rafmacalaba/datause-encoder-data huggingface dataset:
# **I. Background**

The period spanning from the second half of the 1980s until the late 1990s is important for the Mexican economy, as it encompasses a major structural change from a protected, public-sector driven economy to a globally integrated, private-sector led one. For all its merits, this change seems to have produced an increasingly unequal distribution of the fruits of economic growth. The World Bank Report "Earnings Inequality after Mexico's Economic and Educational Reforms" (2000) showed that the most plausible hypothesis for the worsening in earnings inequality in Mexico is the increased rate of skill-biased technological change brought about by trade liberalization. This World Bank Report also found that Mexico is experiencing increasing returns to higher education, and that the skill composition of employment in manufacturing and other export sectors has moved toward demanding a higher proportion of skilled workers, particularly in industries that are most open to international competition.

When rising demand for skills is not met by supply, the result is a persistent shortage of skilled labor and constrained growth. The excess demand also forces firms to pay above marketclearing wages in order to retain the workers they train. On the supply side, the roots of the shortage problem can be traced to thrqe main factors. The first is low educational attainmentparticularly among the poor. The second is insufficient financial support to those students who are academically qualified but who are financially needy. The third is the persistence of antiquated and unresponsive training mechanisms-vocational and technical systems are not providing new entrants with appropriate skills.<sup>2</sup> Effective technical training is Mexico's primary tool for reaching an equilibrium in the market for skilled labor.

> **2** Evidence on the low educational achievement in technical education is drawn from the Council of the National System of

_5_
# **Table 8: Impact of household welfare indicators and their variation during the 2008 food crisis on the**

**frequency of visits to the health facility. OLS Regressions.**

|**VARIABLES**|**Weeks**<br>**between**<br>**two visits**|**Weeks**<br>**between**<br>**two visits**|**Weeks**<br>**between**<br>**two visits**|**Weeks**<br>**between**<br>**two visits**|
|---|---|---|---|---|
|**Income variation**|-0.00170<br>(0.00188)||||
|**Income level in 2007**|-0.00164<br>(0.00288)||||
|**Consumption variation**||-0.00262<br>(0.00472)|||
|**Consumption level in 2007**||-0.00389<br>(0.00613)|||
|**Food consumption variation**|||-0.00625<br>(0.0107)||
|**Food consumption level in 2007**|||-0.000468<br>(0.0122)||
|**Weight-for-age z-score variation**||||6.200<br>(7.930)|
|**Weight-for-age z-score in 2007**||||4.794<br>(8.406)|
|**Constant**|0.967|0.611|0.604|0.829|
||(0.714)|(0.780)|(0.774)|(0.883)|
|**Observations**|475|428|428|196|
|**R-squared**|0.207|0.227|0.229|0.286|

Notes: Standard errors clustered at the individual level to allow more than one observation per patient. *** p<0.01, ** p<0.05, * p<0.1. All coefficients are *100 to make table more readable. Controls not shown in the table but included in the regression: individual characteristics (age, sex, marital status, education), household characteristics (size, age, education and marital status of head), duration of treatment at second wave of survey, health facility indicators. The dependent variable is the standardized deviation from the health facility mean of the number of weeks between 2 visits.

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# Impacts of Sovereign Rating on Sub-Sovereign Bond Ratings in Emerging and Developing Economies<sup>*</sup>

Sanket Mohapatra<sup>a</sup> , Manabu Nose<sup>†</sup> and Dilip Ratha<sup>§</sup>

> a _Indian Institute of Management, Ahmedabad_ † _International Monetary Fund_ § _World Bank_

**Keywords:** Sub-sovereign rating, International debt markets, Spillover effect, Sovereign ceiling

**JEL Codes:** F21, F30, G10, G15.

_________________________

> * Thanks to Augusto Lopez Claros, Douglas Evanoff, Jamus Jerome Lim, Jayanth Varma, Linda Wu, and seminar participants at the 28<sup>th</sup> Australasian Finance and Banking Conference in Sydney and the International Atlantic Economic Society conference in Savannah, GA, for their useful suggestions on earlier drafts. Thanks to Akash Issar and Sergio Kurlat for excellent research assistance. The authors acknowledge financial support for this work from the World Bank's DEC Research Support Budget and from the Research and Publications Committee of the Indian Institute of Management, Ahmedabad. The views expressed in this paper are the authors’ own, and do not represent the views of the institutions in which they are employed. Contacts: <u>sanketm@iima.ac.in, mnose@imf.org, and dratha@worldbank.org</u>
**Figure 9. Net cash and fiscal position, 2020**

<!-- Start of picture text --> 350.0<br>300.0<br>250.0<br>200.0<br>150.0<br>100.0<br>109.0<br>50.0<br>0.0<br>9.4<br>-50.0 -6.3 -10.9 -16.2 -20.5 -23.9 -25.5 -29.0 -30.7<br>1 2 3 4 5 6 7 8 9 10<br>Deciles<br>Direct transfers Direct taxes Contributions<br>Indirect taxes Indirect subsidies Education<br>Health Net cash Fiscal position<br>% of MYPP<br><!-- End of picture text --> _Source:_ Authors’ estimates based on 2021 EU-SILC, 2021 HBS and CEQ Methodology.

Note: This study categorized the population into ten groups based on their market income plus pensions (pre-fiscal income). For each group, the figure displays the incidence of fiscal measures relative to their market income plus pensions. Fiscal measures that result in increased household income are depicted above the zero axis, encompassing direct transfers and non-monetary education and healthcare benefits. Conversely, fiscal measures leading to reduced household income appear below the zero axis, including direct taxes, indirect taxes, and indirect subsidies. The net cash position illustrates the overall financial stance, which sums up all monetizable interventions (encompassing all taxes, direct transfers, and indirect subsidies) for each group. Additionally, the total fiscal position comprises all monetizable interventions and non-monetary benefits.

What is the impact of the fiscal system on child poverty, and which programs contribute more to child poverty reduction?

**As mentioned above, child poverty is a significant issue in Bulgaria, and fiscal policy is not very effective in reducing it.** In 2020, child poverty stood at 30.7% before fiscal policy interventions, showing a marginal decrease to 30.4% after the impact of fiscal measures (see Figure 10). This child poverty rate is notably higher than the overall poverty rate for the entire population, which was 25.3% before fiscal policy interventions and increased to 29.1% after these policy measures were applied. These results can be attributed to the impact of indirect taxes, such as VAT, which have a negative effect on poverty reduction. However, it is worth highlighting that the impact of VAT on children is mitigated to some extent, as reduced rates are applied to essential goods consumed by children, including baby food, school supplies, and books. In contrast, direct transfers positively influence poverty reduction, particularly among low-

32
discussion with SSC experts, it was determined that the dramatic fall in the number of births between 1999 and 2003 was in part an ‘echo effect’ of the sharp drop in fertility and high death rate during the Second World War. The low fertility and high death rates during WW2 resulted in a much smaller birth cohort during this period. The population pyramids for Azerbaijan have since been marked by a regular drop in the number of births from this cohort (and their offspring) in a 25 years cycle. The period between 1999 and 2002-3 coincided with the second 25 years cycle. As we can see from Figure 1, there has been a sharp decline in the Total Fertility Rate from 1999 to 2001.Comparison of household size and age structure between the census and the HBS revealed a systematic change in both size and age structure, with a significant drop in the number of young children in the household.

One of the key assumptions inherent in the poverty mapping procedure outlined by Elbers et al. (2002) is that the models estimated from the survey data apply to census observations. Given the sharp drop in fertility between the implementation of the 1999 Census and HBS 2002, it was decided to modify the dependent variable and to use adult equivalent monthly household consumption as the welfare indicator as opposed to the commonly used average per capita consumption (Elbers et al. 2003; Mistiaen et al. 2002). The equivalence scale used gives less weight to young children than other household members and so is less sensitive to changes in the numbers of young children compared to a more straightforward per capita measure<sup>9</sup> . This minimizes the impact of the drop in the number of young children in the household at the time of the 2002 HBS.

> 9 Adult Equivalent: children aged below the age of six have been assigned a weight of 0.2, children aged between 7 to 12 have been assigned a weight of 0.3, children age 13 to 17 have been assigned a weight of 0.5, and a weight of 1.0 if the household member is older than 17 years. The equivalence scale is designed to account for differences in ‘need’ due to age and sex. It does not however account for economies of scale of household size as each adult carries the same weight on 1.0. The World Bank's 2003 Poverty Profile for Azerbaijan undertaken within the Programmatic Poverty Assessment, proposes a different per adult-equivalent consumption measure that assumes a scale parameter of 0.8 and that the cost of a child (individuals of age 18 and below) is 70% of the cost of an adult. This study was completed earlier and therefore did not use this definition.

36
<!-- Start of picture text --> Q Jo<br>x<br>=<br>Fo}<br>2 nl<br>Q<br>179)<br>is}<br>2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022<br>(SS) GOoDSs =) SERVICES - ~ WORKS<br><!-- End of picture text -->
<!-- Start of picture text --> 20<br>15 y = 0.018 x + 2.53<br>t stat: 0.15, R2 = 0.002<br>10<br>5<br>0<br>-5<br>-10<br>-15<br>Growth Rate of Agriculture<br>1971 1973 1975 1977 1979 1981 1983 1985 1987 1989 1991 1993 1995 1997 1999 2001 2003 2005 2007 2009 2011 2013 2015 2017<br><!-- End of picture text --> 2 C: Acceleration in services has been the fastest <!-- Start of picture text --> 14 y = 0.09*** x + 3.63<br>12 t stat: 2.69, R2 = 0.159<br>10<br>8<br>6<br>4<br>2<br>0<br>-2<br>-4<br>Growth Rate of Industry<br>1971 1973 1975 1977 1979 1981 1983 1985 1987 1989 1991 1993 1995 1997 1999 2001 2003 2005 2007 2009 2011 2013 2015 2017<br><!-- End of picture text --> 2 D: Services have emerged as the largest contributor to GDP growth, followed by industry <!-- Start of picture text --> 16<br>y = 0.097***x + 5.67 Contribution to Growth<br>14 t stat: 3.63, R2 = 0.21<br>12 8<br>6<br>10<br>4<br>8 2<br>6 0<br>-2<br>4<br>-4<br>2 -6<br>0 -8<br>-2<br>Agriculture Industry<br>Growth Rate of Services<br>Services<br>1971 1973 1975 1977 1979 1981 1983 1985 1987 1989 1991 1993 1995 1997 1999 2001 2003 2005 2007 2009 2011 2013 2015 2017<br>1971 1973 1975 1977 1979 1981 1983 1985 1987 1989 1991 1993 1995 1997 1999 2001 2003 2005 2007 2009 2011 2013 2015 2017<br><!-- End of picture text --> Note: Central Statistics Office data. Years refer to respective fiscal years. Agriculture includes crop, livestock, forestry and fisheries; industrial sector includes mining and quarrying, manufacturing, electricity, gas, water and other utilities, and construction; services include trade, hotels, transport, communication and services related to broadcasting, financial, real estate and professional services, and public administration, defense and other services.

The fact that growth has not just accelerated, but has also become more stable over time is reflected in its steadily declining standard deviation, and a declining coefficient of variation (Figure 3).<sup>10</sup> Particularly remarkable is the sharp increase in the stability of GDP growth in the post-reform period since 1991.<sup>11</sup> Even if growth accelerated episodically in the decades prior to 1991, it was

> 10 While the figure only shows the coefficient of variation, the results are very similar for standard deviation.

> 11 The figure also documents a decline in the coefficient of variation in the 1980s, coinciding with some acceleration in growth in the 1980s.

7
production data. In Syria, no fields were observed in 2013 nor 2014. Twelve fields in Syria were observed only in 2012 and two fields were observed in 2012 and 2013. One field in Iraq was missing data in two years and had no RH in another year. The final data thus has 48 field year observations.<sup>8</sup> The estimation employs additional data on the geological characteristics of each flare site to account for heterogeneity in the estimation of the flaring-production relationship. Field-level GOR were obtained from Energy-Redefined, and matched to the field locations according to the field name.<sup>9</sup> Each flare was subsequently assigned the GOR of the nearest field with a known GOR. We do not have field-level GOR data for recent years, so that our statistical inferences assume constant GOR. Field GOR however increases over time as the field ages and crude oil is being extracted. This implies that the assumption of constant GOR will lead to inferences on oil output that are biased upward.

# **A.4.2 Aggregating RH data**

Since the dependent variable, field oil production, is measured yearly and at the field level, we aggregate daily observations on flare-level radiant heat into one yearly field-level measure. The linear relationship between RH and the volume of crude oil extracted allows linear aggregation.

First, each oil field in the production data was linked to gas flares located within its boundaries. In some instances, oil fields were linked to gas flares located beyond the boundaries if clear pipeline infrastructure linking the gas flare to wells within the boundary was observed in daytime satellite imagery (see section A.3 in Annex A for a discussion of the use of satellite imagery). Oil fields were not linked to sites in the VIIRS output where no oil stack infrastructure was observed in daytime imagery, even if the site’s coordinates were within the boundary (see Annex A section A.3 for discussion of infrastructure).

> 8Four observations for the whole country of Syria were added to the final data for robustness check.

> 9Data were obtained from http://www.energy-redefined.com/

40
29 that estimates how much a country’s per capita income today is lower to the extent that some of its workers today were stunted in childhood, and a forward-looking exercise that estimates the net present values of the costs and benefits of a package of interventions aimed at reducing stunting among today’s children. We estimate that, on average, GDP per capita globally is 7% lower as a result of some of today’s workers being stunted in childhood, and that across 34 countries accounting for 90% of the world’s stunted children, the rate-of-return to the package of nutrition interventions is 17%, with a benefit-cost ratio of 15:1.

Our approach has strengths and weaknesses. Among the strengths is that the fact that we conduct the backward- and forward-looking exercises in the same study, using the same methodology and assumptions. By contrast, in the literature to date, the two exercises have been done in different studies, using different methodologies and different assumptions. Another strength of our study is the fact that that the methodology we use (development accounting) is in line with other studies that try to pinpoint some or all of the sources of differences across countries in per capita income. The third strength of our study is its comprehensiveness: our methodology allows for three channels by which stunting affects income (years of schooling, cognition and stature), rather than just one or two; we base our parameters on all the relevant micro-econometric studies, rather than just one or two; and our backward-looking estimates are for the entire developing world while our forward-looking estimates are for countries accounting for 90% of the world’s stunted children.

There are limitations to this exercise that leave scope for future research. In line with the literature, we are looking at how childhood stunting translates into adult earnings via human capital while holding everything else constant. There might be important externalities and spillover effects that arise from human capital formation that are not captured in the estimates of the private returns to reduction of childhood stunting. Equally, there might be general equilibrium effects from scaling
Column 4 shows results of pooled regression with standard errors clustered at the individual level

*** p<0.01, ** p<0.05, * p<0.1

Source: Saraf, 2019; World Bank Group, 2018 Figure 3. Changes in Scores of Depression and Anxiety Symptoms <!-- Start of picture text --> a. PHQ‐ADS Score<br>8<br>6<br>4<br>2<br>0<br>Baseline 5 weeks 3 months<br>Control Treatment<br><!-- End of picture text --> Source: Saraf, 2019; World Bank Group, 2018

# **6.1.2 PHQ‐ADS Odds‐Ratio (Prevalence)**

Prevalence of depression and anxiety symptoms in the study population is measured by the proportion of respondents with PHQ‐ADS scores greater than or equal to 10, which is indicative of whether someone is experiencing these symptoms for more than 3 days during the previous 2 weeks. The prevalence of depression and anxiety symptoms was balanced between the treatment and control groups at baseline. Impact on prevalence is expressed in odds‐ratios, which indicates the odds of an outcome occurring given a particular exposure (in this case, conditional on being in the treatment group), compared to the odds of the outcome occurring in the absence of that exposure (in this case, being in the control group).

Improvements are reflected in the lowered odds ratio of prevalence of depression and anxiety symptoms. Intention to Treat (ITT) estimates (see Table 5 below) show dramatically lower odds of individuals in the treatment group experiencing depressive symptoms relative to those in the control group at both the 5‐week and 3‐month post‐intervention marks.<sup>9</sup> Compared to SME entrepreneurs in the control group, those in the treatment group have 0.65 times the odds of experiencing depression and anxiety symptoms at 5 weeks, immediately after the intervention (Column 1). The effect is larger at 3 months post intervention, with those in the treatment group having 0.46 times the odds of experiencing depression and anxiety symptoms compared to those in the control group (Column 2).

> 9 <mark>The results show the odds of an outcome occurring given a particular exposure (in this case, conditional on being in the treatment group), compared to the odds of the outcome occurring in the absence of that exposure (in this case, being in the control group). An odds ratio of 1 means that a person will have the same odds of experiencing depression and anxiety symptoms irrespective of whether he/she belongs to the treatment or control group. By contrast, an odds ratio of x/y means that the odds of a person in the treatment group experiencing such symptoms is x/y times that of the odds of a person experiencing such symptoms in the control group.</mark>

17
|**Table 2.4: Fertility rate by region in rural Mexico,**|
|---|
|**1980 and 2000**|

||**1980**|**2000**|
|---|---|---|
|**Total Mexico**|2.8|2.6|
|**North**|2.8|2.5|
|**Capital**|2.6|2.2|
|**Gulf**|2.7|2.5|
|**Pacific**|3.0|2.7|
|**South**|2.8|2.9|
|**Center North**|3.2|2.8|
|**Center**|3.0|2.7|
|Source: Census 1980 and 2000.|||

The demographic change that demands the most urgent policy response is the growth in the economically active population in rural areas. During 1990–2000 the number of those aged 12 to 64 rose by more than 300,000 (0.6 percent). The growth rate is low primarily due to out-migration and some due to reduced fecundity. Migration is important in rural Mexico. Mostly young people leave their village in search of employment and find work in a wide variety of economic sectors, either in Mexico or the U.S.

**Table 2.5: Female and male labor market participation** **<u>rate in Mexico, 1980 and 2000, Percent</u>**

||**Male**|**Female**|**Male**|**Female**|
|---|---|---|---|---|
||**1**|**980**|**2**|**000**|
|**Total Mexico**|75.2|26.5|70.2|29.8|
|**North**|73.3|25.6|71.5|32.8|
|**Capital**|73.5|31.9|71.2|35.0|
|**Gulf**|77.7|25.3|73.3|28.2|
|**Pacific**|74.3|23.6|71.6|32.2|
|**South**|77.5|30.3|68.0|24.2|
|**Center North**|75.2|23.1|65.5|27.2|
|**Center**|75.2|26.1|70.5|28.8|
|Source: Census 1980 and|2000.||||

Poverty analyses reveal that many rural Mexican workers, particularly those in the informal sector and agriculture, are poor. The challenge of creating employment is therefore not only to provide new jobs for the new entrants to the labor force, but also to increase the number of jobs that are able to provide sufficient income to lift the employee’s household out of poverty or cushion against it. Creating jobs regardless of quality is not enough—people need good jobs. As the labor market, particularly the informal one, is relatively flexible, the concern is about generating sufficient income via employment rather than simply having a job. The trend in this regard since 1999, as 7
<!-- Start of picture text --> Ingee: Aigh-incone counines = 100<br>40<br>55<br>ft]<br>25<br>20<br>15<br>10<br>5<br>o<br>East Asia and South Asia Europe and Middle Eastand Sub-Saharan Latin America Low- ane<br>the Pacific Central Asia North Africa Alica andthe Garbbean rmicdle-incorne<br>COUNnbies<br><!-- End of picture text --> Sowree: World Bank simulacnons using the Linkage model. Note: Ratio of PPP-adjusted per capita incomes relative to high-income average. PEP is feed at base year (2001) level.
TABLE I: Progresa Transfer Amounts (bimonthly, in pesos)

||1998, sem. 1|1998, sem. 2|1999, sem. 1|1999, sem. 2|
|---|---|---|---|---|
|Type of beneft|||||
|Nutrition support|190|200|230|250|
|Primary school|||||
|3|130|140|150|160|
|4|150|160|180|190|
|5|190|200|230|250|
|6|260|270|300|330|
|Junior secondary: girls|||||
|7|400|410|470|500|
|8|440|470|520|560|
|9|480|510|570|610|
|Junior secondary: boys|||||
|7|380|400|440|480|
|8|400|400|470|500|
|9|420|440|490|530|
|Maximum support|1170|1250|1390|1500|

_Source_ : Attanasio et al, 2011. Sem. denotes semester.

# _V.B Key Facts about Child Labor Supply and Income_

As will be discussed in our identification strategy below, we estimate income and price effects for children of secondary school age only (children aged 12-15 years, inclusive), as nearly all children below the age of 12 attend school. Before doing so, we first investigate the extent to which children in this age range work, their earnings relative to their parents, and evidence that schooling induces a discrete loss in household consumption. Table II summarizes the labor supply statistics of children aged 12-15 years in 1997 (pre-Progresa grants). It can be seen that of those children not in school, 63% of boys report having a job, while 20% of girls report having a job. Conversely, of those children who report they attend school, only 7.5% of boys and 2.9% of girls report having a job. Further, when we look at hours worked for those children with a job, those not attending school work an average of over 40 hours per week, whereas those attending school report an average of less than 15 hours per-week. Thus, it appears that going to school places a major constraint both on labor market participation and on hours worked in the labor market for children.

23