical partners such as Uruguay, Switzerland, and the US and active involvement in the CRS. These announcements, combined with the Panama Papers leak in the same year, likely created the perception among taxpayers that opportunities for tax evasion had substantially diminished compared to previous years. Moreover, the policy’s success can be attributed to its appeal to the elite. It was supported by generous tax incentives, including a temporary reduction in the wealth tax rate, and was 3
totally exempt from liberalization while more than 80 percent of the footwear, leather, wood and non-metallic mineral product sectors could be excluded from the EPA.

**Table 5. Sectoral shares of excluded products from a 80 percent EPA liberalization, US$ million and percent**

||Total<br>imports<br>from the<br>EU|Imports from<br>the EU<br>Excluded from<br>EPA<br>Liberalization|Share of<br>excluded<br>products<br>in sector<br>imports|
|---|---|---|---|
|333- watches and clocks|0.23|0.23|100.0%|
|160- TobaccoProducts|9.16|9.16|100.0%|
|192 – Footwear|2.35|2.34|99.6%|
|191 - Tanning andDressing of Leather;Luggage,Handbags, Saddlery etc|1.27|1.23|96.7%|
|50- Fishing andrelated activities|3.47|3.20|92.3%|
|202 - Products of Wood, Cork, StrawandPlaitingMaterials|15.08|13.63|90.3%|
|261 -Glass and GlassProducts|15.62|13.99|89.6%|
|293- DomesticAppliancesn.e.c.|26.76|23.85|89.1%|
|269- Non-metallicMineral Productsn.e.c.|156.56|134.31|85.8%|
|343- Parts andAccessoriesfor Motor Vehicles andTheir Engines|39.21|33.51|85.5%|
|172 -Other Textiles|9.49|7.04|74.1%|
|141 –Quarrying ofStone, Sand and Clay|8.07|5.83|72.2%|
|289-Other FabricatedMetal Products;Metal Working ServiceActivities|89.14|63.18|70.9%|
|153-Grain Mill Products, Starch Products, andPreparedAnimal Feeds|62.00|38.52|62.1%|
|252 – PlasticsProducts|69.68|42.64|61.2%|
|272 - BasicPrecious andNon-ferrousMetals|289.76|12.54|4.3%|
|142 - Mining and Quarryingn.e.c.|8.64|0.36|4.2%|
|241 - Basic Chemicals|789.38|28.95|3.7%|
|221 – Publishing|81.77|2.73|3.3%|
|12 - Farming of Animals|0.40|0.01|3.0%|
|361 – Furniture|13.10|0.25|1.9%|
|291 –generalpurposemachinery|782.55|14.10|1.8%|
|20- Forestry,logging andrelated activities|3.52|0.06|1.7%|
|11 –Growing ofcrops andhorticulture|343.22|5.26|1.5%|
|321 –electronicvalves and tubes and otherelectronic components|8.34|0.12|1.4%|
|331 - medicalappliances andinstruments and appliances etc|65.62|0.85|1.3%|
|101 - Mining andAgglomerationof Hard Coal|0.39|0.00|1.1%|
|292 -Special PurposeMachinery|692.20|5.14|0.7%|
|351 - Building andrepairing ofships and boats|207.75|1.21|0.6%|
|322 – Televisionandradio transmitters and apparatusfor line telephony|232.00|0.01|0.0%|

Source: Computed by authors based on TRIST database At the aggregate level, Nigerian imports from the EU would be 7.4 percent higher after preferential tariff liberalization of import tariffs. As shown in Figure 2, a large fraction of this increase comes at the expense of other partners. In terms of proportional changes, the trading partners that experience the largest decline in exports are the United States (4.8 percent) and other (non-ECOWAS) African countries (4.4 percent). Official imports from ECOWAS 13
**Figure 2. Policy interest rates and size of economies with negative rates**

**A. Policy interest rates**

<!-- Start of picture text --> Percent<br>2 Euro Area Sweden Japan<br>Switzerland Denmark Hungary<br>1<br>0<br>-1<br>Jan-14 Jul-14 Jan-15 Jul-15 Jan-16 Jul-16<br><!-- End of picture text --> <!-- Start of picture text --> B. Size of economies with negative interest<br>rates<br>Percent of world GDP<br>30 Policy rate 5 year 10 year<br>20<br>10<br>0<br>Jan-14 Jan-15 Jan-16 Latest<br><!-- End of picture text --> Sources: European Central Bank, Bank of Japan, Swedish Riksbank, Swiss National Bank, Danmarks Nationalbank, Central Bank of Hungary, Haver Analytics, Bloomberg, World Bank. A: Policy rates are the following: euro area, overnight deposit facility; Sweden, repo rate; Japan, current account deposit; Switzerland, middle point of target range for three-month LIBOR; Denmark, one week certificate of deposit; Hungary, overnight deposit. Last observation is June 2016.

B: Share of world real GDP (in 2010 US$) accounted for by economies with negative policy rates, 5- and 10- year government bond yields. Monthly averages. Last observation is June 2016.

A cut in policy rates slightly below zero should in principle have similar effects as a cut in rates to low but positive territory. However, policy rates substantially below zero for a protracted period of time could lead to greater risks of financial market disruptions. NIRP, like other unconventional monetary policy measures, could also have spillover effects to emerging market and developing economies (EMDEs), as search for yields in reaction to negative rates in advanced economies may affect capital flows to EMDEs. However, deteriorating macroeconomic fundamentals in EMDEs could limit potential gains from exceptionally favorable financing conditions in NIRP countries, while balance sheet pressures stemming from a strengthening U.S. dollar could heighten policy challenges.

The introduction of NIRP has led to an intensive debate, as illustrated by the quotations at the beginning of this paper. Some argue that NIRP have so far served the intended purpose, complementing the broader set of expansionary measures employed by central banks. Others, however, emphasize financial stability risks associated with NIRP and claim that they may have weakened banks’ willingness to lend, contributed to financial market distortions, further inflated asset prices, and delayed the implementation of necessary macroeconomic and structural policies.

Despite these highly polarized discussions, research on NIRP has been quite limited.<sup>3</sup> This paper aims to shed light on the issue by presenting a comprehensive analysis of the sources and the short-

> 3 This study builds on an earlier essay we produced on the implications of NIRP employed by some European central banks (World Bank 2015a). Hannoun (2015), Jackson (2015), Cœuré (2016), Citi (2016a), Deutsche Bank (2016a), Hamilton (2016), Morgan Stanley (2016a), Vinals, Gray and Eckhold (2016) and Wessel and Olson (2016) present

7
The duration of youth unemployment, compared to adult unemployment, gives an indication as to whether young people have a more difficult time integrating into labor markets than do adults. It has long been recognized that unemployment spells among European youth are shorter than those of adults (Martin et. al. 1984, OECD, 1983), although 20 percent of European youth had spells lasting more than a year (Quintini 2007). The US finds similar trends; over the period 1968-2000, teenagers were six times as likely as those age 35-54 to experience unemployment spells of four weeks or less and those age 1924 were 3 times as likely as the older age group to have short unemployment spells (Abraham and Shiner, 2001). The same trend emerges for Latin America and the Caribbean where youth unemployment spells last an average of 4.9 months, as compared to 7.1 months for workers age 25-65 (SEDLAC, 2009).<sup>3</sup> Notably, unemployment spells are shorter than employment or inactivity spells (Bosch and Maloney, 2007).

Finally, the rates of youth entry and exit from unemployment allow us to determine if young people move around more in the labor market than do adults. The OECD literature suggests that high youth unemployment rates are primarily due to frequent job turnover. Young people in all 20 countries in the sample had higher “job changing” rates as compared to adults (Quintini, 2007). Similar results are found for the US (Leighton and Mincer, 1982). The common interpretation of this observation is that young workers are engaged in a search process where they experiment with various “low-quality” jobs – temporary, low-skilled, poorly paid – until the young person identifies a “career” path she wishes to follow. This process seems to emerge in Latin America, as well. Bosch and Maloney (2007) find that informal sector employment is of a short duration relative to other employment sectors and contributes to unemployment, as opposed to being a substitute.

# **III. Methodology and Data**

Three methodologies are used to understand the dynamics of youth unemployment, drawing on techniques developed in Bosch and Maloney (2007). First, transition matrices<sup>4</sup> are estimated to understand the amount of turnover, namely the share of people who move into unemployment and out of unemployment every period. This allows us to determine if

> 3 The SEDLAC data report truncated unemployment spells, so they may underestimate the actual duration of unemployment.  Since they are drawn from a random point in time, though, there is no reason to expect that the age-trends would differ from completed unemployment spell durations.

> 4 Transition matrices are referred to as “intensity matrices” in Bosch and Maloney (2007).

4
34 Appendix Table A1: <u>Volume of cotton</u><sup>a</sup> <u>production, yield, trade and utilization, 2005-06</u>

||Output<br>(Kt)|Change i<br>stocks<br> (Kt)|n<br>Exports<br>(Kt)|<br>Imports<br>(Kt)|<br>Utilization<br>(Kt)|Share of<br>supply<sup>b</sup><br>exported<br>(%)|<br> <br>National<br>share of<br>global<br>output(%)|<br><br>National<br>share of<br>global<br>exports<br>(%)|<br>Yield per<br>ha, % of<br>global<br>average|
|---|---|---|---|---|---|---|---|---|---|
|China|5819|9|10|2800|8600|0|23.5|0.1|163|
|United States|4735|408|3039|7|1296|70|19.1|37.5|122|
|India|4250|550|225|125|3600|6|17.1|2.8|63|
|Pakistan|2308|42|100|250|2415|4|9.3|1.2|103|
|Brazil|1191|-85|425|50|900|33|4.8|5.2|161|
|Uzbekistan|1100|14|837|1|250|77|4.4|10.3|110|
|Turkey|805|0|25|770|1550|3|3.2|0.3|181|
|<br>Australia|496|-97|582||11|98|2.0|7.2|258|
|Greece|358|6|258|5|100|73|1.4|3.2|144|
|Syria|298|-9|150||158|49|1.2|1.9|192|
|Egypt|263|-8|125|75|220|46|1.1|1.5|137|
|Burkina Faso|254|-14|264||4|99|1.0|3.3|64|
|Mali|250|-1|247||4|98|1.0|3.0|68|
|Turkmenistan|219|6|114||100|54|0.9|1.4|52|
|Tajikistan|162|6|132||25|85|0.7|1.6|80|
|Argentina|155|-5|50|20|130|31|0.6|0.6|63|
|Mexico|152|-33|45|287|428|24|0.6|0.6|169|
|Kazakhstan|147|5|134|5|12|94|0.6|1.7|99|
|Benin|140|-49|186||3|98|0.6|2.3|67|
|Côte d'Ivoire|124|11|103||10|91|0.5|1.3|62|
|Iran|120|0|10|10|120|8|0.5|0.1|114|
|Cameroon|112|-78|57|1|132|30|0.5|0.7|69|
|Spain|110|0|63|15|62|57|0.4|0.8|178|
|<br>Sudan|96|0|92||4|96|0.4|1.1|67|
|Tanzania|96|-24|104||16|87|0.4|1.3|31|
|Paraguay|90|42|43||5|90|0.4|0.5|49|
|Nigeria|87|2|30|15|70|35|0.4|0.4|33|
|Zambia|76|0|55||20|72|0.3|0.7|39|
|Chad|72|-5|77||1|100|0.3|0.9|33|
|Zimbabwe|72|-13|58||26|68|0.3|0.7|36|
|Peru|70|1|2|23|90|3|0.3|0.0|118|
|Togo|70|-9|79||0|100|0.3|1.0|54|
|Myanmar|59|0|11||47|19|0.2|0.1|29|
|Colombia|55|21||78|111|0|0.2|0.0|109|
|Azerbaijan|55|5|41||8|82|0.2|0.5|71|
|<br>Kyrgyzstan|38|0|39|3|3|103|0.2|0.5|121|
|<br>Uganda|37|-5|38||4|90|0.1|0.5|52|
|<br>Mozambique|25|-3|26||2|93|0.1|0.3|16|
|Ethiopia|22|0|2||20|9|0.1|0.0|38|
|South Africa|21|0||39|60|0|0.1|0.0|73|

Source: ICAC (2005).

a Cotton, refers to ginned lint or raw cotton. It does not include seed cotton, linters, cotton mill waste, or cotton fibers subjected to any processing other than separation of lint from seed by the gin. Annual data are for the cotton year beginning 1 August.<sup>b</sup> Supply is output plus change in stocks.
Table 9 Financial Assets and Liabilities of Corporate Sector (billion yen)

||1955/12|1960/12|1971/3|1981/3|1991/3|
|---|---|---|---|---|---|
|Total assets|na|12847|83122|276993|668961|
|Cash|na|150|442|1492|3137|
|Demand deposits|784|1687|9868|30563|44344|
|Time/savings dep|<br>775|2251|14525|47235|134118|
|Securities|na|1018|4039|17688|156850|
|Trust accounts|na|210|1322|5390|38700|
|Trade credit|2566|7323|51765|163813|255030|
|Liabilities||||||
|PFI loans|3893|10135|56315|174266|435397|
|GFI loans|506|6181|21189|63759|63759|
|Domestic bonds|227|693|3151|10307|39715|
|Foreign bonds|--|--|--|1768|32514|
|Stocks|964|2827|8985|16977|64989|
|Trade credit|1973|5646|40580|132789|196657|

Source: Bank of Japan, _Economic Statistics_ Annual, various issues.

40
Lagged dependent variable Including the lagged dependent variable controls for any underlying momentum or autocorrelation in firm growth. The results, summarized in Table 4, are generally consistent with the original regression. For instance, in Model 4, the coefficient for temperature uncertainty in the baseline regression (Eq 10) decreases slightly from -0.768 to -1.078, indicating that the negative effect of temperature uncertainty on firm growth becomes more pronounced when accounting for the autocorrelation in fixed asset growth. The results for industry heterogeneity by capital intensity and income levels remain largely unchanged, with the coefficients moving from -1.038 to -0.947 for industry differences and from -0.903 to -0.923 for income-level variations.

Table 4: Weather uncertainty and fixed asset growth: Lagged dependent variable

|||Dep.|variable: Fi|xed assetgro|wth||
|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|
|Lagged Growth|0.049<sup>∗∗∗</sup>|−0.014<sup>∗∗∗</sup>|−0.014<sup>∗∗∗</sup>|−0.014<sup>∗∗∗</sup>|0.024<sup>∗∗∗</sup>|−0.014<sup>∗∗∗</sup>|
||(0.004)|(0.004)|(0.004)|(0.004)|(0.002)|(0.004)|
|Uncertainty|−4.348<sup>∗∗∗</sup>|−0.700<sup>∗∗</sup>|−0.630<sup>∗</sup>|−1.078<sup>∗∗∗</sup>|−5.568<sup>∗∗∗</sup>|7.663<sup>∗∗</sup>|
||(0.269)|(0.356)|(0.359)|(0.369)|(0.351)|(3.405)|
|Maximum|||−0.070||0.203<sup>∗∗∗</sup>|−0.058|
||||(0.048)||(0.022)|(0.048)|
|Mean||||−0.169<sup>∗∗∗</sup><br>(0.043)|||
|Capital Intensity|||||−2.298<sup>∗∗∗</sup><br>(0.405)||
|Income||||||−5.174<sup>∗∗∗</sup><br>(1.168)|
|Uncertainty:Capital Intensity|||||−0.947<sup>∗∗∗</sup><br>(0.184)||
|Uncertainty:Income||||||−0.923<sup>∗∗</sup><br>(0.373)|
|Constant|8.372<sup>∗∗∗</sup>|19.164<sup>∗∗∗</sup>|21.834<sup>∗∗∗</sup>|24.536<sup>∗∗∗</sup>|14.348<sup>∗∗∗</sup>|53.936<sup>∗∗∗</sup>|
||(0.605)|(2.344)|(2.965)|(2.708)|(1.283)|(10.227)|
|Year FE|No|Yes|Yes|Yes|Yes||
|Country FE|No|Yes|Yes|Yes|No||
|Industry FE|No|No|No|No|Yes||
|Observations|46,922|46,922|46,922|46,922|183,756|46,507|
|R<sup>2</sup><br>|0.009|0.140|0.140|0.140|0.060|0.142|
|Adjusted R<sup>2</sup>|0.009|0.138|0.138|0.139|0.060|0.140|

_Notes:_ This table presents the regression of fixed asset growth on weather uncertainty, measured by the standard deviation of deviations from expected temperatures, with the inclusion of a lagged dependent variable. Models (1) through (4) progressively expand the baseline specification (Eq 10) by incorporating fixed effects (Model 2) and adding controls for mean and maximum temperatures (Models 3 and 4). Models 5 and 6 report the results for industry heterogeneity by capital intensity (Eq 11) and income-level heterogeneity (Eq 12), respectively. Standard errors are shown in parentheses. Significance level:<sup>∗</sup> p _<_ 0.1;<sup>∗∗</sup> p _<_ 0.05;<sup>∗∗∗</sup> p _<_ 0.01.

17
- 19 -

information on the likelihood of sustainability as a supplement measure of OED ratings.

# VI. Results of the Statistical Analysis

# VI.A Unsatisfactory projects and economy policies

Through a simple bi-variate statistical analysis, the incidence of unsatisfactory projects under each policy distortion category is calculated first. Policy variables are divided into high vs. low distortion segments and the likelihood of project failure is computed for each policy distortion segment. The following policy variables are used in the analysis: high/low currency overvaluation (measured by the black market premia), positive/negative real interest rate, high/low degree of trade restrictiveness (measured by Thomas-Halevi index of tariffs and quantitative trade restrictions), high/low fiscal deficit as a share of GDP, and high/low degree of relative price distortions in tradable goods (measured by the index constructed by D. Dollar). In addition, high/low growth rate of GDP is included.

Among 259 social projects, fifty-two (20 percent of the samples) are rated as unsatisfactory. Results from cross-tabulations show that the economic regime's trade restrictiveness, currency overvaluation, fiscal deficit, price distortions, and GDP growth are significantly associated with the performance of social projects, which is similar to what was found for projects in other than social sectors (Table 2). No social project failed when trade barriers are low, as compared with a probability of failure of almost 30 percent when trade restrictions are relatively high or very high. When the black market premium is high, 37 percent of the projects failed, which is more than double the likelihood of project failure when there is low premium (17.4 percent). Similarly, when the fiscal deficit is big, the projects have a much higher probability of performing poorly. And when relative prices are distorted, the likelihood of poor project performance is higher. The overall performance of the economy, as measured by GDP growth, (which in turn is associated with the overall policy framework) matters as well:
# **References**

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23
|Table2:Long-runCoefficientEstimates oftheMark-upM<br>|odels<br>|
|---|---|
|Regressor<br>Mark-upModel I|Mark-upModel II|
|Constant<br>-13.2 (6.1)|-10.9 (2.9)|
|wt<br>0.30 (3.5)<br>|0.32 (2.3)|
|(et+p<sup>*</sup><br>t<sup>)</sup><br>1.30 (6.5)|1.10 (3.0)|
|apt|0.05 (0.2)|
|Trend<br>-0.03 (3.3)|-0.02(1.9)|

a: Numbers inside the parentheses are absolute values of the t-ratios calculated by using the asymptotic standard errors of long-run coefficients.

In the case of mark-up model I, empirical results suggest that a 1 percent rise in wages will increase the price level by 0.3 percent in the long-run. The findings indicate that the long-run impact of a rise in foreign price on the domestic price level is considerably higher: a 1 percent increase in (et+p<sup>*</sup> t<sup>) will raise the price level by 1.3</sup> percent. These findings document the importance of the exchange rate on the evolution of the price level in Turkey. The empirical findings for the mark-up II are fairly similar to that of mark-up model I, confirming the significance of the exchange rate on the evolution of the price level in Turkey.

Table 3 presents the results of the corresponding error correction models to investigate the short-run dynamics. In this stage, I also rely on the AIC to select the lag length of each variable.

22