rafmacalaba/datause-encoder-data huggingface dataset:
<!-- Start of picture text --> 100<br>90<br>80<br>70<br>60<br>50 @ Technical<br>40<br>30 @ General<br>20<br>10<br>0<br>1983 1987 1993 1999 2004<br><!-- End of picture text --> <!-- Start of picture text --> 1800<br>1600<br>1400<br>» 1200<br>ZG<br>c<br>5 1000<br>re)<br>~ 800<br>c<br>— 600<br>200on<br>0<br>1983 1987 1993 1999 2004<br><!-- End of picture text --> <!-- Start of picture text --> 80 —— % of Eng. in<br>technical<br>70<br>60<br>—— % of Eng. In<br>total higher<br>50 :<br>%, education<br>= a0 attending<br>$<br>a —— Linear<br>30 . (% of<br>Eng. in<br>technical}<br>10; _ZN\= —— Linear(% of<br>Eng. In total<br>° higher<br>1983 1987 1993 1999 2004 education<br>attending)<br><!-- End of picture text -->
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_SLOP_ is negative and significant. This may be related to the historical fact that the railways are placed on relatively flat terrain. Therefore, when the slope of the terrain where a firm exists is steep, it is more difficult to utilize rail operations.

**<u>Table 6. Cobb-Douglas Cost Function: OLS and IV regression</u>**

||OLS<br>Coef.|Std.Err.||IV<br>Coef.|Std.Err.||
|---|---|---|---|---|---|---|
|RAIL|-1.072|(0.546)|**|-5.451|(3.186)|*|
|lnZ|0.035|(0.039)||0.049|(0.210)||
|lnWT- lnWM|0.030|(0.045)||-0.357|(0.361)||
|lnWL- lnWM|0.138|(0.039)|***|0.117|(0.049)|**|
|lnWE- lnWM|-0.074|(0.058)||-0.105|(0.087)||
|lnWF- lnWM|0.282|(0.044)|***|0.441|(0.164)|***|
|lnWW- lnWM|0.322|(0.065)|***|0.409|(0.086)|***|
|lnWC- lnWM|0.180|(0.069)|***|0.298|(0.108)|***|
|lnY|0.431|(0.061)|***|0.479|(0.086)|***|
|lnAGE|0.243|(0.104)|**|0.525|(0.210)|**|
|EDU|0.282|(0.066)|***|0.230|(0.085)|***|
|GLBL|0.364|(0.299)||0.445|(0.380)||
|FORN|-0.005|(0.004)||-0.003|(0.005)||
|Constant|11.366|(1.161)|***|11.068|(1.375)|***|
|Obs.|345|||343|||
|R squared|0.7355|||0.6107|||
|F stat.|32.64||||||
|Wald chi||||945.43|||
|No. of dummy vari|ables:||||||
|Industry|4|||4|||
|Region|16|||16|||
|Exogeneity test:|||||||
|Chi2 (3) stat.||||4.882|||
|Overidentifying res<br> Chi2(1) stat.|triction test:|||0.387|||

The dependent variable is ln _C_ -ln _WM_ . Robust standard errors are shown in parentheses. *, ** and *** indicate the statistical significance at 10, 5 and 1 percent, respectively. For the IV regression, three variables are instrumented: _RAIL_ , ln _Z_ and ln _WT_ .
many work days in the analysis as planned in the study design, and truncates the observation period over which we can estimate the productivity/activity impacts of treatment.

# **4. Malaria: Measurement and Treatment Protocol**

Before describing the health intervention and its effect on physical activity in more detail, it is important to understand both the measurement and expected impact of malaria infection as the particular biology of infection informs the identification strategy. Malaria is endemic in our study setting and workers in our initial qualitative interviews described it as an important health issue. As malaria symptoms generally include fever, chills, sweats, headaches, nausea, vomiting, body aches, and general malaise, the potential for malaria infection to impact labor outcomes is high. Severe malaria can impair consciousness, cause seizures, and result in coma (Najera and Hempel 1996). Individuals affected are also often dehydrated and hypovolemic (Miller et al., 2002). The duration of an episode of malaria varies widely.<sup>8</sup> An episode of malaria lasts up to 14 days, with an average of 4-6 days of total incapacitation and the partially incapacitated days characterized by nausea, headaches, and fatigue (Najera and Hempel, 1996).

Three methods are commonly used to assess malaria infection in large-scale surveys: selfreport, Rapid Diagnostic Testing (RDT), and microscopy. While self-reported malaria is often used as a proxy, careful measurement of malaria infection requires testing of a blood sample, as the diagnosis of malaria depends on the demonstration of parasites in the blood. Because the symptoms of malaria are generic, subjects may, through self-assessment, categorize other illnesses with similar symptoms as malaria infection.<sup>9</sup> At the same time, especially in areas where malaria and diseases with similar symptoms are endemic, habituation to these symptoms may lead to underreporting of malaria infection. Self-reported malaria can therefore suffer from both Type I

> 8 Duration may depend on the endemicity level of malaria in the area. Highly endemic areas may, for instance, have higher levels of immunity, and episodes may be longer in areas with less stable malaria presence (Deressa 2007). 9 This is further enhanced for the study area by the word referring to fever in the local language being the same as that referring to malaria.

10
||||Margina<br>|l Effects<br>|||
|---|---|---|---|---|---|---|
||Allowed|Conditioned|Blocked|Allowed|Conditioned|Blocked|
|ln(HHI)|-0.134|0.118|0.016|-0.132|0.122|0.010|
|HIST|-0.064|0.045|0.019|-0.117|0.087|0.030|
|EQ|-0.647|0.137|0.510|-0.502|0.245|0.257|
|EF|-0.263|0.249|0.014|-0.269|0.255|0.014|
|IMP|0.089|-0.071|-0.019|0.092|-0.080|-0.011|
|EB|-0.075|0.076|-0.001|-0.057|0.057|0.000|
|MKT||||0.029|-0.024|-0.005|
|FRG||||0.227|-0.204|-0.023|
|Observations||239|||239||
|Pseudo R2||0.19|||0.23||

Notes: (1) Robust z-statistics in brackets; *, ** and *** represent statistical significance at 90, 95 and 99 percent level of significance, respectively. (2) All marginal effects of binary variables measure the effect of a change from 0 to 1. (3) “Allowed” is the base category.

# V. Conclusions

Based on a newly created dataset with information on Mexican mergers, we estimate the probability that a would-be merger falls into any of three possible resolutions reached by the Mexican FCC: allowed, condition or blocked. Given the discrete nature of the problem and the unknown ordinality of the dependent variable, an ordered and a multinomial logit models were estimated. The results indicate that, overall, the FCC’s decisions are in fact reflecting a consistent application of the mergers provisions outlined in the Federal Economic Competition Law and expanded in the Merger Guidelines. Three variables included in the merger’s provisions have a significant and robust impact in the Commissions’ decision: market concentration, entry barriers and the equity share. A model containing just the variables explicitly mentioned in the mergers provisions would correctly predict 8 of every 10 Commission’s decision.

23
Table 9: Effects of forecast errors and forecast growth on worker compensation: longer-tenured vs newly hired workers

||(1)|(2)|(3)|(4)|
|---|---|---|---|---|
|Dep. variable:|log monthly<br>wage|log monthly<br>base wage|log (1 +<br>overtime pay)|log (1+ other<br>pay)|
|A. Newly hired workers (less|than 4 years)||||
|Weighted forecast error|0.0091|0.0058|0.0234|0.0233|
||(0.0062)|(0.0063)|(0.0207)|(0.0252)|
|Weighted forecast growth|0.0004|-0.0019|0.0331**|-0.0140|
||(0.0037)|(0.0037)|(0.0148)|(0.0174)|
|Period x region FE|Y|Y|Y|Y|
|Period x industry FE|Y|Y|Y|Y|
|Period x export share|Y|Y|Y|Y|
|Firm FE|Y|Y|Y|Y|
|N (obs.)|18700|18700|18700|18700|
|N (firms)<br>|8149|8149|8149|8149|
|Adj. R<sup>2</sup>|0.0139|0.0248|0.0277|0.0105|
|RSS|920|811|15993|16824|
|B. Longer-tenured workers (a|t least 4 years)||||
|Weighted forecast error|0.0094**|0.0075**|0.0430***|0.0099|
||(0.0046)|(0.0036)|(0.0159)|(0.0232)|
|Weighted forecast growth|0.0036|-0.0001|0.0331***|0.0110|
||(0.0030)|(0.0023)|(0.0113)|(0.0153)|
|Period x region FE|Y|Y|Y|Y|
|Period x industry FE|Y|Y|Y|Y|
|Period x export share|Y|Y|Y|Y|
|Firm FE|Y|Y|Y|Y|
|N (obs.)|21418|21418|21418|21418|
|N (firms)<br>|9019|9019|9019|9019|
|Adj. R<sup>2</sup>|0.0331|0.0618|0.0228|0.0088|
|RSS|582|306|14946|17485|

Notes: In each column, the dependent variable is the change between the averages of each 3-year period. Standard errors are clustered at firm-level. *10% level of significance, **5% level of significance, ***1% level of significance.
monetary resources. While it is possible that aspirations changed over time, a key difference between the studies is how they measure aspirations. GHC’s survey question captures something closer to what we would define as parents’ aspirations (“ _Which educational level would you like your child to attain?_ ”), while CSC measure something more akin to parents’ expectations (“ _By the time the child is age 18, what is the probability the child will have completed secondary education?_ ” and “ _What is the probability that the child will graduate from tertiary education?_ ”). While each concept is interesting in its own right, both studies talk about aspirations but with different questions that might be measuring different concepts. Expectations, which refer to the most likely outcomes given one’s perceived situation and barriers, can be expected to be lower than aspirations, which capture a notion of wishes. GHC indeed show that expectations are sizably lower than aspirations in this very context: in 2003, 33 percent of beneficiary parents aspire to university for their children while only 14 percent expect it to be the case (being asked “ _which educational level do you think your child is more likely to attain?_ ”).

Another type of question used to capture aspirations relates to “needs.” Some studies frame their survey questions on income aspirations in terms of the level of income respondents consider “sufficient” and an “absolute minimum” (Stutzer 2004; Knight and Gunatilaka 2012). One study in Myanmar finds that a measure of income aspirations based on “wants” (“ _What level of income would you like to achieve in your life?_ ”) and one based on “needs” (“ _What level of income do you need to feel financially secure?_ ”) correlate similarly with people’s real estate expenditures, but correlate in opposite direction with their peers’ average (“wants” positively while “needs” negatively), suggesting they are different concepts (Bloem 2021).

Other measures that have been used are: i) girls’ perception of gender roles and of suitable age for marriage and childbearing as a proxy for aspirations influenced by social norms (Del Carpio and Guadalupe 2018; Bandiera et al. 2020; Dhar, Jain, and Jayachandran 2020); and ii) attitudes about the future thought to be linked to aspirations, such as measures of depression and feelings towards the future (Macours and Vakis 2014) or planning horizon for future expenditures (Laajaj 2017). These are related but distinct concepts to aspirations and may not relate similarly with other variables of interest.

# **3.3. Measurement issues: Quantification, survey design, and validation**

We identify three main measurement issues.

First, like many other concepts in human development and economics, aspirations are tricky to measure. Broad dimensions of aspirations for the future beyond material standard of living —e.g. “a good life”, “dignity”, “recognition”— can hardly be translated into quantifiable measures. This implies that measures often focus on a limited set of quantifiable dimensions (e.g., years of education, 9
or close to zero, this would suggest that the new CPS did not fundamentally improve the available provision of education.

To identify the subLATE parameters, we would ideally need an extra source of variation that affects the share of _a-compliers_ (within the compliers) without directly affecting the CPS treatment effects. These additional instruments (which we refer to as **X** ) should affect the treatment effects only through the share of _a-compliers_ and not because they capture other forms of heterogeneity (e.g. heterogeneity driven by gender). This assumption is called constant treatment effect (Kline and Walters, 2016) or homogeneous LATE (Hull, 2018). We refer to this approach as conditional sub-LATE. Kline and Walters (2016) (see also Hull (2018) and Feller et al. (2016)) show that sub-LATEs can be identified by interacting the random assignment with observed covariates. The structural equation takes the following form: where _Yi_ is a follow-up outcome, 1 _Di_ = _a_ a variable taking one when the child i is enrolled in an ASP and zero otherwise, **_X_** a set of additional variables orthogonal to the treatment, and **_W_** the preferred set of control variables used for the reduced form estimation. _γ_ 1 captures the _LATEhc_ , and _γ_ 2 captures the effect of going to ASP. To derive the _LATEac_ , we subtract _γ_ 2 from _γ_ 1. 1 _Di_ = _c_ and 1 _Di_ = _a_ are both endogenous and instrumented using following first stage equations: The identification of _γ_ 1 and _γ_ 2 relies on the independence of _Z_ and _Z ∗_ **_X_** and on the assumption that the h and a compliers have a constant return to preschool on **_X_** . Since the X variables are selected from the baseline survey, their independence is guaranteed by randomization. However, these extra instruments could capture sources of variation that are not related to the variation in counterfactual enrollment, therefore causing a violation of the constant treatment assumption. To be valid, the conditional LATE approach therefore requires that the extra instruments capture the heterogeneity caused by the variation in counterfactual enrollment (i.e. the fact that some would have stayed at home and some would have enrolled in an ASP) but not other forms of more standard heterogeneity. Arguably, variables at the province or village level that capture infrastructure quality are less likely to be correlated with the standard heterogeneity associated with observed characteristics. Note that when there is more than one instrument in **_X_** , the validity of the constant treatment assumption can be tested using an over-identification test.

21
Policy Research Working Paper 6320

# **Abstract**

The infrastructure gap is one of the most significant impediments to India realizing its growth and poverty reduction potential. Although India’s transport network is one of the most extensive in the world, accessibility and connectivity are limited. Only 20 percent of the national highway network (which carries 40 percent of traffic) is four-lane and one-fourth of the rural population does not have access to an all-weather road. It is estimated that the transport sector alone will require an investment of nearly US$500 billion over the next 10 years. This paper investigates the impact of the Golden Quadrilateral highway project on the Indian organized manufacturing sector using enterprise data. The Golden Quadrilateral project upgraded the quality and width of 5,846 km of roads in India. The analysis uses a difference-in-difference estimation strategy to compare non-nodal districts based on their distance from the highway system. It finds several positive effects for non-nodal districts located 0–10 km from the Golden Quadrilateral that are not present in districts 10–50 km away, most notably higher entry rates and increases in plant productivity. These results are not present for districts located on another major highway system, the North-South East-West corridor. Improvements for portions of the NorthSouth East-West corridor system were planned to occur at the same time as the Golden Quadrilateral but were subsequently delayed. Additional tests show that the Golden Quadrilateral project’s effect operates in part through a stronger sorting of land-intensive industries from nodal districts to non-nodal districts located on the Golden Quadrilateral network. The Golden Quadrilateral upgrades further helped spread economic activity to moderate-density districts and intermediate size cities.

This paper is a product of the Economic Policy and Debt Department, Poverty Reduction and Economic Management Network. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http:// econ.worldbank.org. The author may be contacted at EGhani@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
- 31 -

Table 9: Proporion of Less-Developed Countries Offering Instruction in Selected Subjects of the Primary School Curriculum by World Region, 1960s and 1980s (Constant Cases)

|Region:|L|AC|As|ia|SS|A|EME|NA|
|---|---|---|---|---|---|---|---|---|
||(N|=l_1)_|(N|=12)|(N=|15)|(N=|15)|
||**12619**|I8.0s|190sa|I28|I|1260|12 &|12Bs|
|Comb.Lang.|100.0|100.0|100.0|100.0|100.0|100.0|100.0|100.0|
|NatLang.|90.9|90.9|100.0|100.0|40.0|46.7|100.0|100.0|
|Loc.Lang.|0.0|0.0|25.0|8.3|40.0|26.7|0.0|6.7|
|For.Lang.|9.1|18.2|16.7|25.0|6.7|13.3|46.7|53.3|
|<br>Off.Lang.|9.0|9.0|25.0|33.3|86.7|86.7|6.7|0.0|
|Mathematics|100.0|100.0|100.0|100.0|100.0|100.0|100.0|100.0|
|Science|100.0|100.0|91.2|100.0|100.0|100.0|86.7|100.0|
|Comb.Social Sci.|100.0|100.0|91.7|100.0|93.3|100.0|100.0|100.0|
|Social Studies|54.6|81.8|50.0|66.7|26.7|60.0|13.3|53.3|
|History|54.6|36.4|41.7|33.3|80.0|46.7|93.3|53.3|
|<br>Geography|54.6|36.4|41.7|25.0|60.0|46.7|93.3|53.3|
|Civics|45.5|9.1|25.0|25.0|46.7|40.0|33.3|33.3|
|Comb.Moral/Rel|. 63.6|63.6|83.3|91.7|86.7|86.7|86.7|80.0|
|Religion|36.4|54.6|25.0|41.7|66.7|66.7|86.7|80.0|
|Moral Ed.|45.5|36.4|75.0|66.7|26.7|20.0|20.0|13.3|
|Aesthetic Ed.|100.0|100.0|100.0|91.7|93.3|100.0|93.3|100.0|
|Music|81.9|90.9|91.7|58.3|86.7|86.7|86.7|80.0|
|Art|100.0|81.8|91.7|83.3|93.3|100.0|93.3|100.0|
|Physical Ed.|90.9|90.9|100.0|91.7|93.3|100.0|100.0|93.3|
|Hygiene|54.6|36.4|50.0|58.3|53.3|40.0|40.0|53.3|
|Comb.Voc.Ed.|90.9|100.0|66.7|58.3|86.7|93.3|46.7|66.7|
|Manual Training|45.5|81.8|33.3|50.0|40.0|26.7|40.0|40.0|
|<br>Agriculture|<br>54.6|45.5|33.3|16.7|60.0|80.0|<br>6.7|6.7|
|<br>Domestic Sci.|72.7|54.6|41.7|16.7|40.0|66.7|26.7|26.7|
|Vocational Ed.|27.3|9.1|8.3|8.3|0.0|0.0|0.0|13.3|
|Business|9.1|9.1|0.0|0.0|0.0|0.7|0.0|0.7|
|Other|18.2|27.3|16.7|58.3|46.7|33.3|33.3|40.0|

*Key for Regions: LAC: Latin America and the Caribbean

EMENA: Southern Europe, Middle East and North Africa SSA = Sub-Saharan Africa
education is key to human capital accumulation and hence poverty reduction, it may be worth investigating the feasibility of targeted subsidies in a broader context of cost recovery.

**Table 3.3. Guinea (1994): Distributional Characteristics of Components of Expenditure**

**on Health and Education.**

||Headcount|PovertyGap|Squared PovertyGap|Watts|
|---|---|---|---|---|
|Consultation Fees|0.00|1.00|1.22|1.07|
|Medical Test|0.00|0.34|0.23|0.30|
|Hospitalization|0.00|0.37|0.19|0.29|
|Pharmaceuticals|0.05|0.61|0.59|0.61|
|Other Health|0.38|1.07|1.04|1.06|
|School Fees|0.00|0.12|0.09|0.11|
|PTA|0.00|0.96|1.05|1.01|
|School Supplies|0.00|0.73|0.68|0.70|
|Other Education|0.00|0.50|0.53|0.52|

Source: Author’s calculations

**Table 3.4  Guinea (1994):  Distributional Characteristics of Some Components of Non-Food Expenditure**

||Headcount|PovertyGap|Squared PovertyGap|Watts|
|---|---|---|---|---|
|Tobacco|0.96|0.93|0.90|0.90|
|Hygiene|0.65|0.72|0.68|0.70|
|Clothing|0.43|0.95|0.90|0.92|
|Personal Effects|0.00|0.74|0.70|0.72|
|Rent|2.19|1.83|2.06|1.94|
|Water|0.00|0.15|0.12|0.13|
|Electricity|0.00|0.14|0.10|0.12|
|Energy|0.87|1.01|0.99|1.01|
|Transportation|0.00|0.26|0.26|0.26|
|Recreation|0.00|0.34|0.29|0.31|
|Communication|0.00|0.05|0.04|0.05|
|Ceremonies|0.34|1.01|1.10|1.05|

Source: Author’s calculations.

Social desirability does not necessarily entail feasibility. There is a potential efficiency cost associated with taxation as socioeconomic agents are expected to adjust their optimal behavior in the face of a new configuration of incentives induced by a tax reform. There could also be significant administrative costs associated with a finer differentiation of commodities for the sake of protecting the poor. As noted earlier, the 20
28 for ECE translate poorly to radio, and limited access for rural and poor families compounded existing inequities in service provision (Jeffery & McLaren, 2021).

In Jamaica, the COVID-19 response in ECE benefited both from greater availability of credible indicators and a wider variety of available interventions. Data sources included follow-up phone interviews with a subsample of households from JAKIDS, a long-running birth cohort study, conducted from April to June 2020; and a UNICEF study of a random sample of households with at least one child, conducted in June and July 2020 (Samms-Vaughan & Pellington, 2021). Parents of ECE-age children were much more likely to report receiving educational materials from school “few or no days per week” relative to parents of primary- and secondary-age children. The surveys also surfaced concerns about equity of access, insufficient support for home-based learning, and increasing levels of violence. At the same time, the response benefited more than in the other countries from existing resources which could be leveraged for continuity of learning. For example, WhatsApp was already in widespread use for parent-teacher communication, so teachers were able to continue using the medium to share learning materials and encouragement for homebased learning activities. The ECC used this network as well as its social media presence to direct parents to a dedicated webpage for children 0-5 years old (https://ecc.gov.jm/covid-19-corner/) with lesson plans, storytelling and tips for parents.

# **Discussion**

Across many factors, the COVID-19 era conditions for prioritization of ECE represented a continuation of the status quo, which resulted in relatively low prioritization in these four countries/regions. Both before and during the crisis, resource provision from donors and