rafmacalaba/datause-encoder-data huggingface dataset:
procedure to cross-section data from 43 countries across the world. To the best of our knowledge, this is the first paper that uses this many datasets to validate and estimate welfare and mobility predictions.

Some previous studies have proposed methods that harness cross-section data to infer about dynamic welfare changes. “Synthetic Panels”, developed by Dang, Lanjouw, Luoto, and McKenzie (2014), is the most recent.<sup>3</sup> The authors estimate a (log) income model in both the first and second rounds of cross-sectional data, including time-invariant, deterministic, and retrospective regressors. Parameters estimated in the first round are then used to predict unobserved first round income for all households interviewed in the second round. Depending on the assumptions on the correlation between error terms in the underlying regressions in both rounds, this “non-parametric” approach provides upper and lower bound estimates of poverty and mobility. The methodology has been validated using seven panels in Chile, Nicaragua, and Peru by Cruces et al. (2015), while Ferreira et al. (2013) applied it to predict intra-generational poverty mobility in 18 countries in Latin America and the Caribbean (LAC) using harmonized crosssectional micro data.

Because this synthetic panel approach only provides bounds, Dang and Lanjouw (2013) proposed a method that obtains a point estimate of intra-generational poverty mobility by assuming that the error terms in the underlying regressions follow a binormal distribution and using the agecohort correlation of residuals from cross-sections. This “parametric” method was validated by the authors on panel data from five countries and has been applied by Dang and Ianchovichina (2018) to study welfare dynamics in six Arab countries; by Dang and Dabalen (2018) to analyze whether growth has been pro-poor in 21 countries in Africa; and by Vakis, Rigolini, and Lucchetti (2016)

> 3 The synthetic panel method builds on the poverty mapping technique developed by Elbers, Lanjouw, and Lanjouw (2003).

3
|P070092|2002|2010|12/28/2009|19 Closed|Yemen,Republic of|
|---|---|---|---|---|---|
|P070092|2002|2010|6/30/2010|20 Closed|Yemen,Republic of|
|P070092|2002|2011|1/9/2011|21 Closed|Yemen,Republic of|
|P070092|2002|2012|10/16/2011|22 Closed|Yemen,Republic of|
|P070092|2002|2012|11/27/2011|23 Closed|Yemen,Republic of|
|P070092|2002|2012|6/30/2012|24 Closed|Yemen,Republic of|
|P070092|2002|2013|12/6/2012|25 Closed|Yemen,Republic of|
|P070092|2002|2014|7/9/2013|26 Closed|Yemen,Republic of|
|P070092|2002|2014|12/28/2013|27 Closed|Yemen,Republic of|
|P070092|2002|2014|6/16/2014|28 Closed|Yemen,Republic of|
|P070092|2002|2015|12/24/2014|29 Closed|Yemen,Republic of|
|P070108|2003|2003|6/4/2003|2 Closed|Mexico|
|P070108|2003|2004|6/15/2004|4 Closed|Mexico|
|P070108|2003|2005|4/20/2005|6 Closed|Mexico|
|P070108|2003|2006|4/9/2006|7 Closed|Mexico|
|P070108|2003|2007|10/4/2006|8 Closed|Mexico|
|P070108|2003|2007|5/16/2007|9 Closed|Mexico|
|P070108|2003|2007|6/27/2007|10 Closed|Mexico|
|P070108|2003|2008|11/29/2007|11 Closed|Mexico|
|P070108|2003|2008|5/5/2008|12 Closed|Mexico|
|P070108|2003|2009|10/17/2008|13 Closed|Mexico|
|P070108|2003|2009|5/8/2009|14 Closed|Mexico|
|P070108|2003|2010|12/16/2009|15 Closed|Mexico|
|P070108|2003|2010|6/16/2010|16 Closed|Mexico|
|P070108|2003|2011|3/2/2011|17 Closed|Mexico|
|P070108|2003|2021|7/22/2020|18 Closed|Mexico|
|P070112|2002|2002||0 Closed|Lithuania|
|P070112|2002|2003|4/16/2003|3 Closed|Lithuania|
|P070112|2002|2004|6/23/2004|5 Closed|Lithuania|
|P070112|2002|2005|2/23/2005|7 Closed|Lithuania|
|P070112|2002|2006|9/27/2005|8 Closed|Lithuania|
|P070112|2002|2006|6/22/2006|9 Closed|Lithuania|
|P070112|2002|2007|6/22/2006|9 Closed|Lithuania|
|P070122|2001|2001|6/20/2001|1 Closed|Zambia|
|P070122|2001|2002|5/8/2002|3 Closed|Zambia|
|P070122|2001|2003|5/28/2003|7 Closed|Zambia|
|P070122|2001|2004|6/30/2004|11 Closed|Zambia|
|P070122|2001|2005|6/13/2005|13 Closed|Zambia|
|P070122|2001|2006|12/13/2005|14 Closed|Zambia|
|P070122|2001|2006|6/22/2006|15 Closed|Zambia|
|P070123|2000|2000||0 Closed|Algeria|
|P070123|2000|2001|5/30/2001|3 Closed|Algeria|
|P070123|2000|2002|5/23/2002|5 Closed|Algeria|
|P070123|2000|2003|6/27/2003|8 Closed|Algeria|
|P070123|2000|2004|6/14/2004|11 Closed|Algeria|
|P070146|2000|2000||0 Closed|Bosnia and Herzegovin|
|P070146|2000|2001|6/27/2001|2 Closed|Bosnia and Herzegovin|
Bone district in South Sulawesi Province is one of the first districts that introduced the Community Based Development Information System (CBDIS) program, a community-based mapping mechanism to identify OOSC by name, address and other details. Using CBDIS data, the local government organizes the “Back to School Movement” ( _Gerakan Masyarakat Lisu Massikolah or Gemar Limas_ ) to provide necessary support for these children. During the period of 2017-19, the initiative has returned 4,998 OOSC to school, either formal or non-formal schooling. This model has now been taken to scale through the government’s village development planning system to enable its wider and sustained use by village governments nationwide.

# 3.3.1. Child Marriage

Another determinant of enrollment is child marriage, often linked to poverty and gender, and also contributing to dropping out. The rate of child marriage in Indonesia has been declining in the recent years, though it is still higher than in neighboring countries (Figure 6). Indonesia’s child marriage rate— defined as the proportion of women aged 20-24 married before age 18—is lower than India’s but twice Vietnam’s (Figure 6). And <mark>because of the huge size of the Indonesian population, the country is among the top 10 countries with the highest absolute number of child brides, ranking seventh globally with 1.4 million women aged 20-24 married before age 18. S</mark> ince 2008, the rate has been relatively stagnant at 25 percent. More recent data in 2015 show a modest improvement to 22 percent.<sup>29</sup> While overall early marriage rates are high across the country, prevalence varies by district. At the provincial level, West Sulawesi had the highest prevalence of early marriage in 2015, with 34.2 percent of ever-married women aged 20-24 married before age 18. In contrast, the Riau Islands had a rate of 11.7 percent. East Java, South Kalimantan and Central Java have the greatest number of high prevalence districts (female adolescent marriage higher than 25 percent). Child marriage is particularly an issue for girls at the senior secondary level, though in some areas child marriage sometimes occurs as early as primary school, and principals noted that girls often must leave formal school if this happens.

**Figure 6. Proportion of women aged 20-24 married before age 18**

<!-- Start of picture text --> 30<br>25<br>20<br>15<br>10<br>5<br>0<br>Percent<br><!-- End of picture text --> Source: UNICEF Global database (countries’ data from 2014-16).

> 29 - UNICEF (2016). Child Marriage in Indonesia: Progress on Pause. <u>https://www.girlsnotbrides.org/wp content/uploads/2016/11/UNICEF-Indonesia-Child-Marriage-Research-Brief-1.pdf</u>

15
have been helped by other in their job search ‐‐ about 50% of treatments vs. 28% of controls, respectively. Finally, the increased job search activity among unemployed graduates reflects their own belief that the program made them more employable. 76% of OJT graduates and 64% of YJC graduates reported that they believed that participation in a program has increase their employability. Focus group discussions with program participants highlighted the beneficial effects of program participation on expanding their social networks and exposure to potential jobs.

In addition to improving employment outcomes, the project also provided access to the formal financial system, which could lessen the demand for economically motivated crime. In particular, the program set up a bank account for each participant to receive stipends. Not surprisingly, this resulted in much higher share of youth with bank accounts in the treatment group versus the control group. While both groups started with similar levels of bank account ownership, at the time of the follow‐up survey 70.9% of treatments had a bank account versus 23.1% of controls. During the program, access to bank accounts was almost universal among the treatment group, but some youth decided to close their bank accounts after graduation from the program due to the associated bank fees.

Overall, the program appears to have had strong effects on employment, job search, and access to bank accounts. Employment rates appear to be substantially higher for the treatment then for the control groups, although the follow‐up question on employment was not directly comparable. Program graduates that were not employed were much more likely to be actively searching for jobs and working with others to do so, and were much more likely to have a bank account. Employment, or even greater expectation that searching for work could pay off, both reduces the economic need and raises the opportunity cost of crime. This evidence is consistent with a program that reduced anti‐social behavior for participants that were less likely to commit crimes, while having smaller effects on the behavior of those engaged in crime.

The results reported up to now have been based on simple difference‐in‐difference estimates that compare treatments and controls. As a robustness check, we also examine estimates obtained using a slightly different method, in which outcomes during the final period are regressed on a set of baseline variables. The results are reported in Table 8. In general, controlling for baseline characteristics has minor effects on the estimates, and make the estimates only slightly more precise. However, the estimated effect of the program on gratuitous property damage (damaging someone’s property for fun or joke) declined slightly, making the estimate no longer statistically significant at the 5 percent level. This further reinforces the notion that the program’s strong effects on anti‐social behavior did not necessarily translate into as strong a reduction in criminal activity.

30
be at play. For example, the very idea that internal conflicts are linked to rational individualism is sometimes questioned given the importance of other social and historical conditions and constraints (see, e.g., Cramer, 2002). Since the 2010s, the use of conflict location data and satellite imagery has offered new avenues for understanding in which local contexts violence develops. Empirical frameworks based on small spatial units (especially grid-cells) and georeferenced data introduce key sources of heterogeneity at the local scale, like the location of mineral deposits (Maystadt et al., 2014; Berman et al., 2017) or the volume of production/exports in each area (Dube and Vargas, 2013; Berman and Couttenier, 2015; McGuirk and Burke, 2020). These variations can then be exploited using quasi-experimental frameworks to isolate and test the validity of one mechanism among others (Blattman and Miguel, 2010; Couttenier and Soubeyran, 2015; Laville, 2019). For example, using grid-cells and georeferenced data on conflicts in Sub-Saharan Africa, Berman and Couttenier (2015) conclude that positive income shocks - proxied by shocks in global demand for the main good exported by the cell - decrease conflict probability in the cell,<sup>3</sup> and von Uexkull (2014) find that negative income shocks - proxied by the exposure of rainfed croplands to drought - significantly and substantially increase the risk of conflict. Although these two studies reach similar results, they present key methodological differences in the sign and proxies used to define the shock. This makes it difficult to establish income as a key local factor of conflict.

Figure 1 presents the distribution of the _t_ -students from 1.391 point estimates from 61 studies on income shocks and conflicts.<sup>4</sup> Figure 1a shows that the literature associates almost twice as often income changes with adverse rather than positive effects on conflict, suggesting a possible asymmetric research focus on the link between income and violence. In Figure 1b, we adjust the _t_ -students so that the _ex-ante_ assumed effects all work in the same direction.<sup>5</sup> It reveals that finding a result in line with _ex-ante_ expectations is twice more frequent (66.8 % of estimates) than finding one with an unexpected signs (33.2 % of counter-intuitive or null effects). This coincidence between the obtained and expected results may suggest the presence of genuine effects or manipulations by researchers, namely, publication selection bias (i.e., to prefer results with signs consistent with their expectations or prior understanding, or results with higher statistical significance).

> 3This effect disappears when spatial units are countries, suggesting a local nature of the explanatory mechanism.

> 4Figure 1 only presents the sample of direct estimates from the MRA database of this study.

> 5 We reverse the sign of _t_ -statistics for regressions focusing on theoretically pacifying mechanisms as they are the only channels with an _ex-ante_ expectation of reducing risks of conflicts.

3
that SOEs that are very unprofitable or subject to chronic government interference can sell debt is a sign of that private investors believe that the government will step in, if necessary, to prevent default.

This is not to argue that selling debt has no beneficial effect on firm behavior. The information generated may be an important aid to customers and taxpayers, particularly because the sale of debt often requires that the firm submit to external auditing. Another route of influence is that bondholders may insist on covenants that constrain the firm’s behavior in important ways. The most famous example is the covenants on the revenue bonds issued by the Port of New York Authority in the 1930s and 1940s that prohibited the use of the authority’s bridge and tunnel toll revenues for any use besides maintaining the bridges and tunnels and related roadways. This requirement was allegedly engineered by Robert Moses, the head of the Port Authority and famous “master builder” of New York, to insure that politicians couldn’t raid his enterprise’s revenues for causes he didn’t approve of, such as subsidizing mass transit (Caro, 1972). The effectiveness of this strategy diminished when it became apparent that private investors were willing to buy Port Authority bonds without such covenants. But it is common for bond covenants to require that the firm maintain certain coverage ratios or reserves.

The empirical evidence on whether selling equity improves efficiency is mixed but generally encouraging. Boardman and Vining’s two studies described earlier give different results: mixed equity enterprises perform no better (and by some measures worse) than other SOEs using the sample of the 500 largest non-US industrial firms in the world (Boardman and Vining, 1989) but they perform better than other SOEs (although not as well as private companies) using the sample of the 500 largest non-financial firms in Canada (Vining and Boardman, 1992). A study of some 400 Indian non-financial companies of which 42 were partially privatized finds that the initial listing of an SOE on the stock exchange is associated with an increase in profits and productivity and that the effects increase with the percentage of shares sold (Gupta, 2005). And a comparison of 26 mixed enterprises in Singapore with a matched set of 26 private Singaporean 40
are found in the public sector relative to those with less education. Those with postsecondary education have inexplicably disappeared from the scene, with only small proportions found in both the public and the private sectors. (In fact, the GLSS for 1991-2 reports that fully a quarter of those with post-secondary education were in the "non-working" category). Whether or not this disappearance of post-secondary employees is a real phenomenon or not, the higher proportion of the educated with secondary levels in the public sector in 1991-2 compared to the earlier date is a clear characteristic of the sample. In so far as the recruitment to the public sector is correlated with secondary education this change in characteristics would tend to increase the importance in earnings differentials relative to the public-private divide.

Secondly, and perhaps more importantly, the large increase in public sector wage scales in July 1992 is unlikely to be reflected in the earnings data from the 1991-2 GLSS survey. Unfortunately we do not yet have an earnings survey of the GLSS type for a more recent date.

In the absence of such analysis, we try to draw some tentative conclusions using additional data sources. We have some detailed information on earnings in the manufacturing sector of Ghana from the sample surveys conducted by the World Bank under the auspices of the Research Program on Enterprise Development (RPED). These surveys of enterprises and a sample of workers attached to each enterprise were conducted in three 'waves' in each of the years of 1992, 1993 and 1994. The data of mean earnings by size group of firms are reported in Table 3.5 together with an estimated mean of monthly earnings in the public sector after the 80 per cent wage hike of 1992. The spread in mean earnings over the firms of different size groups is enormous, and will be discussed later. For the moment we can note that compared to the manufacturing sector, average earnings in the public sector were generally at a higher level than even those prevailing in the large firms. In 1992 mean earnings in the public sector were 58 per cent higher than the average in large firms and more than four times higher than the average earnings of workers in micro enterprises. It might be objected that the survey data for private manufacturing firms in 1992 had been collected too close to the period of the public sector wage hike, and the ripple effect of the latter might be more truly captured in the data for 'wave 2' of 1993. But even this comparison (which does not allow for price increase over the year) shows that mean public wages were 28 per cent higher than the average earnings in the large firms.

_15_
# **8. Systemic Risk Supervision and Challenges**

There is substantial scope for improvements in systemic risk supervision in LAC. About 45 percent of respondents saw their ability to monitor systemic risk as good or excellent (including bank supervisors in 4 of the LAC 5 countries), and 40 percent indicated it needs strengthening (Figure 6). Two respondents indicated that effective consolidated supervision of conglomerates (including cross-border ones) would be necessary to improve systemic risk monitoring16. There are several key hindrances to the agencies’ capacity to conduct effective systemic risk supervision including (i) the need to develop proper staff skills, (ii) better coordination with other domestic supervisors, (iii) need to strengthen legal powers, protection and independence of supervisors and (iv) better use of market data (Figure 6). Issues related to the supervisory architecture appear relevant as well; only about a third thought that rethinking the organization of supervision was moderately or not important factor. More than 50 percent of respondents saw assessment of risk profiles, strategic directions and internal controls as extremely important and virtually all thought this was extremely or very important for systemic risk monitoring. About 85 percent of respondents thought that analysis and monitoring of compliance and trends observed from reported prudential reports was extremely or very important and 65 percent thought the same about reviewing the accuracy of reports of regulatory compliance.

The majority of respondents thought that there should be a fundamental redefinition of the role and functions of the supervisor and making it more proactive, at least to some extent. Only about 15 percent of respondents did not think any reform was necessary (Figure 6). Some supervisors emphasized the need to decisively change the traditional scope of supervision to become more risk-based. Several of them also indicated that supervisors should be more proactive and adopt pre-emptive measures to preserve financial instability when perceived risks are building up. Supervisory agencies should then clarify their strategies and mechanisms to deal with such risks. It was also noted that there may be excessive reliance on rating agencies and that it is necessary that some agency be willing to tackle asset price bubbles. Monetary authorities commented on the need to adopt a macroprudential approach both in regulation and

> 16 One country noted the need for international efforts to develop standards on systemic risk monitoring. Other country indicated the need for better models (in the profession in general) to understand the impact of financial instability on the economy.

22
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Table 1: Summary statistics: analytical sample compared to other municipalities in Brazil, 2011

||_A_|_~~(1)~~_<br>_nalytic sample_|<br>_~~(~~_<br> <br>_Excl_|_~~2)~~_<br>_uded_|
|---|---|---|---|---|
||_Mean_|_SD_|_Mean_|_SD_|
|Reports (per 100,000 women ages 15–19)|75.46|112.98|57.83|129.75|
|Homicides (per 100,000 women ages 15–19)|6.74|5.44|5.51|12.66|
|Hospitalizations (per 100,000 women ages 15–19)|11.59|18.83|7.16|17.93|
|Leave-out F-M wage ratio|1.01|0.10|1.15|0.16|
|Raw F-M wage ratio|0.89|0.13|0.96|0.19|
|Share of women w/Bolsa Família|0.18|0.13|0.38|0.20|
|Per capita cash transfer|10.10|7.71|20.73|12.03|
|F-M ratio with high school degree|1.38|0.16|1.54|0.33|
|Female share of population a ges 15–49|0.51|0.01|0.49|0.02|
|Clinics per 1,000 people|8.87|3.78|12.41|5.30|
|Hospital beds per 1,000 people|16.05|8.56|17.78|18.05|
|Human development index|0.75|0.06|0.65|0.07|
|Female share in formal sector employment a ges 15–49|0.41|0.05|0.44|0.11|
|2011population between ages 15–49|1017769.34|1860906.63|9367.98|4722.43|
|Observations|841||3365||

_Note:_ The universe of municipa lities is defined as the municipa lities on which data on the control variables are availa b l e . These statistics are computed for 2011, which is the baselin e year of the analysis. Means are weighted by the population of women ages 15–49. F-M = female to male. SD = standard deviatio n .

30