rafmacalaba/data-use-mentions-extended huggingface dataset:
NIGER’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE time, at 48 hours, is the highest among West African ports. The global best practice for truck cycle time is one hour, and it is six hours in the Port of Cotonou (table 3). Delays in the Port of Lomé and administrative requirements are responsible for the bulk of the time consumed importing to Niger using the Lomé-Niamey corridor. This port is one of the busiest in West Africa, handling around 460,000 TEUs<sup>5</sup> per year. Crane productivity in Lomé is 23 tonnes per hour, substantially better than elsewhere, although still short of the international benchmark. The international standard for the time containers spend in the terminal (dwell time) is 7 days or less, but in the Port of Lomé it is 13 days (table 3).

Time-consuming regulatory processes related to customs clearance and technical controls further augment the total time of trading across borders. In the Cotonou-Niamey corridor, administrative procedures account for about 23 percent of the total time required to move imports, and in the LoméNiamey corridor they consume around 35 percent (figure 6b). The administrative process in Niger is particularly cumbersome; unnecessary and often redundant formalities significantly prolong the time needed for customs clearance. This results in the costly immobilization of goods and means of transport (Enhanced Integrated Framework 2008).

**Table 3. West African port indicators**

|Port|Unit|Lomé,<br>Togo|Cotonou,<br>Benin|Dakar,<br>Senegal|Abidjan,<br>Côte<br>d’Ivoire|Tema,<br>Ghana|Apapa,<br>Nigeria|Harcourt,<br>Nigeria|
|---|---|---|---|---|---|---|---|---|
|Total container cargo handled|TEU,annual|460,000|158,
there are no statistically significant differences in demographic characteristics between parents with two boys and those with another sibling composition, and also between those with two girls and otherwise, except for the variable _age_ . Parents with two girls and just the fathers with two boys seem to be slightly older than those with another sibling composition. The magnitude of these differences represents only 1% of the sample means of age for mothers and fathers, which is quite small. However, the variable _age_ is used as a control in all the specifications of the paper in order to avoid any concerns.

Another possible threat to the validity of the exclusion restriction in this identification strategy is put forward by Rosenzweig and Wolpin (2000). They argue that having mixed sex siblings may violate the exclusion restriction by directly affecting both the marginal utility of leisure and child rearing costs and, thus, labor-force participation. For example, if spending on various child-related goods is different for parents who have a son versus those who have a daughter, the analysis will be contaminated by the direct impact of a child’s sex on childcare costs. Using Indian data, they find that expenses for clothing of the third child are significantly lower if the older siblings are of same sex. They attribute this effect to “hand-me-down” savings, which are

> 17Several methods to discriminate among unborn girls co-exist today in the world. The most recent methods are based on pre-conception selection and require access to the elaborate equipment necessary to perform sperm sorting, pre-implantation genetic diagnosis (PGD) or in-vitro fertilization (IVF). Cost and accessibility factors, however, restrict these technologies to developed countries and to the most affluent populations. These services were not generally offered by the public health service in Albania during the period studied. On the contrary, sex selective abortions offer a far easier and more accessible route to avoid female births (UNFPA, 2012).

15
# I – Introduction

## I.I – Monitoring Electricity Reliability

Sustainable Development Goal 7 aims to ensure universal access to affordable, reliable, and modern energy services by 2030. Electricity outages have serious negative consequences on wellbeing, productivity, and livelihoods. Best-practice monitoring systems in upper-income countries rely on sophisticated monitoring equipment that relay detailed information on the provision of electricity service to users. However, such systems are absent in most developing countries, and even where the infrastructure is present, the data are only seldom reliable and publicly available.

To address data gaps, outages, and the constraints service disruptions impose on users, are often monitored using surveys. Data collection efforts as diverse as the World Bank Enterprise Surveys, the U.S. Consumer Expenditure Survey, Living Standards Measurement Surveys, and a wide range of theme-specific research surveys all collect data on electricity outages for the purposes of understanding the effects of electricity outages on economic and social outcomes. In most cases, these surveys collect information by means of traditional recall methods.

However, survey data that identify electricity outages are subject to imprecision arising from several sources and establishing the effects of outages is often complicated by shortcomings in measurement. Some of the better-known challenges relate to recency, recall, desirability, and rounding biases. The imprecision that arises from these errors can have practical consequences for policy in several ways. For instance, outage monitoring is often used as a measure of accountability, and imprecision or bias in reporting could lead to inefficient management of resources. Likewise, systematic reporting bias between consumer subgroups could generate inefficiencies in the allocation of scarce resources. Biases in consumer perceptions could also lead to invalid estimates of the impact or potential outcomes of policy reform.

There is empirical evidence in the consumer behavior literature that systematic reporting differences may be a concern when using surveys for accountability purposes. Levanon et al. (2015), for instance find that across 12 countries, consumers with high social status tended to register more service failures and to complain more frequently than customers of lower
# 1.2 Data for Cross-Country Cross-Sector Analysis

For country-sector speci...cations, we estimate the impact of country-sector determinants in year t-3 on country-sector GVC participation in year t, e¤ectively covering the period 19992015. Summary statistics for the EORA GVC participation shares and all the determinants at the country-sector level are provided in Table A5 and correlations among all determinants at country-sector level are provided in Table A6.

We interact each country-year determinant with its corresponding sectoral intensity of use across manufacturing sub-sectors to obtain measures that vary at the country-sectoryear level. For labor endowments we use the country-level share of high-skilled workers following the ILO’s de...nition of low skill (skill levels 3 and 4) based on the International Standard Classi...cation of Occupations. We interact the country skilled labor endowment with a benchmark measure of skill intensity in the US de...ned as the ratio of skilled workers to unskilled workers by sector taken from the NBER-CES Manufacturing Industry Database. For capital endowments, we use the ratio of real capital stock to total employment at the country level from Penn World Tables which we interact with a benchmark measure of capital intensity in the US by sector taken from the NBER-CES Manufacturing Industry Database.

To capture natural resources endowments, we interact the natural resources to GDP ratio from WDI with an indicator for natural resource intensive sectors based on Braun (2003). For country institutional quality we use the rule of law index from the World Governance Indicators database which is interacted with the measure of contract intensity proposed by Nunn (2007). The measure of contract intensity is determined by the share of specialized and customized intermediate inputs used in the production of the ...nal good for each sector based on a U.S. input-output table.<sup>42</sup> Additionally, our model controls for three types of tari¤ measures: tari¤s imposed at home on imports of the sector, tari¤s imposed at home on imports of intermediate inputs used by
seemingly contradictory views may exist. For example, in urban areas, tolerance levels are generally lower and refugees are more likely to be perceived as an economic burden, and yet hosts are more likely to hold progressive (cosmopolitan) attitudes to refugee rights. The Turkana hosts around Kakuma see refugees as a security threat while generally regarding them as friendly and valuing their economic contribution. Ugandan landowners in Nakivale view refugees as both competitors and contributors. These ‘mixed’ sentiments – positive and negative – are not mutually exclusive, but rather co-existent in host society. However, we also observe some clear patterns across contexts. For example, the role of ethno-linguistic proximity (notably revealed through the so-called ‘Somali bond’) plays a role in shaping attitudes, the importance of neighbourhood and household-level attitude formation, and the role of a household’s own socio-economic position in relation to refugees (landlord or tenant, employer or employee, landowner or non-landowner) are important themes across context. Furthermore, a clear theme is the distinction between urban and rural contexts: through our Uganda data there is evidence that direct interactions may play a causal role in shaping attitudes in cities, but not in camp-like settings. Further, we find evidence across the countries that attitudes in camp-like settings are more positive than in

> 21These two statements were asked to hosts in all sites except the Kakuma Camps in Kenya.

28
dynamics which could give rise to a poverty trap. Pseudo-panel analysis reveals more persistence in labor earnings, suggesting that measurement error is rather large in the panel data. Nevertheless, although we find some evidence of non-linearities in income dynamics, we estimate that the mapping from lagged income to current income always crosses the 45 degree line with a derivative less than unity. Hence, there is no evidence for a poverty trap in labor income. This result is robust to allowing different education groups to exhibit different income dynamics, and to using full income or expenditure in place of labor earnings.

In related work, Antman and McKenzie (2005) show that absolute mobility in Mexico is very low, suggesting that the high level of cross-sectional inequality in incomes will persist over time. Thus although the lack of a poverty trap suggests that poor individuals can experience income growth little by little over time, and not be trapped below some threshold level, this will be a rather slow process. The “Mexican dream” may therefore take a long time to be realized.

# 7 References:

Antman, F. and D. McKenzie (2005) “Earnings Mobility and Measurement Error: A Pseudo-Panel Approach”, Mimeo. The World Bank and Stanford University.

Arellano, M. and S. Bond (1991) “Some tests of specification for panel data: Monte Carlo evidence and an application to employment equations”, Review of Economic Studies 58: 277-97.

Azariadis, C. and J. Stachurski (2004) “Poverty Traps”, forthcoming in P. Aghion and S. Durlauf (eds.) Handbook of Economic Growth.

Bardhan, P., S. Bowles, and H. Gintis (2000) “Wealth Inequality, Credit Constraints, and Economic Performance”, pp. 541-603 in A.B. Atkinson and F. Bourguignon (eds.) Handbook of Income Distribution, North-Holland: Amsterdam.

Banerjee, A. and A. Newman (1993) “Occupational Choice and the Process of Development”, Journal
# **Figure 2. Long clearance through customs adds to transport costs...**

_Average days required for customs clearance for imports_ <!-- Start of picture text --> High Income<br>Morocco<br>Poland<br>Malaysia<br>Turkey<br>Hungary<br>Mexico<br>Brazil<br>Ukraine<br>Russia<br>Korea<br>Panama<br>Thailand<br>China<br>Argentina<br>Indonesia<br>Peru<br>Chile<br>Bolivia<br>India<br>Ecuador<br>0 2 4 6 8 10 12 14<br><!-- End of picture text --> _Source:_ World Bank, World Business Environment Surveys and Investment Climate Surveys, reproduced in Bagai, Newfarmer, Wilson (2004).

Complex requirements in cross-border trade increase the possibility for corruption. For example, at the key border crossing point between India and Bangladesh as many as 1,500 trucks queue up on both sides of the border with waiting times varying between one and five days to complete documentation requirements. Expediting customs clearance procedures reduces the discretionary power of customs officials, thus reducing the scope for corruption. An efficient, friendly and corruption free customs can help boost trade and investment. An empirical study by Cudmore (2004) finds that reducing border delays is critical for trade liberalization to have a positive impact on welfare.

TF in the context of customs involves the avoidance of unnecessary trade restrictiveness – while strengthening the quality of controls in an internationally harmonized manner. Customs can play an important role in TF by speeding up its reforms, providing best practices to other trade sectors, enhancing implementation capacity – contingent on support from all stakeholders (See Box 3).

13
the ENLACE. Furthermore, in conversations with INEE, the suggested proportion of students copying was reported to be in nearly all cases less than 10%. Thus, we consider unlikely that the presence of some cheating/copying biases our results on the ENLACE scores.

For our main analysis, we begin with the database of all sixth graders taking the ENLACE test in the school year 2007-2008, the second year in which ENLACE was applied. We focus on this group because it is the group for which we can merge the largest number of follow-up tests. In particular, we can merge to this database of sixth graders in 2007-2008, test results in 2008-2009 (seventh grade for most), 2010-2011 (ninth grade for most) and 2013-2014 (twelfth grade for most). In this way we construct a panel of achievement tests over a period of six years. Because program effects on enrollment and attendance in the primary level are lower than in subsequent levels (Behrman, Sengupta and Todd, 2005), we treat the 2007-2008 sixth grade scores as a baseline, that is unlikely to have been significantly impacted by PROSPERA although households in which the children in our database reside, may have been receiving grants before this period. To the extent achievement of PROSPERA children had already been affected in our data before the school year 2007-2008, the results we report here represent an underestimate of total program effects on achievement.<sup>5</sup> This approach also allows us to treat taking the ENLACE test as a proxy for being enrolled in school. Although defining enrollment in this way underestimates the overall level of children enrolled in school, estimates of the impact of PROSPERA on enrollment will be unbiased assuming the implementation of the ENLACE does not differentially affect who attends school the day of the application with regard to PROSPERA receipt. This seems a reasonable assumption.

The construction of the panel allows us to study educational trajectories of youth between sixth grade and twelfth grade beginning with sixth graders in 2007-2008. We choose this panel as it
perceptions of social mobility, satisfaction with life and perceptions of government performance. We use the second Life in Transition Survey (LiTS II), which allows such an exploration of the effects of the crisis, based not only on outcomes but also subjective perceptions, beliefs and choices, for 29 transition countries and five comparator countries in western Europe (France, Germany, Italy, Sweden and the United Kingdom).

While the poor report being affected by the crisis more (subjectively), household budget surveys suggest that the crisis had a marginal impact on poverty incidence and affected mostly the 2
get more in transfers than what they pay in taxes, except the two richest deciles.<sup>22</sup> The total expenditure in transfers (monetary and in-kind) amounts to 4 trillion MNT in 2016. If indirect subsidies in mortgage interest, electricity and central heating tariffs were included, an additional 0.8 trillion should be added. But tax collections in PIT, VAT, SSC and excises is 3.2 trillion (see Table 1).<sup>23</sup> This means that 25 to 35 percent of redistribution though monetary and in-kind transfers is funded by taxes not collected form households and individuals.<sup>24</sup> Given the instability of royalties and corporate taxes related to mining commodity exports, sustainability of transfers depends crucially of responsible fiscal management.<sup>25</sup> Absolute progressivity can be summarized in the concentration index. The concentration index is defined as the twice the average difference between the cumulative proportion of the population ranked by market income, beginning with the poorest, and the cumulative proportion of a given tax (or transfer). The index ranges from -1 to 1 taking a negative value when the poorest groups of the population get a more than proportionate concentration of transfers, and a positive value when they get a share of transfers that is smaller than their share of population. A negative concentration index implies that they are progressive in absolute terms (i.e., the poorer groups get a larger share of total transfers), whereas a positive index means the transfer is regressive in absolute terms (i.e.,

> 22 This statement is based on survey data. Since survey data do not replicate the amounts of taxes and transfers from administrative data, the statement should be taken as an approximation, if survey data at least capture the relative proportion of taxes and transfers, although not its absolute levels.

> 23 These, in contrast to the previous footnote, are figures from administrative data.

> 24
and the Cognitive Reflection Test (CRT). Our measure of locus of control indicates children think they are unable to control what happens in their lives. More specifically, a decrease in locus of control indicates that students feel they can manage their experiences, thus demonstrating an increase in self-efficacy. Raven is a measure of abstract reasoning and a nonverbal estimate of intelligence. It is implemented as a set of matrices in progressive order. Finally, CRT is a test designed to measure if an individual tends to automatically choose an initially incorrect response and then engage in deeper reasoning to find a correct answer.

During the third stage, right after the students finished the battery of psychometric tests, we obtained emotional response intensity for negative and positive stimuli in terms of valence locus. Here, we exposed students to alternate series of images selected to elicit positive and negative emotional responses in order to estimate poststimuli valence indexes.

Then, using this response intensity measure and the valence-at-resting-state index from the first stage, we estimated emotional responsiveness as the difference between both levels. Specifically, the positive (negative) valence difference indexes measure the variation in valence index recorded in the third stage when the stimulus was positive (negative) net of the individual’s baseline resting state valence index recorded in the first stage. Both differences can be interpreted as a lower level of overreaction of participants—they become more phlegmatic or cold headed—or that individuals move toward a greater withdrawal behavior or attitude.

# **5.3 Attrition Analysis**

The final sample, after filtering the EEG data and accounting for attrition, is 308 valid EEG recordings of students; that is, an average attrition share of 49%. In this subsection, we present some checks to verify that this attrition rate was not correlated with the intervention. Our argument is that attrition was caused mainly from the quality of the data recordings. For example, long, dense, or unclean hair, and/or freezing computers were the most common troubles for the Matlab toolbox in
problems between surveys arising from differences in questionnaires, interviewing procedures or processing methods. These errors can greatly weaken the power of the tests found in the literature using cross-country and inter-temporal comparisons for detecting the true relationship.

There are a couple of things we can do to test robustness to time varying measurement errors. One is to use data over longer periods. Figures 1 and 2 use whatever time periods are available between successive surveys. If instead one uses changes over three surveys (taking the log difference between the survey for date t and t-2) the correlation over the whole period becomes significantly negative (r=0.24, n=206), and that remains true for the data points after 1992. Alternatively, one can use the longest spell for each country; again there is no significant correlation (r=0.10, n=80).

A second test is to use growth rates in consumption from national accounts (NAS) as the instrumental variable for the growth rates based on the survey means. This assumes that the measurement errors in the two data sets are uncorrelated. While in practice there are sometimes overlaps in the underlying data sources used (such as when specific consumption items in the national accounts are benchmarked from household survey data), by-and-large the assumption is probably defensible for the purpose of testing robustness. Using this test, one finds no significant correlation (in either direction) between changes in inequality and (instrumented) growth in survey means for either the 1990s, or the period as a whole since the early 1980s. When estimated over all available observations, the IV estimate of the regression coefficient of the change in log Gini index on change in log survey mean using the change in log private consumption per capita from the NAS as the instrument is 0.04 with a standard error of 0.26. Confining the estimation to the post 1992 period, the IV regression coefficient rises substantially to 0.15, but this is only significantly different from zero at the 15% level
to buyers/suppliers that<br>create shorter paths between two<br>nodes)|0.082|0.094|0.158|
|**Eigenvector centrality**<sup>**a**</sup>(higher values<br>given to buyers/suppliers that are<br>connected to buyers/suppliers with<br>more ties)|12.462|1.167|11.313|

_Note:_ a. These aggregated centrality measures are network measures based on the centrality scores of each node in the network. The higher the centrality scores, the more likely the nodes that are further apart interact through central agencies/firms.

To illustrate the different types of state capture patterns found in Bulgaria, we show in Figure 21 the three largest clusters found in 2017–2019. Cluster A is the largest cluster and although it is not fully captured, it demonstrates a high risk of state capture pattern where the high corruption risk ties dominate the center of the network with the lower corruption risk ties remaining on the periphery. Cluster B is the second largest and shows a partial state capture pattern as well. High corruption risk ties are found in the center, mixed with other low corruption risk ties.<sup>22</sup> Cluster C shows a network with several centers that are not captured and one captured center. This shows a pattern where high corruption risk procedures are not conducted in a systemic pattern throughout the network.

> 21 Each of the betweenness and eigenvector centralization metrics are commonly used network-level metrics stemming from the node-level centrality scores. The betweenness centralization attributes higher centrality scores to nodes that are connected to more edges. A node’s eigenvector centrality, on the other hand, considers the importance of neighboring nodes as well. In our case, the top 30 percent sample has a higher betweenness centralization of 0.158 compared to 0.094 for the bottom 30 percent subnetwork. Similarly, the top 30