rafmacalaba/datause-encoder-data huggingface dataset:
## **Figure 6: STRI by subsector and mode of supply, 2016**

<!-- Start of picture text --> Mode 1 Mode 3 Mode 4<br>Notes: Mode 1 is not computed for Road, Telecom, Maritime cargo-handling, storage, etc., Legal host country advisory and<br>representation services. Mode 4 primarily reflects entry barriers such as quantity restrictions and labour market tests for the<br>supply of services by natural persons - especially with respect to intra-corporate transferees (ICT).<br>70<br>60<br>50<br>40<br>STRI 2016<br>30<br>20<br>10<br>0<br>Wholesale Retail Telecom Insurance Commercial banking Road Maritime Air Rail Accounting Auditing Legal<br><!-- End of picture text -->

# **3.2  Evolution of services trade policy since 2008-11 (55 economies)**

The purpose of this section is to chart the evolution of services trade policies in recent years. This analysis is conducted on the basis of a similar set of subsectors and modes (i.e. excluding maritime services, internet and wholesale distribution) and for the same economies (data for 55 economies are available for both 2008-11 and 2016). The information used for 2008-11 is drawn from the World Bank Services Trade Restrictions Database, with some adjustments as explained in Borchert et al. (2019).<sup>17</sup> Regulatory information in the 2008-11 series pertains to 2008 for all economies covered except for Brazil, China and Mexico, respectively, for which data were updated in 2011. Annex 3 describes the difference between the 2016 scores for all the subsectors and modes available in the new STPD (analyzed in the previous subsection) and the 2016 scores for the subset of sectors and modes for which data were collected in 2008-11 (i.e. those described in this subsection). In this part, we present the economies in the income groups to which they belonged in 2008, to show how these economies' regulatory frameworks have evolved in the past decade.<sup>18</sup> Looking at changes over time in the level of restrictiveness (Figure 7) reveals a secular and broad pattern of liberalization in services trade policies of the 55 economies covered in our analysis. The median STRI decreased by 4 points, from 49 in 2008-11 to 45 in 2016. The STRI ranged from 34 to 73 in 2008-11, and between 25 to 72 in 2016. It appears that the decrease of the STRI is greater for many high-income economies (mainly European Union members). However, some dynamic developing economies such as Bangladesh, India or China have also liberalized significantly in the past decade. Other lower- and middle-income economies that have liberalized their services trade

> 17 See in particular section 2 (Comparing policy information over time), for a detailed comparison and a full decomposition of changes in STRI scores relative to the World Bank 2008-11 STRI.

> 18 Groups as of 2008:

> <u>High income economies (H): AUS AUT BEL CAN CZE DEU DNK ESP FIN FRA GBR GRC HUN IRL ITA JPN KOR NLD</u> NZL OMN PRT SWE USA; Upper-middle income economies (UM): ARG BRA CHL COL CRI DOM KAZ LTU MEX MYS PAN PER POL RUS TUR URY ZAF; Lower-middle income economies (LM): CHN ECU EGY IDN IND LKA NGA PAK PHL THA TUN UKR; Low income economies (L): BGD KEN VNM.

16
# **Table 9:  Total Issuance Costs as Percent of Issue Size (for US$100 mn issues)**

||**Brazil**<br>**C**|**hile**<br>**M**|**exico**|
|---|---|---|---|
|Domestic debt|2.39%|2.74%|1.18%|
|Domestic equity|4.39%|1.62%|3.93%|
|International debt|2.22%|2.22%|2.22%|

The next table takes another view of the transactions costs data. For each category of fees, Table 10 shows its percentage contribution to the total fee costs. The first point of note is that investment banking fees clearly dominate the total costs. The exception is Chile, where even though banks charge Chilean firms the usual ballpark range of 1% fees, the tax represents a higher amount (1.6% of face).

**Table 10:  Comparative Costs: Category Fees as a Percent of Total Costs**

||Brazil|Chile<br>**Domestic De**|Mexico<br>**bt**|Brazil<br>**D**|Chile<br>**omestic Equ**|Mexico<br>**ity**|**Intl. Debt**|
|---|---|---|---|---|---|---|---|
|Investment Banking|86.93%|36.56%|67.75%|91.22%|92.39%|89.06%|56.21%|
|Legal|1.11%|1.83%|3.60%|0.34%|3.08%|2.04%|24.73%|
|Regulatory|1.15%|0.23%|13.32%|0.63%|0.39%|4.00%|0.57%|
|Tax||58.79%||||||
|Publication|3.75%|0.26%|1.27%|1.33%|1.23%|0.69%|0.90%|
|Rating|5.78%|0.42%|7.39%|2.82%|2.46%|2.22%|13.49%|
|Other|1.27%|0.31%|6.67%|3.67%|0.44%|1.99%|4.10%|

There are a few other variations of note in the table above. Companies pay more to investment banks for their services in issuing equity than debt in all countries. In Brazil, publication fees account for a much higher percentage of total costs than the other countries (described above in the Brazil section). The regulatory fees in Mexico are high by comparison to the other two countries. Finally, rating agency fees do vary to a small degree by country, though not significantly.

In conclusion, the differences in the costs of issuance across these three countries and across instruments suggest that cost should be a factor in the firm’s decision making process regarding security issuance. However, it appears that the nature of the investor base, at least in Chile and Mexico, is an overwhelming factor in determining the cohort of companies that can actually access domestic capital markets. In these cases, it is the firm or issue’s credit quality that gains it access to the largest supply of finance, which is typically the pension fund industry. This finding suggests that there should be further investigation into the impact of investment restrictions for institutional investors on the development of local capital markets. Regarding access to international capital markets, the larger firms are those that can access capital abroad, as minimum size issues are more pertinent due to cost and liquidity reasons.

Further work will look at the costs of issuance in other regions, particularly across countries that do not have a substantial institutional investor base and therefore may display different demand and supply dynamics. The issuance costs can then be correlated with a host of firm characteristics and issuance patterns to more narrowly define the role of transactions costs in the firm financing decision.

19
# **2.2 Night lights and GDP**

HSW provide illustrative examples of the link between night lights and economic or political events by focusing on the Asian financial crisis and changes in night lights in Indonesia, the difference between the two Koreas, and the Rwandan genocide. Our sample includes many more salient examples. The intensity of night lights closely tracks Zimbabwe’s economic decline between 2000 and 2008, when real GDP contracted by almost 50 percent (Figure 1). When GDP growth finally turned positive again after 2008, the intensity of night lights also reversed its declining trend. On the other extreme, Equatorial Guinea’s phenomenal growth following the discovery of vast amounts of oil (per capita GDP increased 27-fold between 1992 and 2012) is reflected in the strong growth in night lights over the same period (Figure 2).<sup>2</sup> Figure 1: Zimbabwe’s economic decline <!-- Start of picture text --> ln(GDP) ln(lights)<br>23.6 11.8<br>23.4 11.6<br>23.2 11.4<br>23 11.2<br>22.8 11<br>22.6 10.8<br>22.4 10.6<br>Ln(GDP) Ln(lights)<br>1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012<br><!-- End of picture text --> Source: WDI (2014) and NGDC (2014). Authors’ calculations Figure 2: Equatorial Guinea’s growth

> 2 Despite the 27-fold increase in per capita GDP, poverty was estimated at 77 percent in 2006 (based on national poverty line).

4
thirty years of autocratic rule that followed the Congolese independence from Belgium colonial rule paved the way to a violent transition, culminating in two internationalized wars from 1996 to 1997, in the aftermath of the Rwandan genocide, and from 1998 to 2003. Since the end of the Second Congo War in 2003, eastern Congo has remained unstable and violent, with many domestic and foreign-backed armed groups – including elements of the state military, FARDC – using violence against civilians and each other (Autesserre 2010). The violence and instability have resulted in poor living conditions and regular forced displacement for Congolese civilians.

This project focuses on three provinces of eastern DRC that are especially impacted by forced displacement and political violence: North Kivu, South Kivu, and Ituri.<sup>4</sup> These three provinces account for 4.5 million out of an estimated 5.268 million total (85%) IDPs in DRC 2020 ( _UNHCR Operational Data Portal: Democratic Republic of Congo_ 2021). Other provinces not included in this study but hosting IDPs include southern and central provinces such as Kasai, Kasai-Central, Kasai-Oriental, Lomani, Sankuru, and Tanganyika. The analysis in this paper focuses exclusively on dynamics in eastern Congo where the authors collected data.

The large number of interconnected conflicts in these provinces involving non-state armed groups and state actors create a continuous ebb and flow of displacement in eastern DRC (Jacobs & Kyamusugulwa 2018). In June 2020, UNHCR estimated that over 4.5 million persons were internally displaced in Ituri (1.6M), North Kivu (1.9M) and South Kivu (1M) provinces alone (UNHCR 2020). DRC hosts an additional 536,000 refugees (UNHCR 2020) from neighboring countries with recent experiences of violence, especially Burundi, Uganda, CAR, and South Sudan. Figure 2 plots the trend in the new IDPs in the DRC between 2009 and 2020.<sup>5</sup> Most IDPs in DRC favor staying with host families as opposed to camp displacement (Haver 2008, Rohwerder 2013). In 2017, UNOCHA estimated that around 500,000 IDPs were in camplike settings, whereas 3.3 million sought refuge in host communities (Jacobs & Kyamusugulwa 4The qualitative analysis covers only the Kivu provinces, but the quantitative analysis covers all three. 5Data provided by the Internal Displacement Monitoring Center DRC Page, accessed May 14, 2021.

6
ix governments about the time use problems that arise when just a few textbooks are available for classes.

- Negotiating with and informing the teacher unions about the consequences of time loss on the poor, publicizing local-level linkages between time use and academic performance, exerting social pressure against political influence that enables nonperforming teachers to continue drawing salaries.

- Specifically training teachers to use time well, particularly through dissemination of effective classroom management practices that can keep students occupied all the time; training principals and supervisors to recognize the major components of instructional time use with brief observations and to use this system in their work.

- Publicizing the cost of time loss at the local level and making communities aware of how well children’s time is used.  In particular sensitizing lower-income parents to the signs that show whether their school teaches efficiently and to the simple means they can use to ascertain whether their children learn basic skills (e.g., oral reading fluency).

- If feasible, structuring development policy lending disbursements against significant improvements in instructional time use.

_Instructional time parameters as monitoring indicators_ . The instructional time parameters are useful for monitoring institutional performance and accountability. Well- managed schools should stay open the number of days that governments specify, have few teachers absent, and ensure that students of absent teachers are engaged in learning. Visitors should find school registries with numbers of days and hours per subject matter approximating those specified by the government. Reduced teacher absenteeism should constitute evidence of effective schoolbased management. Finally, time on task in class should constitute evidence of effective supervision by principals, inspectors, or supervisors. Additional work is needed to develop modular surveys that accurately and inexpensively estimate instructional time loss. The benefit may be worth the cost and effort. Measurements obtained in this study suggest that an overall 80% use of time could be used as a standard to attain for lower-income countries.

The effects of time use must be tested more extensively vis-à-vis learning outcomes. Much needs to be learned about the effects of time use on the delivery of curricula, on test scores and on reading fluency acquisition in the all-important early grades. Memory issues, such the rate and efficiency of consolidating new information need to be studied in relationship to the amount and distribution of learning time, in order to identify the activities that maximize long-term recall.

The future of the Education for All initiative may depend on how seriously governments and donors take instructional time wastage. Hopefully this research is only the beginning.
and monilasis. Differences in disease incidence across countries mean that competitiveness depends on more than simple comparisons of yield differentials. Producers in other countries might have higher production costs related to disease prevention or treatment; costs of labor and other inputs may also vary.

# **C.  Quantities Produced and Exported**

Cocoa production increased from about 51,000 MT in 1991/2 to about 70,000 MT in 1997/98 with inter-year fluctuations during that period (see table IV.4). Subsequent to Hurricane Georges, the sector recovered and production in 2001/2002 was estimated to be about 46,000 MT. Export earnings from cocoa exports had grown from US$35 million in 1991 to about US$93 million in 1998 but then fell by 75% to only US$27 million in 1999. Fluctuations in production and export revenues are not new. Significant hurricane damage occurred in 1979 and 1987. In fact, the World Bank’s Cocoa and Coffee Project 1982-1989 was a response to damage caused by Hurricane David in 1979. Implementation was interrupted by Hurricane Emily in 1987.

Some Dominican cocoa is processed and consumed domestically, and some is exported in a processed form. From 1990 to 2002 domestic consumption averaged about 14% of total production. A steady increase in cocoa used for internal consumption was observed from 1991 to 1998, parallel to the increases in production. Domestic consumption peaked in 1997/8 and dropped in 1998/99. Since 1999, domestic consumption has not recovered to parallel increases in production and exports.<sup>55</sup>

**Table IV.4  Production and Exports of Cocoa, 1991/2-2001/2**

||**Production**<br>**(MT)**|**Internal**<br>**Consumption**<br>**(MT)**|**Exports**<br>**(MT)**|**Value of**<br>**Exports**<br>**Million USD **|
|---|---|---|---|---|
|**1991/1992**|51,210|7,664|43,546|35.3|
|**1992/1993**|54,552|8,164|46,388|34.2|
|**1993/1994**|63,960|9,712|54,298|58.5|
|**1994/1995**|60,741|9,091|51,650|59.3|
|**1995/1996**|61,937|9,269|52,667|63.7|
|**1996/1997**|52,442|7,849|44,594|59.3|
|**1997/1998**|69,727|10,435|59,292|92.6|
|**1998/1999**|25,861|3,870|21,991|27.0|
|**1999/2000**|37,107|4,840|32,367|26.7|
|**2000/2001**|44,908|3,435|39,035|38.7|
|**2001/2002**|45,468|2,150|44,410|65.7|
|**Annual Average **|51,628|6,953|44,567|51.0|

Sources: various publications of the Comisión Nacional del Cacao and Secretaria de Estado de Agricultura, Departamento de Cacao.

# _Fermented and unfermented cocoa beans_

Cocoa beans are marketed after drying or after fermentation and drying. Fermented cocoa beans are the industry standard for export, and the Dominican Republic is the only country in the world that exports significant amounts of unfermented cocoa. International markets consider 55 Actually, data on domestic consumption are estimated based on exports. And the method of estimation has changed in recent years. Therefore changes in domestic consumption are spurious.

55
# Table 3: Sibling and Intergenerational Correlations: Full Sample

||All ch|ildren|Brothers|(Men)|Sisters (|Women)|
|---|---|---|---|---|---|---|
||1993|2006|1993|2006|1993|2006|
||||PANEL<br>|A<br>|||
|Sibling Correlation (SC)|0.642***|0.616***|0.614***|0.624***|0.780***|0.696***|
||(110.901)|(97.428)|(71.203)|(67.213)|(89.719)|(57.455)|
|Intergenerational Correlation (IGC)|0.574|0.540|0.541|0.523|0.622|0.559|
||(107.42)***|(105.48)***|(84.35)***|(82.41)***|(82.91)***|(77.53)***|
|IGC squared|0.329|0.292|0.293|0.274|0.387|0.312|
|Proportion of SC explained byIGC|0.514|0.474|0.476|0.439|0.496|0.449|
|Controling for Caste and religion|||PANEL|B|||
|Sibling Correlation (SC)|0.624***|0.586***|0.598***|0.597***|0.764***|0.675***|
||(102.885)|(87.493)|(66.670)|(61.089)|(81.993)|(51.828)|
|Intergenerational Correlation (IGC)|0.551|0.505|0.521|0.491|0.594|0.522|
||(99.15)***|(94.70)***|(77.93)***|(74.12)***|(75.73)***|(69.17)***|
|IGC squared|0.304|0.255|0.271|0.241|0.353|0.272|
|Proportion of SC explained byIGC|0.487|0.435|0.454|0.404|0.462|0.404|
|Controling for Neighborhood FE|||PANEL|C|||
|Sibling Correlation (SC)|0.395***|0.385***|0.375***|0.406***|0.460***|0.389***|
||(45.036)|(42.959)|(29.361)|(30.421)|(21.848)|(17.429)|
|Intergenerational Correlation (IGC)|0.479|0.443|0.474|0.452|0.519|0.455|
||(97.66)***|(95.66)***|(76.56)***|(75.56)***|(70.65)***|(66.39)***|
|IGC squared|0.229|0.196|0.225|0.204|0.269|0.207|
|Proportion of SC explained byIGC|0.580|0.509|0.599|0.503|0.585|0.532|
|No. of observations|34,585|39,562|21,895|23,625|12682|15,937|

Robust t statistics in parentheses. Standard errors corrected for clustering at family level * significant at 10%; ** significant at 5%; *** significant at 1% 38
# (d 'T'raininrg

Imnplemnentation of the training program was somewhat troublesome. The main initial probitmn was the lengthv negotiations of several agreements with the<sup>three training institutions</sup> and their representatives in the different sites. This was aggravated<sup>by the fact that Brazilian</sup> legislation requires state--owned companies to justify the execution<sup>of agreements without a</sup> biddifig process. which was the case since all parties involved in these agreements<sup>were public</sup> enterprises. In the end, ninieteeni asreements were signed between the company<sup>and SEBRAE,</sup> while 21 and 12 contracts were signed witth SENAI and SENAC,<sup>respectively.</sup> The overall training rrecord so far is also lower than expected. Out of the 5,886 employees that were eitlher dismissed or voluntarily separated form<sup>RFFSA, 3,046 joined the</sup> training program. _Thlis_ number includes the 997 employees that had<sup>been previously trained</sup> by the cwnpany before an agreement was reached on the overall program.<sup>Since each eligible</sup> emplovee could sign up for up to two trairiing colurses, the total number<sup>of diplomas awarded</sup> iy the three institutions was 4,573.

Why did relatively tew workers take the training? TIhere are several reasons.<sup>The first</sup> is that the time that elapsed betweeni when the workers left the company<sup>and the beginning of</sup> the trafnine. program was often excessive, as seen in Table 8. L 1~~''able 8: DELAYS IN IMIPLEMENTING TRAINING

|S< t~s<br>et<br>97|96|Dec<br>||
|---|---|---|---|
|<br>.<br>...|<br>No|<br>Dec.<br>|96<br>|
||Mach. 96|Jun.|96|
|:|<br>M~~~Nay7<br>96<br>;tMay||97|
|;|t~~~~~~~ept.<br>96|Dec.|97/|
||0<br>0 0Nov. 96|Dec.|96<br>|
|;g §.g ....|.....<br>Nov. 96<br>)tJun.<br>||96|
||N~rI~stOct.<br>96|Oct.|97|

The second factor is probably that the financial penalty imposed on those who did not apply for traininmy was not correctly planned. All payments were made when the<sup>workers</sup> leaving the company were asked to state whether they would apply for training, which was before the training program began. Since the actual separation took place<sup>before the begirming</sup> of the training prograni, RFFSA had no control over those who decided not<sup>to attend.</sup> This problenm would have been solved if the training program had started immediately atfter the acceptance of the employee's adhesion to the incentive program<sup>and before the actual</sup> separation. Payt of the employee's severance package should also have been withleld until compietion of the training program. Unfor-tunately, it was already too late when<sup>the problem</sup> was detected.
Table 9 shows how the expected climate changes described in the previous section would likely affect life expectancy in Peru over the next 50 years (holding all other factors constant).

At the aggregate level, future climate change in Peru is estimated to cause a small reduction in average life expectancy of about 0.2 years. This average, however, hides much bigger losses in the already hot areas as well as substantial gains in currently cold areas. The state that is estimated to benefit most from expected future warming is Puno located high up in the Andes Mountains. In contrast, the simulations suggest that Loreto in the jungle would suffer a 2 year reduction in life expectancy due to future warming of 2oC.

**Table 9: Simulated effects of future climate change on** **<u>life expectancy (in years), by region</u>**

|**Region**|**Estimated effect of**<br>**future climate change**|
|---|---|
|Amazonas|-1.0|
|Ancash|0.2|
|Apurimac|1.1|
|Arequipa|0.8|
|Ayacucho|0.9|
|Cajamarca|-0.0|
|Callao|-0.5|
|Cusco|1.1|
|Huancavelica|1.2|
|Huanuco|0.0|
|Ica|-0.3|
|Junin|0.7|
|LaLibertad|-0.3|
|Lambayeque|-0.8|
|Lima|-0.5|
|Loreto|-2.2|
|Madre deDios|-1.2|
|Moquegua|0.4|
|Pasco|0.8|
|Piura|-0.8|
|Puno|1.8|
|San Martin|-1.6|
|Tacna|0.2|
|Tumbes|-1.0|
|Ucayali|-2.0|
|**Total **|**-0.2 **|

_Source:_ Authors’ estimations.

Figure 6 plots the estimated change in life expectancy in each district against current life expectancy. There is a significantly negative correlation (  = -0.45) between the two, implying that currently rich districts are likely to lose from future climate change, whereas 20