rafmacalaba/datause-encoder-data huggingface dataset:
52

**Table** **_5_ Dummy variable for bubbles in the. stock market 1984:12-2001:03***

|Dates|Argentina|Brazil|Chile|Colombia|Mexico|USA|
|---|---|---|---|---|---|---|
|1985-<br>1989|*<br>1985:01-<br>1985:09<br>*<br>1987:04<br>*<br>1989:05-<br>1989:09|*<br>1985:08-<br>1986:10|*<br>1985:03-<br>1985:07<br>*<br>1986:03||*<br>1987:02-<br>1987:09|*<br>1986:03-<br>1987:10<br>*<br>1989:07-<br>1989:12|
|1990-<br>1994|*<br>1991:03-<br>1992:07|*<br>1990:02<br>*<br>1992:01-<br>1992:02<br>*<br>1993:11-<br>1994:11|*<br>1991:09<br>*<br>1992:03-<br>1992:07<br>*<br>1994:01-<br>1995:10|*<br>1992:01-<br>1993:01<br>*<br>1993:10-<br>1995:02|*<br>1991:12-<br>1992:05<br>*<br>1992:12-<br>1994:03||
|1995-<br>2001||||*<br>1997:08-<br>1998:01|*<br>1999:12-<br>2000:06|*<br>1998:02-<br>1998:05|

*Except for Colombial985:12-2001:03

# **Number of Bubbles and Crashes**

|LAC|BUBBLE|CRASH 1-CMAX|CRASH 2-DLPRICE|
|---|---|---|---|
|Total|22|24|41|
|Total bubbles ending with a crash<br>|14<br>|11<br>|24<br>|
|Independent bubbles and crashes<br>|6<br>|9<br>|13<br>|
|Crashes during bubbles or vice versa|2|4|4|
Other advanced economies involved in the trade dispute would also be hurt but by much less. Their exports to the United States would fall significantly, ranging from about 5 to 12 percent, and on average by 10.6 percent, relative to the baseline. As these countries reorient their exports, diverting trade around the United States, the adverse effect on their total exports is much less, a decline by 2.2 percent. Canada and Mexico, tightly integrated with U.S. markets, are an exception, and are likely to experience significant losses, with an estimated 0.4 percent decline in GDP in each country.

In response to the higher tariffs on their exports to U.S. markets, developing countries may be tempted to retaliate with their own tit-for-tat measures. While these would help recapture some of their terms of trade losses, the results suggest that in terms of aggregate effects, efficiency losses would dominate any terms of trade gains (Figure 3).

Figure 3: Alternative strategies for developing countries: impact on GDP <!-- Start of picture text --> Percent Join the trade dispute<br>0.6 No action<br>RTAs with non-US regions<br>0.5<br>Turn the other cheek<br>0.4<br>0.3<br>0.2<br>0.1<br>0.0<br>-0.1<br>-0.2<br>-0.3<br>Regions involved in Developing Global<br>trade dispute countries*<br><!-- End of picture text --> Source: Authors’ simulations.

Note. Comparative static results. Percent deviation from the baseline in real GDP. All scenarios assume an escalated trade dispute between the United States on the one hand, and China, Mexico and other high-income countries (regions involved in trade dispute), on the other hand. *Developing countries other than China and Mexico.

For developing countries, joining the trade dispute is found to be the least desirable strategy, translating into GDP losses of 0.2 percent and a decline in exports of 0.3 percent. Compared to no action, aggregate losses are nearly twice as big if retaliatory measures are imposed.

9
# **V. Results**

# **A. Academic Participation**

The fact that the research groups are ultimately comparable allows us to causally attribute any changes in the groups at follow-up to the individual treatments. This allows us to assess families’ responses to the various programs by comparing directly the students’ who receive the treatments to the control group and to compare directly the different treatment groups. It is important to note, however, that within secondary school, different aspects of the treatments create separate incentives for attendance and enrollment. Within the academic year, the attendance targets encourage students to attend school regularly. Between academic years, however, the anticipated value of the transfers in the following year, encourage families to reenroll children in school.

Because the program started during the 2005 academic year and required that all students already be enrolled, we first consider attendance rates as measured by our team of attendance monitors during the last few months of the 2005 academic year (Figures 4 and 5 and Table 5). Since all of the treatments incentivize students to achieve the 80 percent attendance target, we first consider the aggregate effects of combined incentive schemes. The overall average effects of the treatments combined was to increase verified attendance at school by 2.8 percentage points which is statistically significant at the one percent level. The pooled effects of the treatment are graphically depicted in Figure 4 which contains a plot of a kernel density estimate of verified attendance for the treatment and control groups. Based on this graph, the treatment effect seems to operate by reducing the number of students who attend none<sup>12</sup> of the time or between 40 and 70 percent of the time and increasing the number of students who attend over 80 percent of the time.

Another way to look at the data is to plot actual attendance rates for each group verse our proxy baseline attendance rates, which is presented in Figure 5. Using a kernel weighted local polynomial estimator, we plot the relationship of actual measured attendance (on the vertical

> 12 It is important to note that students with a verified attendance rate of zero may have actually attended school at some point, but just not frequently enough to be caught during one of the visits (up to 13) conducted during the 2007 academic year.

- 20 -
informative as to what one should expect to happen to remittances as destination countries continue to adopt more skill-intensive migration policies.

This paper revisits the relationship between remittances and the educational level of migrants using microdata, allowing us to compute the association between an individual’s education level and remitting behavior. An intensive effort allows us to put together the most comprehensive micro-level database on remitting behavior currently available, comprising of data on 33,000 immigrants from developing countries from 14 surveys in 11 OECD destination countries. Using this new dataset, we begin by establishing the factual relationship between the propensity to remit and education.<sup>2</sup> With microdata we can ask whether or not more educated individuals are more or less likely to remit (the extensive margin), and whether they send more or less remittances if they do remit (the intensive margin). We find a mixed association between education and remittances at the extensive margin, and a strong positive relationship at the intensive margin. Combining the two, the fact is that more educated migrants do remit significantly more – migrants with a university degree remit $300 more yearly than migrants without a university degree, where the mean annual remittance over the entire sample is $730.

Theory provides reasons why the relationship between the amount remitted and education could be positive or negative. The more educated are likely to earn more, be repaying education loans, have more access to financial institutions, but also have their family members accompanying them, have wealthier families with less need for remittances, and have presumably less intention of returning to their home country. Using other variables from the microdata we investigate which channels seem to explain the differential remitting behavior of the more educated. We find remitting behavior to have the associations with these different individual characteristics that are predicted by theory, and that the higher income of more educated migrants appears to be the main reason they remit more.

The remainder of the paper is set out as follows. Section 2 summarizes several theories of remitting behavior and the predictions they give for the relationship between education and

> 2 We do not attempt to estimate the causal impact of education on remittances. From a policy perspective, the interest is in whether migration policies which shift the education composition of migrants affect remittances, not on whether education policies to change how much education individuals have affects remittances. Moreover, we lack convincing instruments to identify the latter.

3
Table 6: Early Life Exposure to Coffee Mills and Test Scores: Alternative Measure of Exposure

|||All Subject|s||STEM|||Non STEM||
|---|---|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|
|Exposure (% of age 0-5 )|0.0336**|0.0136|0.0336***|0.0242*|0.0073|0.0215*|0.0370**|0.0166|0.0393***|
||(0.0152)|(0.0135)|(0.0129)|(0.0137)|(0.0125)|(0.0122)|(0.0159)|(0.0142)|(0.0133)|
||[ 0.173 ]|[ 0.433 ]|[ 0.433 ]|[ 0.233 ]|[ 0.59 ]|[ 0.581 ]|[ 0.173 ]|[ 0.433 ]|[ 0.433]|
|Controls|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|School FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Exam Year FE|Yes|No|No|Yes|No|No|Yes|No|No|
|YOB FE|Yes|No|No|Yes|No|No|Yes|No|No|
|Municipality X Exam Year FE|No|Yes|Yes|No|Yes|Yes|No|Yes|Yes|
|Municipality X YOB FE|No|Yes|Yes|No|Yes|Yes|No|Yes|Yes|
|CatchmentArea X Exam Year FE|No|No|Yes|No|No|Yes|No|No|Yes|
|R-squared|0.3866|0.4260|0.4260|0.3796|0.4152|0.4152|0.3609|0.4029|0.4030|
|Observations|1487355|1487036|1487036|1487358|1487039|1487039|1487354|1487035|1487035|

Notes: Dependent variable is the standardized aggregate test scores in the respective subject groups in the Rwanda Primary School National exam. STEM subjects include Math and Science while Non STEM courses include English, Social Studies, and Kinyarwanda. Exposure (% of age 0-5 ) measures the share of a years between conception and age 5 that a student was exposed to an operational coffee mill (within ( _≤_ 5 _km_ ) of locality). Controls include gender of the student, average annual temperature, and precipitation in the exam year, as well as baseline controls such as coffee suitability and road density interacted with time trend. Robust standard errors clustered at the school level in parenthesis. False discovery rate (FDR) adjusted p-values for Exposure (% of age 0-5) are reported in square brackets (Anderson 2008). The outcome variables accounted for in the p-values adjustments include the standardized test scores of: All subjects (aggregate), STEM, and Non-STEM subjects, as well as the subject-specific test scores i.e., Maths, Science, Social, English, and Kinyarwanda.

> _∗_ Significant at 10 percent level _∗∗_ Significant at 5 percent level _∗∗∗_ Significant at 1 percent level

36
<!-- Start of picture text --> Sleeping Working Household Tasks<br>-7<br>-<br>| /\ rm<br>So / | \ ne<br>3 [ 5 I \ yp 5” | i<br>| Bs) 7 3 i} IN<br>= \ = [ \ o- /<br>.\<br>|||<br>o—= o ° Z<br>0 400 800 1200 1600 2000 2400 0 400 800 1200 1600 2000 2400 0 400 800 1200 1600 2000 2400<br>Time of Day Time of Day Time of Day<br>Education Leisure<br>© ©<br>N<br>ct l\ h ct “| |<br>.rot \ \ 5oe (| ———_ Internet<br>re) Vv °<br>| ~ \<br>oO oO<br>0 400 800 1200 1600 2000 2400 0 400 800 1200 1600 2000 2400<br>Time of Day Time of Day<br><!-- End of picture text -->
<!-- Start of picture text --> Figure 2 ‐ GDP (in constant 2000 US$) per<br>labor force<br>18000<br>16000<br>14000<br>12000<br>10000<br>8000<br>6000<br>4000<br>Fast growing countries Comparison group<br>1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005<br><!-- End of picture text --> <!-- Start of picture text --> Figure 3 ‐ Industry, value added (% of GDP)<br>45<br>40<br>35<br>30<br>25<br>20<br>Fast growing countries Comparison group<br>1970 1972 1974 1976 1978 1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000 2002 2004 2006<br><!-- End of picture text --> 46
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26
Financial Regulation and Government Revenue 3. Raising new deposits through the CBE. This would imply expanding the branch infrastructure and would dedicate all new resources to funding the government.

In the following subsections, we describe the three alternative revenue scenarios, and we estimate their cost and the revenue gains. In general, we find that imposing the NBE bill purchase on private banks generates a moderate revenue for the government, around 11–19% of the deficit and 1.5–2.6% of tax revenue, considering their respective 2011 levels of 8.220,19 and 58.980,8 million birr (National Bank of Ethiopia 2012).

In order to offer a comparison with the literature, in Appendix D we propose a calculation of the government revenue from financial repression, following the methodology introduced by Giovannini and De Melo (1993). Though their approach relies on national accounts, rather than bank balance sheets, we find that our results are consistent in terms of sign and magnitude. Beyond being a useful check of our methodology, we find this result to be useful in linking the macro approach on government revenue with the richer micro approach we employ in this paper.

# **4.1 Borrowing from International Financial Markets**

Ethiopia stopped borrowing from international financial markets after the 1991–1999 years of debt restructuring and relief (Borensztein and Panizza 2008). After the recent decade of exceptional growth and development, in December 2014 it issued its first internationally traded sovereign bonds, which were favorably received and priced at 6.625%.<sup>6</sup> This is a comfortingly low rate, provided that only in May 2014, Ethiopia was assigned its first sovereign rating by Moody’s.<sup>7</sup> and that this was a B1 rating, indicating it as a speculative trade with a high risk of insolvency.

The rating and first rate provide a useful benchmark for a counterfactual analysis, so that we can provide a few scenarios on rates that Ethiopia needs to charge in order to replace the 27% rule with international bonds. It is also useful to refer to the experience of its two neighboring countries, Kenya and Uganda, who also enjoy a B1 valuation from Moody’s and have been successfully issuing sovereign debt bonds on international financial markets over the last five years.

> 6Refer to the _Financial Times_ article“Investors pile into Ethiopia’s $1bn debut debt sale,”by Javier Blas, December 4, 2014 (http://www.ft.com/intl/cms/s/0/0efe83f2-7bbb-11e4-a695-00144feabdc0.html#axzz3pUUOSsou).

> 7Refer to the _Financial Times_ article “Ethiopia receives first sovereign rating,” by Javier Blas and Katrina Manson, May 12, 2014 (http://www.ft.com/cms/s/0/cb3a02ee-d9c7-11e3-b3e3-00144feabdc0.html#axzz3Cl8WopSH).

14
At the time of the IHS2 survey (2004/05), 43 percent of young people 15-24 years of age had resided in another village or neighborhood (Panel A, Table 1).<sup>2</sup> These numbers show how common geographic mobility is among young people in Malawi. Compared to working-age adults, rates among adults over 50 years of age are lower. About one-fifth of working-age adults have resided in their current communities for fewer than five years (Panel B, Table 1). Among younger Malawians (between 15 and 24 years of age), the rates are even higher, showing that younger adults move with high frequency. Nearly one-third (33%) of women aged 15-24 have been in their current community for fewer than five years, a finding consistent with marriageassociated migration; for men this statistic is 23%. Many of these moves occur across district lines, as shown in Panel C of Table 1. Among men in particular, almost half of movers had resided previously in another district.

Never-married women and men are less likely to have resided in a different community or village, compared to their ever-married counterparts. Among never-married men, 80% resided in the community for five or more years, compared to 66% of married men. For women, the rates are 82% and 57%, respectively. This suggests that marriage contributes to relocation. Evermarried young movers are more likely to have moved within the district (more locally), whereas among the never-married, a larger share of moves are across districts.

Young adults report a variety of reasons for their moves. Generally, local moves are dominated by marriage for men and women. These moves are within the district and therefore

> 2 The examination of rates of migration across age groups captures two effects: aging and cohort effects. The former captures the effect that older people have had more years exposed to the possibility of a move; once a person has moved, s/he is forever identified as having a migration experience. Cohort effects are the idea that processes and patterns than may evolve over time and not be driven by age but be associated with one’s cohorts.. In the case of Panel A in Table 1, the lower rate of mobility of women in the oldest age group (in terms of those who have not always resided in the current location) compared to prime-age women is evidence that propensity of a woman to move out of place of her birth has increased over time in Malawi.

7
# **c. Mortality and morbidity**

Data on mortality, years of life lost, and morbidity have been derived from the Global Burden of Disease (GBD) Project as of 2016. For each country, the years of life lost have been calculated as the total number of years lost because of premature deaths attributable to smoking among the working-age population of women and men below age 65 (table 3).

Table 3. Years of Life Lost, 2016

|_Country_|_Smoking-relate_<br>|_dyears of product_<br>|_ive life lost_<br>|
|---|---|---|---|
||_Males_|_Females_|_Total_|
|Bangladesh|346,044|5,340|351,514|
|Bosnia and Herzegovina|15,656|5,805|21,591|
|Chile|21,925|12,148|34,203|
|Indonesia|1,124,484|83,549|1,208,163|
|Moldova|29,075|2,661|31,866|
|Russian Federation|1,260,265|196,823|1,457,218|
|South Africa|66,823|33,915|100,863|
|Ukraine|517,300|64,192|581,622|

_Source:_ GBD Results Tool (database), Global Burden of Disease Study 2016, Global Health Data Exchange, Institute for Health Metrics and Evaluation, Seattle, <u>http://ghdx.healthdata.org/gbd-results-tool.</u> _Note_ : Smoking-related years of productive life lost are calculated based on Global Burden of Disease Study data by age-group among men and women below the minimum value of age 65 or life expectancy at birth..

# **d. Tobacco-related medical costs**

To maintain methodological consistency across countries, direct medical expenditures have been adapted from the calculations of Goodchild, Nargis, and Tursan d’Espaignet (2018). As part of their broader calculation of the global economic costs of smoking, these authors use the cost of illness approach to estimate the direct medical costs of smoking-attributable diseases in 2012. They apply their method to 152 countries, representing 97 percent of the world's smokers.

Table 4 presents these estimates in national current units and PPP adjustments for the eight countries in this study. It is assumed that the values calculated by Goodchild, Nargis, and Tursan d’Espaignet (2018) hold for 2016 in real terms. This assumption results in a conservative scenario for most countries in the sample, given the middle-income status of these countries.

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