rafmacalaba/datause-encoder-data huggingface dataset:
TABLE A12: ITT on individual intermediary outcomes: micronutrients, ECD attitudes and observed hygiene

||**ITT**<br>(1)|**S.e**<br>(2)|**P-Value**<br>(3)|**Mean**<br>**Control**<br>(4)|**Obs**<br>(5)|
|---|---|---|---|---|---|
|**_Micronutrients_**||||||
|**Micronutrients index**|0.08|( 0.04)|0.06|-0.00|2,495|
|Received Vitamin A|0.02|( 0.02)|0.21|0.73|2,495|
|Received deworming medecine|0.02|( 0.02)|0.37|0.76|2,495|
|Received iron supplementation|0.04|( 0.02)|0.07|0.49|2,495|
|**_ECD Attitudes of main caregiver_**||||||
|**Attitudes index**|0.11|( 0.04)|0.01|0.00|2,396|
|Should always answer child’s questions|0.02|( 0.02)|0.28|0.87|2,396|
|Boys can play with dolls|0.07|( 0.02)|0.00|0.36|2,396|
|Girls can play with cars|0.04|( 0.02)|0.04|0.51|2,396|
|Early language develops from stimulation|-0.00|( 0.02)|0.87|0.67|2,396|
|Brain develops from very early on|0.01|( 0.02)|0.51|0.86|2,396|
|**_Hygiene (observed by test administrator)_**||||||
|**Hygiene index**|0.09|( 0.04)|0.03|0.00|2,506|
|Child has clean face|0.03|( 0.02)|0.09|0.67|2,506|
|Child has clean hair|-0.00|( 0.02)|0.83|0.80|2,506|
|Child has clean hands|0.06|( 0.02)|0.00|0.56|2,506|
|Child has clean clothes|0.07|( 0.02)|0.00|0.56|2,506|
|Child is wearing shoes|-0.04|( 0.02)|0.05|0.33|2,506|
|Child does not cough|-0.01|( 0.02)|0.40|0.85|2,506|
|Child has clean nose|0.00|( 0.02)|0.99|0.84|2,506|
|Child does not have skin problems|0.00|( 0.01)|0.83|0.93|2,505|

Every row corresponds to a separate estimation (equation 1). The vitamin index is based on three variables: the child has received vitamin A, ferrous sulfate and deworming medicine during the last six months (yes/no questions). The hygiene index captures the hygienic condition of the child observed by the test administrator. The Attitudes index is calculated based on answers by the caregiver to the following questions: 1. Do you answer your child’s questions? 2. Do you think boys can play with dolls? 3. Do you think girls can play with cars? 4. Do you think children start talking by nature? 5. Does the child’s brain develop from gestation or when the child starts going to school? Each outcome variable is standardized using the mean and standard deviation for control households. The rows shows ITT estimates. All the regressions include controls for the stratification variables, child age and gender, enumerator fixed effects, and dummy variable for a set of households surveyed in 2016, as well as village fixed effect. Standard errors reported in parentheses. p<0.1, p<0.05, p<0.01 60
11 this effect to be of second order and not homogeneous among the different countries of our sample. Moreover, the US interest rate has been much more smooth than the risk premiums over the period of our study, as figure 6 shows for Mexico and Poland.

_Mexican and Polish Discount and Par Bonds, stripped spreads and US interest rate_ <!-- Start of picture text --> 2000 2000<br>1600 1600<br>1200l 1200-<br>Boo. ..-...- --- 800 .------<br>.,, , .- A;; ... ~~~~~~~~~~~~~~~~~~~~~~~~~~~............... I-., ;<br>400 400)<br>7/2 r/9 '4i '22M -O6 i94 i'1l1'i8i4 225 96 "/2 '96<br>- MEXDIS --- USRATE - MEXPAR USRATE<br>1200 1200<br>1000 1000<br>800 ~~~~~~~800.--<br>600. p r A , " ' " " ' 00 1 8 0 0 4 t w , i. -0<br>400- W400.<br>200 -- 200 -V 9<br>7/2 /94'1 16/4 5/05/95 9/2/95 2/09/96 i/96 7/2 /94 12/16 /09/95 9/2 /952 6/2 6<br>- POLDIS USRATE | POLPAR USRATE<br><!-- End of picture text -->

_Figure 6_ Second, even if the stripped spread is a rough measure of the country risk, we are in fact interested in cross-country comparisons. For us, it is more important to have a very homogeneous measure, as we believe it is the stripped spread on the most liquid Brady bonds.

Third, it would be ideal to have time series of the risk spread for given maturity instead of following the same bonds over time. However, the Brady market has not reached the dimension to make this possible. The point is that yields spreads may not provide a valid way of comparing the relative risks of bonds with different maturities. But the
short-lived, they have long-term effects for household food security and welfare. For example, households try to cope with such shocks by lowering calorie intake. Torlesse et al. (2003) show that the percentage of underweight children in Bangladesh is negatively correlated with expenditure on rice (because lower rice expenditure allows more income to be spent on higher quality food). Rice expenditure in turn rises with rice prices. In addition, households sell assets such as livestock and spend less on children‘s education as shown by a recent survey in Bangladesh (Wodon and Zaman 2008). Such coping strategies can lead in the long run to lower labor productivity and less accumulation of human capital, which can prevent households from getting out of poverty again once food prices are back to lower levels. As a result, households that are pushed into poverty because of temporary shocks may become chronically poor.

In order to evaluate the future potential of renewed food crises, the factors that caused the recent price spike have to be understood. The literature suggests that several causes have contributed to the price increases.<sup>16</sup> First, higher fuel prices have led to high production costs in agriculture by raising the cost of inputs such as fertilizers. Second, the depreciation of the dollar vis-à-vis previous years has led to higher prices of US$ priced commodities. Third, diet changes around the world, and especially in China and India, have caused a higher demand for meat and thereby for feed grains. Fourth, increased bio-fuel production in the United States, the European Union and Brazil increased the demand for feedstock and put pressure on the land area used for food production. The European Union and the United States subsidize production while Brazil is the only competitive producer of biofuel. Fifth, weather-related shocks leading to droughts and low harvests have hit countries like Australia and Russia. Finally, financial speculation in the agricultural commodity markets may have contributed to food price increases (von Braun and Torero 2009). Although some of these factors had more impact than others, all of them seem to have jointly contributed to the spike in food prices over the past years.

An additional factor that has amplified the rise in the level and volatility of food prices has been the low level of global stocks of food staples. In 2007, the stock-to-use ratio of grains and oilseeds reached the lowest level since 1970. Stocks can function as a buffer for market

> 16 See, for example, FAO (2008), Timmer (2008) and Trostle (2008). Timmer (2008) and FAO (2008) suggest that high bio-fuel production is one of the main reasons for increased food prices, whereas the diet taste changes in India and China seem to be less relevant for short-term price behavior.

40
# **A new tail-based correlation measure and its application in global equity markets**

## Jinjing Liu<sup>∗</sup> World Bank

Keywords: Tail dependence, correlation asymmetries, Expected Shortfall, heavy-tailed distributions, Value at Risk JEL classification number: C12, C15, G15

> ∗I am grateful for comments and suggestions from John Galbraith, Deniz Anginer, Francesca Carrieri, Maarten van Oordt, Jean-Marie Dufour, Victoria Zinde-Walsh, and participants at the 50th Annual Conference of the Canadian Economics Association (CEA), 2015 Annual Meeting of the Eastern Finance Association (EFA), and 11th Centre Interuniversitaire de Recherche en Economie Quantitative (CIREQ) PhD Students’ Conference. This paper’s findings, interpretations, and conclusions are entirely mine and do not necessarily represent the views of the World Bank, its executive directors, or the countries they represent.
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17
axis) against the proxy attendance measure (on the horizontal axis). The effect is fairly uniformly distributed across families. Families who would have met the 80 percent target without the intervention seem to respond slightly less strongly than those who would not have met the target, but interestingly the program seems to make a difference even for students who would not seem to have a strong incentive to increase their attendance rates.

We can perform the same exercise using our administrative enrollment rates in 2006. Figure 6 depicts the results. Unlike the results for attendance rates, here the effect of the program seems to be largest for families least likely to have their children re-enroll in school. Students who would have been likely to re-enroll show no effect from the program while those with a probability of re-enrolling of less than 80 percent show an effect that is fairly evenly distributed across students.

Dividing up these effects to test separately for the individual effects of each treatment, we turn to Table 5 in which we investigate the effects of the individual programs on students’ attendance during the end of the 2005 academic year. Panel A contains the results for our primary outcome variable – verified attendance for all students in the 68 schools selected for surveying during the end of 2005. Panel B contains the same verified attendance measure but just for those students who were found in the baseline survey. Columns 1-3 provide the results for the first experiment in San Cristobal with column 1 providing the average for control students and columns 2 and 3 providing the results for the basic and savings treatments. Columns 4-7 provide the results for Suba. Columns 4 and 6 contain the results for the basic (grades 6-8) and tertiary (grades 9-11) treatments while columns 5 and 7 contain the respective controls. Finally, the last column contains the overall average effects by pooling the effects of the individual treatments.

Turning to Panel A, the individual treatments did cause changes in the verified attendance rates. The first row contains the average results for all students. The second row contains students in grades 6-8 and the third row contains grades 9-11. Overall the basic and savings treatments increase attendance in San Cristobal by 3.3 and 2.8 percentage points respectively. The results are evenly distributed across grades. For grades 6-8 in San Cristobal, the savings treatment increases attendance by a similar amount as the basic treatment (3.5 and 2.6 percentage points respectively), despite the lower monthly transfer. Interestingly, the basic transfer has no effect on attendance in Suba, though if we pool the sample the estimated effect is a statistically

- 21 -
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# **Sea Intelligence Schedule Reliability Index**

Sea-Intelligence consultancy<sup>5</sup> publishes a Schedule Reliability Index benchmarking carrier on-time performance within an 8-hour window. Despite being available globally and by shipping line, this metric unsurprisingly correlates with the Stress Index, as both leverage the same underlying vessel movement data. However, the Stress Index is port-centric and scalable, while the reliability measure focuses on services, trade lanes, and carriers.

# **Global Supply Chain Pressure Index**

The Federal Reserve Bank of New York’s Global Supply Chain Pressure Index<sup>6</sup> (GSCPI) is a meta-indicator that integrates several existing series to compound into supply chain disruption indicators. Global transportation costs are measured by employing data from the Baltic Dry Index (BDI) and the Harpex index,<sup>7</sup>

> 4 Each month, Sea-Intelligence measures schedule reliability across more than 11,000 vessel arrivals on average, in more than 270 ports, which is the underlying data for the monthly global on-time performance, as well as the individual carrier, trade lane and service on-time performance. The trade lane and service schedule reliability are based on a two-month rolling averages. In other words, February trade lane on-time performance is based on the average on-time performance of vessel arrivals in both January and February. The definition of “on-time” has in accordance with the widely used calendar-day definition been settled as arrival within plus or minus 1 calendar day from the proforma schedule.

> 5 https://www.sea-intelligence.com

> 6 <u>https://www.newyorkfed.org/research/policy/gscpi#/overview</u>

> 7 <u>https://www.harperpetersen.com/harpex</u> This index reports the chartering rate for container shipping.

13
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affect the level and management of inventories by allowing a closer tracking of levels, demand and trends.

The following simple stockout inventory model demonstrates the effect that poor infrastructure would have by increasing transport time. Assume that daily raw materials usage, which fluctuates with current or expected sales, follows a normal distribution with standard deviation 6. If daily deviations from expected usage are independent and additional inventory can be ordered immediately, the safety stock is: _S= ka-oi;_ k = firm intolerance for running out of inventory 5 = daily standard deviation of inventory levels T = order time + transit time + handling time A firm that set k equal to 2 would run out of raw materials inventory less than 2.5% of the time.<sup>8</sup> According to this model, if your supplier is located across the street, you don't need to hold any safety stock as long as the supplier holds finished goods inventories. On the other hand, if the supplier is located two weeks away, an unexpected increase in raw materials consumption during any two week period must be met from raw materials inventory. If daily deviations were instead perfectly correlated-more demand today means more demand tomorrow-then the safety stock would be proportional to T rather than the square root of T.

Inventories should be affected by a number of other factors which are common to developing countries that we will try to control for. First, developing countries which import intermediate goods as manufacturing inputs are likely to have higher inventory levels because the import of raw materials involves longer and more uncertain delivery times as well as greater transaction costs leading to larger and less-frequent shipments. We know from our analysis of 9
men and women are statistically significant across some of the countries except Ukraine, Armenia, and Georgia. Research in psychology has also found that differences in cognitive abilities between gender groups might not be statistically significant but might show statistically significant differences in the case of groups defined by race or ethnicity (Hough and Oswald, 2000).

**Table 4. Average Reading Proficiency Scores for Men and Women, by Country**

||Men<br>Mean|SD|Wome<br>Mean|n<br>SD|
|---|---|---|---|---|
|Armenia|254.37|2.41|253.96|1.89|
|Bolivia|191.14|6.89|172.57|6.01|
|Colombia|232.08|3.62|223.43|3.69|
|Georgia|237.47|2.63|243.63|1.89|
|Kenya|182.45|3.97|158.06|4.55|
|Ukraine|263.16|4.64|268.73|1.90|
|Vietnam|236.17|3.32|228.05|2.93|

_Note_ . Reading proficiency scores are measured on a 500-point scale. The sample includes 25- to 64year-olds in self-employment or wage work. _Source_ : STEP Surveys (2014).

As mentioned previously, we also use a second measure of cognitive skills: complexity of computer use on the job. In Table 5, we present the proportion of men and women in the analytic sample who use computers on the job. In most countries, less than half of men and women use computers on the job. In Kenya, 22 percent of women and 28 percent of men reported using computers. In Ukraine, Georgia, and Armenia, 41 percent of men reported using computers. In Bolivia, the proportion of men using computers at work is slightly higher, at 43 percent, while in Colombia and Vietnam it is under 40 percent. The highest proportion of women reporting using computers on the job is in the ECA countries (55 percent in Georgia, 48 percent in Armenia, and 45 percent in Ukraine). The latter countries are also the ones where the proportion of women using computers on the job is higher than the proportion of men doing so. The difference in the proportions between men and women is not statistically significant in Armenia and Colombia.

16
Table 3. Description of World Bank Enterprise Surveys on GVC Participation: Selected Countries in Sub Sharan Africa

||**Re**<br>|**source Ric**<br>|**h Group of**<br>|**Countries**<br>|<br>||**Non-Res**<br>|**ource Rich**<br>|**Countri**<br>|**es**<br>|
|---|---|---|---|---|---|---|---|---|---|---|
||**Oil Expo **<br>|**rters**<br>|**Non-Oi**<br>|**Resourc**<br>|**e Rich**<br>|**Middle**<br>|**Income**<br>|**L**<br>|**ow Inco**<br>|**me**<br>|
||Cameroon|Nigeria|Congo,<br>Dem. Rep.|<br>Ghana|Zambia|Côte<br>d’Ivoire|<br>Kenya|Ethiopia|Malawi|Tanzania|
|**_Full sample:_**|||||||||||
|# Survey waves<br>available|3|3|3|2|3|2|2|2|3|2|
|# Survey waves used|3|3|2*|2|2*|2|2|2|2*|2|
|# Observations|931|8220|888|1214|1204|887|1438|1492|673|1232|
|# Enterprises|799|7619|673|1166|1052|742|1199|1120|582|1117|
|# Exporters|192|608|72|207|185|99|437|129|86|182|
|# Importers|379|1024|160|379|425|217|415|270|158|337|
|# Exporters or<br>importers|434|1417|230|466|511|258|587|344|193|430|
|# Exporters and<br>importers|133|207|26|118|92|52|259|52|47|88|
|# Holders of|55|445|24|89|141|16|146|151|65|101|
|TechnologyLicensee|||||||||||

* The reported results use data from the last two waves.

36