rafmacalaba/datause-encoder-data huggingface dataset:
involving an EU country is composed of Japan-Singapore, which is ranked 12, followed by JapanKorea, somewhat further below in the ranking.

Table 2: Top 10 country-pairs for digital services trader by data model in 1000 Mln. USD (2015)

|Open<br>Proc|Transfers<br>essingM|and<br>odel|Condi<br>and Pr|tional Tra<br>ocessing|nsfers<br>Model|Limite<br>Proc|d Transfer<br>essingMo|s and<br>del||None||
|---|---|---|---|---|---|---|---|---|---|---|---|
|Exporter|Importer|Trade|Exporter|Importer|Trade|Exporter|Importer|Trade|Exporter|Importer|Trade|
|USA|CAN|115.49|GBR|DEU|27.60|RUS|CHN|10.70|USA|CHN|89.70|
|CAN|USA|61.58|DEU|FRA|27.37|IDN|CHN|4.16|USA|JPN|61.61|
|USA|MEX|55.04|GBR|IRL|27.29|CHN|RUS|3.67|GBR|USA|60.20|
|MEX|USA|48.58|FRA|DEU|26.29|CHN|VNM|3.65|IND|USA|51.19|
|USA|BRA|29.95|GBR|FRA|25.42|RUS|KAZ|3.37|USA|GBR|47.76|
|USA|AUS|18.18|DEU|GBR|23.38|KAZ|CHN|2.91|USA|DEU|46.83|
|USA|SAU|13.75|FRA|GBR|22.84|CHN|IDN|2.86|CHN|USA|45.53|
|BRA|USA|10.72|CHE|DEU|21.54|VNM|CHN|2.16|USA|IRL|43.08|
|USA|TWN|9.60|GBR|LUX|20.11|KAZ|RUS|1.68|DEU|USA|39.71|
|HKG|USA|9.08|NLD|DEU|19.85|CHN|KAZ|0.49|USA|KOR|36.76|
|Total||371.97|Total||241.69|Total||35.65|Total||522.38|
|Share wo|rld (%)|7.67|Share wor|ld (%)|4.98|Share wor|ld (%)|0.74|Share wo|rld (%)|10.77|

Source: Authors’ calculations using TiVA trade data. Note: Trade is based on exports data. Countries are categorized on the basis of the data model regarding the cross-border data transfers.

The 10 largest country-pairs that follow the limited model represent a meagre 1 percent of global digital services trade. The largest bilateral trade relation is between Russia and China, followed by Indonesia-China. Note that the amount of services trade between any of the Top 10 country-pairs is much lower compared to countries sharing the other data models. Not all Top 10 country-pairs following this data model include China. Russia and Kazakhstan share strong digital services trade links as shown in Table 2, but also Russia and Vietnam as well as Indonesia and Vietnam have substantial links in digital services trade, falling just outside the Top 10 list. Other country-pairs applying the limited model, such the ones involving Brunei or Tunisia, only cover a small part of digital services trade.

Over 10 percent of global digital services trade is covered by the Top 10 country-pairs that do not share the same data model. This high share is consistent with the high share of total global digital services trade covered by country-pairs with different data models (Figure 3). The largest bilateral trade relationship in digital services in this list is the one between the US and China, followed by USJapan. Note that two Asian countries are reported in this Top 10 list of country-pairs with dissimilar data models, namely India and Korea. Moreover, three EU Member States also appear in this list, which are the UK, Germany and Ireland. Note that if the EU is counted as one, the largest trade relationship in this category would be US-EU with 282 Mln. USD, representing alone a world share of 5.8 percent.

16
Seker, Murat. 2009. "Importing, exporting and innovation in developing countries," Washington D.C.. World Bank Policy Research Working Paper Series 5156.

World Bank. 2004. World Development Report 2005. A Better Investment Climate for Everyone. Oxford: Oxford University Press.

World Bank. 2010. Getting Electricity – A pilot indicator set from the Doing Business Project. <u>http://www.doingbusiness.org/Documents/GettingElectricity_pilot_indicator_project.pdf.</u> World Bank. 2014, _Doing Business 2014: Understanding regulations for small and medium-size enterprises._ Washington, DC: World Bank Group.

23
# **1.  Introduction**

During the past three decades, indiscriminate use of chemical pesticides in agriculture has created serious health and environmental problems in many developing countries (World Resources, 1998-99). The World Health Organization (WHO) and the United Nations Environment Program estimate pesticide poisoning rates of 2-3 per minute, with approximately 20,000 workers dying from exposure every year, the majority in developing countries (WHO, 1990; Kishi et al., 1995; Pimental _et al_ ., 1992; Rosenstock _et al_ ., 1991). From an environmental perspective, chemically-polluted runoff from fields has contaminated surface and ground waters, damaged fisheries, destroyed freshwater ecosystems and created growing "dead zones" in ocean areas proximate to the mouths of rivers that drain agricultural regions (Pimental and Lehman, 1993; Tardiff, 1992).<sup>1</sup> Many of these impacts are a direct result of the overuse and misuse of pesticides, often wildly deviating from recommended application procedures. Several recent studies investigating this behavior found that inadequate product labeling and farmers’ lack of information often lead to widespread overuse or misuse of dangerous pesticides in developing countries (Huan _et al_ ., 2000; Dung and Dung, 1999; Dung _et al_ ., 1999). Anecdotal evidence also suggests that farmers use toxic chemicals extensively due to their reputation for speed and effectiveness; sometimes associating higher amounts with increased effectiveness.

The health and environmental hazards of pesticides can be partly avoided by education and the creation of incentives to curb the trend of overuse. However, a clear understanding of farmers’ perception of risk and pesticide application behavior is necessary in the design of any policy intervention. Currently, systematic studies of the application of pesticides are scarce in developing countries. This paper is an attempt to close this research gap by providing quantitative estimates of pesticide overuse as well as

> 1 In addition, what was initially deemed as localized pollution has now become a global problem, as toxic compounds from pesticides accumulate in oceanic food chains, and even in the tissues of land mammals in "pristine" polar regions (Blais _et al_ ., 1998).

2
9

**Figure 4: Retail kerosene prices in March 2015** (US$/liter)

<!-- Start of picture text --> Rwanda<br>Chile<br>Benin<br>Thailand<br>Guatemala<br>Uganda<br>Nepal<br>Bangladesh<br>Senegal<br>Dominican Republic<br>Philippines<br>Tanzania<br>Mozambique<br>Uruguay<br>United States<br>Niger<br>Madagascar<br>Cape Verde<br>Ghana<br>South Africa<br>Fiji<br>Vietnam<br>Côte d'Ivoire<br>Ethiopia<br>Togo<br>Jordan<br>Kenya<br>Pakistan<br>Cameroon<br>Gabon<br>Sri Lanka<br>Tunisia<br>Bolivia<br>Malawi<br>Kuwait<br>Equatorial Guinea<br>Angola<br>Nigeria<br>India<br>Indonesia<br>Iran, Islamic Rep. of<br>0.00 0.20 0.40 0.60 0.80 1.00 1.20 1.40<br><!-- End of picture text --> _Sources:_ Government websites and announcements, local news articles.

_Notes:_ The prices are for kerosene for household use where available. There are fewer countries in this figure than in those for gasoline and diesel, in part because kerosene is not commonly used as a household fuel in a number of countries, several of which have large price subsidies for LPG, which displaces kerosene. For Malawi, the Malawi Energy Regulatory Authority (MERA) in April 2014 stopped publishing the price of domestic kerosene. As of April 2014, the price of domestic kerosene was less than one ‐fourth of the price of industrial kerosene and had been frozen at that low price since a new automatic pricing mechanism was implemented in July 2012. In a list of historical prices announced by MERA through February 2015, MERA labels kerosene prices as those for “paraffin – industry” (MERA 2015a). In the absence of an official announcement from MERA that the heavily subsidized price of domestic kerosene had been eliminated, this paper assumes that the price in March 2015 was the same as that in April 2014.
- (iii) Since pre-COVID mobility measures are based on years of education and not learning, one must devise a way of translating COVID-induced learning losses to loss in educational attainment, to be able to measure changes in intergenerational mobility relative to the baseline.

# **3.1 Baseline mobility estimates**

For baseline estimates of inter-generational mobility, we rely on data from the Global Database on Intergenerational Mobility (GDIM) constructed by the World Bank, which contains estimates of absolute and relative intergenerational mobility (IGM) by 10-year cohorts, covering individuals born between 1940 and 1989 (GDIM, 2020). Ideally, we would want to have estimates of inter-generational mobility with and without COVID-19 for a cohort of students who are currently in school. Neither of these is observable, however. Following Neidhofer et al. (2021) we rely on mobility estimates from a cohort of children who are closest in terms of age to the children who were in school in 2020-21, when school closures took place. In the case of GDIM data, this is the 1980s birth cohort, comprised of children who were born between 1980 and 1989, most of whom would have completed their secondary education during the 2000s.<sup>11</sup> How consequential are the data constraints that prevent us from working with a more recent birth cohort? The consequences are limited and defensible in our view, even though the current school-going cohort (who were born during 2000-2015, approximately) would have had better education outcomes than the 1980s cohort in the baseline case (absence of the pandemic) in most developing economies. This is because our analysis is not focused on the absolute changes in educational attainment, but rather on the difference between two scenarios represented by two vectors of educational attainment generated for the same cohort of individuals – one without COVID-19 (baseline) and the other accounting for the impacts of the pandemic (counterfactual). Any secular trend in educational attainment from the 1980s-cohort to the current school-going cohort would affect both scenarios equally and have no effect on the impact of the pandemic obtained from the difference between these scenarios. For similar reasons, Neidhofer et al. (2021) estimates counter-factual mobility measures for the 1987-1994 birth cohort, who are also much older than the children currently in school. Moreover, even though there is a secular time trend in educational attainment for most developing economies, the rate of increase appears to have slowed down over time. As seen in Figure 1, there is a steady increase in enrollments in the early 2000s, but enrollments have flattened out across all income groups of countries over the period 20112019 and remained unchanged since 2012 for the Low-Income Countries.

> 11 The 1980s cohort is the most recent cohort in GDIM for whom educational mobility could be accurately measured, given the dates of the underlying surveys and the fact that educational mobility can only be accurately measured for those who are old enough to have completed their education at the time of the survey.

7
- Dunn, L. M., Dunn, L., and Arribas, D. (2006). Peabody, test de vocabulario en imágenes. _Madrid: TEA ediciones_ .

- Fernald, L. C., Gertler, P. J., and Neufeld, L. M. (2008). Role of cash in conditional cash transfer programmes for child health, growth, and development: an analysis of Mexico’s Oportunidades. _The Lancet_ , 371(9615):828–837.

- Field, E. and Maffioli, E. M. (2020). Are behavioral change interventions needed to make cash transfer programs work for children? Experimental evidence from Myanmar. Mimeo.

- Fitzsimons, E., Malde, B., Mesnard, A., and Vera-Hernandez, M. (2016). Nutrition, information and household behavior: Experimental evidence from Malawi. _Journal of Development Economics_ , 122:113–126.

- Frankenburg, W. K. and Dodds, J. B. (1967). The Denver developmental screening test. _The Journal of Pediatrics_ , 71(2):181–191.

- Gaskell, A. and Mills, R. (2009). Using mobile technology for learner support in open schooling: A report to the commonwealth of learning.

- Grantham-McGregor, S. M., Powell, C. A., Walker, S. P., and Himes, J. H. (1991). Nutritional supplementation, psychosocial stimulation, and mental development of stunted children: the Jamaican study. _The Lancet_ , 338(8758):1–5.

- Hall, C. M. and Bierman, K. L. (2015). Technology-assisted interventions for parents of young children: Emerging practices, current research, and future directions. _Early Childhood Research Quarterly_ , 33:21–32.

- Hamadani, J. D., Mehrin, S. F., Tofail, F., Hasan, M. I., Huda, S. N., Baker-Henningham, H., Ridout, D., and Grantham-McGregor, S. (2019). Integrating an early childhood development programme into Bangladeshi primary health-care services: an open-label, cluster-randomised controlled trial. _The Lancet Global Health_ , 7(3):e366–e375.

29
# **Almost Random: Evaluating a Large-Scale Randomized Nutrition Program in the Presence of Crossover**

Sebastian Linnemayr<sup>♦</sup> Harvard University Harold Alderman<sup>‡</sup> The World Bank <u>Key words:</u> Nutrition; Impact evaluation <u>JEL Codes:</u> I12; O12

> ♦ Corresponding author: Sebastian Linnemayr, Harvard School of Public Health, Harvard University, Cambridge, USA; email: slinnema@hsph.harvard.edu.

> ‡ Harold Alderman, World Bank, Washington DC, USA; email: halderman@worldbank.org.
- 15 -

Table 2.1s Share of Agriculture in GDP and Employment, 1970 and 1983, and Per Capita GNP, 1984

|Country|Shar<br>Agricultu<br>(perc|e of<br>re in GDP<br>ent)|Labor F<br> <br>Agric<br>(per|orce in<br>ulture<br>cent)|Per Capita GNP<br>(US dollars)|
|---|---|---|---|---|---|
||1970|1985|1970|1985|1984|
|Southeast Asia||||||
|Malaysia|30<sup>a</sup>|21|53|36|1,980|
|Thailand|28|17|80|59|860|
|Philippines|<br>28|27|54|49|660|
|Indonesia|47|24|66|55|540|
|South Asia||||||
|Pakistan|37|26|59|53|380|
|Sri Lanka|33|24|55|46|360|
|India|47|31|69|59<sup>b</sup>|260|
|Burma|38|48|67|66|180|
|Nepal|68|57<sup>c</sup>|94|93<sup>b</sup>|160|
|Bangladesh-|58|.|86|64|130|

Note: Countries are listed in order of decreasing per capita GNP. Figures are rounded.

a 1971 b 1983 c 1984 Sources: World Bank. World Development Report 1985 and World Development Report 19S6 (New York: Oxford University Press for the World Bank, 1985, 1986). World Bank, World tables, Third Edition, Volume II Social Indicators. Asian Development Bank Key Indicators, July 1987.
The present paper analyzes the relationship between the number of days of paid maternity leave and the proportion of workers in a typical registered private firm that are females. As indicated above, we also explore the idea of lower demand by employers for female workers when the cost of maternity leave falls on the employers as opposed to the government (discussed in detail below). The analysis is based on firm-level survey data for a large cross-section of mostly developing countries. Thus, we capture the full impact (short-run plus long-run) of maternity leave. In this sense, our approach is like that of Fipps et al. (2001), who argue that “ever having” rather than “currently having” a child is generally the more appropriate measure for studying the family gap.

We contribute to the existing literature in several ways. First, estimates of the long-run effects of maternity leave are largely restricted to a handful of rich countries. Whether the results apply to other countries is not discussed. We fill this gap in the literature by focusing on a large cross-section of countries. Second, there is substantial variation across countries in who bears the cost of maternity leave. Demand for female workers may contract when such costs fall on the employers as opposed to the government. However, there is no formal evidence on the issue. We attempt to fill this gap in the literature by analyzing how the maternity leave and female workers relationship differs depending on who pays for maternity leave (employer, government, or both). Our results confirm that the maternity leave and female workers relationship is much larger (more positive) when maternity leave is funded by the government vs. the employer. Third, the use of individual-level data that is pervasive in the existing literature suffers from selection bias. Women who are eligible for maternity leave and those who decide to have children are different in several characteristics from the rest. Failure to control for these selection effects can significantly bias the estimates of the impact of maternity leave (see for example, Hashimoto et al. 2004). Our results 5
**Table 4: Women in the North and East earn lower wages in manufacturing and in the private sector**

Women’s median monthly wages (Rs.)

|||Northern|Eastern|Western|Other<br>provinces|
|---|---|---|---|---|---|
||Manufacturing|10,218|11,908|17,129|12,634|
|Industry|Service|28,921|28,346|25,986|27,795|
||Agriculture|7,393|10,124|10,395|9,087|
|Sector|Public sector|31,166|33,000|34,198|29,973|
||Private sector|10,218|10,124|17,129|11,593|
||Professional|32,019|32,250|34,302|31,906|
||Clerks/sales|18,730|24,809|20,789|17,572|
|Occupati|on<br>Craft/machine workers|10,218|9,924|16,112|12,655|
||Elementary|7,437|10,000|13,305|9,661|
||Other occupations|24,789|39,000|26,321|16,423|
||All wage workers|18,592|20,247|20,763|14,018|

<!-- Start of picture text --> Professional 32,019 32,250 34,302 31,906<br>Clerks/sales 18,730 24,809 20,789 17,572<br>Occupation Craft/machine workers 10,218 9,924 16,112 12,655<br>Elementary 7,437 10,000 13,305 9,661<br>Other occupations 24,789 39,000 26,321 16,423<br>All wage workers 18,592 20,247 20,763 14,018<br><!-- End of picture text --> Source: Labor Force Survey 2015 (Sample: 15+ year olds) The finding that wages of women and men in the Northern and Eastern provinces are not necessarily lower than the rest of the country may result from both supply and demand factors working to limit employment opportunities for women in the North and East. In other words, women may both be more reluctant to work in these areas, and simultaneously in less demand by employers, either because they are less productive or actively discriminated against. The resulting shift inward in the supply curve and shift downward in the demand curve would greatly depress employment in certain sectors, while the wage effects work in opposite directions and may largely negate each other.

In conclusion, residents of the Northern and Eastern provinces are less likely to work than those in the rest of the country, particularly if they are women in the middle of the age distribution, have less than 12 years of education, or are Tamil. Women in these provinces are also underrepresented in agricultural and industrial jobs, and in the private sector.

# 3.2 Youth

So far, the analysis has focused on gender differences in employment between the North, East, and regions other than Western Province. While low employment rates for women explain most of the low employment rates in these regions, the employment rates for youth in the Northern and Eastern provinces have also stagnated at rates below the national average. Figure 6b plots the trends in employment rate of youth of ages 15‐24 years for 2011‐15. The employment rate for youth in the Northern and Eastern provinces is either at or below the national average for the entire period. The employment rate of youth in the Eastern province is the lowest, consistently below that of youth in the Northern province. Since the Northern and Eastern provinces have larger shares of youth population than the rest of the country, it is worthwhile investigating the sources of low youth employment in these areas.

13
**Table 1: Consumption poverty rates for China 1981-2005**

||1993 PPP|With<br>for lo|out adjust<br>wer rural p|2005<br>ment<br>rices|PPP<br>W<br>for l<br>(poverty|ith adjustm<br>ower rural p<br>line in urb|ent<br>rices<br>an prices)|
|---|---|---|---|---|---|---|---|
||_Z_=$1.08|_Z_=$1.25|_Z_=$1.45|_Z_=$2.00|_Z_=$1.25|_Z_=$1.45|_Z_=$2.00|
||||%living b<br>|elow pover<br>|tyline<br>|||
|1981|63.8|87.4|92.0|98.2|83.8|89.8|97.8|
|1984|41.0|78.1|84.4|95.0|69.1|78.6|92.8|
|1987|28.7|67.7|74.6|87.8|53.8|64.6|83.5|
|1990|33.0|71.8|77.4|87.9|60.7|69.9|84.9|
|1993|28.4|67.3|72.9|82.7|54.7|64.4|79.4|
|1996|17.4|52.3|59.9|73.4|37.1|46.8|65.9|
|1999|17.8|49.1|55.9|68.4|35.7|44.3|61.5|
|2002|13.8|40.1|46.3|58.4|27.8|34.9|50.5|
|2004|9.9|33.9|40.0|52.2|22.1|28.4|43.3|
|2005|5.5|26.4|32.9|46.7|15.6|20.9|35.7|

Note: Poverty line in $’s per person per day.

**Table 2: Income poverty rates for China 1981-2005**

||National<br>poverty|1993<br>PPP|With<br>for lo|out adjust<br>wer rural p|2005<br>ment<br>rices|PPP<br>W<br>for l<br>(poverty|ith adjustm<br>ower rural p<br>line in urba|ent<br>rices<br>n prices)|
|---|---|---|---|---|---|---|---|---|
||lines*<br>|_Z_=$1.08<br>|_Z_=$1.25<br>%l<br>|_Z_=$1.45<br>iving below<br>|_Z_=$2.00<br> povertyl<br>|_Z_=$1.25<br>ine<br>|_Z_=$1.45<br>|_Z_=$2.00<br>|
|1981|52.8|62.3|85.3|89.8|97.1|81.6|87.5|96.7|
|1984|24.1|25.6|70.5|78.5|91.8|54.6|67.4|87.5|
|1987|16.8|21.5|63.0|70.2|83.5|45.7|57.2|78.0|
|1990|22.2|23.0|61.8|68.6|81.2|46.0|56.3|75.3|
|1993|20.0|21.0|55.0|62.2|74.9|39.9|49.0|67.4|
|1996|9.8|10.4|38.4|46.6|63.7|23.9|31.7|50.9|
|1999|7.6|8.0|30.2|37.6|54.0|17.8|24.2|41.0|
|2002|7.3|7.6|25.1|31.4|45.8|15.3|20.0|33.5|
|2004|5.3|5.8|20.0|25.5|39.0|11.8|15.6|27.0|
|2005|5.2|5.4|17.3|22.0|34.9|10.4|13.5|23.4|

<u>Note: * from Ravallion and Chen (2007). Poverty line in $’s per person per day. Urban population shares used here</u> are at the end of year from China’s Statistical Yearbooks (NBS, 1982-2006).

15
It is also important to know the salient administrative/institutional details of the program. For NX evaluations, such information is key to designing a survey that collects the right data to control for the selection process. Knowledge of the program’s context and design features can also help in dealing with selection on unobservables, since it can sometimes generate plausible identifying restrictions, as discussed further in sections 6 and 8.

The data on outcomes and their determinants, including program participation, typically come from sample surveys. The observation unit could be the individual, household, geographic area or facility (school or health clinic) depending on the type of program. Clearly the data collection must span the time period over which impacts are expected. The sample design is invariably important to both the precision of the impact estimates and how much can be learnt from the survey data about the determinants of impacts. Section 9 returns to this point.

Survey data can often be supplemented with useful other data on the program (such as from the project monitoring data base) or setting (such as from geographic data bases).<sup>17</sup> Integrating multiple data sources (such by unified geographic codes) can be highly desirable.

An important concern is the comparability of the data on participants and nonparticipants. Differences in survey design can entail differences in outcome measures. Heckman et al. (1999, Section 5.33) show how differences in data sources and data processing assumptions can make large differences in the results obtained for evaluating US training programs. Diaz and Handa (2004) come to a similar conclusion with respect to Mexico’s _PROGRESA_ program; they find that differences in the survey instrument generate significant biases in a propensity-score matching estimator (discussed further in section 5), although good approximations to the experimental results are achieved using the same survey instrument.

There are concerns about how well surveys measure the outcomes typically used in evaluating anti-poverty programs. Survey-based consumption and income aggregates for nationally representative samples typically do not match the aggregates obtained from national accounts (NA). This is to be expected for GDP, which includes non-household sources of domestic absorption. Possibly more surprising are the discrepancies found with both the levels and growth rates of private consumption in the NA aggregates (Ravallion. 2003b).<sup>18</sup> Yet here

> 17 For excellent overviews of the generic issues in the collection and analysis of household survey data in developing countries see Deaton (1997).

> 18 The extent of the discrepancy depends crucially on the type of survey (notably whether it collects consumption expenditures or incomes) and the region; see Ravallion (2003b).

14