rafmacalaba/datause-encoder-data huggingface dataset:
- 13 -

On the other hand, agricultural production could be increased by any road improvement work, even though it is implemented under the traditional input-based contracts. For instance, RD585 and D60 in Katete were selected as paired roads in the control group but improved outside of the ADSP OPRC. Based on the users’ perspective, in total, about 62 percent of the roads received significant rehabilitation works in both treatment and control groups (RDA 2013).

To examine this impact, the following equation is considered: where _WORK_ is equal to one if actual road work is carried out. To deal with the endogeneity issue, the same instruments are used. In addition, _OPRC_ is also included as another instrument. It is believed that the existence of OPRCs could affect the likelihood of receiving road improvement and maintenance works, because the contractors were obliged to keep the roads in good condition.

# **IV. DATA**

Micro data were collected though two household surveys carried out before the project in 2009 and after the project in August to November 2012. The follow-up data were collected before the full completion of the OPRCs, which started in March 2009 and ended in March 2014. Thus, the impacts measured by the surveys are presumably associated with the initial rehabilitation works in the first year of the contracts. However, some emergency and maintenance works were also implemented in not only treatment but also control areas before the follow-up survey. Hence, the measured impacts may also reflect these effects.
SOUTH SUDAN’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE

**Table 8. Benchmarking air transport indicators for South Sudan**<sup>**1**</sup>

|**Country**|**South**<br>**Sudan**|Sudan|Ethiopia|Chad|CAR|Kenya|Egypt|
|---|---|---|---|---|---|---|---|
|**Traffic (2007)**||||||||
|Domestic travel (‘000 seats per year, excluding<br>South)|**0.7**|218|729|n.a.|n.a.|2,093|5,959|
|International travel within Africa (‘000 seats per<br>year)|**164**|302|1,837|110|21|3,145|1,886|
|Intercontinental travel (‘000 seats per year)|15|2,052|2,005|89|24|2,755|15,793|
|Seats available per 100 people|**1.6**|9.8|5.8|1.8|1|21|27.5|
|Herfindahl-Hirschmann Index—air transport<br>market(%)|**18.49**|15.29|70.61|36.35|50.26|39.47|24|
|**Quality**||||||||
|Percent of seat-km in newer aircraft|**19.7**|71.9|98.5|99.5|100|80.2|90.7|
|Percent of seat-km in medium or smaller<br>aircraft|**93.3**|72|39.7|93.6|23.5|20.8|51.2|
|Percent of carriers passing IATA/IOSA Audit|**0**|0|100|0|0|11.1|50|
|FAA/IASA Audit Status|**No audit**|No audit|Passed|No audit|0|0|Passed|

_Source:_ Bofinger 2009. _Note:_ The Herfindahl-Hirschmann Index (HHI) is a commonly accepted measure of market concentration. It is calculated by squaring the market share of each firm competing in the market and then summing the resulting numbers. A HHI of 100 indicates the market is a monopoly; the lower the HHI, the more diluted the market power exerted by one company/agent. FAA = U.S. Federal Aviation Administration; IASA = International Aviation Safety Assessment; IATA = International Air Transport Association; IOSA = IATA International Safety Audit. n.a. = Not applicable.

# **Challenges**

As in other infrastructure sectors, years of conflict have resulted in the neglect and destruction of air transport infrastructure. As of 2007 none of South Sudan’s four airports receive _regularly_ scheduled services. Three of the four have strips that are unpaved or appear in dire need of maintenance **.** South Sudan has made very few strides in attaining adequate levels of air safety. The EU currently has all airlines based in South Sudan blacklisted, meaning they are not allowed to fly into any EU member country. A complete ban of all airlines from a country is a strong indicator of systemic weakness in governmental oversight, as could be expected in a conflict/postconflict country. A number of small domestic carriers, registered generally as charter operators, are operational, but do not report information to a booking or ticket sales agency. The existence of these operations poses a danger to air safety because these operators could have minimal oversight, could operate questionable or faulty aircrafts, and the airlines’ training staff, maintenance crew, and pilots may not have skills consistent with acceptable EU standards.

1 All data are as of 2007 based on estimations and computations of scheduled advertised seats, as published by the Diio SRS Analyzer. This captures 98 percent of worldwide traffic, but a percentage of African traffic is not captured by these data.

22
_Ways Out of Poverty - Diffiusing Best Practices and Creating Capabilities_ _17_ But because poor people cannot simply move to areas where capable institutions already exist, pro-poor growth involves diffusing best practices and capabilities to areas where poor people live.

The mechanics of organizing institutions can be learned. For firms, organizational practices can also be "imported," through foreign direct investment or the movement of firms within countries. The really critical local element of good institutions that is hard to import is the ability of people to work well together-their behavior and their culture. Even firms cannot transplant cultures. A study of a large firm in Italy with operations in both the North and the South shows that worker behavior varies significantly in different parts of the country. When workers from the North move South, they start behaving like those in the South and vice versa (figure 10).

**Figure 10: Frequencies of misconduct of workers In an Italian firm, by region of birth**

<!-- Start of picture text --> Workers In the North Workers in the South<br>0 016 0.016<br>0.012 0.012<br>0 008 - 0.008 -<br>0 004 - 0.004 -<br>0 000 _ 0.000<br>* Born north a Born center * Born south * Born north 3 Born center * Born south<br>Source: Ichino and Ichino (1997).<br><!-- End of picture text -->
cities of 1990 by 6 to 21 (Ghana border, table 1) or 7 to 13 km (Mali border, table A.1), depending on the estimates. However, the BDs are reversed when considering the distance to locations that were already cities in 1960, especially at the Mali border. We link this post-independence urbanization process to the rapid economic growth of Cˆote d’Ivoire and, more locally, to the expansion of either cocoa (Ghana) or cotton (Mali) production. Lower population density on the Mali side could also be linked to the persistence of ”river blindness” (onchocerciasis), which was previously fought and eradicated in Cˆote d’Ivoire.

Finally, in the case of Guinea, half of the alignment is based on rivers; “parts of watercourses, from map evidence, are tortuous, indecisive and many-armed” (Brownlie 1979, 374). The southern part of this border is also rather mountainous. A few discontinuities are found for elevation and distance to rivers that could put the BD estimates in question. We give it less weight in our comments and conclusions. However, no discontinuity in welfare will be identified at this border.

# **III. Data and main results**

After a short presentation of the survey data, we analyze border discontinuities in development outcome variables.

## **Survey data on development outcomes**

We gather a database composed of 15 multi-topic household surveys: seven for Cˆote d’Ivoire, six for Ghana, one for Mali, and one for Guinea. These surveys were implemented between 1986 and 1998 (table A.2). We mainly use “income surveys”, which correspond to the frame of Living Standard Measurement Surveys (LSMS), 18
Table 13: Heterogeneous Effects - By Wealth

||(1)|(2)|(3)|(4)|(5)|(6)|(7)|
|---|---|---|---|---|---|---|---|
|Dependent Variable:|Time-use|Efort|Self-discipline|Confdence|Aspiration|Math|English|
||Index|Index|Index|Index|Index|Z-score|Z-score|
|||P|anel A: Below|Median Wealth|at Baseline|||
|Goal-Setting|0.151***|0.115*|0.123**|-0.025|-0.034|0.012|0.054|
||(0.049)|(0.065)|(0.054)|(0.045)|(0.049)|(0.063)|(0.059)|
|Goal-Setting + Recognition|0.132***|0.064|0.099*|0.007|-0.039|-0.013|0.064|
||(0.044)|(0.071)|(0.055)|(0.049)|(0.046)|(0.061)|(0.052)|
|Observations|6,451|5,984|6,632|6,586|6,118|6,852|6,852|
|Baseline Outcome Controlled|Yes|No|No|No|Yes|Yes|Yes|
|||P|anel B: Above|Median Wealth|at Baseline|||
|Goal-Setting|0.071|0.098**|0.056|0.052|0.056|0.089|0.048|
||(0.049)|(0.045)|(0.044)|(0.040)|(0.045)|(0.087)|(0.078)|
|Goal-Setting + Recognition|0.068|0.036|0.038|-0.030|-0.009|0.072|0.074|
||(0.062)|(0.052)|(0.047)|(0.056)|(0.053)|(0.090)|(0.072)|
|Observations|6,264|5,924|6,417|6,395|6,027|6,574|6,574|
|Baseline Outcome Controlled|Yes|No|No|No|Yes|Yes|Yes|

_Notes_ : This table reports the heterogeneity (by baseline wealth level of student’s household) of the impact of interventions on main outcomes of interest: Time-use Index, Effort Index, Self-Discipline Index, Confidence Index, Aspiration Index, Math test score, and English test score. Construction of these indices is discussed in Section 2. Panel A reports the results for sample which is below the median level of wealth at baseline and Panel B reports the results for sample which is above the median level of wealth at baseline. Wealth is measured using the standardized index of the sum of all the self-reported assets owned by the household. Standard errors are clustered at the level of school. *p<0.1; **p<0.05; ***p<0.01.
Malawi’s land expansion has been the second fastest among comparators averaging 2.2% per year between 1980 and 2005.

**Table 5: Land in comparator countries**

||**Arable land per capita**<br>**(hectares)**|**Total land per capita**<br> **(hectares)**|
|---|---|---|
|Zambia|0.46|6.36|
|Namibia|0.40|40.2|
|Benin|0.32|1.26|
|Tanzania|0.24|2.24|
|Mozambique|0.21|3.75|
|**Malawi**|**0.20**|**0.69**|
|Uganda|0.19|0.66|
|Kenya|0.15|1.56|
|Rwanda|0.13|0.26|
|Burundi|0.12|0.31|
|Vietnam|0.08|0.36|
|Bangladesh|0.05|0.08|

_Source: WDI, 2006_ Additionally some land is lying fallow as households are only cultivating about 80% of their plots. The IHS (2005) reported that on average household plots were about 1.2ha of which 1ha was cultivated. The PVA (World Bank, 2006) further concludes that the relationship between household wealth and land holding size is not very strong indicating that availability of land is not an immediate binding constraint on smallholder incomes<sup>21</sup> . This is confirmed by the experience of Asian countries which surmounted the problems of population density through irrigation and technology. Even after the expansion of arable area ceased, the intensification of smallholder farming has provided the underlying driver of diversified growth.

Much greater reserves of unutilized land still exist in the estate sector. A 1998 study estimated that 45% of the 1.1 million hectares of estate land was not under cultivation<sup>22</sup> . Low government rents have encouraged the retention of this land for speculation. Furthermore Malawi has made significant progress in developing a land policy – yet to be enacted into law - which brings all ownership under a single land tenure arrangement. The short-term land rental market is extremely active and expropriation of land has not been, or perceived to be a problem for investors (ICA, 2006). Although the regularization of land tenure and the resettlement of idle estate lands is a priority, it is unlikely that access to land is currently a constraint to growth.

> 21 Agricultural growth and productivity in Malawi, , Malawi PVA Volume II, World Bank, 2006 22 ibid

- 38 -
## 4.3 Summary of the findings

The simulation results presented above, which confirm and extend the findings of Drukker and Liu (2019), have important implications for our work.

Given that in our application we only have data on 282 trade agreements,<sup>20</sup> we cannot expect any of the methods considered to be able to precisely identify the set of provisions that matter for trade. The task of identifying the correct set of explanatory variables is particularly challenging in our application because many of the provisions have very strong correlations with others, and there are even cases of perfect collinearity. In this challenging context, the iceberg lasso and bootstrap lasso emerge as providing a good compromise between parsimony and the ability to identify the relevant variables. The iceberg lasso has the practical advantage of being easier to implement and not requiring the choice of additional parameters, such as the number of bootstrap samples. Consequently, the iceberg lasso is our preferred approach to select the relevant variables, but the bootstrap lasso is a credible alternative that can be used, at least, as a robustness check. Additionally, the bootstrap lasso is the only approach we have considered that can provide information about model uncertainty, but exploring that is beyond the scope of this paper.

If the objective of the researcher is to accurately predict the trade impact of a given PTA, the preferred approach is to compute the predictions using the post-lasso estimates obtained with the plug-in penalty. Indeed, this approach performs extremely well in all cases, and is only marginally outperformed by the adaptive lasso in the larger sample we considered.<sup>21</sup> However, the bootstrap lasso is also a credible alternative in this context and it can serve as a useful robustness check.

# 5 Empirical Results

In this section, we present the lasso results obtained using the methods described and studied in the previous sections. We first present results for the plug-in method before briefly discussing the results obtained using cross-validation. We then turn to the iceberg and bootstrap lasso results, which each build in their own way on the selection done by the plug-in lasso. We also include a brief discussion of using these methods for prediction.

> 20Note that the information on the effect of the different provisions is limited by the relatively small number of PTAs that are observed. Therefore, despite having a large number of observations, we effectively only have a small sample to identify the effect of the different provisions.

> 21One may wonder why the PPML-lasso with the tuning parameter chosen by the plug-in method predicts so well, even if it often fails to select the right regressor. The answer, of course, is that when the purpose is simply to predict the outcome, the results change little if the regressor with a causal impact is replaced by another that is highly correlated with it.

20
<!-- Start of picture text --> 8.2<br>8<br>|<br>oD<br>7.6<br>— Rural<br>74 Urban<br>-2 0 2 4<br>z_ndvi2018<br><!-- End of picture text -->

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# **7 REFERENCES**

- Al-Riffai P., B. Dimaranan and D. Laborde (2010). “Global Trade and Environmental Impact Study of the EU Biofuels Mandate”. Study carried out by the International Food Policy Institute (IFPRI) for the Directorate for Trade of the European Commission, March.

- BBVA Banco Francés (2008). “Luces y sombras de los Biocombustibles”. Servicios de Estudios Económicos. Año 4/No13. Primer trimestre.

- Böhringer C. and T. Rutherford (2008). “Combining bottom-up and  top-down”, _Energy Economics_ , 574-596. Brooke, A., D. Kendrick and A. Meeraus (1992). _GAMS: A User ́s Guide_ , _Release 2.25_ , Scientific Press.

- Cámara Argentina de Energías Renovables (2009). “Estado de la Industria Argentina de Biodiesel”. Reporte Primer Trimestre 2009: mercados débiles, incremento de conflictos comerciales internacionales y propuesta de solución para la Argentina, Abril.

- Chisari, O. (2009). Economic assessment of gains and losses of biofuels production in Argentina. Unpublished study prepared for the World Bank.

- Chisari O. and C. Romero (2009). _Un modelo de equilibrio general computable para la Argentina_ , UNDP, Buenos Aires.

- Diao X., E. Diaz-Bonilla, S. Robinson, and D. Orden. (2005). “Tell Me Where It Hurts, An’ I’ll Tell You Who To Call: Industrialized Countries’ Agricultural Policies And Developing Countries”. IFPRI Discussion Paper 84. Washington, D.C.: International Food Policy Research Institute.

- Ferris M. and J. Pang (1997). “Engineering and Economic Applications of Complementarity Problems”, _SIAM Review_ , Vol.39, No.4, December, 669-713.

- Folgar C. (2007) “Argentina’s Biofuels Policy”. Energy Secretariat. Korea Biofuels Conference. September 2007.

- Fridfinnson B. and J. Rude (2009). “The effects of biofuels policies on global commodity trade flows”, CATPRN Working Paper 2009-01, http://www.catrade.org.

- ICONE-Instituto de Estudos do Comércio e Negociaçŏes Internacionais. Institute for International Trade Negotiation (2008). “Overview of the biofuel sectors in selected Asian and Latin American Countries”. Asia latin-America Agri-Food Research Network (ALARN).

- IICA-Instituto Interamericano de Cooperación para la Agricultura (2007). “Atlas de la Agroenergía y los Biocombustibles en las Américas”.

23
Table 4.9

|**HomeCoun**|**es/Regionso **<br>**Average**<br>**Network**<br>**Spread**<br>**Index**|**ftheTopS0**<br>**Average**|**Non-finan**<br> **TNI**|**cialTNCsf**<br>**ShareInt **<br>**of**|**rom Devel**<br>**otalforeig**<br> **top50(%**|**opingE **<br>**nassets**<br>**)**|**onomies**<br>**Num**|**berofent**|**ries**|
|---|---|---|---|---|---|---|---|---|---|
|**Reglon/Country**<br>|**2000**|**2000**|**1999**|**2000**|**1999**|**1998**|**2000**|**1999**|**1998**|
|**WestAsia**||19.3||0.5|||1||-|
|LatinAmerica|**.**|**28.2**|**48.3**|**21.8**|**22.0**|**28.3**|**12**|**10**|**9**|
|Africa|**.**|**41.4**|**46.0**|**4.4**|**5.9**|**6.3**|**4**|**4**|**3**|
|South, East and**Southeast**Asia||**32.4**|**39.1**|**73.3**|**72.0**|**65.7**|33|36|38|
|China|2.9|28.5|**.**|3.9|-|8.8|3||3|
|HongKong|1.4|42.0|45.4|38.9,|26.4|22.0|11|11|10|
|India||.|9.6|<br>-|0.7|0.8||1|<br>1|
|Korea|7.6|23.9|27.8|13.4|23.2|16.7|5|9|6|
|Malaysia|1.6|38.1|24.1|7.2|7.0|6.3|5|5|6|
|Philippines|5.0|28.1|25.0|1.1|1.1|1.5|**1**|1|1|
|Singapore|4.0|43.2|58.9|7.4|11.2|**7.2**|6|7|9|
|Taiwan|4.8|23.1|43.9|1.4|**2.4**|**2.4**|2|2|2|
|Average/total'||31.3|34.5|100.0|100.0|100.0|50|50|**50**|
|FromTop100List for Comparison:<br>||||||||||
|U.S.<br>|<br>1|43.01||1<br>27.21|-<br>I|<br>1|<br>**231**|||
|EU||67.1||53.0|||49|||
|Japan||35.9||10.7|||16|||

a Numbers may not add up exactly due to rounding. **Note:** List does not include countries **from Central and Eastern** Europe. Source: Compiled from UNCTAD. World Investment Report 2002, Table tv.i6., 108 and Fiure IV.8., 109.

> South, East and Southeast Asia have the greatest number of companies (33) on the top 50 list and the largest share of total foreign assets (73 percent), as of 2000. Among these countries, the positions of Hong Kong, South Korea and Singapore are significant.

> While Hong Kong has largely maintained the same number of firms on the list since 1998

> (11), the number for Singapore has declined since then (from 9 to 6). South Korea also lost some firms between 1999 and 2000, but this is reportedly due to a lack of data.

> Furthermore, among the 11 newcomers to the top 50 list, seven were from East

> Asia, three of which were from China. The foreign expansion of Chinese firms has progressed rapidly. China's top 12 TNCs, which are mostly State-owned enterprises, controlled over $30 billion in foreign assets, had over 20,000 foreign employees and reported foreign sales of $33 billion in 2001. Non-State-owned enterprises, although mostly small and medium-sized TNCs, are following the same path and have investments

> in over 40 countries around the world, including Asia.<sup>97</sup>

These data indicate the prominence of East Asia's TNCs among those of developing countries. However, it is possible to see the potential for East Asian firms to become "global players" by looking at the top 100 "worldwide" list, which includes firms from both developed and developing countries. In 2000, among a record five firms from developing countries, three East Asian firms made it to this list: Hutchison Whampoa (Hong Kong), LG Electronics (South Korea) and Petronas (Malaysia), the latter two for the first time.

36
# **Table 4. What We Know (and Don’t Know) About Financial Literacy and Financial Education**

||**What we know**|**What we don’t know**|
|---|---|---|
|**Financial**<br>**literacy**<br>**levels**| **National surveys**have found that<br>financial literacy tends to be associated with:<br>-Gender, age (elderly/youth), level of income<br>and education, race/ethnicity, and region<br>(part of country, urban/rural)<br>In Africa and Pakistan, the**FinScope**<br>**surveys**indicate:<br>-Awareness of and access to different<br>financial products can vary greatly by<br>country (e.g. microfinance, mobile banking,<br>types of insurance)<br>-Most people cite lack of income and savings<br>as primary reason for not having a bank<br>account<br>-Business literacy among entrepreneurs may<br>be somewhat lacking<br>-Most people also seem to have a desire to<br>obtain financial education<br>FinScope surveys on access to finance for<br>SMEs in Africa indicate that**business**<br>**literacy**among entrepreneurs may be low,<br>based on lack of record-keeping and other<br>factors<br>Studies in developed countries show that<br>financial literacy are**correlated with**:<br>-Retirement planning (found across many<br>national studies)<br>-Investments (participation in stock markets,<br>portfolio diversification)<br>-Debt and credit performance, including<br>mortgages| **National surveys**are still lacking for<br>most middle-income and low-income<br>countries<br>Likewise, there is not enough**cross-**<br>**country**analysis of financial literacy<br>levels and financial knowledge<br>In Africa, the**FinScope surveys**do<br>not test financial literacy as such, and<br>may be missing other measures of<br>financial capability as well<br>What is the**relative importance**of<br>financial literacy? To what extent is it a<br>proxy for other indicators?<br>We lack more specific knowledge of<br>gaps in**business literacy**|

44