rafmacalaba/datause-encoder-data huggingface dataset:
strong communication strategy to reduce the risk of stigmatization and implementing the CBT workshops and math tutoring during normal schooling hours will be crucial.<sup>11</sup> Policy makers in Mexico and throughout Latin America need to recognize that secondary school dropouts are the outcome of elementary education systems that are not providing all students with sufficient foundational skills. Basic numeracy, literacy and socioemotional skills are essential to face the academic requirements of upper secondary and, later in life, to secure a formal, wellremunerated and stable job. PODER begins to show that, even among youth with a large deficit in foundational skills at age 15, progress can still be possible.

# References

- Angrist, J.; and Imbens G. (1994), “Identification and Estimation of Local Average Treatment Effects”, _Econometrica_ , 62 (2): 467-475.

- Adelman, M.; Haimovich, F.; Ham, A.; and Vazquez, E. (2017). “Predicting School Dropout with Administrative Data: New Evidence from Guatemala and Honduras”, _World Bank Policy Research Working Paper_ #8142.

- Atkin, D. (2016). “Endogenous Skill Acquisition and Export Manufacturing in Mexico”, American Economic Review, American Economic Association, vol. 106(8), pages 2046-2085, August

- Avitabile, C.; and de Hoyos, R. (2018). “The Heterogeneous Effect of Information on Student Performance: Evidence from a Randomized Control Trial in Mexico”, _Journal of Development Economics_ 135: 318-348.

- Barrera-Osorio, F.; Bertrand, M.; Linden, L.L.; and Perez-Calle, F. (2011). “Improving the Design of Conditional Transfer Programs: Evidence from a Randomized Education Experiment in Colombia”, _American Economic Journal: Applied_ 3(2): 167-195.

- Behrman, J.; Parker, S,; Todd, P.; and Wolpin K. (2015). “Aligning Learning Incentives of Students and Teachers: Results from a Social Experiment in Mexican High Schools”, _Journal of Political Economy_ 123(2): 325 - 364

- Benhassine, N.; Devoto, F.; Duflo, E.; Dupas, P.; and Pouliquen, V. (2015). “Turning a Shove into a Nudge? A ‘Labeled Cash Transfer’ for Education”, _American Economic Journal: Policy_ 7(3): 86-125.

- Blattman, C.; Jamison, J.C.; and Sheridan, M. (2017). “Reducing Crime and Violence: Experimental Evidence from Cognitive Behavioral Therapy in Liberia”, _American Economic Review_ 107(4): 1165-1206.

> 11 Some of these lessons are already incorporated in new versions of PODER currently implemented in the Mexican state of Nuevo León and in the province of Buenos Aires.

22
> <sup>11</sup>

of the legislature, the less attractive rent-seeking is to them. Even if individual politicians can each make credible commitments to some co-ethnics, the absence of a unifying ethnic party makes it more likely that they will shirk on commitments to the entire ethnic group.

Though political parties organized to pursue the collective interests of an ethnic group allow the group to limit shirking by co-ethnic politicians, parties are often not organized for this purpose. Citizens do not believe that parties will discipline members who shirk; party candidates do not believe that their support base hinges on their party affiliation. If such a party does exist, members of the targeted ethnic group should be less likely to indicate, in answer to an Afrobarometer question, that they do not feel “close to any party.” They should also be more likely to express a partisan preference than members of ethnic groups that are not the target of appeals, depending on the salience of ethnic preferences. There are three cases. In the first, non-ethnic parties appeal equally to all ethnic groups: the fraction of each ethnic group that “does not feel close” to these non-ethnic parties is similar across all ethnic groups. In this case, members of an ethnic group that is targeted by a credible ethnic party should exhibit lower levels of partisan indifference or distance than members of other groups. The more important is ethnicity, the larger should be the difference. In the second, non-ethnic parties actually appeal more to targeted ethnic groups than to those unrepresented by a party. Here again, as long as the ethnic party is credible, the targeted ethnic group should exhibit significantly lower rates of partisan indifference or distance compared to other ethnic groups.

In the third case, non-ethnic parties appeal more to non-targeted ethnic groups. Members of the targeted ethnic group could express _higher_ levels of partisan indifference, depending on whether ethnicity is more salient to the targeted ethnic group than non-ethnic issues are to the non-targeted groups. For example, ethnic groups may not be targeted by an
expedite the process of container handling, a routine usually seen more frequently in ports with limited infrastructure. Moreover, some ports in the Caribbean and in adjacent regions also benefit from quicker container turnaround due to the transshipment nature of their container traffic, _i.e._ transferring containers between vessels without requiring much terminal space and container processing time. In this paper we propose a methodology to account for the impact of these two characteristics on technical efficiency. The explicit inclusion of a variable that measures the impact of ships’ cranes on productivity is a novel contribution.

In summary, we attempt to address several aspects of port technical efficiency: (i) the contribution of the different inputs related to container traffic; (ii) the level of technical efficiency in LAC ports and their relative position in the region; (iii) the growth of port technical efficiency between 1999 and 2009; and (iv) the explanatory factors of port technical efficiency.

The paper is structured as follows: Section 2 summarizes concepts and approaches used to assess efficiency, and the existing literature on port efficiency. Section 3 shows an analysis of the database. Section 4 provides a discussion of the model. Section 5 presents the estimation results and Section 6 provides an analysis of the results and benchmark of port technical efficiency in the region. Finally, Section 7 concludes.

# **2. Methodological Review**

# **a. Port Efficiency and Other Measures of Performance**

Oftentimes, port performance is associated with various measures of partial productivity, commonly defined as ratios of output volume to input volume, and with different measures of efficiency. The former productivity indicators are usually related to time variables that aim to assess, for example, how fast cargo is handled. Examples of these indicators include _moves per ship-hour, moves per crane-hour, ship delay, ship dwell time_ and _ship productivity_ , among other indicators (Box 1). These types of port indicators provide important operational efficiency measures and, combined, can draw a detailed picture of performance along the various stages of maritime shipping. However, there is a difficulty in gathering consistent time series data of partial productivity indicators for very large samples of ports. In LAC, for example, recent efforts to compile partial performance indicators in small sets of ports in the region include Kent (2011) and IADB (2013).

4
Agriculture Organization (FAO) of the United Nations estimates that 780 million people were undernourished between 1997 and 1999.

Several regions have made considerable progress on this goal in the 1990s, including Latin America and South Asia, still, only East Asia and the Pacific is on track to meet this goal by 2015 at current rates. In Eastern Europe and Central Asia and the Middle East and North Africa, the proportion of the population which was undernourished increased in the 1990s, posing a significant challenge if the goal is to be met. It is also useful to note that while progress on hunger is correlated with income growth and poverty reduction, this relationship is complex. For example, the rate of decline in under-nourishment in East Asia was only half the rate of decline in income poverty.

||_Table _|_3:Povertyandhunger_|_1990-99_||
|---|---|---|---|---|
||(1)<br>**Undernourished**<br>**population1990-92(%)**|**(2)**<br>**Undernourished**<br>**population1996-98(%)**|**(3)**<br>**Averageannualrateof**<br>**change1990-98(%)**|**(4)**<br>**Averageannualrateof**<br>**changenecessary toachieve**<br>**goal(%)**<br>**(1998-2015)**|
|**All**developingcountries|21|18|-2.2|-3.0|
|East AsiaandPacific|**21**|**17**|**-3.0**|**-2.7**|
|EuropeandCentralAsia||**6**|||
|Latin Americanand<br>Caribbean|13|**l**I|-2.4|-2.9|
|MiddleEastandNorth|||||
|Africa|8|IC|3.2|_-5.1_|
|SouthAsia|27|23|-2.3|-3.0|
|Sub-SaharanAfrica<br>__________________|__<br>~35|34|-0.4|-3.7|
|Source:FAO|||||

# _Goal 2 - Universal Primtary Education_

The Millennium Development Goals call for reaching universal enrolment of children in primary school by 2015, so that every child may be able to complete a full course of primary education. Progress toward this target is commonly measured by the net enrollment rate, which measures the ratio of enrolled children of official school age to the number of children of the same age in the population. Unfortunately data on net enrollment rates are available for fewer than a half of all developing countries, and even where data are available they are often late or incomplete. The enrollment rates shown in Table 4 are based on regional estimates produced by UNESCO using a special data set constructed for the 2000 Education for All conference in Dakar. UNICEF is currently developing new estimates for the U.N. Special Session on Children, to take place in May 2002.

6
the above discussion, this finding indicates that either the voracity effect does not hold in practice or, most likely, that POLCON is not the appropriate variable to capture the detrimental of more claimants on available resources.

Finally, Column (5) includes the four explanatory variables in the same regression. Due to multicollinearity, no explanatory variable is significantly different from zero at the 10 percent level.<sup>25</sup> However, the F-test for the joint significance of financial openness, corruption control, and GDP volatility (i.e., the variables that validate the respective theoretical explanations) is significant at the 5 percent level.<sup>26</sup> We thus conclude that all of our theoretical expectations hold to econometric scrutiny, except for the voracity effect hypothesis that would require further research to find an index that captures with more precision what the theory has in mind.<sup>27</sup>

# 4 Testing institutional variables

In this section, we test whether institutional variables may play a role in reducing procyclicality. Based on the stylized facts in Section 2, we expect government stability and law and order to be important factors in reducing fiscal procyclicality. As already mentioned, the source of these two variables is the International Country Risk Guide (ICRG). The ICRG views government stability as the government’s ability to carry out its intended public policies and its ability to stay in offi ce. It has three subcomponents: government unity, legislative strength, and popular support. Law and order are assessed separably. For “law,”strength and impartiality of the judicial system are the critical features, while for “order” the critical factor is the public’s observance of the law.

A new variable that we will introduce in these institutional regressions is whether countries have a sovereign wealth fund or not.<sup>28</sup> More specifically, we are thinking

> 25We should note, though, that the p-values for financial openness, corruption control, and SD(Y) are 0.13, 0.12, and 0.12, respectively; very close to the 10 percent significance level.

> 26In line with Lane (2003), and to check the robustness of our results, we added GDP per capita, size of the public sector relative to GDP, and openness (export + imports as a proportion of GDP) as controls in regression (5) one at a time. GDP per capita is significant at the 5 percent level but the two other control variables are not significant. In all three cases, the F-test for the joint significance of the three relevant explanatory variables is significantly different from zero at least at the 10 percent level.

> 27See the discussion above, Lane (2003), and Tornell and Lane (1998).

> 28This is a dummy variable that takes a value of 1 if a country has a sovereign wealth fund and 0 otherwise.

16
important to check household and individual characteristics across these two groups of households.

**Table 2: Summary Statistics of Household Demographics and Individual** **<u>Characteristics by Covid-19 Status</u>**

||Full Sample|Control|Treatment<br>(Covid)|Difference (T-<br>C)|Standard Error|P-value|
|---|---|---|---|---|---|---|
|**Individual Characteristics**<br>|||||||
|Male|0.48|0.48|0.48|0.00|0.01|0.87|
|Age|25.19|25.09|25.24|0.15|0.25|0.56|
|Literate<br>|0.64|0.66|0.63|-0.03|0.01|0.00|
|**Location and Settlement Type**|||||||
|Moroni|0.11|0.12|0.10|-0.02|0.01|0.00|
|Rest of Ngazidja|0.40|0.39|0.40|0.00|0.01|0.72|
|Ndzuwani|0.43|0.40|0.45|0.05|0.01|0.00|
|Mwali|0.07|0.09|0.05|-0.03|0.00|0.00|
|Urban|0.32|0.35|0.29|-0.07|0.01|0.00|
|**Household Characteristics**|||||||
|**Amenities**|||||||
|Water Access|0.86|0.83|0.88|0.05|0.00|0.00|
|Sanitation Access|0.59|0.58|0.60|0.02|0.01|0.00|
|Electricity Access<br>**Dwelling Features**|0.84|0.85|0.83|-0.02|0.00|0.00|
|Improved Roof|0.99|0.99|0.99|0.01|0.00|0.00|
|Improved Wall|0.48|0.48|0.48|0.00|0.01|0.85|
|Improved Floor|0.81|0.81|0.81|-0.01|0.01|0.14|
|**Other characteristics**|||||||
|Female- Headed|0.34|0.31|0.35|0.04|0.01|0.00|
|Dependency Ratio|1.12|1.14|1.10|-0.03|0.01|0.01|
|Polygamous|0.07|0.06|0.07|0.00|0.00|0.30|
|Single- Headed|0.10|0.10|0.11|0.00|0.00|0.24|
|People per Room|2.37|2.48|2.30|-0.19|0.02|0.00|
|**Head's characteristics**|||||||
|Age|45.76|45.92|45.66|-0.26|0.17|0.13|
|Literate|0.75|0.78|0.73|-0.06|0.01|0.00|
|No Education|0.59|0.58|0.59|0.01|0.01|0.08|
|Primary Educ.|0.12|0.13|0.12|-0.01|0.00|0.01|
|lower secondary|0.10|0.10|0.11|0.00|0.00|0.31|
|upper secondary|0.06|0.07|0.06|-0.01|0.00|0.03|
|Tertiary|0.13|0.12|0.13|0.00|0.00|0.52|
|**Samples**|**29,192**|**17,480**|**11,712**||||

**Note** : “Difference” captures the raw difference between the post-Covid-19 sample (treatment) and the pre-Covid-19 sample (control). Statistical significance: *** p < 0.01, ** p < 0.05, * p < 0.1

The survey data provides important individual and household characteristics like age, gender, marital status, location of settlement, educational attainment, access to basic amenities, and other household demographics. Table 2 presents summary statistics of these characteristics by Covid19 status. The regional distribution shows no difference between the pre- and post-Covid-19 samples for the rest of Ngazidja, the main island, which accounts for 40% of the total sample. About 43% of the sample is resident in Ndzuwani Island, the second largest in the Comoros, with observed differences between the treatment and control groups. The individual demographics are similar for pre- and post-Covid-19 except for the literacy rate, which is higher for the control group. There are some differences in household access to basic amenities 17
The results in the first column indicate that areas with more fighting have seen a larger increase in female primary attainment. Casting this result in terms of the distribution of conflict violence, this implies that an increase in violence by one standard deviation of the district-level distribution of casualties during the conflict (0.98 casualties per 1000 inhabitants) increases female primary schooling attainment by 5.6 percentage points. Put differently, this is roughly the effect of a dramatic move from the 5<sup>th</sup> to the 75<sup>th</sup> percentiles of the district-level conflict distribution of total casualties. The sign of this effect is confirmed when comparing cohorts born only five years apart observed at the same age (between 10 and 18 years old) in 2001 and 2006 (Column 5), although this effect is somewhat larger but less precisely estimated and so only marginally significant. When investigating separately exposure to conflict at ages 6-10, the “normal” primary schooling age when over 80% of children are enrolled in primary school (author’s calculations based on DHS 2006 data) and at 11-14, when the share enrolled decreases sharply from 70.5% to 16.1%, neither conflict exposure coefficient is statistically significant, nor are these coefficients statistically significantly different from each other.

Across all specifications in Table 4, conflict exposure does not appear to affect male primary schooling completion.

The estimates reviewed so far suggest that any positive factor contributing to an increase in female educational attainment is broadly common to all girls aged 6-14, which rules out an explanation due to a reduction in the incidence of early marriage and indicates that, although parents may be reluctant to send young female teenagers to school due to the risk of sexual violence, this effect is compensated by other channels favorable to girl schooling, as otherwise one would expect that conflict experienced while 11-14 would tend to decrease educational attainment. The results in Table 4 also suggest that such favorable factors are not shared equally by boys. Although boys are more likely to be recruited by the insurgents, this is unlikely to be affecting children under the age of 11, and so the results from column (4) showing no effect of the conflict at age 6-10 on primary attainment do not give support to the hypothesis that Maoists may have encouraged schooling for all, e.g., due to their “view that schooling is essential to the creation of a new generation of enlightened revolutionaries” (Hart, 2001, p.33), their sometimes mentioned role in enforcing attendance of teachers and other government employees (Hart, 2001; Collins, 2006), or overall encouragement of education among deprived ethnic minorities.

Table 5 goes about here 22
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**Annex 4: Variable Descriptions and Sources**

|**Variables**|**Description**|**Source**|
|---|---|---|
|Tax collection<br>(Tax/GDP)|(Average 1994-2003). Tax revenue refers to compulsory<br>transfers to the central government for public purposes.<br>Social security contributions are included.|WDI (2006)|
|Revenue collection<br>(Revenue/GDP)<br>GDP per capita|(Average 1994-2003). Revenue is cash receipts from<br>taxes, social contributions, and other revenues such as<br>fines, fees, rent, and income from property or sales. Grants<br>are also considered as revenue but are excluded here.<br>(Average 1994-2003). GDP per capita is gross domestic<br>product divided by midyear population. GDP is the sum of<br>gross value added by all resident producers in the<br>economy plus any product taxes and minus any subsidies<br>not included in the value of the products. Data are in<br>constant U.S. dollars.|WDI (2006)<br>WDI (2006)|
|Population Growth|(Average 1994-2003). Total population between the ages<br>15 to 64 is the number of people who could potentially be<br>economically active.|WDI (2006)|
|Age Dependency Ratio|<br>(Average 1994-2003). Age dependency ratio is the ratio<br>of dependents--people younger than 15 or older than 64--<br>to the working-age population--those ages 15-64. For<br>example, 0.7 means there are 7 dependents for every 10<br>working-age people|WDI (2006)|
|Trade Openness<br>(Export + Import)/GDP|.<br>(Average 1994-2003). Trade is the sum of exports and<br>imports of goods and services measured as a share of gross<br>domestic product.|WDI (2006)|
|Agricultural Value<br>Added/GDP|<br>(Average 1994-2003). Agriculture corresponds to ISIC<br>divisions 1-5 and includes forestry, hunting, and fishing,<br>as well as cultivation of crops and livestock production.<br>Value added is the net output of a sector after adding up<br>all outputs and subtracting intermediate inputs. It is<br>calculated without making deductions for depreciation of<br>fabricated assets or depletion and degradation of natural<br>resources.|WDI (2006)|
|Bureaucratic Quality|(Average 1994-2003). The institutional strength and<br>quality of the bureaucracy is a shock absorber that tends to<br>minimize revisions of policy when governments change.<br>The original score ranges from 1 to 4. High points are<br>given to countries where the bureaucracy has the strength<br>and expertise to govern without drastic changes in policy<br>or interruptions in government services. The score is<br>recalculated to -10 to -1 where low points are countries<br>with strength bureaucracy and high points are countries<br>with weak bureaucracy.|ICRG (2006)|
|Corruption Index|<br>(Average 1994-2003). The assessment of corruption<br>refers to the political system. Corruption index ranges<br>from 1 to 6. High points are given to low corruption<br>countries and low points are given to high corruption<br>countries. The scores are recalculated to -10 to -1 where<br>low points mean low corruption and high points mean<br>high corruption.|ICRG (2006)|

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