rafmacalaba/datause-encoder-data huggingface dataset:
percent. Botswana and Kenya share 11 percent (Table 1). There is a need for aligning to international standards as most of Africa's imports come from countries outside the continent. These countries are better integrated into the global value chain (Table A1). Though the demand is not as important as in ECCAS, the ECOWAS market also offers Chad export possibilities. Data indicate that Togo is the top supplier of edible meat to the ECOWAS region, with 54 percent of the market share over the past five years. South Africa and Tunisia respectively rank second and third with 17 percent and 8 percent.

Table 1: Top 5 supply countries in Africa of meat and edible meat to the Central Africa region (in thousands of USD)

|Exporters|2015|2016|2017|2018|2019|Average Share|
|---|---|---|---|---|---|---|
|Africa Aggregation|26 536|12 929|24 019|27 352|34 200|-|
|Namibia|14 067|4 550|8 910|16 419|15 809|48%|
|Zambia|65|532|1 517|1 969|13 771|14%|
|South Africa|6 775|4 824|5 545|4 381|1 994|19%|
|Botswana|594|258|373|2 240|1 419|4%|
|Kenya|1 369|2 012|2 477|1 781|598|7%|
|Source: Trademap|||||||

# **2.4. Harnessing Cross-Border Trade Potential**

While the opportunities for cross-border trade are recognized, there is no consensus on how to unlock them. For example, the government has indicated its intention to limit or ban the export of animals; various programs have focused on the production side of the cross-border movements and less on the trade aspects (Kamuanga et al. (2008)) The implication of all these actions is not understood by all relevant actors, which could lead to poor implementation or reversals. Actions are needed in the following direction:

- Education and empowerment of public and private stakeholders to enable an understanding of the benefits and costs associated with cross-border trade. This includes consensus building on what is realistic to undertake in the short to medium term to limit or ban cross-border exports of live animals.

- Profiles of informal and unrecorded trade and traders will be required. More specifically, there is a need to understand the policy drivers of trade outside the formal channels, or why there is low compliance with laws and regulations.<sup>8</sup>

- Harness the benefits of cross-border trade of animals as an integral part of the long-term objective

- of promoting the livestock value chains with export only after value addition.

- Enable herders to sell their products (livestock) and purchase their basics for survival at fair prices,

- thereby fostering herders' wellbeing and other actors in border areas.

- Enhance investment by the regional government in pastoral livestock production and productivity to ensure sustained supply of good quality livestock to the growing cross-border market without depleting the livestock resource base.<sup>9</sup>

> 8  Berhanul (2017) shows that informal cross-border trade between Kenya and Ethiopia could be reduced and welfare increased with cooperation to reduce livestock cross-border restrictions.

> 9 Investments in value addition and livestock product processing facilities could provide additional marketing outlets to herders.

8
||||core FHH|Non-FHH|FHHs are less poor than non-FHHs when poverty is measured by per capita<br>expenditure.|
|---|---|---|---|---|---|
|4|Quisumbing_et al._<br>(2001)|10<br>developing<br>countries|Self-<br>reported<br>FHH,<br>females|Self-<br>reported<br>MHH, males|FHHs and individual females contribute disproportionately to overall poverty in 25-<br>50% of the dataset when headcount poverty is measured by (total/per capita/per<br>equivalent) household income and consumption expenditures and are insensitive to<br>the poverty line. FHHs and individual females are similar to MHHs or males when<br>using stochastic dominance criteria, but they are constantly worse off in Ghana and<br>Bangladesh.|
|5|Horrell and Krishnan<br>(2007)|Zimbabwe|Self-<br>reported<br>widowed<br>FHH|Self-<br>reported|The income per capita/ adult equivalent is lower in widowed FHHs than in the<br>MHHs.|
||||Self-<br>reported de-<br>facto FHH|MHH|The income per capita/ adult equivalent is higher in the de facto FHHs than in the<br>MHHs.|
|6|Medeiros and Costa<br>(2007)|8 Latin<br>American<br>ti|FHH,<br>females|MHH, males|Poverty is higher among FHHs, but there is no clear evidence of a recent and<br>widespread feminization of poverty in Latin America. Differences in poverty<br>among FHHs and MHHs increased in Argentina and Mexico, showing specific<br>types of feminization of poverty. The results are robust to different values of<br>poverty lines, the use of equivalence scales, and the distribution of household<br>income.|
|||counres|Self-<br>reported<br>FHH w/o<br>children|Couple HH<br>w/o children|The insignificant increase in poverty indices when comparing FHHs without<br>children to couple-headed HH without children in Bolivia. The rise in poverty<br>indices is significant at 5% when comparing FHHs with children to MHHs with<br>children in Costa Rica.|
|7|Deere_et al._(2012)|Latin<br>American<br>countries|HHs where<br>women have<br>ownership<br>rights|Self-<br>reported<br>FHHs who<br>have<br>ownership<br>rights|The gender of the household head is a poor substitute for a gendered analysis of<br>asset ownership within and among households since an analysis based on headship<br>tends to underestimate women’s ownership of assets.|
|8|Van de Walle (2013)|Mali|Self-<br>reported<br>widowed<br>FHH<br>Self-<br>reported<br>widowed<br>MHH|Self-<br>reported<br>married<br>FHH<br>Self-<br>reported<br>married<br>MHH|Widowed FHHs have significantly lower consumption per capita than married<br>FHHs, while MHHs do not have any significant differences in per capita<br>consumption between widowed MHHs and married MHHs.|
||||Self-<br>reported<br>widowed<br>FHH rural|Self-<br>reported<br>widowed<br>FHH urban|Widowed FHH living in rural areas have lower per capita consumption than all<br>other households living in rural areas. The gap between widowed FHHs and other<br>HHs is lower for HHs residing in urban areas.|

42
As it has been suggested in the literature, we also examine whether the introduction of the Euro has significantly modified the dynamics of external balance fluctuations. The virtual elimination of exchange rate risk, along with the reduction of sovereign risk perceived by the markets prior to the crisis, and the loss of independent monetary policy have most likely changed the transmission and variance of macro-shocks in Eurozone countries. The deepening of financial integration and the greater availability of credit could have increased the interest-rate elasticity of savings, thus possibly increasing the impact of interest rate on the current account of individual countries.

Figure 11 shows GIR for the sub-sample 1996-2011, including the 3 years preceding the formal launch of the euro in 1999, as expectations of this fact seem to have driven behaviors well ahead of its realization. Again, the direction of the response of the current account to different shocks remains mostly unchanged compared to the previous estimation. Nevertheless, the effect of _REER_ on _CA_ becomes insignificant. The results from the forecast error GVD of _CA_ contain some further insights (Table 4): In particular, the importance of real interest rate shocks appears to have notably increased in determining external balance fluctuations (8 percent after four periods against 2 percent when the estimation covers the period prior to 1996—see Table 1). This is not a result of larger interest rate shocks (as measured by unexpected interest rate variations) but a result of greater transmission of these shocks on the current account.<sup>16</sup> Furthermore, the combined contribution of _GR_ and _YGAP_ to _CA_ variance remains practically unchanged compared to the full period while, consistent with the GIRs, the contribution of _REER_ is negligible. These results remain unchanged when the alternative VAR, with the _REER_ replaced by _ULC_ , is used to obtain the relative contributions of the factors explaining the current account balance.

**Figure 11:** Responses of the _CA_ to one standard deviation shocks for **Eurozone countries [1996-2011]**

<!-- Start of picture text --> R shock REER shock GR shock YGAP shock<br>0.6<br>0.2<br>0<br>0<br>0.1<br>-0.4 -0.5 -0.8 -0.5<br>1 5 1 5 1 5 1 5<br><!-- End of picture text --> _Note._ The y-axis measures deviations from the baseline, while the x-axis represents years after shock.

> 16 Actually, the standard deviation for the _R_ shocks in the baseline VAR in the full sample [1975-2011] is higher

> (1.62) than in the more recent sample [1996-2011] (1.35).

19
**Table 3: HIV incidence analysis. Medical Research Council, General Population Cohort. Individuals aged > 17. By age, gender and period. Proportional Hazard Model (Cox Model).**

|**Stratified by gender, by year of**|**birth, by round of e**|**ntry and length of follo**|**w-up:**|||
|---|---|---|---|---|---|
||**All**|**Males**|**Females**|**Rounds 1-6**|**Rounds 6-12**|
|**All**||||||
||Hazard Ratio|Hazard Ratio|Hazard Ratio|Hazard Ratio|Hazard Ratio|
|**years of**<br>**education**|1.0005|0.99|1.013|1.019|0.978|
|**Z- Stat**|[0.02]|[-0.32]|[0.35]|[0.6]|[-0.65]|
|**N**|6405|2959|3446|3852|5434|
|**Failures**|230|124|106|127|103|
|**Time at risk**|33793|15887|17906|11685|19406|
|**Born after 1970**||||||
||Hazard Ratio|Hazard Ratio|Hazard Ratio|Hazard Ratio|Hazard Ratio|
|**years of**<br>**education**|0.933|0.959|0.918|0.988|0.88|
|**Z- Stat**|[-1.9]|[-0.68]|[-1.9]|[-0.21]|[-2.71]|
|**N**|2503|1153|1350|992|2246|
|**Failures**|82|33|49|40|42|
|**Time at risk**|9467|4712|4755|2702|6765|
|**Born before 1971**||||||
||Hazard Ratio|Hazard Ratio|Hazard Ratio|Hazard Ratio|Hazard Ratio|
|**years of**<br>**education**|1.052|1.002|1.16|1.04|1.069|
|**Z- Stat**|[1.66]|[0.08]|[2.56]|[0.96]|[1.47]|
|**N**|3902|1806|2096|2860|3188|
|**Failures**|148|91|57|87|61|
|**Time at risk**<br>Note:<br>Robust z-stat in square brac|24326<br>kets.|11175|13151|11685|12641|

<u>Note: Robust z-stat in square brackets.</u> Based on individuals present at least 2 rounds and who were HIV negative at the first round. Failure is seroconversion from HIV - to HIV + Education is measured at age 18. In case of discordance, the most often cited grade is taken, otherwise the one mentioned just after age 18 41
|Foreign ownership (25+%)|‐0.002|‐0.453|‐0.294|‐0.926**|‐0.286|‐0.946|‐0.709|‐1.395*|
|---|---|---|---|---|---|---|---|---|
||(0.334)|(0.400)|(0.342)|(0.433)|(0.419)|(0.750)|(0.432)|(0.795)|
|Export (directly or indirectly)|0.047|‐0.117|0.118|0.041|0.263|‐0.029|0.364|0.277|
||(0.207)|(0.293)|(0.220)|(0.310)|(0.281)|(0.552)|(0.293)|(0.593)|
|Constant|‐0.043|‐2.570***|0.224|‐2.266***|‐0.656|‐4.059***|‐0.190|‐3.725**|
||(0.555)|(0.749)|(0.599)|(0.798)|(0.862)|(1.345)|(0.903)|(1.478)|
|Observations|7,095|7,095|6,288|6,288|1,608|1,608|1,444|1,444|

Standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1 Country & ISIC 2‐digit FE not shown 31
conscientious, and less emotionally stable. These results point to differentiated skill needs in wage and self-employment, which can explain part of the observed substitution between wage employment and self-employment. Findings also suggest that different behavioral skills are shaped through different formative experiences, and that the process of writing an academic thesis may be relatively more effective in shaping traits such as conscientiousness than entrepreneurship training. As such, these results confirm that behavioral skills are indeed malleable, but also highlight that the content of skills training requires particular attention to the extent that different skills may be needed for different occupations.

As stated above, the design of the impact evaluation does not allow us to formally disentangle the effects of the entrepreneurship track (business training and personalized coaching) from the start-up capital offered to winners of the business plan competition. However, fewer than fifteen winners actually cashed their prize and evidence that the intervention alleviated credit constraints for participants is very limited. Indeed, most of the findings also remain robust when restricting the sample to students who did not win a prize. We interpret this as suggestive that the results are mainly driven by participation in the entrepreneurship track (training and coaching) rather than by the prizes. In fact, participants report lack of access to credit as the largest constraint to entry into self-employment.

Finally, the follow-up survey was conducted 3-6 months after the Tunisian revolution.<sup>27</sup> The occurrence of the revolution does not affect the internal validity of the findings, but the postrevolution political and economic context may have implications for their external validity. On the one hand, between 2010 and 2011, a large number of jobs were lost in the country, with immediate consequences for youth unemployment. In 2011, new social programs were rolled out and may also have affected job search intensity among graduates. On the other hand, students perceived the revolution as having improved prospects for growth opportunities in the medium term. The follow-up survey shows that most respondents had positive perceptions of their prospects in the labor market, including a stronger desire to find a job and to seek selfemployment. Overall, it is therefore not clear how the context of the revolution may have affected the external validity of the findings related to labor market outcomes, and in particular

> 27 The baseline survey was collected between December 2009 and February 2010 and the follow-up survey was collected between April and June 2011. The Tunisian revolution took place in January 2011.

26
**Table 4**

**Donor ranks with different weightings (2007), compared with CGD and EP ranks**

|||**Rank on**|**Ranks on alte**|**rnative sum**|**mary indexes**|<br>**Rank g**|**iven by**|
|---|---|---|---|---|---|---|---|
|**Donor name**|**Code**|**overall**<br>**index**|**Index_18**|**Index_20**|**Allocation**<br>**index**|**CGD/**<br>**Roodman**|**Easterly &**<br>**Pfutze**|
|African Dev.Bank|AFDB|13|14|14|9|2|3|
|Asian Dev.Bank|ASDB|1|1|1|2|7|4|
|Australia|AUS|12|10|10|12|29|12|
|Austria|AUT|34|33|32|33|33|15|
|Belgium|BEL|21|23|22|21|19|13|
|Canada|CAN|29|30|28|30|16|19|
|Czech Republic|CZE|37|37|38|37|26|.|
|Denmark|DNK|3|7|5|4|6|19|
|EBRD|EBRD|16|22|26|22|15|26|
|EC|EC|23|25|25|23|10|25|
|Finland|FIN|15|15|16|13|14|21|
|France|FRA|24|21|18|25|18|9|
|GAVI Alliance|GAVI|5|3|3|8|.|.|
|Germany|DEU|17|18|20|19|34|24|
|Global Fund|GLF|22|19|19|17|8|.|
|Greece|GRC|35|35|35|35|25|27|
|Hungary|HUN|31|16|17|31|13|.|
|IDB|IADB|9|12|12|15|1|4|
|IFAD|IFAD|7|6|7|3|3|31|
|IMF|IMF|8|5|8|6|.|23|
|Ireland|IRL|4|4|4|5|5|15|
|Italy|ITY|28|28|24|28|35|14|
|Japan|JPN|27|26|21|27|36|8|
|Korea|KOR|36|36|36|36|31|.|
|Luxembourg|LUX|18|24|27|16|9|22|
|Netherlands|NLD|6|8|6|7|12|18|
|New Zealand|NZL|20|20|23|20|21|30|
|Norway|NOR|14|17|15|14|20|6|
|Poland|POL|19|13|13|18|32|.|
|Portugal|PRT|38|38|37|38|24|9|
|Spain|ESP|25|27|29|24|27|29|
|Sweden|SWE|11|9|11|10|11|7|
|Switzerland|CHE|26|29|30|26|22|9|
|Turkey|TUR|33|34|33|32|30|.|
|United Kingdom|GBR|10|11|9|11|17|2|
|United Nations|UNTOT|30|31|34|29|28|28|
|United States|USA|32|32|31|34|23|15|
|World Bank<br>Notes:<br>Roodman index var<br>100 (average perce<br>"overall index" is t<br>"index_18" equally <br>"index_20" equally<br>_Overhead_sub-inde<br>"allocation index" i|WB<br>ies from 0-<br>ntile rank o<br>he core inde<br>weights all<br>weights all<br>xes<br>s derived fr|2<br>1, computed<br>f 5 sub-index<br>x,reproduce<br>18 indicators<br>18 indicators<br>om overall in|2<br>as quality-adjust<br>es). Other index<br>d from Table 3<br>included in any <br>included in equ<br>dex,but does n|2<br>ed aid divide<br>es are in stan<br>of the 4 sub-i<br>al_weight_18<br>ot adjust indic|1<br>d by net aid. East<br>dard deviation un<br>ndexes<br>index plus Easter<br>ators for factors o|4<br>erly-Pfutze va<br>its with a me<br>ly-Pfutze_Tran_<br>utside the do|1<br>ries from 0-<br>an of 0.<br>_sparency_&<br>nor's control|

Source: Authors’ calculations, based in part on Roodman (2006 and 2009) and Easterly and Pfutze (2008) 25
# **5.3 Impacts on decisions**

Although the reform focused on improving pre-trial procedural efficiency, it may have affected the decision phase in two ways: either through positive externalities or displacement of judges’ effort. We use our rich case-level court data to shed light on these effects.

## **5.3.1 Effort displacement across pre-trial and decision**

One possible unintended impact of the reform is that judges’ zeal in pre-trial displaced attention away from their deliberations. This could lead to an increase in both duration and number of decision hearings. We do not identify a significant jump in the duration of deliberations (Panel A, Figure 6; col 1 Table 6), the hazard ratio of completing deliberations (Panel B, Figure 6; col 2, Table 6), or the likelihood of completing this stage within one month (Panel C, Figure 6; col 3, Table 6). Additionally, cases that entered a chamber after the decree did not, on average, experience a different number of decision-stage hearings (Panel D, Figure 6; col 4, Table 6). Similarly, we see no jump in the probability of a case being heard at any scheduled hearing over the course of the decision phase (Panel E, Figure 6; col 5, Table 6). Finally, pooling across pre-trial and decision phases, we see a jump in the hazard ratio of completing the overall case (Panel F, Figure 6).

## **5.3.2 Quality of decisions**

We then investigate a potential quality-speed trade-off. As discussed above, the pre-trial procedure aims to prepare a case for judgment in the decision phase of the trial. We capture quality of the pre-trial along one dimension (completeness of the evidence brought forward) and quality of the judgment along two dimensions (judges’ documentation of the decision, and parties’ intention to appeal the decision).

First, we assess completeness of the evidence by looking at the incidence of two decision 20
those who have been exposed to extension services and who have access to the financial markets. Figure 4.3 shows that inorganic fertilizer and pesticide use is slightly higher among those who live in a village in which extension services were available in the past 12 months,<sup>20</sup> and considerably higher in the case of improved seed use. Focus groups conducted by the authors with farmers in the Northern region of Uganda underlined that extension workers provide advice mainly on pesticide and hybrid seeds, and some actually expressed a belief that fertilizer may be harmful for the soil in the longer term.<sup>21</sup> While we do not have data on the exact content of the extension services provided to farmers, it may be important in determining the relative adoption rates among the different inputs considered in this study. Similarly, households that report having taken a formal loan also use pesticide at a higher rate, though not other agricultural inputs. This suggests that access to credit may not be a binding constraint in Uganda.

**Figure 4.2: Weather conditions in Uganda 2005/06 to 2013/14 (Water Requirement Satisfaction Index)**

<!-- Start of picture text --> 100<br>95<br>90<br>85<br>80<br>75<br>70<br>2005/06 2009/10 2010/11 2011/12 2013/14<br>Central Eastern Northern Western<br>WRSI<br><!-- End of picture text --> Source: Staff calculations staff calculations using geoWRSI v 3.0, with global PET and RFE v2 (2001‐2017) time series.

**Figure 4.3: Input use seems higher among those with extension services and those taking formal credit**

**1. Extension 2. Credit**

> 20 This includes extension services provided by NAADS (National Agricultural Advisory Services), input suppliers, NGOs and other institutions.

> 21 In June 2017, the authors met with members of farmer cooperative groups in Gulu and discussed how production practices had changed in recent years and why farmers did or did not use certain inputs.

25
# Iviii **INTRODUCTnON**

Table 1 5 Long-Run and Stationary Population, Momentum, Doubling Year, and Year When Net Reproduction Rate Reaches 1 by World Bank Region and Country Department

|||Populat|ion<br>(mill|ions)|Station|-|Year||
|---|---|---|---|---|---|---|---|---|
||||||ary over|Popu-|when||
|Region and<br>country<br>department|1985|2050|2100|Station-<br>ary|<br>1985<br>popula-<br>tion|<br>lation<br>mo-<br>mentun|popula<br>tion<br>doubles|- Year<br>when<br> <br>NRR=1|
|World|4,844|10,035|11,330|11,514|2.4|1.4|2044|2060|
|Borrowers and non-borrowers|||||||||
|Sub-Saharan Africa|457|1,986|2,634|2,716|5.9|1.6<br>|2008<br>|2060<br>|
|Asia and Oceania|2,574|4,715|5,001|5,058|2.0|1.5|(a)|2055|
|Europe, Middle East,<br>and North Africa|1,147|2,187|2,502|2,539|2.2|1.3|2059|2055|
|America|667|<br>1,146|1,192|1,201|<br>1.8|<br>1.4|<br>(a)|<br>2030|
|Borrowers<br>only|||||||||
|Africa<br>Region|415|<br>1,858|2,480|2,559|<br>6.2|<br>1.6|<br>2008|<br>2060|
|1 Occidental<br>and Central|40|<br>185|<br>250|<br>258|<br>6.5|<br>1.5|<br>2008|<br>2050|
|2Eastern|106|<br>486|<br>654|<br>674|<br>6.4|<br>1.6|<br>2008|<br>2050|
|3South-Central and<br>Indian<br>Ocean|52|<br>245|<br>333|<br>343|<br>6.5|<br>1.5|<br>2008|<br>2055|
|4Western|119|<br>472|<br>592|<br>608|<br>5.1|<br>1.6|<br>2009|<br>2050|
|5Sahelian|36|<br>175|<br>252|<br>263|<br>7.3|1.5|<br>2009|<br>2060|
|6Southern|62|295|400|412|6.7|1.6|2007|2055|
|Asia Region|2,384|4,483|4,771|4,827|2.0|1.5|2099|2040|
|1 Other South|134|351|401|412|3.1|1.6|2021|2030|
|2 Southeast|274|568|609|616|2.2|1.6|2042|2030|
|3China|1,040|1,626|1,672|1,681|<br>1.6|<br>1.5|<br>(a)|<br>2000|
|4 India|765|1,602|1,732|1,759|<br>2.3|<br>1.4|<br>2041|<br>2015|
|5 Indonesia and Pacific|170|335|356|360|2.1|<br>1.5|<br>2055|<br>2040|
|ERNRegion|474|<br>1,451|1,766|1,800|<br>3.8|<br>1.5|<br>2017|<br>2055|
|1Pakistan and Turkey|146|502|607|<br>618|<br>4.2|<br>1.6|<br>2012|<br>2040|
|2 North Africa|55|170|<br>197|<br>199|<br>3.6|1.6|2014|2050|
|3 Middle East|168|657|839|<br>859|<br>5.1|1.6|2010|2055|
|4 Europe|105|121|<br>123|<br>124|<br>1.2|1.2|(a)|<br>2030|
|LAC Region|387|800|850|<br>857|<br>2.2|1.6|2043|2030|
|<br>1 Brazil|136|265|278|<br>280|<br>2.1|1.6|2061|2005|
|2 Central America|104|237|<br>254|<br>256|<br>2.5|1.7|2029|2030|
|3 Caribbean and Other|70|150|<br>162|<br>164|<br>2.3|1.6|2037|2030|
|4 Temperate and Tropical|78|148|<br>156|<br>157|<br>2.0|1.5|2101|2025|

Note: World Bank regions and country departments are defined in Exhibit 2. a. Population will not double with projected rates.