rafmacalaba/datause-encoder-data huggingface dataset:
**Table 2.3 Rtegression results with different sample sizes**

||Slope<br>coefficient|t-statistic|R|
|---|---|---|---|
|Full sample|0.917|3.903|0.752|
|Observations I to 37|1.209|1.678|0.703|
|Observations 1 to 36|1.432|1.282|0.701|
|Observations 1 to 35|1.504|0.678|0.698|

intuition behind this finding is as follows. The t-statistic is the ratio of the estimated slope coefficient and the standard error of that estimate. The second of these is square root of the ratio of variance of the error tern and the sum of squared deviation of the independent variable. In the fill sample the denominator is large - there are three observations with a large mean deviation - so that the standard error will be smaller and the t-statistic larger than if these observations were omitted. C(o put it simply: once the tliree influential points are removed there is very little variation in educational expenditLre, which must necessarily result in inexact estimates). Meanwhile the numerator will (taking the fitted line as an approximation of the true relationship) be small in the full sample since these three observations lie very close to the line - removing these observations will not reduce and may even increase the error variance.

Therefore the results reported by Holmqvist are very sensitive to the sample used. Dropping just one observation leaves GNP per capita as the only significant determinant of educational expenditure. Aid - either that targeted to education or for other purposes - has no role.

The examples presented here are of cross-section regressions, and most of the aid effectiveness literature has been of this type. However, the need fur analysis of individual countries using time series has long been recognized (e.g. Papanek, 1973) and some studies have adopted such an approach (for example, Morisset's study of Argentinean savings, 1989). As is now widely recognized, economic time series data are prone to spurious regression.

A variable which assumes its own lagged value plus a random error (white noise) is described as a random walk. It has been shown that the regression of one random walk on 21
# Appendix A1 – Supplementary data from NALO 2018

**Table A1.1 Student reports of selected variables, by school gender**

||||**Male(%)**|**Female(%)**|**_φ/φc_**|
|---|---|---|---|---|---|
||Repeated a year at school because of poor results||14.5|6.6|.129***|
|||_0_|22.6|12.0||
||How man books have ou read?|_1-5_|27.8|33.3|148***|
|Gd 4|y y|_6-10_|15.7|14.4|.|
|rae||_>10_|33.8|40.4||
|||_A lot_|81.4|84.1||
||Math is important in life|_Somewhat_|13.3|12.6|.051***|
|||_Never_|5.3|3.3||
||Repeated a year at school because of poor results||6.9|4.5|.051***|
|||_0_|30.6|19.3||
||H bk h d?|_1-5_|42.6|53.1|137***|
|Gd 8|ow many oos ave you rea|_6-10_|10.6|11.6|.|
|rae||_>10_|16.2|16.1||
|||_A lot_|47.6|36.3||
||Math is important in life|_Somewhat_|38.6|48.0|.115***|
|||_Never_|13.8|15.7||

_Note:_ *p < .05; **p < .01; ***p < .001.

**Table A1.2. Percentage of students** **_not born in Saudi Arabia_ attending schools of various types (parent reports), by school gender**

|||**Male(%)**|**Female(%)**|**_φ/φc_**|
|---|---|---|---|---|
||Government school|85.0|95.2||
|Grade 4|Quran school|1.1|2.2|.206***|
||Private school|13.9|2.6||
||Government school|87.7|96.3||
|Grade 8|Quran school|1.3|1.0|.168***|
||Private school|11.1|2.7||

_Note:_ *p < .05; **p < .01; ***p < .001.

39
# **Figure 11:** Transmission to the **Rest** of **the** OECD **of** Clinton Deficit Reduction Program and **Maastricht Agreement**

<!-- Start of picture text --> ROECD Real GDP ROECD Private Investment<br>IA~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~l<br>1S .0 r . ~ -. s * - -A-<br>ej O.e 1.0~~~~~~~~~~~~~a~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~*9-- - S t.WA-OSl0 tAS S~~~~R M;>&_6__^_- O.S<br>-A~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~<br>v~~~~~~ - - -____________-____________-_-_-- -_- Mastricht 13_______________ - - - - ----- ----- ---<br>. . . .. . .. . . . . . . . . . - <-6 $l4c -0 2 .4~i~cw<br>' 1214 lose 1222 2000 20204 :02 loo 12lS :20W0 2002 004 2006 2= 2010 212<br>Yeaor<br>year<br>ROECD Share Market Value ROECD 10-Year Bond Rate<br>/ ¢ s | 2 -. - A - - - - --- A- - - - - - .- - - *<br>e - r3 - b0-trbt<br>is}~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ tr.-I.SUv<br>s_ ____________________________ _ < - t"lxe"t ~~-2.6 _ S arr<br>-l -5.0<br>2 124 9506 1535 2000 002 204 2008 MO08 1 Q12 20 Ii s KB liii 200t 20t2 204 C6 2 2010 20C)<br>Year Year<br><!-- End of picture text -->
# **V. YOUR JUDGEMENT ABOUT THE DIFFICULTY OF IMPLEMENTING THIS SP SURVEY**

J1. How close do you think the average estimate for this Delphi exercise would be to the number which would be obtained from undertaking an actual SP survey? [Answer on 10 point scale and record your score] Not Very Close 1 2 3 4 5 6 7 8 9 10 Very Close Score:_________ J2. How difficult do you think it would be to successfully implement a SP survey on this topic (value of avoiding losses of Amazon Rainforest), for the entire U.S.?

Not At All Difficult 1 2 3 4 5 6 7 8 9 10 Very Difficult Score:_________ J3. How difficult do you think it would be to successfully implement a SP survey to value non-use values of biodiversity in the U.S.?

Not At All Difficult 1 2 3 4 5 6 7 8 9 10 Very Difficult Score:_________ J4. How difficult do you think it would be to successfully implement a SP survey to value a recreational activity in the U.S.?

Not At All Difficult 1 2 3 4 5 6 7 8 9 10 Very Difficult Score:_________

# **VI. BACKGROUND QUESTIONS**

B1. How familiar are you with the literature on Stated Preference (SP) surveys in general?

Not At All Familiar 1 2 3 4 5 6 7 8 9 10 Very Familiar Score:_________ B1a. How familiar are you with the literature on Contingent Valuation (CV) surveys?

43
# **Table 23e.  Piecemeal Sensitivity Analysis – Hydrocarbon Emissions by Region from WTO Accession (percentage change from base year)**

||**Overall**||**Far**||||||**St.**|||
|---|---|---|---|---|---|---|---|---|---|---|---|
|**Parameter being changed**|**average**|**Central**|**East**|**North**|**Siberia**|**South**|**Urals**|**Moscow**|**Peters.**|**Tumen**|**Northwest**|
|**Central Results for reference**|1.7|2.6|8.6|4.1|2.7|1.8|-0.1|-2.0|4.5|1.9|4.8|
|**esubc = 0.5**|1.2|1.1|9.1|3.3|2.3|0.6|-0.6|-3.4|2.8|1.8|3.3|
|**esubc = 1.5**|1.9|3.1|8.5|4.4|2.9|2.2|0.1|-1.5|5.3|2.0|5.4|
|**esub = 2**|1.8|2.9|7.4|4.2|2.3|2.1|0.4|0.4|5.0|1.8|5.3|
|**esub = 4**|1.6|2.4|8.8|4.1|2.8|1.6|-0.4|-3.1|4.3|1.9|4.7|
|**esubs = 0.5**|-0.5|-2.1|7.5|0.9|0.4|-1.6|-2.2|-5.7|-2.1|0.7|-1.1|
|**esubs = 2.0**|5.2|10.5|10.1|9.5|6.5|7.3|3.1|3.9|11.2|3.9|15.8|
|**esubva = 0.5**|1.9|2.8|8.8|3.7|3.1|2.0|0.1|-1.5|4.6|2.0|5.0|
|**esubva = 1.5**|1.6|2.5|8.5|4.0|2.6|1.7|-0.2|-2.2|4.4|1.9|4.8|
|**etad = 10**|1.2|1.6|8.8|3.5|2.3|0.9|-0.6|-2.7|3.5|1.6|3.9|
|**etad = 5**|2.4|4.1|8.5|5.0|3.4|3.0|0.6|-0.8|6.1|2.4|6.4|
|**etadx = 3**|1.7|2.4|8.3|3.6|2.7|1.9|0.0|-1.8|4.5|2.0|5.0|
|**etadx = 7**|1.7|2.6|9.1|4.1|2.8|1.6|-0.2|-2.3|4.3|1.9|4.5|
|**etaf = 12.5**|1.3|2.1|7.4|3.4|1.9|1.3|-0.2|-1.6|3.7|1.6|4.3|
|**etaf = 17.5**|2.1|3.0|9.7|4.7|3.5|2.1|0.0|-2.4|5.2|2.2|5.3|
|**sigmadm = 4**|1.6|2.5|8.9|4.1|2.7|1.6|-0.4|-3.1|4.4|1.9|5.1|
|**sigmadm = 2**|1.8|2.9|7.5|4.1|2.4|2.1|0.4|0.1|4.7|1.8|4.8|
|**Source: Authors' calculations.**||||||||||||
|**Notes:**<br>a. Thepiecemeal sensitivityanalysis em|ploys centr|al values for a|llparamete|rs(see belo|w)other than|the testedp|arameter.|||||

|**Source: Authors' calculations.**<br>**Notes:**<br>a. Thepiecemeal sensitivityanalysis em|ploys cen|tral values for allparameters(see below)other than the testedparameter.|
|---|---|---|
|**Parameter**|**Central**|**Definitions of theparameter(see figures, especially figure 5)**|
||**value**|**for moreprecise elasticity structure).**|
|esubc|1.25|Elasticityof substitution in consumer demand|
|esubs|1.25|Elasticityof substitution between value-added and business services|
|sigmadm|3|"Armington" elasticityof substitution between imports and domesticgoods|
|etaf|15|Elasticityof multinational service firm supplywith respect toprice of output|
|etad|7.5|Elasticityof Russian service firm supplywith respect toprice of output|
|esub|3|Elasticityof substitution between firm varieties in imperfectlycompetitive sectors|
|esubva|1|Elasticityof substitution betweenprimaryfactors ofproduction in value added|
|etadx|5|Elasticityof transformation(domestic output versus exports)|

71
**Figure 7:** The procyclical nature of hidden debt

_Panel A:_ Which years are being revised?

<!-- Start of picture text --> .2<br>.1<br>0<br>-.1<br>p-value = 0.026<br>-.2<br>-10 -5 0 5 10 15<br>Real GDP growth in year which is revised<br>....<br>Panel B: When do revelations happen?<br>.2<br>.1<br>0<br>-.1<br>-.2 p-value = 0.001<br>-10 -5 0 5 10 15<br>Real GDP growth in year of revelation<br>Hidden debt - normalized<br>Hidden debt revelations - normalized<br><!-- End of picture text --> _Notes:_ Panel A shows the association between hidden debt and GDP growth in the year that is being revised. Panel B shows the association between hidden debt revelations and GDP growth in the vintage of the revision. The vertical axes show normalized hidden debt flows and their revelations, as defined in equations (1) and (2) respectively, standardized to account for outliers and to ease interpretation. Horizontal axes show detrended growth rates, winsorized at the 1<sup>st</sup> and 99<sup>th</sup> percentile. Trends were computed using the Hodrick-Prescott filter with a smoothing parameter of 100. Bins are constructed following the approach of Cattaneo et al. (2024). P-values and regression lines were obtained from panel regression in which we control for country fixed effects and the creditor composition of borrowing.

19
# **4.3.- Regulation of safety and externalities**

Airport safety would also play an important role in determining quality. Its objective is to ensure that passengers will have a normal waiting time and flight, and that the possibilities of suffering a terrorist or criminal attack are minimal. The procedures required to comply with safety standards impose some costs on passengers and airlines. Different components of the airport security system are shown at Table 4.9.

Security queues are considered an important determinant of airport quality. BAA reports that among the three airports mentioned above, 90% of passengers waited less than five minutes and 95%, less than 10 minutes. Airlines have suggested that a maximum waiting period of five minutes at London airports for a security search would be desirable.

In economic jargon, externalities are considered a market failure, hence wherever they appear intervention is regarded as necessary. The main negative externalities at airports are noise, congestion and pollution.<sup>30</sup> Traditionally, airport operators or even corresponding regulators, have left externalities aside, and only recently have they started worrying about their environmental impact. Today, it is common to find airports where night operations have been banned or restricted to less noisy aircraft's. Higher fares for noisier planes is another technique that aims to reduce the social cost of noise. Peak-pricing is also spreading as a practice for relieving congestion. Air pollution, however, has not been given much importance.

The increasing sensitivity toward environmental concerns has led to special treatment for externalities in most infrastructure project contracts. Usually an environmental impact study is required as a pre-requisite for airport infrastructure construction. Such a study should also consider the monitoring of possible negative impacts during the operation phase. In general, the environmental impact study will reflect environmental law.

# **4.4.- Regulation of investment obligations**

As was shown in section 2, the possibilities for private sector participation in airports are numerous. However, if involvement in the activity does not comprise long term objectives such as maintenance of facilities and future investments, airports would end up obsolete and highly deteriorated. This could be the case when a concession contract does not consider investment obligations. Fortunately, this is not often the case in most airport infrastructure concession contracts.

On the contrary, investment plans are usually an essential part of the contract. For instance, the concession contract that was recently prepared for the operation of Argentine airports required the operator to present a detailed investment plan. The concessionaire is obliged to invest a minimum amount of something more than US$2 billion, in addition to

**30** See section 1.

44
**Figure 41: Public and Private Health Expenditure per Capita, 2006 (RHS), and Type of health specialist visited, satisfaction with treatment and reasons for not seeking treatment, Togo 2006 (LHS).**

|50|**Went to see health specialist**<br>|Total Urban|Total Rural|
|---|---|---|---|
|45<br>Public|**Type of institution**|||
|35<br>40<br>Private|**Yes**<br>public|6.3%|7.3%|
|30|_of which satisfied with treatment_|_65.4%_|_73.5%_|
|25|private|7.4%|3.7%|
|15<br>20|_of which satisfied with treatment_|_81.9%_|_77.1%_|
|10<br>|**No**|86.2%|88.9%|
|5|**Reason for not seeing health spe**|**cialist**||
|0|not necessary|88.0%|87.4%|
||too expensive|11.4%|11.7%|
||not available/ too far|0.2%|1.5%|
||other|0.7%|0.5%|

_Source:_ DDP data, World Bank, and author’s calculations using CWIQ 2006.

**The high repetition rate has created a vicious circle, leading to an additional drain on limited public funds.** The repetition rate is the highest of all the benchmark countries for primary and the third highest for secondary schooling, and is part of the reason for ‐ as well as an implication of ‐ the high pupil teacher ratios (Figure 42).

**Figure 42: Repetition Rate and Pupil‐Teacher Ratio for Primary and Secondary Schooling, 2007.**

<!-- Start of picture text --> 60<br>40 Pupil‐teacher ratio, secondary<br>35 Primary Secondary 50 Pupil‐teacher ratio, primary<br>30 40<br>25<br>20 30<br>15 20<br>10<br>5 10<br>0 0<br>Source: DDP data, World Bank.<br><!-- End of picture text -->

**Despite some problems, Table 10 below suggests that skills are not a current constraint to economic activity as both unemployment and underemployment are high for all levels of education.** However, there is an important difference across education levels. Unemployment increases with the education level, indicating that the better educated, and probably better off, prefer to be unemployed looking for employment than to work and be underemployed. Conversely, the lower educated and poorer segments of the population have to accept whatever employment they can find and, therefore, are more often under‐employed. So it seems that Togo still has potential to use more of the educated workers and to use the ones already employed in a more productive fashion.

**Table 10: Un‐and Underemployed by Education Level for Rural, Urban Areas and Lomé, Togo 2006.**

||**U**|**nemploye**|**d**|**Un**|**deremplo**|**yed**|
|---|---|---|---|---|---|---|
||**Urban**|**Rural**|**Lomé**|**Urban**|**Rural**|**Lomé**|
|**None**|3.9%|0.6%|5.4%|19.4%|24.5%|22.5%|
|**Primary**|7.4%|2.3%|7.8%|20.7%|25.2%|21.2%|
|**Junior high**|7.4%|3.9%|8.3%|15.7%|21.4%|17.6%|
|**Senior high & above**|11.4%|2.3%|12.2%|13.7%|18.6%|14.6%|
|**Total**|7.4%|1.4%|8.5%|17.8%|24.2%|19.0%|

_Source_ : CWIQ 2006 and author’s calculations.

**There is a strong correlation between low education levels and poverty, already discussed in the Profile of the Economic Actors section and confirmed in the regression results presented in Table 11.** Compared to households

39
Table 11: Eect of a direct SLM training intervention on other farmers' adoption of pit planting, by CFs' characteristics

|CF characteristics|Educ > Median|Age _≥_Median|Land _≥_Median|Same Crop|
|---|---|---|---|---|
|T|0.029|0.035*|0.018|0.025|
||[0.021]|[0.020]|[0.019]|[0.019]|
|CF characteristics|0.011|-0.010|-0.014|0.003|
||[0.020]|[0.016]|[0.016]|[0.023]|
|TÖCF characteristics|0.000|-0.011|0.025|0.026|
||[0.025]|[0.023]|[0.022]|[0.027]|
|N|9,836|9,836|9,836|9,968|
|R<sup>2</sup>|0.010|0.011|0.010|0.010|
|Control mean|0.068|0.068|0.068|0.069|
|T+TÖCF characteristics|0.029|0.023|0.044*|0.052*|
||[0.025]|[0.023]|[0.024]|[0.030]|

Sources: Contact farmer survey, 2010; Household Survey, 2012, 2013. Notes: Regressions include the same explanatory variables as models in Table 10. The cuto values for CF characteristics correspond to the median values of education (7 years), age (41 years at ML, 43 years at EL), and landholdings (2.75 ha at ML, 3.5 at EL) in the sample of CFs. T+TÖCF characteristics (bottom row) presents the total eect of the treatment T and its interaction with the CF characteristic. The associated standard errors are in brackets. Signicance on the additive eect is determined by a Wald test.

***, **, and * indicate signicance at 1, 5, and 10 percent critical level. Ö=multiplied by; CF=contact farmer; SLM=sustainable land management. ML=midline; EL=endline.

42
9. The potential for crop growth depends on the number of days during which rainfall and soil moisture combined can meet the evaporation demands of the local climate. Only 53 percent of Africa's land area is capable of sustained pro ion of rainfed crops, of which over one-third can support short season e ips only (i.e. crops with 75-180 days growing period). Even where rainfall totals appear adequate, the erratic dist-ibution caused by the behaviour of the TTCZ is a serious constraint.2/ On the other hand, the temperature regimes are generally favourable to year-round cultivation where water is available.

10. The land area capable of sustained production is reduced further by the widespread poverty of the soils. When the constraint caused by shallow low-retention soils and heavy clays is taken into account, only 30 percert of the land area is either marginally or eminently suitable for cultivation. This amounts to 820 million hectares of which FAO estimates that 200-250 million hectares are currently under cultivation. There would appear to be, therefore, a reserve of 600 million hectares of unused land, although other constraints apply as will be discussed below. The most important feature, however, is its uneven distribution across the continent.

II. An FAO/I1SA study describes six climatic zones. By applying these to the cultivable land areas in individual sub-regions, estimates of

- 2/ The ill-fated groundnuts scheme in Tanzania failed despite good average rainfall because the probability of a good growing season was below one in twi.
## **4.9.2 Incorporating labor market regulations**

The model also incorporates some important labor market regulations. For example, the progressive tax structure is taken into account in computing after-tax income. Fees that workers pay for health and disability insurance are also taken into account. Lastly, the model incorporates the fact that informal sector workers typically do not pay these taxes and fees.<sup>35</sup>

# **5 Description of the data**

The estimation and simulations are based on data from three sources: the _Encuesta de Protecci ́on Social_ (EPS) longitudinal survey, linked administrative records of pension balances and contributions to retirement accounts (obtained from the Chilean supervising agency for pension fund administrators (the _Superintendency of Pensions_ or SP) and data on the returns achieved by Chile’s pension fund administrators (the _Administradoras de Fondos de Pensiones_ ).

The EPS survey was first administered in 2002 (originally under the name _Historia Laboral y de Seguridad Social_ ) by the Microdata Center of the University of Chile. Originally, the sampling frame was individuals affiliated with the AFP or the older INP pension systems. The survey data were then linked to the administrative records of the pension accounts of the sampled individuals. In 2004, 2006 and 2009 three follow-up surveys were administered, and the sample was augmented to include individuals that were not affiliated to any pension program, to obtain a total sample of 20,114 individuals, representative of the Chilean population in 2004. We use information on the 16,150 respondents who were interviewed in the 2006 round and use the survey weights to correct for attrition and non response.

The EPS questionnaire was designed specifically to study Chile’s social protection programs including the pension system. It contains rich longitudinal information on sociodemographic variables, household composition, employment histories, earnings and assets. The data include retrospective employment histories back to 1981 as well as earnings from

> 35 We use information on reported earnings and do not explicitly incorporate minimum wage regulation. However, we trim out reported monthly wages over 100 million pesos as they are likely to be reported with error.

20
the ‘user benefits’ of time savings, and their valuation is typically based on studies of the value of time. More generally, outputs may be valued at market prices (as they would be in a commercial decision) or, if outputs are non-marketed, values have to be inferred (e.g. by studies of the value of time, Small and Verhoef, 2007).

What about indirect effects, the shuffling of _L_ and _K_ between places and sectors of the economy due to induced private sector investment decisions?<sup>9</sup> The benchmark case is that the value of these changes is zero. If the economy is efficient then the marginal value of _L_ is the same in all its uses, and so too is the marginal value of _K_ . Moving them between uses is therefore of zero value – additional output in the new use is worth just the same as lost output in the alternative. This argument is the rationale for only looking at the direct effects of policy, as in standard cost-benefit analysis; if the economy is efficient, then other indirect effects combine to have zero value.

Indirect effects are of net value only if policy tends to correct inefficiencies, i.e. operates to raise productivity or to draw resources from lower to higher value use. This in turn arises only if the price-system (or some other mechanism) has not lined up marginal values. In the spatial context in particular there are many obstacles to efficient allocation, as we saw in section 2.1, and valuation requires that such inefficiencies are diagnosed, quantified, and understood. The remainder of this section outlines some of the key markets in which such failures are prevalent. They are linked to particular spatial contexts and policies in greater detail in sections 3 and 4 of the paper.

**_Job creation: spatial and labor market inefficiencies:_** Frequently the main motive for PBP is the creation of new jobs in the affected place, created as a consequence of increased investment and economic activity.  Valuation of these jobs depends on the value of workers in the new activity compared to what they would otherwise have been doing.  Box 2.1 illustrates the simple analytics.

> 9  And perhaps also from abroad or from additional savings, but both of these at a (opportunity) cost.

12
<!-- Start of picture text --> 0.14<br>0.12<br>ees<br>g<br>a¢53S o1<br>8<br>§ 0.08<br>88<br>On<br>Re)<br>% 0.06<br>g<br>8<br>5 0.04<br>B<br>0.02<br>)<br>2015-16 2016-17 2017-18 2018-19 2019-20<br>—NSS-2011 —=CPHS<br><!-- End of picture text --> <!-- Start of picture text --> 02<br>eee<br>0.18<br>= 016<br>&§<br>3 0.14<br>o<br>8<br>012<br>8 o1<br>on<br>Re}<br>a2 0.08<br>g<br>8<br>not& EI 0.06 a<br>© 0.04<br>0.02<br>0<br>2015-16 2016-17 2017-18 2018-19 2019-20<br>—NSS-2011 —=CPHS<br><!-- End of picture text -->