rafmacalaba/datause-encoder-data huggingface dataset:
There are two main reasons that justify the adoption of the privatization model followed by the Argentine government. First, the need to obtain fiscal resources. By selling a firm with a significant market share in a highly concentrated market, with control of key access infrastructure (ports, pipelines), the price that can be obtained in a public auction is much higher than the price that results from selling a much smaller firm that faces the prospects of a very competitive market. Second, in order to gain political support for the privatization, the government decided to privatize YPF following a "national champion" firm model with significant presence in the market, imposing limits to the percentage of stocks each private economic group could hold and keeping a golden share stock -that provided the government with veto power over strategic business decisions. In this way, YPF would keep being a "national pride".

As in all complex privatization processes, the actual objectives are extremely difficult to identify. However, after six years of the privatization, and despite facing complaints about the intensity of competition in the gasoline market, the Argentine government decided to trade off its stake and golden share in YPF for fresh fiscal resources offered by Repsol. The govemment could have taken this opportunity to force YPF to sell some key assets and Jay down the conditions to foster a more competitive gasoline market; instead, it preferred more fiscal resources.

As a general rule, governments should take advantage of the opportunity that a privatization or deregulation process provides to make structural changes in the industry, shaping a more competitive market structure. If they miss this opportunity, imposing structural remedies through the use of competition laws is extremely more difficult. The policy recommendations advocated by the SDCyC for the gasoline market were aimed at reducing strategic barriers to entry, hoping that more effective competition would prevail by new entry. Reshaping market structure by forcing disinvestments of key assets, although a desirable objective for the SDCyC, was an objective impossible to reach given the respect of firns' property rights. The only way that the Argentine Antitrust Agency can force the disinvestments of assets is by proving that firms in the market (one, or a combination of them) implemented anticompetitive conducts. Although the Argentine Antitrust Commission analyzed many filings for anticompetitive conducts in the gasoline 13
cantly, and the incidence of poverty increases by 0.7 percentage point toward the end of the adjustment period, the poverty gap changes only in the short run, but is almost unaffected in the long run. For unskilled workers, both measures of poverty indicate an improvement in the longer run, especially for those workers involved in the informal sector. However, for unskilled workers in the formal sector, poverty increases on impact–by about 0.6 percentage point. There is therefore a potential short-run trade-off emerging between unemployment and poverty: although the reduction in the minimum wage lowers open unskilled unemployment in the formal sector, it also increases poverty for that category of households.<sup>14</sup> For skilled workers in the formal sector, poverty tends to increase slightly, in both the short and the long run by approximately 1 percentage point. Overall, therefore, poverty increases in rural areas and decreases in urban areas, resulting in a slight decrease in poverty at the economy-wide level. Changes in the income-based Gini coefficient indicate that income distribution is affected only modestly by a cut in the minimum wage; the degree of inequality falls by only a small amount in the long run. This effect is directly related to the sharp reduction in disposable income experienced by skilled workers and capitalists and rentiers, relative to other household groups.

Let us now examine in more detail the differences concerning the impact on poverty and inequality as measured by the three approaches to micromacro linkages presented in section 3. With the simple micro-accounting method (in which if changes in disposable income are feed into the household survey without taking into account changes in the employment structure and thus in the size of relative groups), poverty is overestimated on impact compared to the more elaborate approach based on reweighting techniques, but as we can see from Table 2 this differences becomes smaller over the adjustment period. This holds regardless of whether we look at the headcount ratio or the poverty gap. The difference represents between 0.10 and 0.25 percentage points, which may not seem that large; however, it is equivalent to up to 20 percent of the total change in poverty relative to the initial period. In terms of absolute deviations from the baseline, the discrepancy is smaller, because the baseline projection is affected by a bias in the same direction, but, likewise, in relative terms, it is not negligible. Figure 4 traces the absolute deviations from the baseline measured by the simple micro-accounting method relative to the absolute deviations from the baseline measured by the 14See Agénor (2003b) for a more detailed discussion of unemployment-poverty trade-offs.

24
mostly by profit or perks maximization—they seem to be “working for God.” At the same time however faith-based schools may also suffer from weaknesses, especially if they place the pursuit of their religious mandate ahead of the needs of students in regard to what they need to learn to be successful in today’s world.

In this paper, we assess the performance of Fe y Alegría secondary schools in Colombia using a rich multi-year data set with test scores on a wide range of subjects as well as detailed information on the characteristics of the household to which students belong. Fe y Alegría is a catholic education network founded in 1955, serving almost one million students in 17 countries<sup>2</sup> . It targets excluded population and its work program, according to the organization’s mission, is based on the Christian values of justice, participation, and solidarity. The network started operating in Colombia in 1971, and in 2006 it served more than 72,000 students in 61 schools<sup>3</sup> . Even though academic excellence is not explicitly mentioned in its mission or vision, it is one of the variables Fe y Alegría principals and leaders use for their internal evaluations.

Most observers consider Fe y Alegría to be successful, but few rigorous evaluations have been undertaken until now. This paper was inspired by previous research on the performance of Fe y Alegría in Venezuela conducted by Allcott and Ortega (2009) who use propensity score matching methods to estimate the effects on standardized test scores of graduating from Fe y Alegría in comparison with public schools. They find an average treatment effect on the order of 0.1 standard deviation in mathematics, which is small, but nevertheless statistically significant. They argue that the better performance of Fe y Alegría stems not only from its labor contract flexibility and decentralized administrative structure, but also from the peculiar “family culture” of the schools.

> 2 In chronological order of start of operations: Venezuela, Ecuador, Panama, Peru, Bolivia, El Salvador, Colombia, Nicaragua, Guatemala, Brazil, Dominican Republic, Paraguay, Argentina, Honduras, Chile, Haiti, Spain, and Chad. There are no Fe y Alegría schools in Spain.

> 3 Only formal education is included.
|15-24|6.7|16.5|40.9|33.5|1.0|1.4|
|---|---|---|---|---|---|---|
|25-34|17.4|31.0|21.9|19.6|2.4|7.7|
|35-44|17.6|27.7|33.8|16.1|1.1|3.7|
|45-54|19.8|23.7|34.8|15.8|1.5|4.3|
|55-64|30.6|23.8|26.9|12.4|1.0|5.4|
|65+|64.1|19.4|8.8|4.8|0.5|2.4|
|All|21.1|23.4|30.6|19.7|1.3|3.9|

_Source: VHLSS 2008_ But what do these numbers mean for labor market performance? Looking at this issue from the perspective of employers helps locate in which sectors of the economy the skills shortages are likely to be. The existing data provides limited information as to the mismatches that may exist between skills supply and demand in Vietnam. Looking at this issue from the employers’ side, the evidence shows that overall, about 9 percent of firms report suffering from a shortage of skilled labor.<sup>3</sup> This number is somewhat higher than other lower-middle-income countries (Table 6), but it is not particularly high, especially when compared to low-income countries such as Cambodia or Lao PDR.

**Table 6: % of Firms Identifying Labor Skill Level as a Major Constraint**

||All firms|Apparel<br>sector|Exporter|Non-<br>exporter|Small<br>firms|Medium<br>firms|Large<br>firms|Domestic<br>firms|Foreign<br>firms|
|---|---|---|---|---|---|---|---|---|---|
|Cambodia|15.5|-|10.3|15.6|12.8|22.0|18.1|13.4|22.3|
|Indonesia|4.5|4.2|2.4|4.5|4.5|3.7|6.3|4.3|12.9|
|Lao PDR|18.7|**-**|19.1|18.7|10.3|46.9|46.3|16.7|52.4|
|Philippines|7.8|28.7|2.4|8.2|10.0|5.3|5.5|8.4|4.1|
|Vietnam|8.9|11.9|5.5|9.3|5.6|11.1|12.3|7.9|18.3|

_Source: World Bank Enterprise Surveys (Indonesia, Lao PDR, Philippines, Vietnam 2009; Cambodia 2007)_ Looking beyond the national level, it becomes apparent that a significant proportion of foreign firms, as well as medium and large firms, report being constrained by shortage of skills in the labor force. It is important to look beyond the national level, as certain types of firms are more likely than others to be constrained by skills shortages. These results show that firms that tend to use more advanced technologies, and therefore are in need of more skilled workers, are facing greater constraints in terms of the availability of skills. This tends to be true in most Asian comparator countries, except the Philippines (Table 6). Compared with the other countries, the Philippines has an English-speaking workforce, and has had a policy of training its labor force to meet international demand

> 3 These numbers are obtained from World Bank Employer Surveys. An Enterprise Survey is a firm-level survey of a representative sample of an economy’s private sector. The surveys cover a broad range of business environment topics including access to finance, corruption, infrastructure, crime, competition, and performance measures.

11
17 The spatial model only captures time-invariant geopolitical shocks. There could however be time-varying geopolitical conditions capturing the corresponding risks. Does an increase in geopolitical risk in one country spillover over to growth in other countries? To study the issue, we rely on the measure of Caldara and Iacoviello (2017). In the paper, the authors construct geopolitical risk indices based on textual analysis of newspaper articles. We obtain the EM geopolitical shock by averaging the eight EM countries in their sample. As shown in Figure 15, the results point to geopolitical risks having contractionary effects on the own bloc, but the spillover effects being relatively small.
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_Proof._ See Appendix A5.

■ We emphasize two interesting features of Theorem 1 that relate to the global line. First, our axioms imply the use of a _strongly_ relative global line only when the sensitivity to relative income has no upper bound. Indeed, when _σ_ ̄ _→∞_ , we have _R →_ 0 and _Rσ_ ̄ _→ za/y_<sup>_~~z~~_</sup> , and thus the relative part of _z_<sup>_∗_</sup> tends to a strongly relative line, which points to the origin. Hence, on _U_<sup>_σ_ ̄</sup> , the global line is weakly relative when this sensitivity is bounded above ( _σ_ ̄ _∈_ R++), but strongly relative when this sensitivity has no upper bound ( _σ_ ̄ _→∞_ ). Second, under heterogeneous preferences, the global line need not correspond to an indifference curve. Indeed, there is no preference in _U_<sup>_σ_ ̄</sup> that has an indifference curve corresponding to the global line _z_<sup>_∗_</sup> . This means that there is no individual for whom the global line provides the same utility in (all) different societies. Instead, the global line defines an ‘objective’ income poverty status, which is attached to any individual whose income is smaller than the global line.

Observe that the trade-offs between own income and relative income made by index _HHS_ under the global line _z_<sup>_∗_</sup> correspond to those characterized in Theorem 1. Above the subsistence income, the poverty score function of index _HHS_ has the same iso-poverty-score curves.<sup>37</sup> Below the subsistence income, the poverty score function of index _HHS_ does not depend on relative income.<sup>38</sup> Interestingly, Decerf (2017) proposes a family of hierarchical indices whose isopoverty-score curves satisfy the constraints derived in Theorem 1. For a given global line _z_ , index _P_<sup>_H_</sup> is defined by the following poverty score function: where _α >_ 0 and _λ ∈_ (0 _,_ 1). The hierarchical headcount ratio corresponds to the index that would be obtained when setting _α_ = 1 and _λ_ = 0, the latter value being on the frontier of acceptable values.

> 37On _Xz∗ \XA_ , we have that _<u>y</u>_<sup>_<u>y</u>_</sup> _<u>−</u>_<sup>_−_</sup> _<u>y</u>_<sup>_z_</sup> _<u>a</u>_<sup>_~~<u>z</u>~~_=</sup> _<u>y</u>_<sup>_<u>y</u>_</sup><sup>_~~<u>′</u>~~′_</sup> _<u>−</u>_<sup>_−_</sup> _<u>y</u>_<sup>_za_</sup><sup>_~~<u>z</u>~~⇔_</sup> _zz_<sup>_∗∗_</sup> (( _<u>yy</u>_ <u>))</u> _−−zya_<sup>=</sup> _z_<sup>_z∗∗_</sup> (<sup><u>(</u></sup> _<u>yy</u>_<sup>_~~<u>′′</u>~~_</sup> )) _−−zya_<sup>_′_.</sup>

> 38Below _za_ , _HHS_ attributes a poverty score equal to one regardless of own income, which is thus not strictly decreasing in own income as required by Theorem 1.

31
average, _Jefes_ participants increased their hours of work by about 10 hours. In this respect, _SD_ and _DD_ results are similar. The results overall are suggestive of foregone income in that the net increase in hours worked is about half the _Jefes_ stipulated work requirement of 20 hours.<sup>10</sup> It is clear from these results that _Jefes_ did not just displace unemployment. Indeed, roughly as many participants came from those who would otherwise not have been active in the workforce. This implies that assuming that all _Jefes_ participants would have been otherwise unemployed would grossly overestimate the impact of the program on the rate of unemployment.

We can now use our estimates of the program’s impact on labor-market status to estimate the impact on the unemployment rate. Our preferred _SD_ estimates from Table 9 imply that 26% of Jefes participants would have been unemployed if not for the program, while 23% would have been inactive. Table 10 gives the counterfactual unemployment rates (as well as the activity and employment rates). We also compare these to INDEC’s (2002a) estimates that had assumed that all _Jefes_ participants would have been unemployed without the program.

It can be seen that allowing for the behavioral responses implied by our results gives an appreciably lower impact on the unemployment rate. While INDEC’s calculation implies a 5.8 percentage point drop in the unemployment rate due to the program, we get an impact of 2.5 points. In contrast to the claim by a number of observers (including INDEC and World Bank, 2003), _Jefes_ was not responsible for bringing down the unemployment rate in the aftermath of the crisis; we find that the unemployment rate would have fallen between May 2002 and October 2002 even without the program.

> 10 We found that 78% of _Jefes_ participants doing the work requirement reported exactly the legally required number of hours (20). This may reflect the fact that municipalities, in order to generate work for a large number of participants, employed them for the minimum number of hours. It is also possible that some participants over-reported their number of hours worked to accord with the legal requirements.

20
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latter would su¢ ce to produce at least some correlation between an exchange rate and the fundamentals that would guide debt managers in their decisions. What we see as a main problem is the use of relatively high frequency data and the actual exchange rate for estimation of the covariances between the model variables, here exchange rates and economic fundamentals, of the theoretical framework. This is due to the fact that the exchange rate observations carry substantial amount of noise<sup>19</sup> that blurs the information one strives to retrieve from the covariances. To illustrate this point consider the following example. Assume that the DGP for the exchange rate, st; is e where st is the fundamental (equilibrium) value of the exchange rate given by where �t and �<sup>�</sup> t<sup>arethedomesticandtheforeignin‡ation,respectively,following</sup> simple AR(1) processes, i.e.

Finally assume that the domestic output growth is generated as i.e. by a combination of the supply and demand shocks. We set var("AS;t) = var("IS;t) = 1; var("<sup>�</sup> AS;t<sup>)=0:5; var("s;t)=10usingthecommonknowledgethat</sup> ‡exible exchange rates are usually more volatile than other economic fundamentals; further, � = �<sup>�</sup> = 0:8 and �y = 0:6. We generate 10; 000 observations of the variables

> 19In the short run the trading in the exchange rate markets is not based on currencies’fundamentals and is driven largely by trends in the trade set by chartist. Fundamentalists come to trade only if there is a sizable adjustment needed to bring the exchange rate to its fundamental value see de Grauwe and Grimaldi (2005).

21
Table 5: Effect of registration with the NREGS on risk index of crop <u>portfolio</u>

||(1)|(2)|(3)|(4)|(5)|
|---|---|---|---|---|---|
|NREGS registered (2007)|0.019<sup>+</sup>|0.022<sup>_∗∗∗_</sup>|0.034<sup>_∗∗_</sup>|0.026<sup>_∗_</sup>||
||(0.010)|(0.006)|(0.010)|(0.010)||
|NREGS registered (2009/10)|||||0.007<br>(0.006)|
|Rainfall (deviation) at baseline||0.175<sup>_∗∗_</sup>|0.194<sup>_∗∗∗_</sup>|0.205<sup>_∗∗_</sup>|0.227<sup>_∗∗∗_</sup>|
|||(0.054)|(0.055)|(0.061)|(0.056)|
|Risk index at baseline||-0.635<sup>_∗∗∗_</sup>|-0.572<sup>_∗∗∗_</sup>|-0.477<sup>_∗∗∗_</sup>|-0.500<sup>_∗∗∗_</sup>|
|||(0.040)|(0.058)|(0.073)|(0.061)|
|Risk index _×_ Rainfall (deviation)||-0.134|-0.276<sup>_∗_</sup>|-0.373<sup>_∗_</sup>|-0.342<sup>_∗_</sup>|
|||(0.096)|(0.128)|(0.146)|(0.136)|
|Observations|1087|1087|838|1087|1087|
|_R_<sup>2</sup>|0.058|0.402|0.459|0.387|0.395|

Notes: Estimation in FGLS (col. (4) in OLS). Dep. var.: Risk index of a household’s crop portfolio. Cols. (1), (2) & (5) present results for the full sample without matching. Col. (3) restricts the sample to households who have registered with the NREGS by 2009/10. Col. (4) matches households based on baseline characteristics. Additional controls are variable inputs (log), area cultivated (log), share of area under irrigation, fertilizer application, HYV seeds application, labor sharing, annual income from off-farm activities (log), if household benefits from credit/training program, rainfall (deviation), rainfall (deviation, lag), self-reported shocks, and time trend. Standard errors (clustered at the sub-district) in parentheses.<sup>+</sup> _p <_ 0 _._ 10,<sup>_∗_</sup> _p <_ 0 _._ 05,<sup>_∗∗_</sup> _p <_ 0 _._ 01,<sup>_∗∗∗_</sup> _p <_ 0 _._ 001.

40
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54

# Table III.5

Source of Contraceptives, by Residence and Age, for Selected Countries

|Country<br>Location<br>Age|Contraceptive<br>Prevalence|Government<br>(Percent)|Source of Con<br> <br>Commercialb<br> <br>(Percent)|traceptive<br> <br>NGO<br>(Percent|sa<br>Otherc<br>) (Percen|
|---|---|---|---|---|---|
|Colombia (1986)||||||
|Urban||||||
|15-24|34.4|19.1|54.1|26.1|0.6|
|<br>25-34|<br>57.8|<br>23.2|51.4|24.4|1.0|
|<br>35+|40.6|37.4|32.0|28.1|2.5|
|Rural||||||
|15-24|39.4|40.3|54.8|3.2|1.6|
|<br>25-34|31.7|55.7|33.0|9.1|2.3|
|35+|39.4|73.0|15.7|11.2|0.0|
|Egypt (1984)||||||
|Urban||||||
|15-24|23.7|20.3|79.7|0.0|0.0|
|<br>25-34|49.0|21.4|75.7|2.1|0.8|
|35+|43.7|29.1|67.5|1.9|1.5|
|Rural||||||
|15-24|7.5|28.7|69.6|0.9|0.9|
|25-34|22.6|30.5|67.9|0.5|1.2|
|35.|23.0|39.6|58.7|0.5|1.2|
|Morocco (1984)||||||
|Urban||||||
|15-24|31.1|55.1|44.0|--|0.9|
|25-34|39.2|46.9|51.7|--|1.5|
|35+|29.0|54.2|44.0|--|1.9|
|Rural||||||
|15-24|10.1|55.0|40.6|--|4.4|
|25-34|16.2|71.9|26.8|--|1.4|
|35+|13.7|73.0|24.5|--|2.4|
|Thailand (1984)||||||
|Urban||||||
|15-24|50.7|43.8|52.4|0.0|3.8|
|25-34|60.9|59.8|35.4|1.0|3.9|
|<br>35+|63.7|81.2|17.3|0.7|0.7|
|Rural||||||
|15-24|40.6|73.7|22.3|1.1|3.0|
|25-34|61.2|81.7|16.8|0.8|0.7|
|35+|50.6|83.0|15.3|0.3|1.4|

# Source: CPS; DHS

- a. Sample is currently married vomen.

- b. Includes private physicians, hospitals, pharmacies, and any other private, non-NGO.

- c. Unspecified source, may encompass NGOs when private,<sup>nonprofits are not a</sup> category, and may include commercial vhere it is not a separate<sup>category.</sup> Other also includes don't know and not stated.