rafmacalaba/datause-encoder-data huggingface dataset:
standard for second grade of 60 words per minute. They obtain an average score of 3.45 over 9 in the open comprehension questions (Recall Comprehension), but do much better in the direct, literal comprehension questions (Q&A Comprehension), where they have an average of 4.3 over 5 with lower variation (SD=1). In fact, the majority of students (56.4 percent) were able to answer all five questions correctly.

On the written test, the average total raw score is 11.8 over 19, and the average Rasch score 45.5 over 75.<sup>31</sup> A student who finishes second grade is expected to be able to answer all questions correctly, but as noted before, several questions in the test are aimed at third or fourth grade skill level (see Table 3, in page 22). Based on probability calculations and difficulty levels of the items,<sup>32</sup> we estimate that a student with a Rasch score of 72 would be able to answer all second-grade questions correctly.

**Table 5. Descriptive statistics of Fluency and Comprehension Variables**

**(adjusted)**

|**Test**||**Obs**|**Mean**|**SD**|**Min**|**Max**|
|---|---|---|---|---|---|---|
|**Oral Test**|Fluency|475|57.41|29.59|0|157|
||Recall Comprehension|431|3.45|2.83|0|9|
||Q&AComprehension|431|4.30|1.01|0|5|
|**Written Test **|(RaschScores)|475|49.51|8.59|15.09|70.68|

Next, we present the results of statistical analyses carried out to address the issues stated in the objectives of the paper, that is, to compare and evaluate how accurately the tests assess reading skills, to correlate scores of students in both tests, and to explore signs of discontinuity in the fluency-comprehension relationship.

# **4.1. How accurately do oral and written tests assess reading skills?**

To compare the results in the oral and written tests we standardized all variables so that they are in the same scale, with mean of 0 and standard deviation of 1. Initial box plots suggest that there are no obvious differences in the distributions of scores and none of the distributions are normal (see Figure 16 in Annex 7.6).

**Table 6. Assessment of Normality of Standardized Scores of Fluency and Comprehension Variables (Shapiro-Wilk W test)**

|**Test**|**Variable**|**Obs**|**W**|**V**|**z**|**Prob>z**|
|---|---|---|---|---|---|---|
||Fluency|475|0.9876|3.978|3.311|0.00046|
|**Oral test**|Recall Comprehension|431|0.9588|12.139|5.961|0.00000|
||Q&A Comprehension|431|0.9241|22.348|7.418|0.00000|
|**Written T**|**est**(Rasch Scores)|475|0.9182|26.274|7.839|0.00000|

> 31 The scale is set to range between 0 and 100 in theory, but 75 is the equivalent to 3 standard deviations above the mean, and so –although possible– no one is expected to score higher than 75.

> (θ−β) _e_ 32 Probability = (θ−β) , where θ is the individual score, β is the item difficulty, and _e_ is the exponential. 1 + _e_

30
|This table presents the results of<br>statistic is calculated using an as<br>over all assets and by asset class.<br>each fund type. T-tests are two-t<br>indicate statistical significance <br>parentheses. Numbers represent p|Overall Mean<br>**Herding Stati**<br> the regression of <br>set-specific probabi<br> The table presents<br>ailed. One asterisk <br>at the five-percent<br>ercentages (results|Fund A<br>**Table**<br>**stic on Fun**<br>the asset-speci<br>lity of buying<br> in different c<br> indicates stati<br> level. The s<br>are multiplied|Fund B<br>**17**<br>**d Type Fixe**<br>fic herding sta<br>an asset at a m<br>olumns the ove<br>stical significa<br>tandard error <br>by 100).|Fund C<br>**d Effects**<br>tistic on fund-t<br>oment in time. <br>rall mean and t<br>nce at the ten-p<br> from the signi|Fund D<br>ype fixed effec<br> The regression<br>he zero-mean f<br>ercent level an<br>ficance t-test i|Fund E<br>ts. The herding<br>s are calculated<br>ixed effects for<br>d two asterisks<br>s presented in|
|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|
|**All Asset Classes**|0.79**|-0.57**|-0.09*|1.07**|-0.02|-0.37**|
||(0.06)|(0.06)|(0.05)|(0.03)|(0.05)|(0.06)|
|**Domestic Assets**|||||||
|Former Pension System Bonds|-0.24**|0.22|0.26**|-0.78**|0.14|0.15|
||(0.19)|(0.19)|(0.11)|(0.06)|(0.09)|(0.11)|
|Corporate Bonds|2.15**|-0.98*|0.14|1.41**|-0.34|-0.23|
||(0.55)|(0.55)|(0.38)|(0.28)|(0.32)|(0.33)|
|Financial Institutions|0.61**|-0.00|-0.04|0.13|-0.06|-0.02|
||(0.17)|(0.17)|(0.16)|(0.10)|(0.17)|(0.16)|
|Government Paper|0.53**|-0.10|0.20|0.36**|-0.11|-0.35**|
||(0.30)|(0.30)|(0.18)|(0.11)|(0.16)|(0.15)|
|Investment and Mutual Funds|0.51|0.07|0.33|0.27|-0.76|0.07|
||(0.46)|(0.46)|(0.63)|(0.52)|(0.62)|(0.46)|
|Equity|-0.47**|-0.59**|-0.36|0.96**|-0.47|0.47**|
||(0.24)|(0.24)|(0.25)|(0.18)|(0.29)|(0.11)|
|Mortgage Bonds|2.38**|-1.77**|-0.62**|3.86**|-0.21**|-1.25**|
||(0.16)|(0.16)|(0.10)|(0.06)|(0.10)|(0.11)|
|**Foreign Assets**|||||||
|Fixed Income|-0.16|-0.13|-0.12|-0.06|0.12|0.19|
||(0.51)|(0.51)|(0.44)|(0.31)|(0.40)|(0.44)|
|Investment and Mutual Funds|0.91**|0.16|-0.17|-0.11|-0.04|0.16|
||(0.15)|(0.15)|(0.20)|(0.17)|(0.25)|(0.15)|
|Equity|-0.08|0.27|0.11|-0.23|-0.44|0.27|
||(0.64)|(0.64)|(0.52)|(0.60)|(0.96)|(0.64)|
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Share of children that were notfaken jor postnatal checkups within 2 months after birth (24) <!-- Start of picture text --> 100%<br>85% 88% 84% ota<br>so% | 75% 749% 76% 73H OH 70%<br>64% 63% 63%<br>60%<br>40% m= 2000<br>20% 2008<br>0%<br>Urban Rural No education Primary Secondary Higher Educatio TOTAL<br>education Education<br>Urhan/Rural| oration Mother's Fducation TOTAI<br><!-- End of picture text -->
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# Appendix

A1. Optimal time allocation under autarky. Individual i chooses the time share parameter �i so as to maximize utility: subject to 0 � �i � 1. The Cobb-Douglas functional form implies that corner solutions can be ruled out. The ...rst-order condition (f.o.c.) is : which implies a = �i: 11
|Floods EmergencyRecoveryProject<br>Berina Uwimbabazi|
|---|
|Power Sector Reform: Second Development PolicyCredit<br>Richard JeremySpencer|
|Northern Mountain Integrated Child Nutrition Improvement ProjectHuongLan Dao|
|Northern Mountain Integrated Child Nutrition Improvement ProjectHuongLan Dao|
|Northern Mountain Integrated Child Nutrition Improvement ProjectHuongLan Dao|
|Northern Mountain Integrated Child Nutrition Improvement ProjectHuongLan Dao|
|Northern Mountain Integrated Child Nutrition Improvement ProjectHuongLan Dao|
|Northern Mountain Integrated Child Nutrition Improvement ProjectHuongLan Dao|
|Water for Agropastoral Livelihoods Pilot Project<br>Stephen Paul D'Alessandro|
|Water for Agropastoral Livelihoods Pilot Project<br>Stephen Paul D'Alessandro|
|Water for Agropastoral Livelihoods Pilot Project<br>Stephen Paul D'Alessandro|
|Geothermal ExploratoryDrillingProject<br>Almudena Mateos Merino|
|Geothermal ExploratoryDrillingProject<br>Almudena Mateos Merino|
|Geothermal ExploratoryDrillingProject<br>Almudena Mateos Merino|
|Geothermal ExploratoryDrillingProject<br>Almudena Mateos Merino|
|Geothermal ExploratoryDrillingProject<br>Almudena Mateos Merino|
|Geothermal ExploratoryDrillingProject<br>Almudena Mateos Merino|
|Geothermal ExploratoryDrillingProject<br>Almudena Mateos Merino|
|Social Protection Integration<br>Miriam Matilde Montenegro Lazo|
|Social Protection Integration<br>Miriam Matilde Montenegro Lazo|
|Albania Financial Sector DPL<br>Isfandyar Zaman Khan|
|SupportingEducation Reform in Targeted Circuits<br>Ciro Avitabile|
|SupportingEducation Reform in Targeted Circuits<br>Ciro Avitabile|
|SupportingEducation Reform in Targeted Circuits<br>Ciro Avitabile|
|SupportingEducation Reform in Targeted Circuits<br>Ciro Avitabile|
|SupportingEducation Reform in Targeted Circuits<br>Ciro Avitabile|
|SupportingEducation Reform in Targeted Circuits<br>Ciro Avitabile|
|SupportingEducation Reform in Targeted Circuits<br>Ciro Avitabile|
|SupportingEducation Reform in Targeted Circuits<br>Ciro Avitabile|
|Serbia Competitiveness and Jobs<br>Dusko Vasiljevic|
|Serbia Competitiveness and Jobs<br>Dusko Vasiljevic|
|Serbia Competitiveness and Jobs<br>Dusko Vasiljevic|
|Serbia Competitiveness and Jobs<br>Dusko Vasiljevic|
|Serbia Competitiveness and Jobs<br>Dusko Vasiljevic|
|Serbia Competitiveness and Jobs<br>Dusko Vasiljevic|
|Serbia Competitiveness and Jobs<br>Dusko Vasiljevic|
|Serbia Competitiveness and Jobs<br>Dusko Vasiljevic|
|Serbia Competitiveness and Jobs<br>Dusko Vasiljevic|
|Serbia Competitiveness and Jobs<br>Dusko Vasiljevic|
|OECS Regional Tourism Competitiveness<br>Thomas A. Vis|
|OECS Regional Tourism Competitiveness<br>Thomas A. Vis|
|OECS Regional Tourism Competitiveness<br>Thomas A. Vis|
|OECS Regional Tourism Competitiveness<br>Thomas A. Vis|
|OECS Regional Tourism Competitiveness<br>Thomas A. Vis|
|OECS Regional Tourism Competitiveness<br>Thomas A. Vis|
|SEHAT Additional Financing<br>Mohammad Tawab Hashemi|
|SEHAT Additional Financing<br>Mohammad Tawab Hashemi|
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maintenance, (iii) road disruption, (iv) public protest, and (v) road maintenance was one of these “inducement mechanisms.” This observation was the seed for a particularly productive yield in Hirschman’s intellectual journey: as a matter of fact, it would become the starting point for the discussion of “voice” in Hirschman’s analysis of social interactions in his 1970 book, _Exit, Voice and Loyalty_ .

In his “Interim Observations,” Hirschman argued that the Bank had thus far ignored those questions: “the projects appear to be judged wholly on their technical merits.”<sup>44</sup> In the notes taken during their field trips – but not in the “Interim Observations” – Albert and Sarah Hirschman fully elaborated on the limits of judging projects by only their technical merits, directly addressing the fundamental question of social equality and redistributive issues. “Profitability alone,” they maintained, “is no yardstick for social desirability of investment.”<sup>45</sup> Hirschman also focused on some specific Bank policies usually set forth as pre-conditions of lending: the Bank’s preference for dealing with development agencies independent from the national government and with contractors selected through international biddings; the Bank’s insistence that governments should profit from public utilities established through its loans; and the Bank’s insistence on exerting strict control over the internal administrative processes of the borrowing countries. Hirschman underscored instead the need to adapt to specific circumstances.<sup>46</sup> During his travels, he had noticed that certain agencies, which in principle were supposed to be autonomous from the national government, were actually impeded in effective decision-making by disunited Boards, whereas more streamlined chains of command in governmental ministries could prove to be more effective. In other cases, autonomous authorities were turning into semi-feudal centers of power, effectively making coordination among different branches of the national government impossible and destroying the principle of the unity of the budget.<sup>47</sup> As for the Bank’s attempts to control the internal administrative processes of the borrowing countries, Hirschman noted that very often this was plainly impossible: internal difficulties within recipient governments and intra-governmental conflicts were a common characteristic of borrowing countries, and the first casualty of these difficulties was reliable and open communication with external subjects.<sup>48</sup>

> 44 Albert O. Hirschman, “A Study of Selected World Bank Projects – Some Interim Observations,” August 1965, World Bank Hirschman Folders Vol. 1.

> 45 Sarah Hirschman, “Ethiopia,” August 19, 1965, Albert O. Hirschman Papers, Box 57, Folder 2.

> 46 Albert O. Hirschman, “A Study of Selected World Bank Projects – Some Interim Observations,” August 1965, World Bank Hirschman Folders Vol. 1.

> 47 Sarah Hirschman, “Ethiopia,” August 19, 1965, Albert O. Hirschman Papers, Box 57, Folder 2.

> 48 “Ideas – Miscellaneous,” no date, Albert O. Hirschman Papers, Box 57, Folder 2.

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# **3.2 Households in the maize sector: Consumers and producers**

**Patterns of maize consumption in Kenya are complex, with differences across the income distribution both in quantity and form consumed.** The left hand panel of Figure 10 illustrates the average annual expenditure on maize per adult equivalent across expenditure deciles.  Maize consumption generally increases up until the 7<sup>th</sup> decile, after which it remains fairly constant.  This is a common pattern for a staple – as income increases, households tend to consume more of the staple, but if income increases sufficiently then they begin to substitute superior alternatives.  Households also make different choices between _types_ of maize as they become wealthier.  Consumption of dry grain and loose flour first increases and then decreases in total expenditure, while sifted flour increases across all deciles.  This suggests that households view sifted flour as a superior substitute relative to maize grain and loose flour, and they substitute accordingly as they become richer. Green maize consumption increases across the distribution, suggesting that this might be a complement as opposed to a substitute to grain/flour (i.e. used for different dishes that are not necessarily viewed as superior or inferior).

**Figure 10: Maize consumption: Household expenditure per adult equivalent (left panel), Household expenditure** **_share_ (right panel)**

_Source: KIHBS (2005/06), author calculations._

**Maize consumption as a percentage of total expenditure decreases in total consumption expenditure.** While households do consume more maize as their total expenditure increases, the rate of increase of the former is slower than the latter, as shown in the right hand panel of Figure 10 above.  This graph illustrates that the budget share of maize declines monotonically across income deciles.  This makes intuitive sense: as incomes increase households spend more on maize, but they do not spend _all_ of the additional income on maize – clearly there are also other products they wish to consume.  So wealthier households spend a much smaller portion of their total expenditure on maize.  Households in the poorest decile have an expenditure share on maize that is almost seven times that of the wealthiest decile.

## **There are some differences between the makeup and level of urban/rural consumption**

**of maize.** The pattern of increasing overall expenditure, and decreasing expenditure shares, relative to income holds across both rural and urban households. However, on average rural households consume substantially more maize in general and maize _grain_ in particular, while urban households consume more sifted flour. Some part of this pattern is likely explained by

23
relationship between economic activity and nightlight intensity should not come as a surprise. But infrastructure changes relatively little in the short-term. From a statistical point of view, long-term changes in nightlight intensity provide information on fundamental trends in the economy, while short-term changes are “noisier”. One consequence is hence the “attenuation” of the estimated relationship. Despite this attenuation effect, as can be seen in Table 2, the relationship between annual changes in GDP and annual changes in nightlight intensity remains significant both in a large cross-section of countries and in South Asia.

_Table 3: Different Henderson regressions for South Asia._

||**World**<br>ln(GDP)<br>(1)|**South Asia**<br>ln(GDP)<br>(2)|**South Asia I**<br>ln(GDP)<br>(3)|**South Asia II**<br>ln(GDP)<br>(4)|
|---|---|---|---|---|
|ln(lights/area)|**0.267*****|**0.273*****|**0.169***|**0.350*****|
||(0.0303)|(0.0481)|(0.0611)|(0.011)|
|Observations|3,966|178|78|100|
|Countries|187|8|4|4|
|(within<br>country)R2|0.788|0.976|0.987|0.994|

_Note:_ “South Asia I” includes Afghanistan, Bangladesh, India and Maldives. “South Asia II” includes of Bhutan, Nepal, Pakistan, and Sri Lanka. All regressions expect (1) are estimated from 1992 to 2016. The regression specification is the same as before. * p<0.1 and *** p<0.01.

Allowing for differences across groups of countries increases precision of the Henderson regressions (Table 3). Predicting economic activity in South Asia based on a naïve world-level analysis that treats all countries alike results in larger errors compared to estimating the relationship only for countries in South Asia (Figure 2). In India, GDP predicted using this naïve approach increasingly lags behind the observed GDP. Predicting GDP based on a relationship estimated only for South Asian countries results in a much better fit and the mean squared regional prediction errors decreases by 49 percent. But even then, there are periods in which predicted and actual GDP show considerable deviation, at least in some countries. Visual inspection of the data reveals that South Asian countries 12
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