rafmacalaba/datause-encoder-data huggingface dataset:
the-year market capitalization) across these three asset classes. Clearly, government bonds are the asset class with the highest turnover, followed by equity, and corporate bonds. Government bonds and equities not only have higher turnover than corporate bonds, but also are more frequently traded in open exchanges (Panel C). For instance, equities from the 40 listed companies that compose the main Chilean stock market index (IPSA) (where pension funds invest most of their equity portfolio) traded on average 92 percent of the trading days in 2004. Government bonds of maturities between 8 to 10 years also traded almost every day. In contrast, corporate bonds of intermediate maturities (8-10 years) traded 46 percent of the time during that year.<sup>17</sup> Finally, companies listed in the stock exchange are typically larger than those that issue corporate bonds. Panel D compares the median size of the main listed companies and of those companies that have issued corporate bonds. It shows that, despite corporate bond issuers being relatively large in Chile, they are typically smaller than the main companies listed in the stock exchange (median assets of US$ 750 million versus US$ 1,900 million for listed firms in 2005). Even the 40 largest companies with corporate bonds outstanding during 2002-2005 are smaller than the 40 main listed companies included in the IPSA (median assets US$ 1,700 versus US$ 1,900 million in 2005).

In summary, the data show that government bonds are widely available, frequently traded, and have easily available price information. Equity markets are dominated by large corporations, whose stocks trade frequently in open exchanges. Corporations issuing corporate bonds are smaller than those issuing equity, issuances are large, but they are infrequently traded and a non-trivial part of this activity occurs over the counter. These differences suggest that

> However, this should not make each issuance less transparent since the underlying debtor is the same. Summary statistics on amounts per issue are not reported, but are available upon request.

> 17 Data on the trading frequency of corporate and government bonds come from Lazen (2005). Trading frequency for equities come from the Santiago Stock Exchange.

15
<u>Table 12: The beneft-cost ratios of interventions</u>

|Intervention|Aggrega|te GDP|Per Cap|ita GDP|
|---|---|---|---|---|
||_r_ = 4%|_r_ = 5%|_r_ = 4%|_r_ = 5%|
|School-attendance subsidy|3.5|2.1|2.9|1.7|
|Health programme|16.3|13.5|14.6|11.7|
|Combined programme|13.3|10.6|11.7|9.0|

public sector projects in Africa, for there is always the alternative of investing in international paper of good quality. To be on the safe side, we also set the bar a – bit higher at 5 percent. Deciding on an appropriate welfare index is not at all straightforward, because one of the policy instruments – education subsidies – will not save lives, so that troubling ethical problems arise in comparing the above programmes. One crude, but defensible, way of proceeding is to use per capita and aggregate GDP, respectively, as (instantaneous) indices. Since only the latter gives any weight to the – – number of individuals actually alive and then equally these two indices arguably bracket the range of possibilities when output is the underlying measure of performance.

Table 12 reports the net present values of the streams of costs and benefits generated by the three programmes, whereby the streams of benefits after 2040 are interpolated from the differences in growth rates between 2030 and 2040 with and without the intervention, respectively, and then discounted over an infinite horizon. Linear interpolation is used to arrive at the estimates for all intervening years within each decade. All three programmes pass the test with flying colours whatever be the choice of index; but the two that involve the promotion of health have a benefit-cost ratio that is roughly fourto five-fold that involving educational subsidies alone. This striking improvement is due not only to the saving of lives, but also to the fact that the resulting reduction in _expected_ mortality provides an additional potent incentive to invest in education.

43
Table A.9: Under-5 mortality: Heterogeneity by birth order

|Birth order|2nd|3rd|4th|5th|6th|7th|
|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|
|_Firstborn girl_ * _Female_|3.484***|2.971**|1.123|-0.0783|-3.635|20.28|
||(0.692)|(1.111)|(1.666)|(3.108)|(3.826)|(13.75)|
|_Firstborn girl_ * _Female_ * _Post1_|-2.847**|-0.881|1.019|3.061|-0.405|-22.43|
||(1.034)|(1.047)|(1.602)|(4.228)|(4.271)|(18.44)|
|_Firstborn girl_ * _Female_ * _Post2_|-2.093**|-2.463|0.0841|1.044|2.977|-25.86|
||(0.789)|(1.523)|(2.335)|(4.091)|(4.331)|(16.45)|
|N|101,529|64,383|34,318|16,384|6,885|2,539|

NOTES: Estimates corresponding to the specification in column (3) of Table 3 estimated separately for various birth orders. Each column is a separate regression. The outcome measures mortality as % of births that do not survive. Baseline mean refers to the average likelihood of under-5 mortality in the pre-ultrasound period. Standard errors in parentheses are clustered by state. Data: NFHS. *** 1%, ** 5%, * 10%.

56
estimate of the propensity score: _P_ ( _M ij_ 1 = 1 | _X ij_ 0 ) .<sup>6</sup> This leads to efficient and consistent estimates of α as long as either the propensity score or the regression model are specified correctly (Imbens and Wooldridge, 2007).

The validity of our WLS estimates relies on the Conditional Independence Assumption: conditional on the covariates, owning a mobile is exogenous ( _i.e.,_ not driven by unobservables that also affect the decision to smoke). While we cannot directly test this assumption we can assess if it is plausible by regressing _Ln_ ( _Cij_ 0 ) on _M ij_ 1 . The closer the estimated coefficient is to zero, the greater the plausibility that the assumption holds (Imbens and Wooldridge, 2007). Results are available in Table A1. None of the coefficients are statistically different from zero at the usual levels and none of the t- statistics are greater than 0.67. Our results are thus unlikely to be driven by selection on unobservables.7

# **_4. Results_**

In this section, we first empirically assess the impact of mobile phone ownership on smoking behavior by implementing the estimation strategy discussed in Section 3.

> 6 Estimates are available in Table A2.

> 7 While the results indicate that selection on unobservables is not very likely, as a further test of robustness, we also estimate equation (2) through IV. The validity of such estimates relies on the availability of an instrument that explains the decision to buy a mobile phone but is not correlated with the decision to smoke (other than through the impact of mobile phone).  We use the 2003 asset index as an instrument for mobile phone purchase. Results available in Table A2 indicate that this is a good predictor of the decision to buy a phone between 2003 and 2006 (t-stat is equal to 10.8). Further, a regression of the 2003 level of tobacco consumption on the 2003 asset index does not yield any significant results (t-stat is equal to -0.51). (Results available upon request). While not a direct test of the validity of our instruments, this suggests that our asset index is not directly correlated with the decision to smoke. We use the Limited Information Maximum Likelihood (LIML) estimator as it performs better than 2SLS in finite samples.  We test whether our instrument set is weak against the alternative hypothesis that it is strong using the test put forward in Stock and Yogo (2005). In all regressions we can reject the null hypothesis that our instruments are weak. All our IV results are consistent with both our OLS and WLS results.

6
**_Table 6: EBRD qualities of a ‘sustainable market economy’ and select indicators relevant to energy_**

|**Quality**|**Component**|**Select indicators relevant to energy (shaded)* **|
|---|---|---|
|**Competitive**|Market structures for competition and incentives<br>for sound decision-making [53%]|General (e.g. Entry of new firms)|
||Capacityto add value and Innovate[47%]|General(e.g. Credit toprivate sector/GDP)|
|**Well-**<br>**governed**|National-level governance (quality, integrity and<br>control of corruption, rule of law) [60%]|General (e.g. Regulatory quality, transparency of policy-<br>making, regulatoryburden)|
||Corporate-level governance (frameworks and<br>practices, integrity and business standards)<br>[40%]|General (e.g. structure and functioning of Board,<br>stakeholders and institutions)|
|**Green**|Climate change mitigation [35%]| Electricity share from renewables and hydro<br> Power (electricity & heat) consumed per unit of carbon<br>dioxide emissions<br> Renewable energy legislation (existence and degree<br>of enforcement)<br> Rating of nationally-determined contribution to Paris<br>Agreement goals<br> Carbon pricing<br> Fossil fuel subsidies|
||Climate change adaptation[35%]|General(e.g. disaster risk)|
||Other environmental areas [30%]| Population exposed to levels of PM2.5 exceeding<br>World Health Organisationguideline|
|**Inclusive**|Gender equality|General(e.g. female firm ownership)|
||Opportunities foryouth|General(e.g. labor market structure)|
||Regional disparities| Access to heating<br> Access to gas<br> Householdperception of electricityservice satisfaction|
|**Resilient**|Financial stability [70%]|General(e.g. bankingsector health)|
||Energy sector<br>resilience [30%]<br>Liberalization &<br>market liquidity [33%]| Sector restructuring, corporatization, unbundling<br> Fostering private sector participation<br> Tariff reform|
||System connectivity<br>[33%]| Domestic connectivity<br> Inter-countryconnectivity|
||Regulation and legal<br>framework [33%]| Adequate legal framework<br> Independent energyregulator|
|**Integrated**|Trade[50%]|General(e.g. foreign-direct investment)|
||Infrastructure| Quality of electricity supply<br> Losses due to electric outages|

*Note: 1990s model reform elements are shaded more darkly. Source: Adapted from EBRD (2017).

Page 48
be self-employed and provide childcare for their children at the same time. In our sample, 50% of skilled workers were self-employed.

Most importantly, we find a strong effect of childcare attendance on women’s wageearning jobs. Childcare attendance increases the probability of having a wage-earning job by 0.41. Sending a child to childcare can free up time for women and allow them to participate in the labor market. Childcare attendance has no effect on overall employment but a positive effect on wage-paying employment. This means that women switch from self-employed work to wage-earning work. Table 3 shows the negative effect of childcare on self-employed farm work.

There is also a significant positive effect of childcare attendance on women’s formal jobs. Having a child in childcare increases the probability of women having a formal job by 0.257. Formal jobs are medium-term employment with a social insurance contribution. Thus, sending children to childcare helps women not only to participate in the labor market but also to find a medium-term, stable job.

<mark>We measure the effect of having children in childcare on the wages of wage earners. The effect of childcare attendance on monthly wages is positive but not statistically significant. However, there is a significant effect on annual wages. The increase in total wages may be due to an increase in the number of women in formal jobs as well as an increase in productivity.</mark> <mark>In Panel A of Table 3, childcare attendance and maternal employment are measured in the current year. An important question is whether putting children in childcare has an ongoing or medium-term effect on maternal employment. To examine the medium-term effect, we regress the employment variables of women measured in one VHLSS on childcare attendance recorded in the previous VHLSS, using panel data from two consecutive surveys.</mark><sup>16</sup> <mark>This approach aims to measure the 2-year lagged effect of childcare attendance on parental education. We use the same model specification as the model of current effects.</mark>

> 16 Fifty percent <mark>of households sampled in one VHLSS are re-interviewed in the next one. For example, 50% of households in the 2010 VHLSS are resampled in the 2012 VHLSS. Then 50% of the households covered in the 2010-2012 VHLSS panel are resampled in the 2014 VHLSS. In addition, 25% of households in the 2012 VHLSS are also resampled in the 2014 VHLSS. Thus, in two consecutive surveys, around 50% of households feature in panel data, and around 25% of households are included in panel data in three consecutive VHLSS. In practice, the dropout or attrition rate in two consecutive surveys is around 8%.</mark>

16
Third, the recent experience has shown both the bright and dark sides of financial deepening, a trade-off that also applies to the mortgage market as much as to other segments of the financial systems, especially as housing boom and bust cycles have been at the center of the recent financial crises in the U.S., Ireland, and Spain. Developing the benchmarking model that we introduced above further to be able to properly gauge the “temperature” of the mortgage market is an important extension. Such an expansion of the benchmarking exercise would require, however, more detailed data on the mortgage market as detailed above, including price data and longer time series.

26
which gives the probability that farmer _i_ will choose crop j among J species (McFadden 1973, Train 2001).

The parameters can be estimated by the Maximum Likelihood Method, using an iterative nonlinear optimization technique such as the Newton-Raphson Method. These estimates are CAN (Consistent and Asymptotically Normal) under standard regularity conditions. (McFadden 1999)

# **3. Data**

The data this study relies upon came from a World Bank project to study climate change impacts on agriculture in Latin America. The project collected economic surveys at the farm level from the following seven countries: Argentina, Brazil, Chile, Colombia, Ecuador, Uruguay, and Venezuela. The countries were selected to represent the wide range of climate throughout South America and include representatives from both the Southern Cone and Andean regions. Districts within each country were selected to provide as much within country climate variation as possible. The original survey interviewed over 2000 farmers of which 949 farmers selected one of the crops that we are modeling. Some farmers focused strictly on livestock, some farmers picked other crops, and some farmers did not reveal which crops they grew.

Climate data came from two sources: US Defense Department satellites and weather station observations. We relied on satellite temperature observations and interpolated precipitation observations from ground stations (see Mendelsohn et al 2006 for a detailed explanation). Soil data were obtained from the FAO digital soil map of the world CD ROM. The soil data were extrapolated to the district level using GIS (Geographical Information System). The data set reports 26 dominant soil types.

5
**Table 2: Comparative Investment Costs by School Model, USD (2011)**

|**Expenditure**|**Regular Schools**<br>**(20 hours a week)**|**Full-time Schools**<br>**(37.5 hours a week)**|**Extended-day Schools**<br>**(35 hours a weeks)**|
|---|---|---|---|
|**Investment**||||
|-<br>New construction|0|1,700,000|0|
|-<br>Refurbishing|0|1,100,000|120,000|
|-<br>Equipment/Library|0|107,000|12,200|
|-<br>Training|0|5,315|5,315|
|**Total cost(low-case)**|**0**|**1,212,315**|**137,515**|

Note: Total cost assume a low-case scenario of refurbishing rather than new constructions for the ETC schools.

**Table 3: Comparative Annual Operating Costs by School Model, USD (2011)**

|**Expenditure**|**Regular Schools**<br>**(20 hours a week)**|**Full-time Schools**<br>**(37.5 hours a week)**|**Extended-day Schools**<br>**(35 hours a weeks)**|
|---|---|---|---|
|**Wages**|**220,532**|**391,331**|**340,797**|
|-<br>Director|25,559|35,404|35,404|
|-<br>Secretary|16,054|29,188|29,188|
|-<br>Teachers(20 hrs)|128,434||64,217|
|-<br>Teachers(40 hrs)||233,503|116,752|
|-<br>Phys Ed|16,054|16,054|16,054|
|-<br>Other workshops|12,418|16,054|24,837|
|-<br>English teachers||12,418|12,418|
|-<br>HardshipIncentives|5,037|||
|-<br>Other staff|16,974|48,709|41,928|
|**School meals/snacks**|**20,854**|**49,652**|**39,721**|
|**Other direct costs**|**25,917**|**30,582**|**30,582**|
|**Annual cost/student**|**1,371**|**2,418**|**2,108**|
|**Total cost**|**267,302**|**471,565**|**411,100**|

The tables show that the ETC model is approximately 76 percent more costly to operate per student per year over the regular program, and 15 percent higher than the ETE model. On the investment side, the ETC model is nearly eight times more expensive, using a low-case scenario of refurbishing (US$1.1 M) rather than new constructions (US$1.7M).

# **5.3. Cost-Effectiveness Estimates**

As seen in the quasi-experimental evaluation cited in the previous section (Cerdan-Infantes & Vermeersch, 2007), it is estimated that the impact for extending the school day in Uruguay for a child who spends one year of primary school in an ETC would be 0.04 standard deviations for 22
increase in grocery-store prices of 0.08%. The effect is stronger in counties with higher SNAP participation rates and higher grocery market concentration. Increases in sales, after controlling for price effects, are also larger in counties with higher SNAP participation rates. We estimate small and statistically insignificant effects on other store types, firm dynamics such as retailer entry and exit, and market structure. We estimate a positive price effect not only for SNAP-eligible products but also for SNAP-ineligible products, although the sales effect is much stronger in high-participation counties for SNAP-eligible goods but not SNAP-ineligible goods. We regard this as evidence consistent with multi-product pricing models such as that of Chen and Rey (2012), suggesting that retailers are able to raise markups on both eligible and ineligible products.

Next, we use consumer scanner data to estimate the effect of SNAP on household consumption. We classify consumption by eligible and ineligible households, as well as by eligible and ineligible products. We estimate a marginal propensity to consume food (MPCF) out of SNAP of about 0.44 for eligible households, in line with previous literature. We estimate that the MPCs for ineligible products and ineligible households are statistically insignificant and different from the MPCF out of SNAP for eligible households. Using cross-state variation in issuance schedules, we find that the MPCF out of SNAP is larger in weeks of benefit disbursement, complementing previous literature on impacts of issuance schedules (Goldin et al. 2022). We also investigate the effect of SNAP on shopping behavior, and report small and mixed effects.

In order to understand economic incidence, we develop and calibrate a simple partial-equilibrium model of supply and demand, extending upon the tax-incidence analyses from Weyl and Fabinger (2013) to shed light on the incidence of social-safety-net transfers. The exercise allows us both to test the consistency of empirical findings with theory and to interpret the results. The framework takes our reduced-form estimates of the price response and the MPCF out of SNAP as a set of sufficient statistics to measure the local incidence of changes in SNAP benefits. We find that for SNAP-eligible goods, SNAP recipients experience a large increase in consumer surplus as expected. We also find that retailers exercise market power by increasing prices to capture producer surplus. On the other hand, non-SNAP consumers experience a decline in consumer surplus. A marginal dollar of SNAP benefits increases SNAP consumer surplus—specific to groceries consumption—by about $ 0.7, increases producer surplus by about $ 0.5, and decreases non-SNAP consumer surplus by about $ 0.4, or $ 0.05 per non-SNAP consumer on average. We derive pass-through formulas to show that the size of the price response is consistent with the theoretically-calibrated prediction based on the MPCF out of SNAP, demand elasticity, and pass-through rate. Furthermore, the model predicts that the price responses should increase with SNAP 3
of the course curricula reached 61.2 percent, representing an increase in the curricular learning of between 8 and 10 percent per month. All students who received a license participated in the program and the great majority used it the recommended time.

Due to the COVID-19 pandemic, TTCs closed in March 2020. As a result, the rollout of the intervention changed to the extent that students could not use the computer laboratories at their institutes and needed to ensure their means to access the software (through a computer, tablet, or smartphone) and the internet. In other words, the only change in the intervention post-pandemic was the technology delivery modality. Same as in the pilot, instructors from TTC reviewed the curricular contents included in the standard "ALEKS pre-calculus for college readiness" course. They selected the items that they considered relevant based on their course's curricular priorities. A series of item "calibration" workshops occurred in December 2020, which led to the customization and configuration of all ALEKS courses in the system and the provision of teacher credentials. A course would typically consist of about 200 items. But, since not all technical programs have the exact mathematics requirements, the number of items in every course oscillated between 80 items in technical careers related to the provision of services (e.g., health and wellbeing) and 207 items in engineering-related technical programs ( **Table A2** in **Appendix A** ). Teachers participating in the program received training on accessing the software, creating, and modifying the course, viewing student dashboards to monitor their performance (use and progress), and using the software data analytics functionalities.<sup>3</sup> The rollout of the intervention began in January 2021. During the semester, McGraw Hill provided guidance and support and offered periodic reports on the access and use of the platform. Similarly, a local monitoring firm also prepared intermediate monitoring reports showing statistical data on the platform's performance (e.g., number of active vs. enrolled students, initial proficiency and progress, average hours of use, and percentage of students who meet the minimum recommended for weekly use). The monitoring results helped identify institutes with a high share of students who had not used the platform and problems with the take-up of the program. Based on these findings, teachers received additional training the first week of March, which addressed

> 3 All training sessions were recorded to benefit teachers who could not participate. Additionally, the McGraw Hill team set up an email account teachers could use in case they had questions related to using the platform or required technical support.

8