rafmacalaba/datause-encoder-data huggingface dataset:
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# Budget DefIoct(-)/GDP Intensive Adjusters

# Current Aocount/GDP Intensive Adjusters

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# Gross Domestic Investment/GDP Intensive Adjusters

# Domestic Savlngs/GDP Intensive Adjusters

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# Real Export of GNFS Growth Rate Intensive Adjusters

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# Real GDP Growth Rates Intensive Adjusters

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Several other approaches have been suggested for setting new thresholds so that they provide a better basis for policy analysis. One idea, derived from the use of the income classification for aid allocation purposes, is to define low income countries as those that cannot eliminate absolute poverty by relying on their own resources. Ravallion (2012) estimates that most countries with per capita incomes of more than US$4,000 (2005 PPP) would conceivably be able to eradicate extreme poverty (defined as living on less than US$1.25 a day in 2005 PPP terms) without recourse to external assistance. This equates to a per capita income of almost US$2,300 using market exchange rates, or roughly double the value of the current low income threshold.

Another idea, suggested by the AIDS Healthcare Foundation (AHF), is to raise the low income threshold to $10-$15 per day.<sup>19</sup> Their argument is that many countries classified as middle income have poor health outcomes and a high burden of diseases such as AIDS, TB and malaria, but they lose access to preferential pricing for certain medicines or to financial support because some agencies use the low income classification threshold in their resource allocation models: for example, the Global Fund to Fight AIDS, TB and Malaria. There are suggestions for adjusting the value of the high income cutoff as well. Pritchett (2006) argues that a plausible upper-bound poverty line is about US$10 a day (2005 PPP), and according to Kenny (2011), any country in which average incomes are five times that level—about US$18,250 (2005 PPP)— could be defined as rich. This turns out to be quite close to current practice: countries near that level have an average Atlas GNI per capita of about US$11,800, compared with the high income threshold of US$12,735 in 2014. A conclusion from this is that there are widely differing views on appropriate threshold levels, and they largely depend on their intended purpose.

A challenge with any of the new approaches described above is that a number of countries would be reclassified on the basis of a methodology change, rather than as a result of growth or changes in per capita income. This is not problematic if the classification is used purely for analytical purposes, but this review has shown that its use extends into resource allocation models and into policy development. For this reason, any adjustments to the

> 19 See http://raisethemic.org.

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_CHAPTER 5. UNIT LEVEL MODELS: LINEAR INDICATORS_ 27 satellite images. Therefore, their application represents a very early example of integration of different data sources for SAE.

This model has traditionally been used to estimate linear indicators such as area means, employing the EBLUPs defined by Henderson (1950) under the “infinite” population setup, or those derived by Royall (1970, 1976) under a finite population scheme, which agree when the area sampling fractions are negligible. We start describing the latter. Let us decompose the mean of area _d_ into a sum of values for the units observed in the sample _sd_ and for the units in the sample complement _rd_ = _Ud −sd_ , as follows: The values _ydi_ for sample units _i ∈ sd_ are observed, and only those for non-sample units _i ∈ rd_ are unknown. The BLUP of _Y_<sup> ̄</sup> _d_ under the nested error model (5.1) is obtained by fitting the model to the sample data using the WLS estimator **_β_**<sup> ̃</sup> of **_β_** , and then predicting the values _ydi_ for units outside the sample from area _d_ . Specifically, the BLUP is given by with predicted values given by Here, _y_ ̄ _da_ = _a_<sup>_−_</sup> _d·_<sup>1</sup> � _i∈sd_<sup>_adiydi_and</sup><sup>**x** ̄</sup><sup>_da_=</sup><sup>_a−_</sup> _d·_<sup>1</sup> � _i∈sd_<sup>_adi_</sup><sup>**x**</sup><sup>_di_arerespectively</sup> the weighted means of the response and the auxiliary variables, with weights given by _adi_ = _kdi_<sup>_−_2,for</sup><sup>_ad·_= �</sup> _i∈sd_<sup>_adi_,</sup><sup>_u_ ̃</sup><sup>_d_is the BLUP of</sup><sup>_ud_and</sup> _y_ ̃ _di_ is the BLUP of _ydi_ for _i ∈ rd_ , under the model (5.1).

Similar to the vector of response variables for area _d_ , **y** _d_ = ( _yd_ 1 _, . . . , ydNd_ )<sup>_′_</sup> , we construct the corresponding matrix of auxiliary variables for that same area, **X** _d_ = ( **x** _d_ 1 _, . . . ,_ **x** _dNd_ )<sup>_′_</sup> . Under the nested error model (5.1), it holds
medical expenses out of pocket.

# **3.** **_Nikkeijin_ Workers in Recent Years: Settlers with Family**

# **(1) From Temporary Guest Workers to Settlers**

When an increasing number of Brazilian _Nikkeijin_ began to come to Japan in the early 1990s, most of them were “ _dekasegi_ ” workers (i.e., temporary guest workers), who stayed in Japan just for a few years and went back to their home country with a fortune made in Japan. However, in recent years, the Brazilians have begun to settle in Japan, just as the Japanese emigrants did in Brazil about 100 years earlier. Figure 5 shows the result of a survey on _Nikkeijin_ ’s length of stay in Japan, which was conducted by Sangyo Koyo Antei Sentah (Industry Employment Security Center) in 2002. As the table indicates, almost 80 percent of _Nikkeijin_ workers stayed in Japan for more than three years. Twenty-eight percent of them stayed in Japan for more than 10 years. This is probably due to the fact that it was very difficult for _Nikkeijin dekasegi_ workers to quickly make a great amount of money under the stagnant Japanese economy. In addition, it was hard for them to find good jobs in Brazil after returning home due to the Brazilian stagnant economy. Although the long-term resident visa is valid only for three years initially, the Japanese government is generous to allow those Brazilians to apply for renewals. Unless they commit a crime, the renewal is almost automatic.

After two or three renewals, they can apply even for a permanent resident visa, which is extremely difficult to obtain for foreigners other than _Nikkeijin._ As a result, the number of permanent residents from Brazil has been rapidly increasing in recent years. Table 14 presents the change in the number of permanent residents by nationality since 2000. As indicated in the table, the overall number of permanent residents increased only by 18 percent from 657,605 in 2000 to 778,583 in 2004. However, the increase for Brazilians is remarkable. While there were only 9,062 permanent residents from Brazil in 2000, the number in 2004 (52,581) was almost six times larger. As a result, while the Brazilian _dekasegi_ workers used to predominantly be on a long-term resident visa in the early 1990s, the share of long term residents decreased to about half (52%) of the total Brazilians staying in Japan in 2002 (see Figure 6). Thirty-four percent of Brazilians entered Japan with a visa for spouse etc. of Japanese citizens, and 12 percent were staying in Japan with a permanent resident visa in that year.

As _Nikkeijin_ from Brazil stay longer in Japan, they bring their family to Japan. The male _dekasegi_ workers started to bring their wives and children from Brazil. Table 15 shows such a tendency. The table compares the number of Brazilians by age and gender in 2002 with those in 1992. The share of working-age Brazilians (15 to 64 years old) in 1992 was 91.5 percent, but it dropped to 84.4 percent by 2002. On the other hand, the share of children (0-14 years old) increased by seven percentage points from 8.3 percent in 1992 to 15.2 percent in 2002.

As discussed in detail below, the increase in spouses and children has generated various 18
POLICY **RESEARCH WORKING PAPER** 1758

# **Summary findings**

Evaluating the restructuring of large enterprises in transition economies is difficult because it is only one of many economic changes. Such evaluation is nevertheless essential for designing reform policies.

Djankov and Pohl examine 21 case studies of Slovak firms based on detailed financial information for 199196, and interviews with top management. Much of their sample was firms initially classified as "nonviable lossmakers." They found that the majority of large Slovak firms successfully restructured without the help of foreign investors or government restructuring programs.

Privatization to insiders, through managementemployee buyouts, did not hamper restructuring because the new owners (old managers) invested heavily in new technology, laid off a substantial part of the workforce, sought foreign partnerships, and were prepared to sell controlling stakes to outsiders in return for fresh financial resources.

The evidence also suggests that mass privatization did not result in weak corporate governance because it was followed by a rapid consolidation of ownership.

Their findings support the view that the main objective of privatization programs should be the speedy transformation of ownership, not the selection of perfect owners.

Slovakia was an interesting choice for case-study analysis because much of the heavy industry and arms industry of former Czechoslovakia was located in Slovakia, so it inherited a relatively unattractive industrial structure. Slovakia also implemented two very different privatization programs, one of mass privatization and one of leveraged management buyouts or direct sales to (domestic) outside investors.

- This paper a product of the Finance and Private Sector Development Division, Europe and Central Asia Technical - Department is part of a larger effort in the department to study the determinants of enterprise restructuring in transition economies. Copies of the paper are available free from the World Bank, 1818 H Street NW, Washington, DC 20433. Please contact Faten Hatab, room H8-087, telephone _202-473 -5 83 5,_ fax 202-477-8 772, Internet address fhatab@worldbank.org. April 1997. (28 pages)

_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 represen_ **_t_** _he view of the World Bank, its Executive Directors, or the countries they represent._ Produced by the Policy Research Dissemination Center
Policy Research Working Paper 7184

# **Abstract**

This paper examines wage dynamics and turnover using tracking data of adolescents from the Philippines. The analysis uses individual test scores in grade 6 to proxy abilities. The empirical results show that (i) returns to labor market experience are large, nearly a half of the conventional estimate of returns to schooling; (ii) returns to experience are higher if educational attainment and/or test scores are higher; and (iii) ability, measured by test scores, positively influences the upgrading of occupations toward more skilled categories, although educational attainment plays an important role in determining the first occupation. The complementarity between schooling and experience is greater among good performers who show high test scores; education and ability augment gains from accumulating labor market experiences.

This paper is a product of the Agriculture and Rural Development Team, Development Research Group and 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:// econ.worldbank.org. The author may be contacted at fyamauchi@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
# **A5. Information Available on Cost and Revenue**

**Table A.2.** Information availability for crop costs and revenues in LSMS-ISA surveys

|**Country**|**Item**|**Level**|
|---|---|---|
||(+) Total Production|Crop level|
||(-) Seeds|Crop level|
|**Ethiopia**|(-) Fertilizer<br>(-) Labor (post-planting)|Household level<br>Plot level|
||(-) Labor (post-harvest)|Crop level|
||(-) Other uses (e.g. Gift, self-consumption, etc.)|Crop level|
||(+) Total Production|Crop level|
||(-) Seeds|Plot level|
|**Malawi**|(-) Fertilizer|Plot level|
||(-) Labor|Plot level|
||(-) Other uses (e.g. Gift, self-consumption, etc.)|Crop level|
||(+) Total Production|Crop level|
||(-) Labor|Plot level|
|**Tanzania**|(-) Seeds|Crop level|
||(-) Fertilizer|Crop level|
||(-) Land rent|Plot level|
||(-) Machinery rent|Plot level|
||(+) Total Production|Crop level|
||(-) Transport cost, seeds and fertilizer|Plot level|
|**Uganda**|(-) Labor|Plot level|
||(-) Other cost, Machinery, extension services|Household level|
||(-) Other uses (e.g. Gift, self-consumption, etc.)|Crop level|
||(+) Total Production|Crop level|
||(-) Fertilizer|Plot level|
||(-) Seeds|Crop level|
|**Nigeria**|(-) Labor|Plot level|
||(-) Extension charges|Household level|
||(-) Other related cost|Household level|
||(-) Other uses (e.g. Gift, self-consumption, etc.)|Crop level|

_Note:_ Figures in LSMS-ISA surveys are provided in local monetary units. ( + ) = Revenues; (-) = Cost. “Other uses” refers to instances where one crop is used in the production of another crop such as, for example, use of a tree for fertilizer for an annual food crop.

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Finally, in the absence of credit markets, trade is balanced location by location, so that local nominal income equals total expenditure from all locations _s_ on goods produced in a location _r_ . Formally, that implies: where wt(r) and wt(r)Ht(r)L<sup>�</sup> t(r) stand for the nominal wages and income in location _r_ , respectively. Desmet et al. (2018) show that a dynamic equilibrium of the economy exists and is a sequence of static equilibria where, in each time period _t_ , markets for goods and labor clear. That requires equations (1) and (2) to simultaneously hold in each period _t_ .

# _2.2 Data and parameter calibration: Quantifying spatial frictions_

A path-breaking contribution of the presented framework is its ability to quantify spatial frictions in the world economy using real world data. In this application, the Earth’s surface is divided into about 17,000 1<sup>∘</sup> × 1<sup>∘</sup> grid cells. By using global data at this unit of disaggregation for several characteristics, one can use the model’s structure to back out several parameters of the model, including the two mobility parameters of key importance to our investigation – the trade frictions, ζ(s, r), and the migration entry costs, m2(r). In what follows, we explain the conceptual idea behind the estimation of the mobility frictions in Desmet et al. (2018).

Trade costs, ζ(s, r), are estimated using a rich transportation infrastructure data covering the entire Earth’s surface and capturing different modal types of transportation as well as geographical characteristics. Combined with the Fast-Marching algorithm in Allen and Arkolakis (2014), these data allow us to estimate a bilateral matrix, ζ(s, r), containing about 300 million entries for _s_ - _r_ location pairs (17,000x17,000). We estimate trade frictions only for 2000, because the sources of transportation and geographic data are only available for that year.

Migration entry costs, m2(r), are quantified for each location _r_ using a calibration strategy. It starts with the estimation of the fundamental amenities (a�(r)) to initial level of utility (u0(r)) ratio (a�(r)/u0(r)) for each location _r_ of the model in a way that allows the model-implied economic activity 9
|||US|Japan|EU 15|CANZ|
|---|---|---|---|---|---|
|Relative case|Exponential ft|52%|70%|85%|78%|
||Power-law ft|54%|69%|81%|60%|
|Absolute case|Exponential ft|49%|80%|79%|97%|
||Power-law ft|51%|74%|87%|62%|

Table 6: Values for the MAC uncertainty _σC_ , as derived from Ghersi (2003). Taking for each _−_ country/region the lower estimate leads to an overall average of 66%, and a range of 49% 81%, valid for both the absolute and relative case.

(e.g. Metz et al., 2001, Ghersi, 2003) report only data points and not functional forms. Third, most available studies of abatement costs focus on developed countries only.

To get some insights into _σC_ , we use a compilation of modeling results from Ghersi (2003), who reports two-point estimates for marginal abatement costs of 13 different models, both in terms of absolute and relative reductions. The marginal abatement costs are valid in 2010 for decisions made in 2000. For our study, we consider all these to represent random draws of the true cost function, and perform a least-square fit with an exponential and power-law function. Our estimate of _σC_ is then the normalized standard deviation of the residuals, as reported in Table 6. Typical values are around 0 _._ 5 for the US, but significantly higher, between 0 _._ 6 _−_ 0 _._ 9, for other industrialized countries. Because of insufficient data, we cannot provide estimates for developing countries, but it seems reasonable—also for the very fact that no data is available—to assume even higher values for these less researched economies.

# **6 Intensity vs. Quota in a Real-World Setting**

We now test whether an intensity target performs better than a cap vis-`a-vis uncertainty on key policy variables, using the analytical conditions derived in Section 4 and the empirical values in Section 5.

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and almost nonexistent for education. One way of interpreting the differences depending on dimensions could be that people suffering from housing and basic needs deprivations feel that there are living a poorer life. Although there is need to analyze this relationship further to draw sound conclusions statements, one can expect that, e.g., living in a damaged house or receiving humanitarian assistance, is more evidently associated with “feeling poor.”

**Figure 26, Percent of Armenians Who Consider Themselves Deprived in Each Dimension, 2015**

<!-- Start of picture text --> 70%<br>60%<br>50%<br>40%<br>30%<br>20%<br>10%<br>0%<br>Basic Needs Housing Education Labor Health Aggregate MPI<br>Very Poor Poor Below Middle Middle Over Middle<br><!-- End of picture text --> _Source:_ ILCS data _._ _Note:_ Only nine households considered themselves to be rich in 2015 _._

# 6. Conclusion

An MPI is a useful complement to the monetary measure used to monitor deprivations in Armenia. The absolute number of individuals living in households deprived in each dimension of nonmonetary poverty is large: e.g., almost 1.2 million experience deprivations in the labor dimension—the biggest contributor to multidimensional poverty in Armenia. The second most important deprivation is housing, where more than 1 million individuals report multiple deprivations. For health 0.7 million and for education 0.05 million individuals suffer from overlapping nonmonetary deprivations. More than 0.18 million individuals in Armenia fall below the national food poverty line, are vulnerable to major shocks such as a decline in remittances, or report that either in absolute or relative terms they are materially deprived.

In 2015 12.83 percent of Armenians is both multidimensionally poor and lives below the upper national poverty line<sup>10</sup> (Figure 25). For this group, a better understanding of what underlies the persistence of poverty can enable policy makers to target interventions to the specific deprivations these households experience. A second group of households is multidimensionally poor but lives above the upper national poverty line (16.27 percent); deprivations make them more vulnerable to shocks and decrease their quality of life. The third group does not experience multiple deprivations but currently lives below the upper national poverty

> 10 The upper national poverty line is officially used to measure consumption poverty in Armenia. In 2014 the corresponding threshold was 40,264 Armenia drams per adult equivalent per month.

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<!-- Start of picture text --> El Salvador<br>"caro"<br>filtered<br>80 original<br>60<br>40<br>20<br>0<br>2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014<br><!-- End of picture text --> Figure 6: Smoothed results for the average of the “caro” keyword in El Salvador. The smoothed series is computed using the Christiano-Fitzgerald filter with all stochastic cycles at a periodicity lower than 3 months and higher than 12 months removed.

14
_Ingo Borchert and Mattia Di Ubaldo_ lends further credibility to the results and shows that estimated effects are not randomly distributed across observable policy patterns.

Table 4: Trade Effect of PTA Policy Clusters, 2000-16

||(1)|(2)|(3)|(4)|(5)|(6)|
|---|---|---|---|---|---|---|
|Struct(most ambitious)|0.4681<br>∗∗|0.5531<br>∗∗|∗||||
|Struct(very ambitious)|0.1910<br>∗|0.0327|||||
|Struct(not ambitious)|0.0037|-0.0002|||||
|Struct(least ambitious)|0.0021|0.0000|||||
|RoO(liberal M3 M4)|||0.5031<br>∗∗|∗<br>0.3545<br>∗∗|||
|RoO(move profs)|||0.4347<br>∗|0.5627<br>∗∗|∗||
|RoO(most frequent)|||-0.0057|0.0321|||
|RoO(2nd most freq)|||0.0023|0.0001|||
|Account(most ambitious)|||||0.1469<br>∗∗|∗<br>0.1022<br>∗∗|
|Account(very ambitious)|||||0.0079|-0.0006|
|Account(pub requirement)|||||0.0622<sup>∗∗</sup>|0.0580<sup>∗∗</sup>|
|Account(least ambitious)|||||0.0034|-0.0001|
|EU Pair|0.2475<sup>∗</sup>|0.3119|0.2479<sup>∗</sup>|0.3123|0.2474<sup>∗</sup>|0.3121|
|Observations|235,876|203,732|235,876|203,732|235,876|203,732|
|Exporter-year FE|Yes|Yes|Yes|Yes|Yes|Yes|
|Importer-year FE|Yes|Yes|Yes|Yes|Yes|Yes|
|Country-pair FE|Yes|Yes|Yes|Yes|Yes|Yes|

_Source_ : Authors’ estimations using WB-DTA 2.0 and ITPD-E data. Dependent variable: bilateral services trade _Xijt_ . PPML estimation with three-way clustered standard errors. Other policy patterns controlled for but coefficients not reported. Columns 1, 3, 5 include all observations whereas columns 2, 4, 6 restrict the sample to PTA pairs.

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