rafmacalaba/datause-encoder-data huggingface dataset:
# **Driver 1.  Wage decompression and growth of the private sector**

Transition involved the emergence of a private sector which was to grow over time. By 2004, over 60 percent of GDP was produced in the private sector [EBRD (2005)]. This changed the process of wage setting by introducing a tighter link between productivity and wages. It is usual to associate inequality outcomes more closely to labor market conditions, primarily to inequality in wages, which in turn depends on the level of returns to human capital and changes in endowments. Indeed, wage inequality is a major driver of overall inequality. At a first glance, data on wage inequality data appear to mirror those for inequality of consumption. Contrasting different data sources, based both on enterprise records and household surveys, Figure 4 reports available Gini indices for wages in Russia and Poland. While dispersion between different sources is indeed very large, the levels and patterns resemble the trends depicted in Figure 1 closely.

**Figure 4.  Gini index for monthly wages in Russia and Poland, various sources** **_Russia Poland_**

<!-- Start of picture text --> Official wage, GKS RLMS ISSP Official, GUS HBS LFS ISSP<br>0.55 0.4<br>0.5<br>0.35<br>0.45<br>0.4<br>0.3<br>0.35<br>0.3<br>0.25<br>0.25<br>0.2 0.2<br>1989 1991 1993 1995 1997 1999 2001 2003 1989 1991 1993 1995 1997 1999 2001<br>Gini Gini<br><!-- End of picture text --> Sources: ISSP: International Social Survey Program from Paternostro and Tiongson Russia: Russian Statistical office (GKS), and RLMS – Russian Longitudinal Monitoring Survey, Lukianova for contractual wages (cleaning out the effect of arrears). Poland: HBS – Household Budget survey reported net wages Keene and Prasad, 2002 estimate –own; LFS –Labor Force Survey from Newell and Socha (2003).

Why did wage inequality in Russia increase so rapidly? Returns to education alone seem to be insufficient to explain it. Cross country studies find that the returns to education increased from the “pre-transition” period to the “early transition” period. The meta-study by Fleisher and others (2004) suggests that the sharpest increases occurred during the early years of transition. Flabbi, Paternostro and Tiongson (2005) examine the evolution of the skills premium in transition economies through the late 1990s or the period thereafter through 2002 or 2003 using ISSP data, which is an internationally comparable survey. However, neither that study, nor the other sources reported in Table 4 below, produce any evidence that Russia stands out as having particularly large or distorted patterns of returns to education compared to Poland. Indeed it started with a much lower level of returns but already by mid 1990s had converged to Polish levels. This factor therefore cannot be used to explain excess inequality of Russian wages and other explanations are required.

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introduced in 2016 achieved remarkable success by bringing previously undisclosed foreign and domestic assets into tax compliance, amounting to a cumulative total of 21% of GDP and standing out as one of the most successful tax amnesty initiatives globally. The extensive scale of Argentina’s asset disclosures provides an opportunity to utilize straightforward methodologies to unpack the effects of changes in tax enforcement. Third, Argentines are required to report their domestic and foreign assets annually to the tax authority due to the existence of a gross wealth tax on individuals and firms, enabling us to track declared income and wealth over time. Specifically, we use two decades of detailed tax tabulations and employ the generalized Pareto interpolation method to characterize the distributions of income and wealth.

We begin by leveraging the disclosures made under the 2016 amnesty to shed light on the prevalence, nature, and distribution of tax evasion. We find substantial offshore evasion: about 255,000 Argentines admitted to concealing assets under the program, and strikingly, more than 80% of these assets were hidden abroad, mainly in the US and tax havens or low-tax jurisdictions like Uruguay, Switzerland, and the British Virgin Islands. Regarding the types of assets disclosed, over half of the disclosed foreign assets consisted of stocks, one-quarter were held in bank accounts and various currencies, and one-tenth were real estate properties. Furthermore, our analysis reveals that offshore tax evasion is predominantly concentrated among the wealthiest individuals, especially within the top 0.1% of the wealth distribution.

Next, we examine impacts on tax compliance up to five years later. We find that the asset disclosures resulted in enduring increases in reported wealth. The number of Argentines declaring foreign assets in their wealth tax returns tripled after 2016, and the value of reported foreign assets quadrupled from Arg$250 billion in 2015 (4.3% of GDP) to Arg$1 trillion in 2016 (16.5% of GDP) and reached Arg$1.25 trillion in 2019 (20.7% of GDP), bringing the country closer to the macro estimates of offshore wealth by Alstadsæter et al. (2018).<sup>1</sup> Furthermore, the improved reporting of foreign assets significantly expanded the tax bases for both wealth tax and income tax. The wealth tax revenue more capital than doubled, with the amnesty’s one-time penalty recouping the entire wealth tax revenue lost due to tax evasion since 2002. Additionally, taxpayers also reported the income from the disclosed assets more three times more generated truthfully, declaring capital income. Again, this expanded tax base endured even five years later, indicating a lasting improvement in tax compliance.

> 1 While individuals who start reporting their wealth on their tax return without participating in this amnesty could, in principle, be driving the increase, we find that virtually all disclosures went through the tax amnesty program, unlike the "quiet disclosures" in the US (Johannesen et al., 2020).

2
**Table 1. Selection of empirical studies assessing the determinants of sovereign default**

|**Reference**|**Variables**||**Sample**|
|---|---|---|---|
|Detragiache and Spilimbergo<br>(2001)|Short term debt<br>Debt coming due<br>Foreign exchange reserves<br>Total debt to GDP ratio|Concessional share<br>Multilateral share<br>Interest rates<br>Overvaluation|69 countries, yearly data<br>1971‐1998|
||Commercial share|Openness||
|Catao and Sutton (2002)|Terms of trade<br>Real interest rate US bond<br>Government balance over<br>GDP<br>REER|Total external debt service to<br>export ratio<br>Foreign exchange reserves to<br>debt<br>Openness<br>Volatility, fiscal policy|25 emerging markets,<br>1970‐2001|
||Short term debt|Volatility, TOT||
||Foreign exchange control<br>index<br>Real GDP growth|Volatility, money base<br>coverage<br>Volatility, capital control||
|Kruger and Messmacher<br>(2004)|Proportion of new financing<br>needs<br>GDP growth<br>Change in growth rate of TOT|CA deficit to GDP<br>Debt to exports<br>LT debt service to reserves|42 countries, 1970‐2001|
||Export growth|LT debt service to GDP||
||US 3 month interest rate|ST debt to reserves||
||Foreign debt to GDP|||
|Kraay and Nehru (2006)|Present value of debt to<br>exports<br>Debt service to revenues|Rule of law<br>Depreciation|LICs, 94 crisis episodes<br>1970‐2001|
||Debt service to reserves|TOT growth||
||CPIA rating|GDP per capita||
||GDP growth|Inflation||
|Pescatori and Sy (2007)|Openness<br>Overvaluation|ST debt over reserves<br>GDP growth|Several samples, 1975‐<br>2002|
||Total debt over GDP|Inflation||
|Tomz and Wright (2007)|GDP (HP‐filtered)||106 countries, yearly data<br>1820‐2004|

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where _Esr,n_ is an _N ×_ 1 vector with the scalar corresponding to the gross exports from _s_ to _r_ in position _n_ (i.e. _esr,n_ ) and zeros elsewhere. As in previous cases, we can consider the forward production-demand linkages up to the market of final destination as well as a breakdown by sector of origin/final-destination as described in Section 3.3.

Here, the definition of double counted items is even less restrictive than in the bilateral perspective case, which means the **DVA**<sup>_∗_</sup> _sr_<sup>and the</sup><sup>**FVA**</sup><sup>_∗_</sup> _sr_<sup>are smaller than</sup> the sum of the value-added terms in equation (49) across the different exporting sectors. Notably, this sum does not have an economically meaningful interpretation, due to double counted items.

Since tariffs are usually imposed at the sectoral-bilateral level, this perspective is suited to evaluating the incidence of tariffs on the value-added produced by different countries. The ICIO framework allows to single out the countries/sectors that originated the value embedded in the goods on which a tariff is levied. By boosting flows in intermediate goods, across countries and stages of production, GVCs tend to amplify the costs generated by border barriers (Rouzet and Miroudot, 2013; Vandenbussche et al. 2017). Since tariffs are applied on gross trade flows, the value-added that crosses the targeted importer border several times is subject to an ‘inflated’ tariff as compared to the original nominal one.

Given the nominal tariff _τsr,n_ imposed by country _r_ on the goods imported from _s_ in sector _n_ , the ‘effective tariff’ on the value-added produced by a certain country _j_ con be computed as follows: while the incidence of the tariff on the total value-added produced by _j_ can be computed multiplying **ET**<sup>_j_</sup> _sr,n_<sup>bytheratiobetween</sup><sup>**V**</sup><sup>_j_</sup><sup>**B**</sup> _jssr,n Esr,n_ and **V** _j_ � _k_ � _l_<sup>**B**</sup> _jk_<sup>_Y_</sup> _kl_<sup>.</sup>

# **4.3 Other accounting perspectives**

Additional breakdowns of exports can be obtained by using further accounting perspectives. In this section we consider those that may be useful in addressing the most common empirical issues.

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# **1 Introduction**

Timely and comparable poverty estimates are vital to assess countries’ development International estimates serve as a for re- progress. poverty public good searchers and inform the development community on efforts to meet the first Sustainable Development Goal, to end extreme poverty by 2030. Within international development organizations, national development agencies, and NGOs, they also inform the allocation of resources and the development of strategic priorities.

Yet timely and comparable estimates of poverty are lacking for many reasons. In some countries, fragility, conflict, and violence make it difficult to conduct household expenditure surveys altogether, while in other countries, lack of monetary resources is the main obstacle. Even when surveys are frequently conducted, the time it takes to field a survey, collect, process, and analyze the data, often implies a two-year lag from the time of data collection to the release of international poverty estimates. With the world changing at an ever more rapid pace, as illustrated by the unexpected onset of COVID-19, this lag risks painting an outdated picture of poverty in a country. As of October 2021, on average across the developing world, the most recent survey with international poverty data was from 2014. In addition, 16 economies with a population greater than 1 million had no international poverty estimate at all. For these reasons, initiatives that reliably and cost-effectively predict what the poverty rate is today are crucial for informed and effective high-level decision-making.

The objective of this paper is to test various methods to estimate extreme poverty in all countries of the world as of the present year and as of the preceding year – at the time of writing, 2021 and 2020. We will refer to estimates of poverty for the present year as nowcasts and estimates of poverty for the preceding year as nearcasts. For _nearcasting_ , one can rely on all data that are produced with less than a one-year time lag. We will use more than 1,000 variables from the World Development Indicators, the World Economic Outlook, and the Google Earth Engine. For _nowcasting_ , only variables that themselves have been nowcasted by others are available, as well as variables that are produced with little or no time lag, such as certain remote sensing indicators.

We combine all of these possible predictors with the PovcalNet database, which contains more than 2,000 international poverty estimates covering 168 countries. We train our model on these past surveys – essentially pretending a subset of them do not exist – and evaluate the models how well by measuring they approximated the true poverty rates of these held-out surveys. The best performing 2
10 Another development in the past decade has been an increase in FDI flows from developing countries. Multinational firms from Brazil, for instance, have undertaken investments in the countries of West Africa and in other developing areas. Meanwhile, Brazil has received investments by 1985 from such developing economies as Kuwait, Saudi Arabia, South Africa, Iran, Portugal, and Hong Kong--although they totalled less chan 2 percent of Brazil's the FDI stock.

Malaysia, Singapore, and Hong Kong are unusual among developing economies in the extent to which they are both recipients of and sources of FDI, which reflects the intense activity in intraregional FDI in Southeast Asia.<sup>10</sup> Malaysia is especially prominent for the high proportion of FDI that has come from other developing countries as of 1983, 44 percent of its FDI stock in "pioneer industries" was from non-OECD countries, inctluding 33 percent from Singapore. Five Asian developing countries exhibit relatively high proportions of FDI stocks from other developing countries: Thailand, 17.6 percent in 1984; India, 13.2 percent in 1974; Indonesia, at least 9.6 percent in 1983; and the Republic of Korea, close to 9 percent in 1986. In three of the Latin American countries, the comparable figures were generally less than 5 percent. (There are no directly comparable data for Argentina, Nigeria, or Kenya.) Table 2-7 lists additional data on FDI; those data indicate FDI flows from developing countries were usually about I.D percent of world totals. Three-fourths of the outflows were from Latin American countries.11 Table 2-7. FDI Flows from Developing Countries, 1975-87

|Year|SDRs (millions)|Percent of total<br>world FDI|
|---|---|---|
|1975|181|1.0|
|1976|313|2.1|
|1977|310|1.4|
|1978<br>|299|1.2|
|1979|332|1.0|
|1980|485|1.3|
|1981|325|0.6|
|1982|1,057|2.2|
|1983|721|1.6|
|1984|448|0.9|
|1985|1,101|2.4|
|1986|938|1.5|
|1987|1,223|1.5|
|1988|1,159|1.2|

Source: IMF.
Finally, our main treatment variable¬—the negative income shock—is based on changes in labor income, which we construct based on survey responses. A skeptical reader might question the incentives of individuals to truthfully report their income. There is some evidence that top incomes are underreported in household surveys. Nonetheless, our negative shock is created based on the distribution of changes in reported labor income. As long as income is consistently underreported, our results would still hold. We concede, however, that our analysis has little to say on the behavior of households from the top of the distribution, as such households tend not to answer surveys in the first place.

The desirability of social policies depends crucially on how well households can privately insure against idiosyncratic income shocks, which in turn depends on the access to financial markets. Therefore, these results point to some policy implications and directions. If income shocks drive young people out of school and reduce the future stock of skilled labor, then social safety nets or expanded access to educational credit lines that act as insurance mechanisms could play an important role in reducing the adverse effects of shocks on household income and thereby support increases in skilled labor.

# **ACKNOWLEDGMENTS**

The authors thank Emmanuel Skoufias, Rafael de Hoyos, Daniel Lederman, Angel Melguizo, participants at seminars in the OECD and World Bank, and the anonymous referees for their thoughtful comments.

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# **4. Policy implications**

In spite of the limitations stemming from the assumptions we had to make as a result of the poor quality of the information available and accounting for the very risk averse evaluation rule, this quantitative exercise provides a number of useful policy hints. Table 8 summarizes the information for each country grouping.

The first obvious conclusion is that there are very few certainties in general and no clear world wide sectoral pattern. There is no scope for simple encompassing regional or sectoral statements because issues are region- and sector-specific. The second is that at the Global level, the _presumptions_ of concentration problems only arise for the businesses which use to be the main motivation to maintain these services in the public sector: electricity and water distribution as well as fixed phones services. Third, Western Europe is the region with the largest number of actual or potential concentration problems—it is in trouble in electricity and probably in telecoms as well. Eastern Europe follows closely with as many sectoral potentially problematic. North America, Africa and Asia & Oceania have the fewest number of problematic sectors. Fourth, Eastern and Western Europe, Latin America and North America are the only regions with sector wide potential concentration problems. They are also among the most developed in the world—this list only exclude Oceania. This is somewhat surprising since these are also the regions in theory most committed to competition. Both telecoms business—fixed and mobile--have actual or potential problems in these regions sectors. Electricity—generation and distribution--is equivalently problematic in Western Europe and Latin America. Water and sanitation are an actual and a potential problem in North America. Fifth, the telecoms sector is overall the sector with the largest number of potential or actual concentration problems which is also a surprise in view of the widespread sense that the technological revolution made the sector competitive sooner and faster than any other sector. It may be less surprising considering the widespread difficulties in ensuring the design and enforcement of fair access rules in a business in which interconnection is at the core of everything. Sixth, from the viewpoint of one of the poorest regions of the world, it is unfortunate to see that one of the most concentrated sectors is the water sector in Africa in spite of the potential strong role of alternative operators in the region.<sup>16</sup>

> 16 See Kariuki, M. and J. Schwartz (2004) for more on small scale operators in water and energy

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14 More recently, the impact of export taxes on agricultural output has been the focus of many World Bank studies, some of which are summarized in the 1986 World Development Report. The oft-cited experience of Ghana with export taxes on cocoa (either explicit or implicit, through marketing boards and fixed exchange rates) is a particularly dramatic illustration of the incentive effects of taxation. Like Ceylon many years ago, the monopoly posltion of Bangladesh in jute has been eroded as synthetic substitutes have been developed. The gains from monopoly pricing of export goods may be short lived.<sup>5</sup> More recent studies of trade taxation have stressed the complex effects of import and export taxation on the entire economy. There are a number of simulation models which implement econometrically estimated parameters in general equilibrium models of particular countries (Clarete and Whalley, 1987; also see Shoven and Whalley, 1985). The excess burden of trade taxation is then addressed using a "counterfactual" -- what would be production and income if the existing tariffs were replaced by increasing other taxes so that government revenue is held constant? A comparison of real income, or of consumer utility, provides a measure of the excess burden of the tax. That is, these models can calculate the loss in consumer "surplus" caused by tariffs. The disadvantage with the simulation 5 Recent oligopolistic models of trade have emphasized the use of export subsidies or taxes to provide an advantage to the home country in the bargaining game over production and prices. See Eaton and Grossman (1986).