rafmacalaba/datause-encoder-data huggingface dataset:
enrollment is expected for the remainder of this decade. In the 1990/91 session, student enrollment reached 44,000 and by the year 2000 it is projected by the Bank mission that at the present rate of intake it will reach 79,000. In spite of the fact that the University of Sana'a is a "massuniversity", the higher education enrollment ratio is less than 1% of the age cohort, one of the lowest in the Arab world. Female participation in the University's student composition remains low at 14.5% (see Table 5). However, due to an absence of data, it is difficult to ascertain the equity aspects of this enrollment explosion, particularly in terms of socio-economic background and rural-urban origin.

32. Of the 1989/90 intake of 13,253 students, just over 7,300 students were enrolled in the five Faculties of Education -- the Sana'a Faculty alone enrolling about half this number -- making the Faculties of Education the fastest growing element of the University. This rapid growth of the Education Faculty is a marked reversal of past trends. Since 1973/74, the percentage of students enrolling in the Faculty of Education as a proportiyn of all students was decreasing, from 19.7% in 1973/74 to 3.4% in 1981-82. ° <!-- Start of picture text --> Graph 11<br>Education Faculty Enrollment 1970-2000<br>Thousands<br>30<br>20 /<br>actual pjeatud-<br>10/<br>0 .<br>1975 1980 1985 1990 1995 2000<br>%tar<br>?0urces Uaifi5sity o0 8 n5&<br>* PtoJi@0lon b4aed as Wofnd Snas<br>M1211oa Data<br><!-- End of picture text --> 10 Mohamed A. Alkhader. 1985. Low Enroliment of Students at the Faculty of Education and its Effects on the Second Five Year Plan in B.R. Pridham (ed.) Economy. Society and Culture in Contemgorary Yemen. London: Croom Helm. p.190-99 10

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intervention can make “villages across rural Africa ... achieve the MDGs and escape the poverty trap”. The June 2010 report indicates that analysis with a set of comparison sites will be published later in 2010.

This report uses the before-and-after comparison in the Millennium Villages to attribute changes in the villages to the effects of the project. The report lists for each site a series of “biggest impacts,” such as “Proportion of households that own a mobile phone increased fourfold” in Sauri, Kenya (MVP 2010c: 75). Changes in skilled birth attendance over time are called “effects” of the project (MVP 2010c: 5). Other changes in the villages over time are labeled as “quick wins” (MVP 2010c: 3). The report states that further research, to be published later, will allow “more definitive statements” about “whether the observed changes were due to the MVP intervention package or were instead a consequence of secular change” (MVP 2010c: 102). But even this wording suggests that the mid-term evaluation report is a statement about the effects of the project.

In the mid-term MVP evaluation report the treated villages are compared to the same villages before the intervention. This has the advantage of simplicity but the major disadvantage of leaving unknown what might have happened in the villages if the project had not occurred. The before-versus-after evaluation approach requires the very strong assumption that the indicators of interest would not have changed in the absence of the MVP. It attributes any observed changes to the intervention, when in fact some or all of those changes might have occurred in the absence of the MVP.<sup>3</sup> Without both baseline and post-treatment information on a credible control group, it is impossible to know if this is true.<sup>4</sup> The alternative method we explore here is simple: we compare trends in development indicators for each of three Millennium Villages to trends in the same indicators for the same country overall, rural regions of the same country, and rural areas of the province or region where the

> 3 It is worth noting that the counterfactual is not the absence of all interventions of any type, because the MVP evaluation cannot be and should not be an evaluation of all publicly-funded activity of any kind. Rather, the proper counterfactual for an impact evaluation of the MVP is whatever interventions the MVP sites would have received in the absence of the MVP.

> 4 Fisman and Miguel (2008: 202-206) raise concerns about the lack of rigor in the MVP impact evaluation design and posit that broader national trends might be responsible for some of the changes observed in the Millennium Village intervention site at Sauri, Kenya.

10
may not be indicative of a massive rise in crime, but rather a roll of the die that it was the turn of this municipality to be the location of a low-frequency random event. Figure 3 illustrates that, indeed, population size and the portion of observations that are zeros are strongly related. Thus, only the most populous quintile of municipalities with viable observations were retained. This reduced the zeros to 32 percent, 42 percent, and 23 percent of observations, respectively, in the three outcomes of interest—female homicides, female overnight hospitalizations for assault, and mandatory reports by health workers of physical and sexual violence against women. Though only 841 municipalities remain in the sample, they include 72 percent of the Brazilian population of women ages 15–49. Map 2 illustrates the change in the violence measures from 2011 to 2016 using the viable sample. Map 3 shows the same image, but excluding the lower 4 quintiles by population; this restriction eliminates much of the pale areas of the map, representing municipalities with relatively smaller changes. The raw wage ratio is much higher in the excluded municipalities (table 1), which may be attributed to the fact that these municipalities are poorer (a lower human development index and a greater share of women receiving the Bolsa Família benefit) and thus include a larger proportion of laborers in the informal sector. The formal sectors in these small municipalities have a larger portion of public sector employees, which is more gender-egalitarian in salary structure than is the case of the private sector.

# **Empirical Strategy**

Using a six-year panel, 2011–16, the analysis applies a generalized difference in difference approach to estimate the effect of the gender wage ratio on IPV, as follows: , (2) ggmm ggmm ggmm gg mm ggmm where is the measure of violent incidents against women in municipality YY = ββWW + ρρxx + αα + γγ + εε _m_ at time _t_ , is the raw or leave-out gender wage ratio; ggmm is a set of municipality-year controls; YY ggmmis a municipality effect; and is a year effect. Each municipality is weighted by the ggmm WW xx population of women ages 15–49. For each outcome-intervention combination of interest, gg mm αα γγ the regression is run with and without controls. Standard errors are clustered at the municipal level.

# **Results**

# **Main Results**

The estimates are first presented using the raw wage ratio. An improvement in equality in wages for women is shown to be correlated with a reduction in violence at all levels of severity on which there are measures, but the correlation is only statistically 15
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predicted headcount rates at the state level to test whether the model generates plausible predictions. Lastly, we examine estimated poverty rates at higher poverty lines to ensure that they are reasonable. Ultimately, only the estimated urban and rural poverty rates at the $1.90 line were used to determine the official poverty estimates, by calculating an appropriate pass-through rate to apply to consumption growth in the national accounts.

# **A Elasticities and Semi-Elasticities**

Table 10 shows the elasticity and semi-elasticity of the predictions, compared with the typical lining up procedure and past experience. As mentioned above, India experienced a rapid fall in poverty despite moderate growth between 2009-10 and 2011-12. This is reflected in a large swing in the poverty-growth elasticity, from about -0.6 between 2004-05 and 2009-10, to about -3 between 2009-10 and 2011-12. This swing is also reflected in the semi-elasticity, which jumped in magnitude from about -21 to -85. The model predictions for 2014-15 imply an elasticity and semi-elasticity of -2.1 and -40. Both are within the range of the two previous measurements, with the predicted elasticity closer to the measure from 2009-10 to 2010-11, and the semielasticity closer to the 2004-05 to 2009-10 period.

As a point of comparison, the typical method used by the World Bank would scale up the 2011-12 survey welfare measure by the growth rate in Household Final Consumption Expenditure (HFCE). That method would predict a poverty rate of 11.0% in 2014-15, which would imply an elasticity of -3.5 and a semi-elasticity of -60. The implied elasticity is substantially greater in magnitude than even the large values observed from 2009-10 to 2011-12, which were boosted by the post-drought recovery. In short, the implied elasticities and semi-elasticities from the model predictions are much more plausible, in light of past experience, than those generated by the typical line-up approach.

# **B Implied State Level Results**

One would expect that states with greater GDP growth would see larger reductions in extreme poverty. State per capita GDP growth does not enter into the model, and the model includes only one global time trend rather than state-specific time trends. Comparing state GDP growth with predicted poverty reductions therefore reveals the extent to which differences in state level per capita GDP growth are reflected in the predictors included in the model.

18
cycle of religious observance, which prescribes a single rest day in a seven-day cycle. However, in section 5.1 I show that it is possible to generate welfare improvements under the restriction that the labor-leisure cycle is synchronized with the cycle of religious worship.

The rest of the paper is organized as follows. Section 2 presents some institutional background. Section 3 presents a model of production in which labor productivity depends on the work-rest schedule. Section 4 discusses restrictions on the parameterization of the model. Section 5 embeds the production model in a representative agent framework, and discusses welfare implications. Section 6 concludes and discusses avenues for further research.

# **2 Background**

This section presents a brief survey of the week as an economic institution. The purpose of this survey is twofold: first, to illustrate that the adoption of the current five-day workweek was not an outcome of optimal policy; rather, it was shaped by a variety of factors that are unrelated to its productivity attributes. Second (and relatedly), the purpose of this survey is to illustrate the importance of religious observance in shaping the pattern of the workweek in order to motivate the discussion in section 5.1.

The Hebrew Bible, arguably the most influential book in history, opens with a micro foundation of the seven day week.<sup>7</sup> According to the JudeoChristian tradition, after spending six days creating the world, the Lord rested on the seventh day. To honor this, Jewish religion places heavy restrictions on work during the Sabbath. While Christianity and Islam adopted the custom of weekly religious observance during Sundays and Fridays, respec7The seven-day week is also rooted in ancient traditions relating to the planetary cycle. Their primary use was the accounting of time rather than dictating the work-rest schedule. The Romans used an 8-day maket cycle, which was used as a device for coordinating on market days. The adoption of the seven-day week came with the adoption of the Julian calendar in 45 BC.

6
**Figures 5:  Distribution of Countries by (Relative) Income Classification, 1900-2008, %**

<!-- Start of picture text --> 100<br>90<br>80<br>70<br>>0.7<br>60<br>0.5 to 0.7<br>50<br>0.3 to 0.5<br>40<br>30 0.1 to 0.3<br>20 <0.1<br>10<br>0<br>1900 1950 1960 1970 1980 1990 2000 2008<br><!-- End of picture text --> Source: Authors‘ calculations based on Maddison data.

There is also some ―churning‖ where countries not only converge up the ladder, but also diverge down the ladder. This is the case of some former colonies in Africa: many have gone from being lower MICs at independence to LICs in 1980. Since then, some have climbed back up to MIC status. There are also countries at the HIC end of the distribution that have fallen back to MIC status, by this measure.<sup>5</sup> Many middle-income countries face the risk of falling into a middle income trap. Even some previously high growth Asian economies, like Malaysia and Thailand,<sup>6</sup> experienced a substantial deceleration of growth following the Asian financial crisis. The Latin America and Caribbean region, however, probably provides the classic example of MICs failing to progress to high income country status. Figure 6 reveals the persistent lack of convergence of the Latin America region with living standards in the United States. Periods of mild catch-up were followed by periods of declining relative incomes. More recently, most of the economies of Latin America have stabilized and there has been a strong upturn in growth, led by the largest economy of the region—Brazil—as will be discussed in more detail in the next section. It remains to be seen whether the recent improved growth performance in Brazil and several other Latin American economies can be sustained into the future.

> 5 For recent decades, the World Bank‘s open database provides a spreadsheet with the changes in country categories. Heckelman et al (2011) analyze empirically the determinants of countries‘ ―graduation‖ above the notional threshold for receiving World Bank lending.

> 6 Post crisis GDP per capita growth rates were roughly half the level of pre-crisis growth rates.   Malaysia‘s average growth of 6.4 percent from 1988-1997 fell to 3 percent from 1999-2010, and Thailand‘s average of 7.2 percent over 1988-1997 fell to 3.4 percent for 1999-2010.  (GDP per capita growth rates are from _World Development Indicators_ .)  On the other hand, this would still imply convergence—albeit at a slower pace--given HIC per capita growth rates of around 2 percent.

8
Table 31

|Dependent variable: Fisca|l adjustment|when need|ed based o|n debt/rev|enue||||
|---|---|---|---|---|---|---|---|---|
|Variables|(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
|Rigid expenditure minus|0.011***|0.010***|0.007|0.004|0.004|0.005|-0.014|0.010|
|structural as % GDP|(0.003)|(0.003)|(0.004)|(0.005)|(0.008)|(0.020)|(0.014)|(0.018)|
|**Economic factors**|||||||||
|GDP growth (t-1)||-0.070||-0.370|-0.324|-0.371|-0.387|-0.360|
|||(0.287)||(0.509)|(0.512)|(0.509)|(0.511)|(0.509)|
|Inflation (t-1)||-0.188||-0.376|-0.318|-0.377|-0.358|-0.372|
|||(0.238)||(0.257)|(0.245)|(0.258)|(0.250)|(0.258)|
|U.S. Interest rate||-0.019*||-0.033**|-0.034**|-0.033**|-0.037***|-0.033***|
|||(0.011)||(0.013)|(0.014)|(0.013)|(0.013)|(0.013)|
|**Political factors**|||||||||
|Gini|||0.001|0.001|0.002|0.002|0.002|0.001|
||||(0.002)|(0.002)|(0.002)|(0.005)|(0.002)|(0.002)|
|Rule of law|||0.041**|0.045**|0.031|0.045**|0.013|0.045**|
||||(0.019)|(0.021)|(0.024)|(0.021)|(0.035)|(0.021)|
|Margin of Majority|||-0.259*|-0.260**|-0.254*|-0.260**|-0.257**|-0.190|
||||(0.134)|(0.129)|(0.132)|(0.128)|(0.127)|(0.243)|
|Election's year|||-0.040|-0.047|-0.047|-0.047|-0.046|-0.048|
||||(0.030)|(0.030)|(0.031)|(0.030)|(0.031)|(0.031)|
|**Interactions**|||||||||
|EMEs|||||-0.024<br>(0.145)||||
|EMEs * Deviation|||||-0.006<br>(0.011)||||
|Gini * Deviation||||||-0.000<br>(0.001)|||
|Rule of law * Deviation|||||||0.004<br>(0.003)||
|Majority * Deviation||||||||-0.010<br>(0.028)|
|Observations|1,015|1,003|713|709|709|709|709|709|
|Country FE|No|No|No|No|No|No|No|No|
|r2_p|0.0166|0.0246|0.0394|0.0581|0.0614|0.0581|0.0606|0.0583|
|Robust standard errors cl<br>***p<0.01,**p<0.05,*p|ustered by co<br><0.1|untry in pa|rentheses||||||

- 60 -
APPENDIX

# **Online Appendix to “Sovereign Bonds since Waterloo”** Meyer, Reinhart, Trebesch (QJE)

## **Overview:**

Appendix A: Financial and macroeconomic data Appendix B: Bond prices and investor returns (starting on page A4) Appendix C: Debt restructurings and haircuts (starting on page A42)

# Appendix A: Financial and macroeconomic data

This Appendix lists the data sources on financial and macroeconomic variables used in the paper.

### <u>Three-month UK bills, monthly, since 1815</u>

The 3-month UK bill return index in the 19<sup>th</sup> century builds on data by GFD and includes observations from commercial bills (open market rates of discount). For the years 1815-1823 the data comes from Homer (1967). For 1824-1857, the series is retrieved from the NBER Macrohistory database that draws on the open market rates reported in the UK “Parliamentary papers”. For 1857-1899 the bills data comes from The Economist and the Investor’s Monthly Manual. For 1900-1913, the series comes from the NBER Microhistory database and for 1914-1923 from Morgan (1952). The data since 1924 is retrieved from the Central Statistical Office of the British government.

### <u>Three-month US bills, monthly, since 1835</u>

The 3-month US bills return series draws on various sources. From 1835-1918, we use the approximated series by GFD, which is based on the minimum rate of the coupon on US Government bonds and commercial bills. For the years 1835-1862, that data was retrieved from Homer (1967), Martin (1886), The Economist (1854-1861) and the periodical Hunt’s Merchants Magazine (1843-1853). From 18621918, data comes from The Financial Review. From 1919 onwards, data is retrieved from the Federal Reserve Bank using 90-day Treasury bills. From 1920 until 1942, the data comes from the Federal Reserve Bank, National Monetary Statistics. From 1942 onwards, we retrieve 3-month US bills returns from the Center for Research in Security Prices (CRSP). CSRP calculates total returns based on a comprehensive database of historical US bills. In each month, a representative Treasury bill is selected that is closest to the target maturity.

### <u>US Treasury bonds, monthly, since 1815</u>

The long-term US Treasury bond return index is from GFD and constructed using data by Homer (1967) and Martin (1886) for the years 1815-1862, from The Economist (1854-1861) and from the periodical Hunt’s Merchants Magazine (1843-1853). For 1862-1919 data comes from The Financial Review. From 1920 onwards, the data comes from the Federal Reserve Bank and Salomon Brothers (1995). Whenever possible, we use an index of 10-year bonds. During the 19<sup>th</sup> century, the available indices also contain A1
Policy Research Working Paper 6407

# **Abstract**

This paper exploits the emotional connections and viewer attentiveness of mainstream media to evaluate the economic impact of financial education messages on debt management delivered through a popular television soap opera in South Africa. The study uses a symmetric encouragement design to compare outcomes of individuals who were randomly assigned to watch a soap opera with financial messages, “Scandal!” to those of individuals who were invited to watch a similar soap opera without financial messages, “Muvhango.” Both shows overlapped in evening primetime and had similar past viewership profiles. The financial storyline spanned two months and featured one of the leading characters of the show borrowing excessively and irresponsibly through hire-purchase, gambling, and ending up in financial distress; and eventually seeking help to find her way out. Two intermediate and one final follow-up surveys were conducted as part of the study. The analysis finds individuals assigned to watch Scandal had significantly higher financial knowledge of the issues highlighted in the soap opera storyline, in particular messages delivered by the leading character. On behavior, Scandal viewers were almost twice more likely to borrow from formal sources, less likely to engage in gambling, and less prone to enter hire purchase agreements. Messages promoting a national debt mediation helpline delivered by an external character did not sustain traction beyond immediate interest. Three qualitative focus groups highlight the importance of emotional connections with the leading character in motivating behavior change.

This paper is a joint product of the Finance and Private Sector Development Teams of the Development Research Group and Africa Region. 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 gberg@worldbank.org and bzia@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