rafmacalaba/datause-encoder-data huggingface dataset:
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move to measures that place more weight on the poorest deciles, this variance share declines further, to 60 percent for the bottom 20 percent measure, and 52 and 41 percent for the Atkinson A(3) and bottom 10 percent measures.

In Table 3, we disaggregate our results by income level, focusing on the sample of spells lasting at least five years. The results are very similar for low-income, middle-income, and high-income countries. In all but one case we cannot reject the hypothesis that the slope is 1.0. The one exception is the social welfare function corresponding to income of the bottom 10 percent in middle-income countries, where the slope is slightly greater than one, at 1.16, indicating a weak tendency for the income share of the poorest 10 percent to rise as average incomes increase in this sample. We also continue to see that the share of the variance of growth in social welfare due to growth in average incomes is high, particularly for measures such as the Sen index and average incomes of the bottom 40 and 90 percent. This is particularly the case in high- and middle-income countries. In contrast, among low-income countries we find that the share of the variance of growth in social welfare due to growth is somewhat lower than in the middle- and high-income countries.

In Table 4, we disaggregate our results by decade, again focusing on the sample of spells at least five years long. Across all periods and for all social welfare functions, we continue to find a slope coefficient that is very close to, and not statistically significantly different from one, indicating an absence of a significant relationship between changes in inequality and changes in average incomes. One consistent pattern, however, is that the share of the variance of growth in social welfare due to growth in average incomes declines slightly as we move from the 1980s to the 1990s to the 2000s. For example, for the bottom 40 percent social welfare function, this variance share declines from 85 percent in the 1980s to 72 percent in the 2000s.<sup>8</sup> In all of our results so far, we have relied exclusively on household survey means to construct growth rates in average incomes. A large literature has discussed substantial differences between growth in survey mean income and corresponding aggregates in the national accounts in some countries (see for example Deaton (2005) and Deaton and Kozel (2005) for the case of India in particular).

> 8 Interestingly, this decline in the variance share does not appear to be due to compositional effects (noticing that the sample size increases significantly over time).  As a robustness check, we constructed a sample of 43 countries with one survey available near the middle of each of the three decades.  We then constructed two sets of spells, from the mid-1980s to the mid-1990s, and from the mid-1990s to the mid-2000s.  In this smaller set of spells we also find that the share of the variance of growth in social welfare due to growth in average incomes falls in the second set of spells relative to the first.

15
a buyback financed through exit bonds. However, a buyback financed through the use of reserves has the additional effect of reducing the stock of reserves. This reduction in reserves is calculated in the DM and exported to the RMSM-X. There, we take into account the possible macroeconomic implications.

In the case of a buyback financed by equity, that is, a debtequity swap, the user must make an assumption about the "additionality" of the direct foreign investment. The flow of foreign investment is reduced to the extent that the equity that is exchanged for old debt would have been bought otherwise. In addition the DM calculates the increase in profit remittances that results from the increased foreign ownership of real assets. Both the change in direct foreign investment and profit remittances are exported to the RMSM-X. Finally, there is an extra issue to be considered in a debt-equity swap. This is the origin of the equity exchanged for the buyback. If it was previously owned by the government, no monetary implications arise. But, if the equity was owned by the private sector the issue arises of how did the government raise the funds to buy it. It could be through taxes, by borrowing at home or by increasing the money supply. In any case, this action would have macroeconomic implications. Therefore, the user is asked to decide the source of the funds. Once this is done, the increase in taxes, borrowing and/or money creation is fed back to the RMSM-X model.

51
Figure 13

# **NORTHERN CHILE: THE OUTCOME (SO FAR)**

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7 Table 3 also shows the performance of different regions of developing countries. In practically all cases and periods, Asia as a region has shown the greatest growth with Latin America second. But Sub-Saharan Africa and Europe, Middle East and North Africa grew fastest in the last decade.

**Table 3: Growth in World Merchandise Exports and Imports by Income Group and Region**

||An|nual Expor|t Growth(i|n %)|Ann|ual Import G|rowth(in %|)|
|---|---|---|---|---|---|---|---|---|
|CountryGroup|1980-90|1990-00|2000-10|1980-10|1980-90|1990-00|2000-10|1980-10|
|**BY INCOME LEVEL**|||||||||
|Developed economies|7.3|5.2|7.3|6.6|6.6|5.6|7.1|6.4|
|Developing economies|3.4|9.3|11.9|8.2|4.9|9.0|12.0|8.6|
|High income developing economies|3.7|8.9|9.5|7.3|7.9|8.7|8.7|8.4|
|Upper middle income economies|4.6|10.2|13.8|9.5|4.0|10.5|13.5|9.2|
|Lower middle income economies|0.7|8.3|12.3|7.0|2.0|7.0|14.6|7.7|
|Low income economies|1.2|5.2|12.8|6.3|2.1|5.0|13.3|6.7|
|Of which: Least developed countries|1.9|7.1|16.2|8.2|0.4|5.3|14.6|6.6|
|**Memo:**|||||||||
|Developing econ, excl. China & India|2.9|8.7|9.9|7.1|4.6|8.4|9.7|7.6|
|Upper middle income, excl. China|3.4|8.8|9.9|7.3|3.0|9.1|9.7|7.2|
|Lower middle income,excl. India|-0.1|8.2|11.0|6.3|1.5|6.8|12.4|6.8|
|**BY REGION**|||||||||
|World|5.8|6.4|9.0|7.0|5.9|6.5|8.7|7.0|
|Developing economies|3.4|9.3|11.9|8.2|4.9|9.0|12.0|8.6|
|Asia|10.8|10.8|12.0|11.2|10.2|9.8|12.3|10.8|
|Latin America & Caribbean|3.3|9.6|9.4|7.4|0.6|12.0|8.8|7.0|
|Europe, Middle East & North Africa|-4.1|5.9|13.4|4.8|0.4|3.6|14.4|6.0|
|Sub-Saharan Africa|-1.3|3.3|13.7|5.1|-1.4|3.6|14.2|5.2|
|**Memo:**|||||||||
|BRICS 5 countries|10.8|9.1|17.8|12.5|7.8|9.3|18.7|11.8|
|OPEC 12 members|-4.6|5.9|13.2|4.6|-2.0|3.9|15.0|5.4|

Source: Based on UN COMTRADE database.
We assume that wage returns to skills are constant across one’s working life (Buchmann et al. 2016). Improvement from the same intervention I translates to a ∆L ∙β� predicted wage increase. The average annual income of a worker in country j is w� , measured by the labor share of the Gross National Income (GNI) per capita. Hence, intervention I yields an additional income of ∆L ∙β� ∙w� per year, assuming no wage growth,<sup>8</sup> following Miguel and Kremer (2004) and Muralidharan and Sundararaman (2011). Students receive the intervention at age a� and they are expected to enter the labor market at age 20 (Muralidharan and Sundararaman 2011). Wage gains will be further discounted by (20‐ a� ) years. The net present value of additional wage gains can be written as: where _n_ = number of years in the workforce and _i_ = discount rate.

In this paper, the expected work life is assumed to be 40 years and discount rate is taken at 3%, a standard social discount rate in public finance (Hanushek and Woessmann 2010; Hagist et al. 2005; Börsch‐Supan 2000).

## 2.3 Cost Effectiveness of Learning Interventions

Building on the impact metrics, this paper also presents cost‐effectiveness analysis and benefit‐cost ratios of a range of programs with available cost data from J‐PAL (2014). The cost‐effectiveness of selected interventions is estimated in terms of EYOS per US$100. For instance, we first multiply the point estimate of improved learning by ∆L to get the amount of EYOS for each participant by the program. We then divide the present value of total costs of the program (including costs to both the implementer and the beneficiary) by the number of participants to determine the program cost per beneficiary. And we divide the amount of EYOS by the cost per individual, then multiply by 100 to express the outcome per a US$100 investment.

Benefit‐cost ratios or return to investment (ROI) ratios are calculated as the NPV of lifetime increased wage income divided by the NPV of the program costs per individual. All conversions and calculations follow the methodology described previously in this section.

# 3. Data

The primary data used in this paper are the World Bank’s STEP Skills Measurement Program. The STEP household surveys measure skills of the adult population in low‐ and middle‐income countries and provide comparable international scores of reading proficiency on the scale of OECD’s assessment of adult skills, the PIAAC (World Bank 2016a). We use the STEP survey data collected between March 2012 and July 2014 in five countries with available literacy assessment data: Bolivia, Colombia, Ghana, Kenya, and Vietnam.<sup>9</sup>

> 8 Wages are highly likely to increase over time and assuming no wage growth is a conservative approach. Nevertheless, the net present value of increased learning from interventions is considerable.

> 9 Data are also available for Armenia, Ukraine and Georgia but not included for the reason that over 50 percent of the population in these countries have more than 12 years of formal education, which dramatically reduces the sample size for the analysis in this paper.

7
of this assessment is unable to determine what effect, if any, ART had on national HIV incidence trends. It is interesting to note that the inter-survey HIV incidence levels in South Africa are level at 1.9% for 2005-08 (ART coverage in the range of 20-30% [CD4 200 threshold]) and equally 1.9% for 2008-12 (ART coverage rising to 66% in 2011 [CD4 350 threshold]), hence showing no clear impact of the significant ART scale-up on model-inferred HIV incidence. While unable to conclude through this analysis, it is possible that ART might have had an influence on sustaining the downward trajectory of the epidemics in some countries.

50
<!-- Start of picture text -->
1.5<br>1<br>—eq<br>0.5 —cds<br>——sen<br>0 — sub<br>——pfd<br>-0.5 —trp<br>-1<br>Jan-06 Apr-O6 Jul-O6 Oct-06 Jan-07 Apr-O7 Jul-O7 Oct-07 Jan-08<br><!-- End of picture text -->
them, we plot the group average of both personal computers and internet users per population for each year in the period 1990-2004. Countries in the top quartile of regulatory freedom (countries with lower regulations) have much higher levels and speeds of adoption of both technology indicators. Countries in the middle (inter-quartile) range of regulatory freedom also experience an increase over time but, having started their rise much later, show levels of technology adoption in the mid 2000s that are between one-third and one-half of those in the top quartile. Finally, countries in the bottom quartile of regulatory freedom start the adoption process much later and slowly than the others, resulting in enormous technology gaps with respect to the leaders.

The evidence presented above is suggestive that both developmental and regulatory barriers play a role in explaining technological differences across countries. For more careful empirical support, we now turn to cross-country regression analysis. This will allow us to ascertain whether each proposed determinant of technological adoption exerts a statistically significant impact, even after controlling for the effects of the other determinants. Moreover, this analysis will help us understand to what extent the proposed determinants complement each other regarding their effect on technological adoption.

Table 1 presents information and results of the cross-country regression analysis. The dependent variables are the two indicators of technological innovation, that is, personal computers and internet users, both normalized by population. The explanatory variables are intended to capture the most relevant developmental and regulatory characteristics --the first represented by governance and schooling, and the second one, by business regulatory freedom.<sup>6</sup> Specifically, for the quality of public institutions and governance, we use an index based on International Country Risk Guide indicators on the rule of law, bureaucratic quality, and absence of official corruption. For education and human capital, we use the Barro and Lee (2001) measure of average schooling years of the adult population. For regulatory stance, we use the

> 6 We make this selection based on the received literature on growth and technological innovation --see De Soto (1989) and Parente and Prescott (2000) on regulatory freedom; Olson (1982) and Acemoglu, Johnson, and Robinson (2005) on institutions and governance; and Lucas (1988) and Glaeser et al. (2004) on human capital and schooling.

8
time. Tariffs on imports, _τgjt_ , are calculated as the applied duties divided by the FOB import value.

The ad-valorem-equivalent NNTR and NTR statutory rates are obtained from Feenstra et al. (2002) at the 8-digit level of the Harmonized Tariff Schedule (HS). We assign HS-8 products to 5-digit SITC rev. 2 goods using the concordance from United Nations Trade Statistics (2017), and we define the SITC-level tariff as the median tariff within the good. The _NNTR gap_ is the difference between the NNTR rate and NTR rate, which can vary over time as NTR rates change. Following Pierce and Schott (2016), we use the NNTR gap in 1999 as our measure of “the” NTR gap.<sup>12,13</sup> Our baseline sample of exporters to the United States includes China and all the other countries that had normal trade relations with the United States throughout the entire sample period and were not a part of preferential trade agreements (PTA) with the United States. These conditions exclude Canada and Mexico, as well as the other communist countries.<sup>14</sup> A key feature of our baseline sample is that after 1980, all sources should face the same NTR rates with few exceptions.<sup>15</sup> We also exclude all of the goods that include products that were subject to quota removals under the Multi Fiber Arrangement (MFA), as documented in Bambrilla et al. (2010). Table 1 reports the distribution of tariff rates by 1-digit SITC industries by NNTR rates and applied rates. The mean NNTR rate is 28 with a standard deviation of 18. The average applied rates are five in 1979 and two in 2001. For China, the average applied rate is considerably higher than other countries in 1979 (20 versus five), but quite similar to other countries by 2001 (three versus two). Thus, tariffs generally vary little across countries receiving NTR rates, and most of the variation in tariffs in our analysis is between NNTR and NTR rates in the early part of the sample.<sup>16</sup>

> 12All of our results are robust to using the mean HS-8 NNTR gap, as well as a time-varying NNTR gap.

> 13The simple correlation between the NNTR gap and the tariffs imposed on China in 2018 and 2019 at HS-8 level is -0.06. This finding suggests the NNTR gap ceased to be an effective measure of trade-policy risk and justifies our baseline sample-period end.

> 14The list of countries excluded because they held NNTR status at some point in the sample period are Albania, Bulgaria, Cambodia, Cuba, Czech Republic, Hungary, the Democratic People’s Republic of Korea, Romania, the Slovak Republic, Vietnam, and the 15 countries that formed the Soviet Union.

> 15Applied tariffs would differ owing to aggregation, specific tariffs, temporary commercial policy, or measurement error.

> 16The lack of tariff variation among the NTR countries creates some concerns about the elasticities being estimated across countries receiving NTR rates for the whole sample. We return to this in section 4.5.

10
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Qualitative work by Morawczynski (2009) suggests that incomes of rural mobile money transfer recipients have increased due to remittances, which have also led to higher savings by households. These results are based on an ethnographic study conducted in Kibera, a slum in Kenya, in 2007. Participants in a separate qualitative study by Plyler et al. (2010) ranked money circulation as the most important effect of mobile money, and credited mobile money with boosting local consumption. The flow of remittances into rural areas was perceived to have increased local economic activity because M-PESA enabled ―just-in-time‖ remittances that made capital available when it was most needed. Mbiti and Weil (2010) also find some evidence that the growth of M-PESA in communities has been associated with increases in local farm employment.

Mbiti and Weil (2009) find that the major use of M-PESA is for transfers and that there is relatively little storage of value. At the same time, they also show that a significant number of survey respondents indicate that they use their M-PESA accounts as a vehicle for saving. Mbiti and Weil also find evidence that M-PESA use decreases the use of informal savings mechanisms such as ROSCAs, and increases the probability of being banked.

Other important benefits of M-PESA include increased security of funds, and the empowerment of women. Hughes and Lonie (2007) suggest that mobile money transfer is more secure and cost effective than alternative methods of money transfer, such as using couriers or friends. Focus group participants in Plyler et al. (2010) highlight the greater security they enjoy because they are able to keep funds as mobile money, rather than at home in cash. Men in the Kibera slum focus on the aspect of physical danger due to mugging, which has decreased because fewer people carry large amounts of cash. Likewise, other respondents in the study indicate that local businesses and street vendors often convert their cash to M-PESA at the end of the day for safekeeping. Additionally, mobile money empowers women by giving them an independent place to store and manage funds that is private and inaccessible to other family members.

Survey data examined by Aker and Mbiti (2010) show that current users of mobile money transfers in Kenya tend to be wealthier, better educated, urban and already banked, with most transfers occurring within urban areas and remittances being smaller but more frequent **.** They also develop the theory of the general mechanisms through which mobile phones remove constraints to social and economic progress. They assert that improved access and use of 5