rafmacalaba/datause-encoder-data huggingface dataset:
# **_6.1 Baseline results_**

Table 2 presents the results from estimating Eq. (1) without treated group-specific time trends in column (1) and with such time trends in all other columns. Inference is based on standard errors robust to heteroscedasticity with the Huber-White approach, clustered at the product level.<sup>31</sup> The estimates in column (1) show positive and significant impacts on exports of African beneficiary countries of GSP LDC and AGOA non-LDC non-apparel and apparel. But our preferred estimates are those in column (2) that correct for the potential presence of differential pre-treatment trends for beneficiary countries and products. They show a positive and significant impact of GSP LDC for beneficiary countries in Africa: on average their exports to the US of the additional products eligible for duty-free treatment under GSP LDC increase by 12 percent from 1998 onwards, relative to pre-1997 levels.<sup>32</sup> Regarding the impacts of AGOA, column (2) shows they are insignificant on non-apparel products for non-LDCs and LDCs, in contrast to the significant positive effects in column (1). These results reveal the importance of accounting for treated groupspecific time trends. The most important boost to exports to the US provided by AGOA is estimated for apparel products, which increased by 22 percent for AGOA-eligible countries from 2001 onwards relative to pre-AGOA levels.<sup>33</sup> Two crucial remarks need to be made about the estimates in Table 2. First, recall that they are obtained controlling for time-varying country and product changes in US imports, and thus account for any overall surge or drop in US imports from AGOA countries for eligible and non-

> 31 Due to the presence of a very large set of fixed effects, we estimate our equations using the _reghdfe_ Stata command drawing on Guimaraes and Portugal (2010) and Correia (2015). The command eliminates from the number of observations singleton groups and adjusts standard errors for their exclusion. A singleton group is a group with only one observation: e.g., for exporting countryHS 6-digit fixed effects, a singleton group is an exporting country-HS 6-digit cell that is imported by the US in a single year. 32 This marginal effect corresponding to the coefficient of 0.114 in Table 2 is obtained as (e(0.114)-1.)*100. The impact of GSP LDC on exports of LDCs outside Africa is actually negative and significant.

> 33 The treated group-specific trends have a positive and significant coefficient but for apparel the trend is significant only at a 10 percent confidence level.

20
**Table 5. Changes in Poverty and Inequality**

|||Upper<br>povertyrate|Gini<br>coefficient|
|---|---|---|---|
|Baseline||24.3%|0.31|
||Complete pass-through|24.6%|0.31|
|25% cigarette price increase|Medium|24.5%|0.31|
||High|24.1%|0.31|
||Complete pass-through|24.8%|0.31|
|50% cigarette price increase|Medium|24.7%|0.31|
||High|23.8%|0.30|
||Complete pass-through|25.0%|0.31|
|75% cigarette price increase|Medium|24.8%|0.31|
||High|23.4%|0.30|

# **5. Adding the health impacts of a cigarette price increase into the equation**

The simulations presented so far focus on the direct impact of an increase in cigarette taxes on consumption. However, by reducing tobacco consumption, tobacco taxation can also generate longerterm monetary gains because of the improved health of the population. A large body of literature has documented the economic gains associated with reduced tobacco consumption and a healthier population. This section attempts to monetize some of these gains across deciles to highlight that the monetary impacts of a healthier population largely exceed the direct impacts on consumption. The focus is on two types of gains. First, the savings because of reduced medical costs are considered. The most recent comprehensive estimates of the economic cost of smoking in Bangladesh refer to 2004 (WHO 2007). According to these estimates, the annual direct health care cost attributable to tobaccorelated illnesses was Tk 50.9 billion (approximately US$990 million in 2016 prices).

Second, the income gains derived from the increased number of working years are considered. Verguet et al. (2015) analyze the health effects of a price increase in China and conclude that a 50 percent rise in prices would result in 231 million life years gained over 50 years and would have a significant impact among the poor. Pichón-Riviere et al. (2014) estimate that tobacco use in Chile would reduce life expectancy by nearly 4.0 years among women and 4.3 years among men. Alam et al. (2013) estimate that deaths among men ages 25 to 69 years result in an average loss of seven years of life per smoker in Bangladesh. Estimates using data on 2016 indicate approximately 3 million total years of life lost because of premature mortality attributable to smoking.<sup>17</sup> Table 6 summarizes back-of-the-envelope estimates of the savings in medical expenses and gains in consumption arising from the increase in working years across consumption deciles. The savings are

> 17 GBD Results Tool (database), Global Burden of Disease Study 2016, Global Health Data Exchange, Institute for Health Metrics and Evaluation, Seattle, http://ghdx.healthdata.org/gbd-results-tool.

15
Can Digital Personalized Learning for Mathematics Remediation Level the Playing Field in Higher Education? Experimental Evidence from Ecuador<sup></sup> Diego F. Angel-Urdinola, Ciro Avitabile, Marjorie Chinen JEL Classification: I20, I21, I23, I25, I29

**Keywords:** Mathematics remediation; higher education; teaching at the right level, computer assisted learning, digital personalized learning.

>  Special thanks to Segundo Agapito Farias and Nelly Fernanda Paredes from SENESCYT, as well as Flavio Jacome and Silvia Nuñez from McGraw Hill, for their significant contributions. We also acknowledge the contributions of Catalina Castillo and Lorena Moreno during the intervention design and implementation. Juan Baron provided valuable comments on an early version of the paper. This study was registered in the AEA RCT Registry on February 28<sup>th</sup> , 2022, with the RCT ID AEARCTR-0009036.
Croke et al. (2016) the subsample of studies with prevalence greater than 20% and those with prevalence greater than 50%.<sup>26</sup> The full sample estimate of 0.134 kg comes from studies with average worm prevalence of 51%. Assuming treatment only affects weight in the worm-infected population, this implies an average effect of roughly 0.301 kg among those with worms (calculated by dividing estimated impact by worm prevalence for each study and applying a random effects model).<sup>27</sup> This in turn likely represents the average of a considerably larger effect among the small proportion of those infected who have moderate or severe intensity infections and a considerably smaller effect among the majority of those infected, who have light-intensity infections.

While the average weight gain is fairly modest, it is far from negligible relative to the very low cost of deworming. Given the estimated effect of 0.134 kg and the estimated cost of $0.60 per person treated for two treatments per year (in the multiple dose context we focus on in this meta-analysis), the estimated cost per kg gained is ($0.60)/(0.134kg) = $4.48 per kg. For reference, it is worth comparing this to nutritional programs aimed at similar populations, in particular, school feeding programs. Kristjansson et al. (2007) conduct a Cochrane Review of the impact of school feeding, and Galloway et al. (2009) combine these results with data on costs to estimate the cost effectiveness of school feeding. They estimate that over a one year period, “the cost of an extra kilogram of weight ranged from $112 to $252 in the RCTs and $38 to $86 in the [controlled before-and-after

> 26 Results are also robust to maintaining the treatment effects and standard errors reported in TMSDG (rather than using updating treatment effects or standard errors for Awasthi 2001, Hall 2006, and Sur 2005) and only altering the TMSDG sample by incorporating previously-excluded studies.

> 27 Note that this calculation includes estimated prevalence for three studies in the full sample which do not report prevalence within their study – Gateff, Lemarinier and Labusquiere (1972), Alderman et al. (2006), Awasthi et al. (2008). See Table 1 and Section 4 for more details.

26
28

# Table 8: Regression estimates of HCFP impact on out-of-pocket spending and utilization

||OLS full s<br>|ample<br>|OLS trimme<br>|d sample<br>|
|---|---|---|---|---|
||coeff.|t-stat|coeff.|t-stat|
|Total out-of-pocket payments|-3.375|-0.29|10.154|1.06|
|Catastrophic (>10% nonfood consumption)|-0.040|-4.07|-0.033|-3.26|
|Outpatient expenses|8.119|0.91|6.505|0.74|
|Inpatient expenses|16.374|0.63|32.616|1.18|
|Outpatient expenses per visit|7.085|0.55|3.955|0.31|
|Inpatient expenses per admission|49.218|0.40|89.367|0.73|
|Non-prescribed medicine expenses|-3.119|-1.60|-4.310|-2.25|
|Medical equipment expenses|-0.168|-0.87|-0.156|-0.97|
|# outpatient visits|0.156|3.16|0.143|2.75|
|# inpatient spells|0.042|3.34|0.048|3.56|
|Outpatient visits Yes|0.045|4.61|0.040|3.94|
|Inpatient spells Yes|0.020|3.32|0.022|3.63|
|Village station Yes|0.002|0.92|0.003|1.42|
|Commune center Yes|0.059|8.15|0.060|7.71|
|Regional clinic Yes|0.002|0.80|0.002|0.79|
|District hospital Yes|0.021|3.60|0.019|3.09|
|Provincial hospital Yes|0.016|3.66|0.017|4.09|
|Central hospital Yes|0.000|-0.16|0.000|-0.02|
|Other govt hospital Yes|0.000|0.16|0.000|0.14|
|Private provider Yes|-0.035|-5.88|-0.037|-6.10|
|Traditional healer Yes|0.000|-0.01|0.001|0.27|

Note: Expenditures are in thousands of VND, except in case of catastrophic out-of-pocket payments which is a share of the relevant subsample.
Table 5: Productivity by Financial Constrained Indicator Variables Average Productivity

|Indicator variable=|No|Yes|t-stat|
|---|---|---|---|
|Access Mod-Severe|4.212|4.127|1.123|
||(1.15)|(1.068)||
|Access Major-Severe|4.18|4.126|0.7616|
||(1.131)|(1.05)||
|100% Internally fnanced Investment|4.177|4.115|0.7433|
||(1.107)|(1.105)||
|100% Internally fnanced Working Capital|4.193|4.012|1.906|
||(1.108)|(1.09)||
|Overdraft Facility/Credit line available|3.978|4.256|-3.8719|
||(0.9985)|(1.151)||

_Source:_ Bangladesh 2002 Investment Climate Assessment Survey, World Bank

# **5.2 Working Capital and Distance to Export Destination**

The 2002 Bangladesh data set is unique in that the survey asked firms to list their main export destinations and amount exported to each of these destinations for years 2000, 2001 and 2002. From this, a weighted export distance can be calculated for each firm. The availability of data on the distance of the export destination allows a simple test of the relationship between working capital and the export distance by exporting firms. This relationship is a key component of the model: the greater the export distance, the longer the delay in the receipt of payments and the greater the demand for working capital required.<sup>25</sup> The empirical question is whether the amount of working capital, _X_ , is significantly and positively correlated with export distance among exporting firms controlling for factors that also determine the amount of working capital. If working capital does not vary with distance, than this puts into doubt whether the working capital is driven by delays in the receipt of revenues, or is simply a function of costs. The empirical specification is motivated by the determinants for the amount of cash required to export from Equation 13. Working capital demand increases with time delay _S_ , cost of production _Ct_ , expected cost of production _Ct_ +1 (to ensure that costs are covered in the interim between production and when revenue is received upon delivery) and with productivity _A_ . The time delay, _S_ , is proxied by the distance to export destination, _distance_ . Whether the firm will export in the interim periods during the delays also affect how much working capital is needed. Therefore, working capital demands would also increase with expected relative foreign to domestic price, _p_ .

The stock of working capital kept on hand would decrease with supply of liquidity: the amount the firm can borrow which is measured as short-term liabilities, and past export production, proxied by years of exporting, _yearsexporting_ .<sup>26</sup> The empirical specification is as follows: Distance enters the specification as one period ahead as the export distance is realized after the firm

> 25See previous sections as well as refer to Table 2 on page 5 that shows when payments are made and Figure 1 on page 6 for transit times.

> 26 Financial constraint variable was not included as this would restrict the sample to one year, and cannot estimate the relationships. Furthermore, the discount rate _β_ could be proxied by reported interest rates but interest rates were also only available for 2002.

19
Figure 29. Cumulative government and private capital stock (2004-2014) <!-- Start of picture text --> 600<br>500<br>400<br>300<br>200<br>100<br>0<br>Government capital stock Private capital stock<br>Constant 2017 thousand international US$ Equatorial Guinea Gabon Botswana Angola São Tomé and Príncipe Mauritania Congo, Republic of Nigeria Sudan Ghana Cameroon Central African Republic Zambia Côte d'Ivoire Togo Liberia Tanzania Niger Madagascar Mali Uganda Guinea Chad Mozambique Sierra Leone Mauritius South Africa Eswatini Cabo Verde Namibia Comoros Lesotho Eritrea Kenya Senegal Benin Gambia, The Burkina Faso Guinea-Bissau Zimbabwe Rwanda Malawi Ethiopia Burundi<br>Congo, Democratic Republic of the<br><!-- End of picture text --> _Note: Sum of annual general government capital stock per capita and private capital stock in constant 2017 international dollars, between 2004 and 2014. Countries to the left correspond to resource-rich countries and countries to the right correspond to non-resource-rich countries. Source: authors’ calculations using IMF's Investment and Capital Stock Dataset, 2021._

# **The legacy of the boom**

The legacy of the boom for Africa’s resource-rich exporters has proven very mixed. As commodity prices fell in 2014 onwards a challenging period was entered where revenues and exports slumped. Some have weathered the end of the boom well, including managing to sustain impressive rates of growth. Tanzania is an example of this – recording 6.5% during the boom, and 6.3% after the boom. By contrast, a group of countries, and especially those rich in oil and gas, have faced growth slumps induced by the price crash and weak price recovery. Here examples include Ghana, Nigeria and Angola, with boom and postboom growth measured as 7.1% to 5.0%, 6.7% to 1.0%, and 8.2% to -1.0% respectively.

Overall, the growth rates in Sub-Saharan Africa fell following the 2014 price falls. For the region, average growth rates during the boom were around 5.7% and 3.4% for resourcerich and non-resource-rich Sub-Saharan Africa respectively. Following the boom however, the group averages fell to 2.8% and 3.1%. Here the drop was biggest in the resource-rich group, and fell to below the pre-boom average growth rates.

40
_Panel B: Inequality_

|Dependent variable: Gini<br>index|4-year<br>average|5-year<br>average|Adding<br>time-<br>invariant<br>covariates|Adding<br>temperature<br>interaction<br>term|
|---|---|---|---|---|
||(1)|(2)|(3)|(4)|
|∆Temperature|0.309***|0.339***|0.421***|0.977**|
||(0.045)|(0.042)|(0.052)|(0.455)|
|Temperature||||0.333<br>(0.388)|
|Temperature squared||||-0.027***<br>(0.007)|
|Temperature*∆Tempearture||||-0.464***<br>(0.070)|
|Controlling for rainfall|Yes|Yes|Yes|Yes|
|Subnational FE|No|No|No|No|
|Year FE|No|No|No|No|
|Number of countries|90|90|90|90|
|Observations|1,019|1,019|1,019|1,019|

_Notes:_ Robust standard errors in parentheses. Standard errors are clustered at the subnational level. Regressions are weighted by region population. Inequality is measured by Gini index. Control variables in Column (9) are taken from Kalkuhl and Wenz (2020) which include cumulative oil gas, distance to coast, distance to river, and altitude. The long differences estimation is based on cross-sectional data with a smaller sample size compared with panel data. *** p<0.01, ** p<0.05, * p<0.1 49
> - 37 .

# **UNITED NATIONS DEVELOPMENT PROGRAM**

The United Nations Development Program (UNDP) is prepared to support SM activities in the current four-year program cycle (1987-1991). Reflecting the reactions of other donors, UNDP notes that a broad range of development activities influence maternal health and warns of the difficulty of targeting programs without improved definition.

Twelve statistical categories were examined for SM content:

- Primary health care

- * Maternal and child health * Maternal health

- Maternal health services

- Family planning

- Women in development

- Women's participation (and organizations)

- Integrated rural development

- Adult education

- Agriculture Nutrition

- * Disease prevention and control

The total five-year value of the examined programs was S84 million or about $16.8 million a year. On the basis of the criteria followed for this survey, UNDP expenditures for SM amounted to about $4.3 million in 1987. Over the prior three-year period, net flows remained at the same level. Declines in all categories in 1988 were taken to represent incomplete expenditure reporting.

The UN Statistical Office has no financial database to track SM financial trends. National expenditure data, which do not permit SM identification, are collected directly from countries, not from international organizations. Specific health sector statistics, other than financing, are gathered largely from WHO. There is no attempt to disaggrega(e the sector into sub-tomponents.

For population programs, financial trends are identifiable through UNFPA for its own program (see notes on UNFPA). WHO does not maintain a global financial trend database for maternal health except for its own programs.

United Nations Development Program One United Nations Plaza New York, NY 10017
On the other hand, as the bottom panel of Figure 6 shows, there is no evidence that separations have increased because of the pandemic and lockdown. In fact, the actual number of separations is below what would have been expected if the 2018-19 trends had continued in 2020. This is true for layoffs, quits, and contract terminations (Appendix Figure A.1). Certainly, in the case of layoffs, this can be explained by the government measures to protect jobs by prohibiting layoffs in affected industries and by tying income support to the maintenance of employment relationships. The reduction in quits is not surprising since one would expect fewer workers to leave their jobs in a deteriorating labor market. It should be noticed that there is initial evidence of a small uptick in the number of separations at the end of the period, which may reflect the easing of the layoff restrictions.

Using monthly data on net labor market flows, we observe that the crisis has affected sectors and occupations differently. Table 2 presents the difference between 2020 and 2019 in the size of net flows (new hires minus separations) for January, February, March, and April by sector of economic activity. The results suggest that the accommodation and food sector was particularly affected by the crisis. The negative impacts were especially severe in March and April, the months in which tourism would normally be gearing up for the summer season. In March, accommodation and food services accounted for 52% of the 2020 net job decreases, relative to 2019, while by April, this share was 84%.

**<u>Table 2. Comparison of net job flows between 2019 and 2020 by sector</u>**

||Diff<br>|erence: Month<br>|2020–Mont<br>|h 2019<br>|
|---|---|---|---|---|
|NACE Rev. 2 sector of economic activity:|January|February|March|April|
|Agriculture, forestry etc.<br>|402|8|-413|-127|
|Mining and quarrying|22|-75|-164|-58|
|Manufacturing|1151|83|-5014|-482|
|Electricity, gas, steam etc.|-419|829|-42|-248|
|Water supply; sewerage etc.|-187|-188|-53|-18|
|Construction|548|-383|-1472|292|
|Wholesale and retail trade|1009|592|-7310|-8119|
|Transportation and storage|500|371|-6556|-3572|
|Accommodation and food service|3865|3415|-43120|-84491|
|Information and communication|-80|-665|-1965|447|
|Financial and insurance activities|-485|-188|-415|126|
|Real estate activities|-17|-34|-405|-769|
|Professional, scientific and technical|-634|-1303|-3000|-389|
|Administrative and support service|-688|-515|-4896|-3435|
|Public administration, defense etc.|-941|-2612|235|-260|
|Education|299|-350|-1798|214|
|Human health and social work|179|-846|-943|2038|
|Arts, entertainment and recreation|573|-728|-4684|309|
|Other service,households and extra|-119|-295|-1623|-2114|
|Total additionaljobs|4978|-2884|-83638|-100656|

Source: ERGANI and National Institute of Labor and Human Resources (NILHR). Authors’ calculations.

Table 3 presents a similar analysis by occupation. During March and April, the most affected group was workers employed in services and shop and market sales workers.

23
19 countries’ export taxes (Figure 5c). For the developing country group, export taxes were the most significant contributor to welfare losses prior to the 1990s (Figure 5a), but their relative importance has fallen in all regions since then (Table 4).

# [Insert Figure 5 about here]

A comparison of the IWRI results in Table 4 with those in Table 2 for NRA and CTE aggregates reveals the usefulness of the IWRI method. Take import taxes in 2000–04, for example: they account for slightly more than 100 percent of border measure NRAs and CTEs globally but, according to the IWRI, those taxes account for around only three-quarters of the global welfare loss from all border measures.

The global border measure IWRI peaked for the world in 1985–89, after which it nearly halved to just over 30 percent by 2000–04. Table 5 reveals the relative contributions of each of the four border measures to this overall reduction, for each of the studied regions and globally. Import and export taxes contribute just over and a little under half of the overall global reduction, respectively. For developing countries, however, the fall was driven overwhelmingly by falls in export taxes: they account for 86 percent of their IWRI reduction. This dramatic result receives no comment in the previous studies cited at the start of this paper, not only because they include no time series but also because they ignore export taxes (as well as production taxes and import subsidies).<sup>7</sup>

# [Insert Table 5 about here]

Finally, annual time series reveal what happens to the relative contributions of different policies when international prices for farm products spike up or down. Insulation of domestic markets from such shocks, by varying border trade restrictions, is a common practice in both developing and high-income countries. The net effect is clear in Figure 5: when international prices spike up, as in 1973–74, the contribution of import tariffs falls dramatically but the contribution of export taxes rises, and conversely when international prices collapse, as in 1986.

> 7 Dioa, Somwaru and Roe (2001, p 37) find 89 percent of their cost of agricultural policies comes from import tariffs (market access), 10 percent from domestic producer support and 1 percent from export subsidies. Anderson, Martin and Valenzuela (2006) suggest 93 percent of the global cost of agricultural import protection and subsidies is due to tariffs, the costs of domestic support measures are around 5 percent and those of export subsidies are just 2 percent.