rafmacalaba/datause-encoder-data huggingface dataset:
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25

**Box 2.3.  Training of judicial personnel in the application of international human rights conventions in Argentina, Brazil, Chile and Uruguay.**

With grant support from the Inter-American Development Bank, the International Women Judges Foundation undertook training of judicial personnel and law school faculty on the application of international human rights conventions such as Belém do Pará. The project used a train the trainers approach in which a core of trainers from participating countries received training in the preparation of interactive seminars for judges and were given materials for use in future seminars. These core trainers then trained a wider cadre of trainers, who in turn agreed to organize future training seminars in their countries. A web site was created to disseminate decisions by judges trained by the program in which international human rights conventions were cited in their decisions on cases involving violence against women.

An initial evaluation of the project was quite positive:

- Second-generation trainees have evaluated the core trainers as excellent or very good

- Teaching materials were judged of high quality

- Significant numbers of judges have received training (251 in Argentina, 152 in Brazil, 141 in Chile, and 121 in Uruguay)

- Trained judges credit the program with:

- alerting them to the nature and scope of family violence and gender discrimination, as well as the hidden biases and stereotypes that legitimate violence and discrimination

- demonstrating more effective and sensitive ways to question witnesses

Of course, the true test of the project is whether judges ́ rulings are influenced by the project, and this was not examined in the evaluation.

Source: Pfund (2000); International Women’s Judges Federation (2004).

accusatory system, the reform allowed doctors other than government pathologists to collect evidence of sexual crimes and to testify in court, with the goal of increasing access to these services for women and child victims.

The impact of the reform on the handling of GBV cases in Chile is decidedly mixed. On the negative side, the percentage of cases in which a conviction is handed down has declined significantly.<sup>29</sup> Casas and Mera (2004) attribute this to two factors: prosecutors only bringing cases almost certain to result in conviction to trial, and doctors (aside from pathologists) being very reticent to testify in cases of sexual violence. On the positive side, the regional victim service units established by the reform have significantly reduced problems of secondary victimization during the investigative process and trial, and prosecutors have improved their treatment of women victims. In addition, the accusatory system offers somewhat speedier resolution of cases than the previous inquisitory system.

Development banks such as the World Bank and Inter-American Development Bank have a long track record in promoting judicial reforms in Latin American countries. These projects

> 29 The percentage of sexual violence cases that end in conviction is about 1/10th that of homicide cases, and is lower than that for robbery cases (Casas and Mera, 2004).
# **1. Introduction**

Global value chains (GVCs), which only recently entered into the purview of economists, are now viewed as at the center of debate over causes and consequences of the 2009 collapse of global trade. Feenstra (1998) noted the prominence of a global “disintegration” of production, but not until Grossman and Rossi-Hansberg’s (2006) essay was there broad acknowledgment by economists of the extraordinary nature of trade within GVCs – what the authors call “trade in tasks” rather than traditional trade in final goods. Before this, trade in intermediates was generally modeled as a refinement of the international division of labor according to principles of comparative advantage (e.g. Arndt and Kierzkowski, 2001). Since the global downturn, GVCs are seen as central to the dramatic collapse of world trade.<sup>1</sup> Well documented, this collapse has been more severe, rapid and persistent than trade collapses experienced in the past, including during the Great Depression. This paper explores the role of GVCs in the trade collapse and the prospects for world trade and its geographic distribution in light of the dynamics of GVCs.

Trade decline has potentially devastating effects on export-oriented developing countries. As shown in _Figure 1_ (left-hand panel), the decline in US goods imports in the fourth quarter of 2008 and the first two quarters of 2009 was greater than the decline in US GDP and the drop in the ratio of imports to GDP over that period represented by far the greatest three-quarter decline in imports both absolutely and relative to GDP since 1980 at least. The EU has undergone a similar, if less dramatic, import decline (see Figure 1, right-hand panel). A similar pattern has been observed on a global scale as well. In an ominous sign, the drop in trade in the recent crisis has been found to be even more rapid than the decline in world trade at the beginning of the Great Depression in1929.<sup>2</sup>

**<u>Figure 1: US and EU27 Goods and Services Imports (as Percentage of GDP)</u>**

<!-- Start of picture text --> EU27 Imports (% of GDP)<br>22%<br>21%<br>20%<br>19%<br>18%<br>17%<br>16%<br>15%<br>14%<br>Goods imports Services imports<br>1999‐I 1999‐III 2000‐I 2000‐III 2001‐I 2001‐III 2002‐I 2002‐III 2003‐I 2003‐III 2004‐I 2004‐III 2005‐I 2005‐III 2006‐I 2006‐III 2007‐I 2007‐III 2008‐I 2008‐III 2009‐I 2009‐III<br><!-- End of picture text --> Source: Own illustration. Data US: Bureau of Economic Analysis, National Income and Product Accounts Table, Gross Domestic Product, seasonally adjusted at annual rates. Gray bars correspond to US business cycles recessions according to the definition of the NBER. Data EU27: Eurostat, National Accounts, seasonally adjusted and adjusted data by working days. Imports refer to extra-EU27 countries.

Leading up to the recent economic downturn, developed-country imports of goods and services were growing faster than output, as seen in the case of the US and the EU in Figure 1.

> 1 See the array of contributions in Baldwin (2009).

> 2 Eichengreen and O’Rourke (2009).

2
It should be noted that the Brazilian toy industry had retained its high level of tariff protection during the trade liberalization program, on the grounds that there were seasonal imports between July and September every year. Thus, the tariff rates applied on toys were kept at 65% until September 1992, then lowered to 55% between October 1992 and July 1993, when they were reduced to 40% until March 1994. They were then lowered to 20%, though only for three months. Once again, the industry requested a tariff increase to 30% for the June-September period, which was accepted.

The Brazilian government officially notified the WTO, in late 1996, of its decision to extend the enforcement of safeguards for three more years. It established a phase-out schedule, by which the tariff rate should converge to the 20% level agreed among Mercosur’s partners, in 2000 (Table 11).

In 1999, a new request was filed for the extension of the safeguard measure. Once accepted, additional tariffs were re-imposed for a four-year term, though at much lower rates.

In 2001, coconut producers, protected by a countervailing duty in force until August 2000, filed a petition for the adoption of a safeguard measure, a request which was accepted in mid-2002 by imposing an import quota.

Finally, toy manufacturers requested the safeguard measure to be extended for one more year, and the application was granted in late 2003. A 10% added to the common external tariff of 20% was thus imposed.

Table 11

# Safeguard Measures in Brazil: 1988-2003

|Year<br>Case|Preliminary measure (%)|Final Determination 1|o year|2o year|3o year|4o year|
|---|---|---|---|---|---|---|
|1996 Toys|70|tariff (%)|63|49|35|20|
|1999 Toys (review)||tariff (%)|34|33|32|31|
|2001 Coconut||Quota (tons)|3.957|4.155|4.353|4.551|
|2003 Toys (extension)||tariff (%)|30||||

Source: 2003 DECOM/SECEX Report and Customs, Compendium of Laws on Foreign Trade. Author's calculations.

30
**Table 1. Summary statistics of workers and employers in Rozee.pk and the entire labor market**

||Data from R|ozee.pk|Labor Force Surve<br>Enterprise Surv|y 2014‐15,<br>ey 2013|
|---|---|---|---|---|
||Mean|Std|Mean|Std|
|**Workers**|||||
|Female|0.21|0.40|0.25||
|Age|26.02|6.46|34.91|12.89|
|Education|||||
|Secondary|0.10|0.30|0.32|0.46|
|Postsecondary|0.86|0.35|0.09|0.29|
|Salary (2012 PKR)|25,362|31,389|12,271||
|**Employers**|||||
|Size (# employees)|||||
|Micro: 1‐10|0.48|0.50|0.28|0.45|
|Small: 11‐50|0.34|0.47|0.45|0.50|
|Medium: 51‐100|0.09|0.28|0.09|0.29|
|Large: 101‐600|0.07|0.25|0.13|0.34|
|Mega: 601+|0.03|0.18|0.05|0.21|
|Business age (years)|9.72|15.26|18.70|11.30|

_Source_ : Authors’ calculation using Rozee.pk data, Enterprise Survey 2013, and Labor Force Survey 2014 – 15, except for the female proportion and salary in the far right columns. They are based on Pakistan Bureau of Statistics (2015).

Wages are very different between the Rozee.pk and the entire labor market. The average and median annual salaries of the labor force participants in Rozee.pk are PKR 25,362 and PKR 16,781, respectively, while the national average in 2014/15 is PKR 12,271 (Figure 2).<sup>12</sup> This difference may be in part because of the higher education of jobseekers in Rozee.pk. On the demand side, employers post higher paying jobs at Rozee.pk. According to the distribution of monthly salaries advertised in job postings (Figure 2), the average and mean advertised salaries are PKR 33,883 and PKR 22,590, respectively. These are much higher than the national mean salary, PKR 12,271.

> 12 At the website of Rozee.pk, job seekers have an option to enter the information of current and desired salaries. Of job seekers, 45% enter the information of current salary, and 55% enter the information of desired salary.

5
Saharan Africa only 5% of adults report having originated a new loan from a formal financial institution in the past year. Adults in Central Africa are particularly likely to report using store credit (11%) and borrowing from a private informal lender (8%). Regionally, only 3% of adults report having a credit card.

Outstanding loans for funerals/weddings, school fees, and emergency/health purposes are particularly common in Africa (Figure 6). 15% of adults in Africa have an outstanding loan for emergencies or health-care needs, but 56% of them report family and friends as their only source of credit.

**Figure 6: Reported Reasons for Current Loans**

<!-- Start of picture text --> World Africa Central Africa<br>Western Africa Eastern Africa Southern Africa<br>North Africa<br>40%<br>20%<br>0%<br>School fees Emergency/health Funerals/weddings<br>Percent of Adults<br><!-- End of picture text --> Source: Demirguc-Kunt and Klapper, 2012 Only 3% in adults in Africa report having personally paid for health insurance and only 6% of those who work in farming, forestry or fishing industries report having purchased crop, rainfall, or livestock insurance in the past 12 months.

12
associated with the lower amount of funding (in absolute terms) that these countries receive. Initially, the lower level of funding, which is indeed observed, is already accounted for by the regional terms and the intercept. Secondly, when we estimate split regressions for each we still observe that have a and re- region, disruptive firms higher probability ceive a higher amount of funding than non-disruptive firms in the same region, even in specifications where we control for country effects. The lower return to disruptiveness observed in these regions has, therefore, to do with other factors. One of them could be related to a potentially higher perception of risk compared to frontier countries, where there are lower information failures and risks can be more thoroughly evaluated.

When we restrict our sample to firms obtaining funding, that is, the intensive margin of funding, in Table 3, the differences between disruptive and non-disruptive firms in the frontier and Africa become very small and not significant. This points to a critical role of the extensive margin of funding. Once firms obtain financing, there are no important differences in the value of funding according to disruptiveness. However, in the case of LAC, the premium, conditional on receiving funding, becomes negative, particularly for startups. A potential explanation for this result is that these firms associated with disruptive technologies might be perceived as relatively riskier and, therefore, obtain relatively lower tickets (around 27% lower).<sup>14</sup> Table 1: Disruptiveness and Probability of Receiving Funding

||Any Tec|hnology|Excludin<br>Payments|g Mobile<br>and Wi-Fi|
|---|---|---|---|---|
||Ever Funded|Last Funded|Ever Funded|Last Funded|
||(1)|(2)|(3)|(4)|
|Disruptive|0.164***|0.135***|0.179***|0.149***|
||(0.016)|(0.011)|(0.017)|(0.013)|
|Disruptive_×_Africa|-0.134***|-0.103***|-0.152***|-0.114***|
||(0.018)|(0.013)|(0.020)|(0.016)|
|Disruptive_×_LAC|-0.093***|-0.072***|-0.101***|-0.079***|
||(0.024)|(0.016)|(0.030)|(0.021)|
|_R_<sup>2</sup>|0.214|0.185|0.215|0.186|
|Observations|25464|25464|25464|25464|
|Countries|33|33|33|33|

Clustered errors in parentheses. *** 10%, ** 5% and * 1%. All regressions include as controls age (in IHS transformation), dummies for 9 categories of employment size, and the number of similar firms (in IHS transformation), dummies for the number of overlapping sectors where the firm operates (one or two additional sectors). The dependent variable is a dummy variable that takes a value of one if the firm received funding. Standard errors clustered at the country level.

14Tables A2 and A3 in the appendix re-estimates the tables 2 and 3 using a different definition of start-up: firm no older than 5 years. Results suggest again that most of the financial gain among disruptive firms accrues for older firms.

20
48 sectors. The introduction of th rion therefore reduces the overall marginal productivity of labor anu capital, and thereby lowers the country growth rate conditional on the observed growth in labor and capital (this abstracts from secondary effects of taxes on the supply of labor and capital). Regression results using a sample of African countries (Skinner, 1987) and a more complete sample of 111 countries (Engen and Skinner, 1988) found a significant negative effect of taxation on the marginal product of capltal and labor.

There has been a substantial amount of empirical research using cross-country data to test the impact of government spending on output growth rates. Landau (1983; 1986) found a strong negative impact of government spending (and more specifically, government consumption excluding military and educational expenditures) on output growth rates (also see Martin and Fardmanesh, 1987). To test this proposition using the data on 111 countries mentioned above, consider a simple graph which compares the average ratio of government consumption on goods and services to GDP during 1970-80 with GDP growth rates during the same period (Figure 7). The regression line (shown on the graph) is estimated to be That is, the correlation is only moderate. However, when factors such as investment and population growth rates are also included, the results are somewhat stronger: = 0.012 + 0.132(I/Y) + 1.154(L) - 0.118(G/Y) R = .227 (1.00) (4.32) (4.69) (2.61)
<!-- Start of picture text --> @ Produced share @ Imported share<br>100%<br>90%<br>80%<br>70%<br>60%<br>50%<br>40%<br>30%<br>20%<br>10%<br>0%<br>Starchy staples Pulses Fish Meat Eggs Milk and milk Vegetables Fruits Fats and oils Sugars<br>products<br><!-- End of picture text --> <!-- Start of picture text --> so<br>43 4s<br>‘imported share GHG Emissions 40<br>| 30<br>20<br>6 []2 776 10<br>27% _<br>20% 20, 2% 2% ay FtS<br>8 ax é<br>1% 12% z<br>10% “ 10% 9% %<br>bla 3% 3% bed bul 5% 5%<br>ie | |Bmw | |HE HR 05% 0.03% 0.01% 044% 0.09% 0.09% 0.00% 0.04% 0.04% 0.03% 0.00% 0.03% Z<br>Be preg? #2 ble slp ee fig F 2 Sig a e 2 £35 8 G23 53<br>é . 5z 5z@ a é<br>Starchy staples Pulses Vegetables Fruits Meat ‘Milk and milk products. Oils: ‘Sugars<br><!-- End of picture text -->

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21 unit root, equation (17.a) representf a random walk, (17.b) is a random walk with drift, while (17.c) corresponds to a case that exhibits deterministic non-stationarityl8.

(17.a) Yt - a Yt-l + ut (17.c) yt - _p_ + S(t-T/2) + a Yt l + **Ut** The selection criteria based on the PP tests are the following. We start using model (17.c) and we use the statistic Z(t3) to test the null (B-O,Pm/5,Ql) and the statistics Z(a) and Z(tV) to test the unit root hypothesis. If we do not reject the null hypothesis of a random walk with drift, represented by model (17.b), we proceed with Z(02) to test the null of a driftless random walk (p.B=O,A=l). If we do not reject the driftless case based on the statistics obtained from (17.c) we t:hen use equation (17.b) to calculate the statistics Z(01), Z(a*) and Z(.2Q*) which can then be used to perform additional more powerful tests.<sup>19</sup> The regressiors (17.a)-(17.b) are reported in table II.A and the Phillips-Perron tests in 18 The reader should keep in mind that these tests have low power against the alternative hypothesis that the variables are trend stationary instead of difference stationary (De Jong and Whiteman (1989). The latter hypothesis has the appeal of allowing for the possibility of stabilization plans.

19 It is important to start with model (17.c) because if the series (yt) is stationary around a linear trend it can be shown that T(4*-1) and Z(t **a*)** converge in probability to zero.
Just as there are comparability problems in the population data, so too for the compilations of national poverty statistics. On top of the aforementioned inconsistencies in how “urban areas” are defined, there is the problem that different countries naturally have different definitions of what “poverty” means; for example, higher real poverty lines tend to prevail in richer countries, which tend also to be more urbanized. And the urban composition of the poor probably varies with the level of economic development and urbanization. The picture one gets may well be affected by such comparability problems, although (as we will explain later) there are theoretical ambiguities about the direction of bias in estimates of the urbanization of poverty.

We address some of the weaknesses in existing knowledge relevant to Claim 2, but we have no choice but to take as given the empirical foundations of Claim 1 — based on existing national-level definitions of “urban” and “rural.” By estimating everything from the primary data (either directly from the unit-record data when available or from specially-designed tabulations from those data) we are able to assure a relatively high degree of internal consistency in quantifying the urban-rural poverty profile. We introduce a change in the methodology of the World Bank’s global poverty counts using international poverty lines, which have not previously been split by urban and rural areas.<sup>2</sup> We combine country-specific estimates of the differential in urban-rural poverty lines with existing Purchasing Power Parity exchange rates and survey-based distributions.<sup>3</sup> Thus we make the first decomposition of the international “$1 a day” poverty counts by urban and rural areas. We re-affirm Claim 2 from these new data. However, we also point to a number of continuing concerns about the available data.

What does Claim 1 imply for the future validity of Claim 2? Does population urbanization mean that the urban poverty problem will soon overtake the rural problem? We use our new estimates to assess the validity of three further claims:

> 2 The only previous estimate of the urban-rural split of poverty that we know of by Ravallion (2002) was essentially based on the poverty measures from the WDI, using country-specific poverty lines rather than an international line, such as the $1 a day standard.

> 3 PPP exchange rates correct for the fact that non-traded goods tend to be cheaper in poorer countries (where wages are lower). We use the World Bank’s PPPs.

4