rafmacalaba/datause-encoder-data huggingface dataset:
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- City of Vancouver. (2017). _Green Transportation_ . Obtenido de Green Vancouver : http://vancouver.ca/green‐vancouver/green‐transportation.aspx

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DEGLOSEA. (2010). _Best Practice Muangklang: Low Carbon City._ Dinda, S. (2004). Environmental Kuznets Curve Hypothesis: A Survey. _Ecological Economics_ , 431– 455.

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Friedman, J. H. (1991). Multivariate Adaptive Regression Splines". _The Annals of Statistics, 19_ (1), 1‐67.

Glaeser, Edward L. "Are cities dying?" The Journal of Economic Perspectives 12, no. 2 (1998): 139‐ 160.

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32
|**Table 4: Impacts on Employm**|**ent Outcom**<br>Worked<br>at all<br>in 2014|**es in 2014**<br>Number of<br>Months<br>worked|Average<br>monthly<br>earnings|Truncated<br>monthly<br>income|Aggregate<br>Employment<br>Outcome Index|
|---|---|---|---|---|---|
|_Panel A: Intention‐to‐Treat Ef_|_fects_|||||
|Assigned to Intern Program|0.172***|1.406***|9288**|10119***|0.285***|
||(0.050)|(0.442)|(4497)|(3638)|(0.083)|
|_Panel B: Local Average Treatm_|_ent Effects_|||||
|Participated in Internship|0.424***|3.458***|22760**|24797***|0.700***|
||(0.124)|(1.130)|(11057)|(8953)|(0.208)|
|Sample Size|459|459|458|458|459|
|Control Mean<br>Notes:<br>Robust standard errors in par<br>*, **, and *** indicate signific<br>Truncated earnings truncates|0.610<br>entheses, af<br>ance at the<br>at the 99th|3.902<br>ter controllin<br>10, 5, and 1 p<br>percentile.|36245<br>g for strata<br>ercent level|34131<br>fixed effects.<br>s respectively|‐0.213<br>.|

|**Table 5:Gender Impacts on 2014 Empl**|**oyment**<br>Worked <br>at all<br>in 2014|Number of<br>Months<br>worked|Average<br>monthly<br>earnings|Truncated<br>monthly<br>income|Aggregate<br>Employment<br>Outcome Index|
|---|---|---|---|---|---|
|Assigned to Intern Program|0.201***|1.550***|8657|8882*|0.299***|
||(0.058)|(0.525)|(6333)|(4577)|(0.101)|
|Assigned to Intern Program*Female|‐0.136|‐0.791|‐3629|‐431|‐0.142|
||(0.113)|(0.985)|(10453)|(7550)|(0.182)|
|Female|‐0.068|‐0.683|‐19300**|‐16700**|‐0.264*|
||(0.100)|(0.865)|(7204)|(6508)|(0.156)|
|Sample Size|459|459|458|458|459|
|Control Mean Males|0.627|4.084|42169|39036|‐0.139|
|Control Mean Females<br>Notes:<br>Robust standard errors in parenthese<br>*, **, and *** indicate significance at<br>Truncated earnings truncates at the 9|0.575<br>s, after con<br>the 10, 5, a<br>9th percen|3.525<br>trolling for s<br>nd 1 percent<br>tile.|23953<br>trata fixed e<br>levels respe|23953<br>ffects.<br>ctively.|‐0.369|

18
**Table 5:  Radio’s Impact on Citizen Support for Jobs versus Education**

||No Radio X<br>|Children i<br>|nteraction<br>|Radio X<br>|Children i<br>|nteraction<br>|
|---|---|---|---|---|---|---|
||Bivariate|Radio<br>|All<br>|Bivariate|Radio<br>|All<br>|
|||controls|controls||controls|controls|
|Number of stations|||||||
|Public||-0.0334|-0.070||-0.035|-0.070|
|||(0.42)|(0.12)||(0.39)|(0.11)|
|Private commercial||-0.00202|0.012||-0.002|-0.012|
|||(0.92)|(0.59)||(0.92)|(0.59)|
|Religious||0.0368|0.035||0.036|0.034|
|||(0.33)|(0.43)||(0.34)|(0.44)|
|**Private non-commercial**|**-0.016**|**-0.018**|**-0.014**|0.001|-0.0001|0.003|
|**(community)**|**(0.18)**|**(0.14)**|**(0.28)**|(0.09)|(0.99)|(0.85)|
|**Private non-commercial X**||||**-0.006**|**-0.006**|**-0.006**|
|**number of children**||||**(0.03)**|**(0.02)**|**(0.04)**|
|Number of children in|||0.002|.010||.016|
|household|||(.73)|(0.20)||(0.05)|
|Number of children aged 0 – 5|||-0.007|||-.007|
||||(0.47)|||(.48)|
|Respondent has secondary|||0.064|||0.064|
|education|||(0.04)|||(0.04)|
|Respondent Yoruba|||-0.31|||-.015|
||||(0.80)|||(0.71)|
|Number of adult household|||0.016|||0.015|
|members listed|||(0.03)|||(0.03)|
|Respondent single|||-0.075|||-0.078|
||||(0.04)|||(0.04)|
|Respondent Catholic|||-0.070|||-0.069|
||||(0.04)|||(0.05)|
|Income is below 30K|||0.15|||0.15|
||||(0.00)|||(0.00)|
|Dwelling characteristics|||||||
|Wall - Brick/Semi-rigid|||0.002|||0.0003|
||||(0.92)|||(0.99)|
|Floor – Cement|||0.037|||0.039|
||||(0.11)|||(0.10)|
|Rooms - Five or more|||-0.048|||-0.048|
||||(0.07)|||(0.07)|

25
||**T**<br>|**able 5: Girls' Enro**<br>|**llment - Grade 6**<br>|||
|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|
||OLS|FE|FE + Controls|Excluding mosque an<br>primaryschools|d<br>Excluding mosque and<br>primary schools+ Multan<br>+Khanewal|
|Number of Basic Facilities = 3|||-1.975***|-1.977***|-1.888***|
||||(0.509)|(0.510)|(0.544)|
|Constant|25.74***|20.23***|21.60***|22.21***|22.14***|
||(0.639)|(0.133)|(0.494)|(0.496)|(0.530)|
|Observations|71,620|71,620|71,620|70,074|65,476|
|Number of Schools||13,675|13,675|12,165|11,377|
|Adjusted R-squared|0.007|0.037|0.038|0.038|0.039|
|R-square within||0.0375|0.0383|0.0383|0.0389|

* p<0.05 ** p<0.01 *** p<0.001. Standard errors in parentheses are robust to heteroskedasticity and clustered at the level of the school. The dependent variable is girls’ enrollment in grade 6. The average school in a treatment district school at baseline had 16 girls enrolled in grade 6. Column 1 reports estimates from an OLS specification. Column 2 reports estimates from a fixed effects specification at the school level. Column 3 adds to column 2 a series of indicators for how many of the following three facilities a school has: toilets, drinking water and electricity. The excluded group is no facilities. The excluded year is the baseline year 2003. Column 4 excludes mosque schools and primary schools from the sample. Column 5 removes two districts whose treatment status was interchanged once the program began: Khanewal (changed from treatment to control) and Multan (changed from control to treatment).
Doctor Voucher treatment to have a positive RDT. Malaria-positive patients are more likely to get treated for malaria and especially severe malaria, so this difference would bias results _against_ patient-driven and _towards_ doctordriven demand. This is the opposite of what we find.

Panel C of Table 2 shows that patients come from relatively poor families: just under one-third of survey respondents (either the patient or the caregiver accompanying the patient to the clinic, in the case of minor children) are literate; households are quite large, with 10.3 members on average, and monthly per capita income is just under CFA 22,000 ($36). Forty-one percent of household members are children under the age of 15, and households own 0.49 mosquito nets per capita.

Appendix Table B6 shows that the characteristics of individuals selected for the home survey versus not, and interviewed versus not (conditional on selection), are balanced. There is, however, significant selection into taking the home RDT: patients who consented to take the home test are sicker and more likely to have purchased an antimalarial than patients who did not give consent.

# **3.4 Predicting Malaria Risk**

We would like to study how prescription and treatment outcomes vary with patients’ underlying malaria risk ( _π_ in the model). We follow Cohen, Dupas and Schaner (2015) and estimate the relationship between RDT result and observed patient characteristics in the clinic survey with the following probit specification: where _posict_ is a dummy variable equal to one if a patient tests RDT positive and **xict** is a vector including dummy variables for symptoms, days since onset of illness, patient age, a dummy equal to one if the patient is under age 5, the interaction between age and the under 5 dummy, patient gender, and patient pregnancy status.<sup>26</sup> The results of this regression, shown in Appendix Table

> 26We expect malaria risk to change discontinuously at age 5 because the Malian government had a policy of seasonal malaria chemoprevention for children under 5 at the time of the study (PMI, 2017). We also control for demographic characteristics indicative of socio-economic status (which correlates with malaria risk), including the survey respondent’s ethnicity, ability to speak French, literacy in French, and education. Finally we control for the patient and respondent being different people. Results are similar if we

23
interest rate on deposits or the cost of wholesale funds, are a function of the prevailing “risk‐ free rate” in the economy and a premium reflecting the default risk of the institution. The “risk‐free rate” reflects the fundamental equilibrium between savings and investment and is higher the scarcer the supply of capital in the economy is. Nominal rates are further influenced by expectations of future inflation. Caps on lending rates do not influence banks funding costs, but some countries try to reduce costs of funds by implementing caps on deposit rates.

**Taxation:** Finally, the level of interest rates is influenced by explicit and implicit taxation. Explicit taxation consists of the prevailing corporate tax rate in the jurisdiction that the bank operates in. Additionally, lending rates are impacted by implicit taxation, i.e. the opportunity costs of holding required reserves at the central bank. Reserve requirements are an implicit tax on banks if, as is usual, official reserves are remunerated at less‐than‐market rates (Demirguc‐Kunt and Huizinga, 1999).

# 2. Overview of interest rates caps around the world

Collecting data on interest rate caps is inherently difficult as there are no “hard” quantitative data, they come in many different facets, and there is no uniform classification or data source. For this paper we look primarily at the existence of laws and regulations that _de jure_ limit the level of lending rates for at least a part of the financial system. In addition, we include countries where interest rates are _de facto_ constrained, for example by subsidized rates from public banks or through some form of negotiated agreement.

The main data source for the analysis of this paper is the _Global Microscope on Financial Inclusion_ database, published by the Economist Intelligence Unit (EIU).<sup>3</sup> The Global Microscope database provides detailed information on financial regulations relevant for financial inclusion for 55 emerging and developing countries. For this study we extract the information provided by the country notes on Indicator 4.1.1 _“Interest rate restrictions (credit)”_ . We use the latest version of the Global Microscope, which is based on analyses and interviews conducted over the summer of 2016. The data, thus, give up‐to‐date information on the current state of interest rate restrictions. We extend the EIU data for countries not covered in their analysis by using information provided by primary sources, such as national

> 3 With technical support and oversight from the Center for Financial Inclusion at Accion, the Multilateral Investment Fund at the Inter‐American Development Bank, and the MetLife Foundation.

7
level of trade fraud in terms of under-reporting. In the model of this paper, in addition to the potential tari¤ evaded, which depends on trade value, under-reporting also allows traders to evade restrictive NTMs, such as inspections and testing requirements, which generally depend on the quantity traded. While the NTMs are only imposed by the importing countries, they may a¤ect both the importing and exporting ...rms. Nevertheless, di¤erences in the penalty rates and detection rates of the importing and exporting countries give rise to the observed trade discrepancies.

The following model focuses on under-reporting in quantity traded. Let index k = fX; M g indicate exporting or importing ...rms. Exporting and importing ...rms submit respective customs declaration forms for the same shipment, but may declare di¤erent quantities, Q<sup>X</sup> and Q<sup>M</sup> ; in order to minimize their trade costs. For the exporting ...rms, their exporting costs include the importing countries’NTM related transaction costs, such as pre-shipment inspection, testing requirements, conformity certi...cates and other obligations necessary to comply with the requirements of the importing country. These costs depend on the quantity and are captured parsimoniously by the AVE of NTM, multiplied by the size of the shipment, PQ<sup>X</sup> : While these costs provide an incentive for exporting ...rms to under-report export quantities, exporting ...rms may also face penalties if they are detected to have underreported in their export custom forms. The overall penalty cost depends on the penalty rate, �<sup>X</sup> ; the detection rate, s<sup>X</sup> ; the squares of the size of deviation, �Q � Q<sup>X�</sup> =Q; and the true value of the shipment, PQ:<sup>12</sup> Each exporting ...rm will therefore choose to declare Q<sup>X</sup> to minimize the total exporting costs, C<sup>X</sup> :

> 12Here we follow Ferrantino, Liu and Wang (2012) to model the cost of exporting depending on the squares of the size of deviation.

7
# The Technology-Employment Trade-Off: Automation, Industry, and Income effects<sup>∗</sup>

Gene Kindberg-Hanlon<sup>†</sup>

**Keywords** : Employment, Productivity, Technology shocks, SVARs. **JEL classification** : C11, C30, C33, E24

> ∗I would like to thank Alistair Dieppe, John Fernald, Neville Francis, Ayhan Kose, Franziska Ohnsorge, Temel Taksin, and Lei Ye for valuable comments and Aygul Evdokimova for excellent research assistance. I gratefully acknowledge financial support from the PHRD fund. The findings, interpretations, and conclusions expressed herein are those of the author and do not necessarily reflect the view of the World Bank.

> †World Bank gkindberghanlon@worldbank.org
- European Central Bank, 2006, “The Accumulation of Foreign Reserves.” Occasional Paper Series No. 43.

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- Jaimovich, Dany and Ugo Panizza, 2006, “Public Debt around the World.” IDB Research Department Working Paper No. 561.

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26
(Bruns & Luque, 2014; Leyva et al., 2015), elements of what happens in the classroom are inevitably lost as neither tool captures quantity and quality. This problem is even more acute in low- and middle-income countries, which are often characterized by high absence rates and low instructional time (Bold et al., 2017; World Development Report, 2018).

The _Teach_ classroom observation tool addresses this shortfall, as it measures the quantity _and_ quality of teaching. Although the tool is primarily high inference, the Time on Task component includes a simplified version of the Stallings tool to capture whether teachers provide a learning activity and students are on task.

Research indicates effective teachers maximize the amount of time students spend on learning (Wharton-McDonald et al., 1998; Stronge, 2018). In fact, time lost in classroom instruction is associated with behavior issues and poorer student academic outcomes (Bruns & Luque, 2014; Dobbie & Fryer, 2013; Lavy, 2010; 2015). Using data from seven Latin American countries, Bruns and Luque (2014) showed that teachers from schools ranked in the top 25th percentile of student learning spent, on average, 80 percent of their time on task, as compared to teachers in the bottom 75th percentile, who, on average, spent only 30 percent of their time on task.

Like time on learning, student engagement is another important predictor of learning (Castillo, 2017). In the same study, Bruns and Luque (2014) found when students are on task and engaged, they learn significantly more than when they are distracted or off task.

# 3.1.2. What Are Effective Teaching Practices?

This subsection provides the evidence and ‘content validity’ for the teaching practices captured in _Teach._<sup>3</sup> The Quality of Teaching Practices component is organized into three areas: _Classroom Culture, Instruction,_ and _Socioemotional Skills_ . These areas have nine corresponding elements that point to 28 behaviors. The behaviors are characterized as low, medium, or high, based on the evidence collected during the observation. These behavior scores are then translated into a 5-point element scale that quantifies teaching practices, as captured in a series of two, 15-minute lesson observations.

_Classroom Culture_ refers to a jointly-shared set of beliefs, attitudes, and behaviors by the teacher and students. Teachers who create a positive environment where students feel

> 3 This section is a summary of Molina et al. (2018a), which provides additional evidence on _Teach_ ‘content validity’.

6