rafmacalaba/datause-encoder-data huggingface dataset:
**<u>Table 1b: Cell characteristics</u>**

||N|mean|Min|max|
|---|---|---|---|---|
|Cell size|4,293|3.25|1|15|
|nb of children 0 - 4 y.o. in cell|4,293|0.49|0|4|
|nb of children 5 - 14 y.o. in cell|4,293|0.86|0|7|
|nb of children less than 15 y.o. in cell|4,293|1.36|0|8|
|nb of elderly over 66 y.o. in cell|4,293|0.12|0|3|
|cell with 2 adults (15+) or more %|4,293|46.69|0|100|
|cell with 2 adults (21+) or more %|4,293|31.02|0|100|
|cell without any children %|4,293|43.69|0|100|
|cell with mother + children only %|4,293|47.44|0|100|
|cell head is a woman %|4,293|56.81|0|100|
|Excl. household head|2,531|81.19|0|100|
|cell head is the hh head %|4,293|40.23|0|100|
|cell head is a male hh head%|4,293|31.95|0|100|
|cell head is the spouse of hh head %|4,293|35.43|0|100|
|cell head is a child of hh head %|4,293|8.86|0|100|
|cell head is son/daughter in law of hh head %|4,293|2.15|0|100|
|cell head is a sibling of hh head %|4,293|4.46|0|100|
|cell head is a remote parent or non related %|4,293|5.48|0|100|
|cell head is polygamous man %|4,293|10.55|0|100|
|Excl. household head|2,531|1.83|0|100|
|cell head is polygamous woman %|4,293|22.18|0|100|
|Excl. household head|2,531|33.11|0|100|
|cell head has no formal schooling %|4,293|67.00|0|100|
|Excl. household head|2,531|68.57|0|100|
|age of cell head|4,292|42.18|0|98|
|Excl.householdhead|2,530|37.03|0|98|

Source: “Pauvreté et Structure Familiale” (PSF) survey, authors’ calculations using sampling weights. Note: Summary statistics of cells’ characteristics.

37
Policy Research Working Paper 8344

# **Abstract**

How do labor income shocks affect household investment in upper secondary and tertiary schooling? Using longitudinal data from 2005–15 for Argentina, Brazil, and Mexico, this paper explores the effect of a negative household income shock on the enrollment status of youth ages 15 to 25. The findings suggest that negative income shocks significantly increase the relative risk of students exiting upper secondary and tertiary education in Argentina and Brazil, but not in Mexico. For the three countries, the analysis finds evidence that youth who exit school due to a household income shock have worse employment outcomes than similar youth who exit without a household income shock. Differences in labor markets and safety net programs likely play an important role in the decision to exit school as well as the employment outcomes of those who exit across these three countries.

This paper is a product of the Poverty and Equity 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 authors may be contacted at lsousa@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
winners (which is often recommended to reformers in practice; cf. Roland 2000, 49), Assumption 1 materializes.

Second, in light of our application to privatization, Assumption 1 can also result from the fact that better firms tend to find buyers more rapidly (Roland, 2000, 248). This point has also been recognized by policy makers. According to Egyptian government officials in the _New York Times_ of June 27, 2010, Egypt suspended its privatization program in 2009 because "most of the likely candidates had already been either privatized or dissolved, leaving hardto-sell industries that were technologically outdated and overstaffed with ill-trained workers".

In the Appendix, we explore the alternative case in which the government is not able to identify winners and losers in advance. That case is probably more relevant to trade reform because this type of reform does not come with a natural selection process, and it is not clear that the government knows the identity of the winners and losers up front in that setting. Then, reform outcomes are sampled randomly from the true underlying distribution. Crucially, the Appendix shows that the "sampling without replacement" effect continues to be present under random sampling. This leads to two regions in the ( _α,β_ ) space where the dynamics are anomalous (i.e., favorable reform outcomes decreasing support for reform and vice versa). More generally, the importance of the "sampling without replacement" effect is increasing in the tightness of the prior belief on γ: the tighter the prior on γ is, the less responsive voters' beliefs on _γ_ are to news and the more dominant the "sampling without replacement" effect becomes (because the latter works independently of the tightness of _γ_ 's prior). In the limit, because the prior on _γ_ converges on a point, the model collapses to the one discussed in the previous subsection (without aggregate uncertainty).

Turning to the setting in which Assumption 1 holds, it is instructive to first think through what would happen if voters do _not_ account for the selection bias and hold a diffuse prior belief on _γ_ at the start of the reform (call this "time 0"). In particular, let us assume that both _α_ 0 and _β_ 0 (the fractions of winners and losers whose identities are clear ex ante) are close to zero (which minimizes the tightness of the prior). Then, Bayes' rule implies that voters’ beliefs about _γ_ are revised upward when winners are revealed (∂γ�t/∂αt = βt/(αt + βt)<sup>2</sup> > 0). In particular, voters’ beliefs about _γ_ will quickly converge to 1 because voters only observe favorable reform outcomes and erroneously think that this is the result of random ∗ sampling from the underlying true distribution of winners and losers. This implies that αt → 1 (cf. equation (8)), which allows the reforming government to reveal that the median voter is 15
variation in control structures across those types of companies. Third, our sample for the Philippines has only 120 companies, and consequently it was not possible to make any of the groups larger as they currently cover all Philippine firmns.

Size appears to matter significantly in explaining the distribution of control across ownership classes. Table 3 provides the comparisons within each country at the 20% cutoff level (Tables Al-A3 also show the descriptive statistics for the other cut-off levels). In most countries, the share of family ownership increases for smaller-size firms. This pattern is especially strong in Japan, where only one of the largest twenty is in family control, while 57% of the smallest 50 companies are controlled by families. The same dramatic increase in family control is observed in the Korean sample where only four of the largest twenty companies are family-controlled, while forty-eight of the smallest fifty companies fall into that category. The magnitude of the increase of family control in smaller-size companies is similar in Taiwan (from 15% to 80%). In Indonesia, Malaysia, the Philippines, Singapore, and Thailand the same pattern is present although it is not as strong, as many large companies are also controlled by families. The exception is Hong Kong, where about three-fourths of the largest twenty companies are under familycontrol, while less than 60% of the smallest fifty companies are in the same category.

Consistent with the results in Table 2, these statistics also show that the majority of large and medium-size Japanese and Korean corporations are widely-held. All bottom fifty companies in both countries have, however, ultimate owners. In contrast, there is much less variation of control structures across company size in the Philippines, although this result may be driven by the smaller sample in that country. In all other countries, widely-held corporations are the exception, particularly so for small corporations, but also for large and medium-size corporations.

It is useful to compare our results for the largest twenty companies with La Porta, Lopez-de-Silanes, and Shleifer (1998). We focus on Hong Kong, Japan, Korea, and Singapore-the East Asian countries reported in both studies. We obtain identical results for Japan and Singapore, while the differences in Hong Kong and Korea are within -ive percent of ownership (note also that they report ownership data for either 1995 and 1996, while we use exclusively 1996 data, which could explain some of the differences). This implies that the data on corporate ownership across East Asia are fairly robust.

_15_
occupations in the job postings data and use the skills profiles of those occupations as a proxy for the skills profile of missing occupations.<sup>18</sup>

# _Description of the occupational skills profiles_

The average number of, and types of, skills listed in (or imputed from) the job vacancy data differs by occupation. Occupations require an average of 6 skills, ranging from 2.2 to 12 skills (Table 4). Heavy truck and bus drivers require the fewest skills (2.2) while software and applications developers and analysts are at the other end of the distribution requiring 12 defined skills. On average, occupations require one digital skill, less than one cognitive and socioemotional skill each, and more than three other skills, which include languages and other technical skills, highlighting the high degree of jobspecific skills.

**Table 4. Count and share of requirements for skills categories and their subsets across the 127 occupations**

||**Variable**|**Mean**|**Std dev**|**Minimum**|**Maximum**|
|---|---|---|---|---|---|
|**Average number of skills**|**Total**|5.6|1.8|2.2|12.0|
|**(count)**|**Digital**|1.0|1.1|0.0|7.6|
||Advanced|0.3|0.8|0.0|6.4|
||Intermediate|0.3|0.4|0.0|2.5|
||Basic|0.4|0.2|0.0|1.9|
||**Cognitive**|0.7|0.3|0.2|1.3|
||Thinking|0.1|0.1|0.0|0.3|
||Communication|0.3|0.1|0.1|0.6|
||Organization|0.3|0.1|0.1|0.6|
||**Socioemotional**|0.5|0.2|0.1|1.2|
||Emotions|0.0|0.0|0.0|0.2|
||Relationships|0.3|0.2|0.1|1.0|
||Personalgrowth|0.1|0.1|0.0|0.6|
||**Others**|3.4|0.9|1.5|6.5|
|**Average share among total**|**Digital**|12.7|10.6|1.0|58.1|
|**skills**|Advanced|3.2|6.4|0.0|47.5|
||Intermediate|3.9|4.8|0.0|28.5|
||Basic|5.6|5.2|0.3|51.0|
||**Cognitive**|10.9|2.9|3.6|18.5|
||Thinking|1.2|0.7|0.1|3.9|
||Communication|5.3|1.9|1.0|10.3|
||Organization|4.2|1.5|1.2|9.2|
||**Socioemotional**|8.3|5.0|2.1|42.2|
||Emotions|0.5|0.5|0.0|4.3|
||Relationships|5.8|4.5|1.3|38.2|
||Personal growth|2.0|1.5|0.3|8.6|
||**Others**|68.2|10.6|28.6|84.3|

> 18 We use two sources to define similar occupations. ILO (2012) lists related occupations in ISCO-08. The O*NET Career Changers Matrix lists occupations in the US that have similar skills and experience and that workers could transfer between with minimal additional preparation (O*NET 2021). We only use these two sources to identify potential similar occupations. Once identified, the imputed occupations take the average share of skills requirements for each skills category of their similar occupations in the Malaysia data (or the skill profile of the similar occupation when the similar occupation is only one).

15
2. Number of applicants to the job posting 3. Number of job applicants ahead of the candidate (Approximate order in the application line) 4. Status of the search (Options include the following: employers are actively processing applications, employers have stopped processing applications, employers declare a candidate not suitable, employers offer an interview or they are not actively processing applications) 5. Number of days between application submission and a candidate is offered an interview (if applicable) 6. Number of days between application submission and a candidate is declared ‘not suitable’ (if applicable) Companies contacted the candidates directly through email. Any interview request received was manually declined as soon as we observed it. After being contacted, we declined the candidacy within 0-48 hours. However, it is possible that interview requests made on Saturdays was not declined for up to 72 hours. We declined contact using the standard reply message.

# **3.5 Correspondence Study Limitations**

While correspondence studies are the gold standard for measuring discrimination, there are several common limitations (Lahey and Beasley, 2018). The first important limitation is related to external validity. Would the results hold for different age groups, areas of specialization, or regions? Would the results hold if we had used a different job platform? While we cannot directly address the external validity concerns, we consider five areas of specialization to ensure our results speak beyond a targeted field. We also study entry level discrimination for young professionals, which is important given the consequences for future labor market outcomes (Wachter, 2020). Our findings, at best, are representative of Malaysia’s private sector and do not likely reflect hiring decisions for government jobs. Unfortunately, government jobs are not posted on the online platform we utilized so we were unable to incorporate them in our study. In fact, it’s possible the discrimination we observe here could be reversed in that setting.

11
18

**Table** **_5_**

|Inflation equation|for commerce||||
|---|---|---|---|---|
||<br>(1)|(2)||(3)|
|Constant|5.604|1.516|4.798||
||_(3.64)_|_(1.35)_|_(3.24)_||
|_Ac_|0.459|0.383|0.642||
||_(3.78)_|_(2.89)_|_(3.39)_||
|**Ap***|-0.016|_0.035_|0.145||
||_(-0.08)_|_(0.18)_|_(0.67)_||
|y (a)|-5.270||-4.758||
||_(-3.58)_||_(-3. 73)_||
|x|1.100|0.013|0.987||
||_(3.41)_|_(0.13)_|_(3.49)_||
|AGDP (a)||-0.540|||
|||(-1.30)|||
||-0.302|-0.080|-0.257||
||_(-3.61)_|_(-1.30)_|_(-3.20)_||
|J(b)|0.964|0.787|0.953||
||_(32.55)_|_(4.37)_|_(27.69)_||
|i|||-0.006||
||||(-1.25)||
|AdjR<sup>2</sup>|0.668|0.591|0.690||
|**DW**|2.45|2.31|2.29||

Notes: t-statistics in parenthesis. (a) Instnumented by lagged industrial GDP (up to four quarters), and the variations in the lagged real exchange rate and the lagged nominal wage;(b)the coefficients on c.l and **_P*1_** are constrained to sum to one.

The long-nu markup equation is the following:

# **Equation** 6

_p,, - c,, =_ 0.04 _(p*, - p,,)_ - 1.86 Ap 1t The variation in foreign prices does not affect significantly the markups of commerce in the long-run because it is a non-tradable sector. Like in the other sectors, the
_<mark>of this cost</mark>_ <mark>? There are various reasons to turn the literature’s focus in this direction. For example, the distribution of losses has obvious effects on political incentives (especially for democratically elected decision makers). Yet, the empirical literature has been largely silent around this issue. Existing works concentrate on the relationship between inequality and sovereign default, predicting that increased inequality increases the probability of default since both austerity and tax increases are politically costly (Andreasen et al., 2019; Jeon and Kabukcuoglu, 2018). Both models are motivated and parameterized by the case of Argentina, 2001.</mark><sup>4</sup> <mark>We seek to fill that gap and inform further development in this literature by providing findings on the social cost of default, as well as stylized facts on the heterogeneous effects of default on different segments of the income distribution. In this paper, we test whether sovereign default leads to social costs, including those associated with inequality, while previous research focused on the role of inequality as a driver of default.</mark> <mark>Our study uses a sample of 131 sovereign defaults since 1900. Data coverage naturally varies depending on the exercise due to both data availability for the variables of interest and the timevariance inherent to the cornerstone</mark> _<mark>sovereign</mark>_ <mark>default variable – the number of sovereigns covered in our database in 1900 was 50, while it increases to 193 by the 2010s. The exercises on poverty and distributional impacts have the smallest sample since the World Bank’s Poverty and Inequality Platform (PIP) data that we rely on are only available since the 1980s and data is best only for the most recent decades, which saw only limited sovereign defaults. Additionally, for some exercises the PIP require further exclusions, as we explain in the relevant sections below. The analyses of proxies of multi-dimensional poverty rely on longer annual data series that often start in the 1960s. Our analysis of per capita output has the most complete data coverage starting in 1900. However, the sample changes over time owing to waves of independence along with any gaps in the per capita GDP data series from the Maddison Project. Lastly, it is important to</mark>

> 4 <mark>This in itself is somewhat problematic, since Argentina had been in default for more than half of the twenty years prior to the 2001 default. We provide stylized facts using a wider, though still not representative sample. Note also that some related literature exists considering the political economy of inflation inducing decisions and the policies’ differential impact: Albanesi (2007) argues that low-income households are more negatively affected by inflation. As inequality increases the bargaining power of low-income households diminishes, and the government follows policy decisions that are less likely to favor the group with diminished bargaining power, thus raising equilibrium inflation.</mark>

3
Table 12 presents cross-tabulations of interdecile movement. Column [1] shows the percentage of each row that remains in the same decile of the pension capital accumulation distribution regardless of whether real interest is 2% and real earnings growth 2% or real interest 4% and zero real earnings growth. Column [2] shows the percentage who move to a _higher_ decile when changing from i = 2%, dY= 2°/, to i = 4%O dY = 0%, and column [3] shows the percentage who move to a _higher_ decile when changing assumptions from i = 4%, dY = 0% to i = 2%, dY = 2%. As expected from figures 6 and 7, 'young earners' show a very clear tendency to move to higher deciles under the assumptions of column [2], and 'old earners' show a clear tendency to move to higher deciles under the assumptions of column [3]. Since over 90 per cent of 'young earners' are womrn (who accumulate of 30% of lifetime earnings before the age of 30 because caring responsibilities restrict their opportunities for subsequent full-time employment), the outcomes of the different earnings growth and rate of return assumptions have obvious gender implications. Because of the high probability of an interrupted employment history in mid-life, women benefit relatively more than men from a scenario in which real earnings growth is low and real rates of return high.

However, despite these relative effects, women reach pension age on average with a pension capital fund much lower in absolute terms than that for men. From LIFEMOD we can exantine the co-determinants of low pension capital accumulation. The capital sum accumulated by each of the 1508 men (ANUMEN) and 1687 women (ANUWOMEN) who survive in LIFEMOD to retire at 65 and who have contributed 15% of annuial earnings (with i = 3%, dY=1.5%) can be regressed on a number of socio-economic characteristics. In equations [1] and [2] reported in Table 13 the independent variables used are: UNEMPTOT: total period spent unemployed up to age 65 (in years) YRSTERT: years of tertiary education LASTEARN: last full-time earnings YRSCHILD: total number of years with dependent child under 16 in household 51
India (48). Brazil’s position in the ranking is influenced by measures adopted by Argentina, its main partner in the Mercosur. If measures adopted by Argentina (26) and Uruguay (1) were to be excluded, then Brazil would be ranked 14<sup>th</sup> among the exporting countries most affected by antidumping measures.

The productive sectors most affected by antidumping measures between 1995 and 2002 are: metals (31.6% of the total), chemical products (18.6%), and plastics (12.2%). The same trend holds true in Brazil, where the chemical industry, followed by the metal and plastic sectors, accounts for 73.4% of all the measures adopted in that period.

Lastly, according to Zanardi (2003), who evaluated more comprehensively the use of antidumping measures — based on data from the semi-annual notifications submitted by WTO Member countries and on a specific survey for non-Member countries between 1981 and 2001 — considering all the antidumping investigations initiated by the European Union, the United States, Canada and Australia, the proportion of final affirmative determinations were, respectively, 73.7%, 59.3%, 58.4% and 41%. In the countries that started resorting to this instrument in the 1990s (such as India, South Korea, Mexico and Turkey), affirmative determinations amount to 71.9%, 65.1%, 65% and 52.5%, respectively. In Brazil, almost half of the proceedings (49.6%) end up with a final affirmative determination.

Brazil is ranked 15<sup>th</sup> in safeguard measures notified in the same period —having resorted to them only twice (toys and coconut) — lagging behind the United States (12), Czechoslovakia (10), Jordan (9), Chile (8), and Venezuela (6). Safeguard actions have increased substantially since 1998, when they rose from 11 to 34 in 2002 (Table 3). However, the number of cases involving this instrument is still much lower than those involving antidumping measures.

18
**Figure** 4

# EFFECT OF POLICY ON INVESTMENT

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