rafmacalaba/datause-encoder-data huggingface dataset:
**Figure 6: Mitigation potential by agricultural practice.**

<!-- Start of picture text --> Up to $20<br>Restore cultivated organic soils U p to $50<br>Up to $100<br>Cropland management<br>Grazing land management<br>Restore degraded lands<br>Rice management<br>Livestock<br>Set-aside, LUC and agroforestry<br>Manure management<br>0 400 800 1200<br>CO2-eq per year (megatonnes)<br>Note: Abatement costs are given in costs per ton CO2 equivalent<br>in U.S. dollars<br><!-- End of picture text --> Source: Smith et al. (2007)

**Figure 7: Mitigation potential in agriculture by region.**

<!-- Start of picture text --> Technical mitigation potential of agricultural sectors<br>East Asia and Pacific<br>Sub-Saharan Africa<br>Latin Aermica and Carribean<br>Eastern Europe and Central Asia<br>South Asia<br>Middle East and North Africa<br>0 500 1,000 1,500<br>CO2 emissions per year (megatonnes)<br><!-- End of picture text --> Source: Smith et al. (2007).

62
Russian Federation are not unusual, especially when compared to the Transition countries.

# **References**

- Afontsev, Sergey (2004), ―The Political Economy of Tariff Unification,‖ EERC Working Paper number 04/12E, Moscow, Russia. Available at http://www.eerc.ru/details/download.aspx?file_id=3766

- Afontsev, Sergey (2002), ―Endogenous Tariff Protection and the Level of Trade Distortions in Russia,‖ EERC Working Paper number 01/07, Moscow, Russia. Available at http://www.eerc.ru/details/download.aspx?file_id=3583.

- ATF Bank, ―Customs Union‖ No  Big Inflation Shock, but efforts needed to offset impact on non-resource sectors,‖ Kazakhstan: ATF Bank, April 2010.

- Jandosov, Oraz, and Lyaziza Sabyrova (2011), ―Indicative Tariff Protection Level in Kazakhstan:  before and After the Customs Union (Part I),‖ Rakurs discussion paper 5.3, March.

- Michalopoulos, Constantine and David Tarr (1996), "Trade Performance and Policy in the New Independent States," in the Directions in Development Series, Washington: The World Bank.

- Michalopoulos , Constantine and David Tarr (1994), _Trade in the New Independent States, Studies of Economies in Transition No. 13_ , Washington, D.C.: The World Bank.

- Shepotylo, Oleksandr (2011), ―Calculation of the tariff rates of Kazakhstan before and after the imposition of the customs union common external tariff in 2010.‖ Available as part of World Bank (2012).

- Shepotylo, Oleksandr and David Tarr (2007), ―The Structure of Tariffs in Russia: 2001-2005,‖ Policy and Research Working Paper, World Bank. Available at: www.worldbank.org/trade/russia-wto.

- Shepotylo, Oleksandr and David Tarr (2008), ―Specific tariffs, tariff simplification and the structure of import tariffs in Russia: 2001-2005, _Eastern European Economics_ , Vol. 46 (5), September –October 2008, 49-58.

- Tarr, David (2002), "On the Design of Tariff Policy: Arguments for and Against Uniform Tariffs,‖ in B. Hoekman, A. Mattoo  and P. English (eds.), _Development, Trade and the WTO: A Handbook_ , Washington: World Bank, 2002. Available in Russian at www.worldbank.org/trade/russia-wto.

22
35

**TABLB 4.10**

**Scmepk Compadom of Pmuit** **_Bmpkwrtn_ by Skl** **_Group_**

|Group|1975|1980|1984|<br>**1985**|
|---|---|---|---|---|
|A. S.ew|||||
|Top Management|0.9|05|1.0|<br>1.1|
|Top Technicians and<br>'Middle Management|1.S|23|2.6|<br>2.7|
|Technicians and<br>Skilled Workers|165|16.8|21.5|<br>22.0|
|Workers and<br>Apprentices|81.1|80.2|74.9|<br>72.2|
|TOTAL|100.0|100.0|100.0|<br>100.0|
|B. Non-icns||||F'|
|Top Management|_14S|40.2|463|50.9|
|Top Technicians and<br>Middle Management|23A|34.0|36.0|<br>38.1|
|Technicians and<br>Skilled Workers|27.6|20.7|14.5|<br>10.7|
|Workers and<br>Apprentices|_6.5_|S.0|3.2|03|
|TOTAL|100.0|100.0|100.0|<br>100.0|
|C. Other Afrin|||||
|Top Management|1.8|1.7|1.1|1.9|
|Top Technicians and<br>Middle Management|2.3|3.1|3.9|4.5<br>(Continued)|
<!-- Start of picture text --> GFDL Model<br>10000<br>1000<br>100<br>><br>c<br>2<br>eo 10<br>o<br>ro)<br>=<br>é 1<br>0.1<br>w= Current<br>wee Future<br>0.01<br>1 10 100 1000<br>Return Period (Years)<br><!-- End of picture text -->
(4) of Table 3 shows that the sales amount (count) increases 8 percent (11 percent) more for the small merchants with contactless technology compared to their larger counterparties with the same technology (pvalue _<_ 0.001).

Contactless payment technology facilitates small transactions and could increase the tendency for consumers to pay more without cash for small purchases. In addition, I investigate whether the sales of merchants making small transactions are benefiting from the increase in the contactless payment limit. In doing so, I further divide the small merchants into two sub-groups according to their median transaction size per purchase in 2019. Specifically, I define the small merchants with median transaction size in 2019 below the 50th-percentile as the small transaction size type and expect them to exhibit the strongest increase in card sales. Results are consistent with my expectation. Columns (2) and (5) of Table 3 show evidence that the positive effect on card sales for small merchants is driven by the ones featuring small-size transactions. The change in log sales amount after the three staggered shocks for small merchants with small transaction size is positive and about 20 percent higher than the small merchants without small transaction size. The difference is statistically significant at the 1 percent level. The result is 24 percent for the card transaction counts.

The benefits from cheaper and more convenient payment technology are likely to be greater for new entrepreneurs. They tend to operate on a smaller scale, with a higher marginal benefit due to lower transaction costs and improved transaction efficiency. In addition, new merchants have a less stable customer base, resulting in a greater impact from increased consumer traffic. I find consistent evidence from the COVID-19 era and after the increase in the limit of the latest digital payment. I consider a merchant to be new if he was 0 or 1 year old in 2019. Others are considered old merchants. As shown in column (3) of Table 3, while older merchants with contactless payment technology saw an increase in card sales of about 8 percent over merchants without the latest technology after the shocks, when compared to younger entrepreneurs, the younger ones increased their card sales by 3 percent more.

20
6 Sometimes this was because there was no estimate reported in the survey report or online tool. But often it was because indicator definitions can vary from one survey ‘family’ to another, and sometimes even within a survey family, either over time or between the survey report and the online tool. Although we have re-analyzed the raw microdata, the estimates we report are simply harmonized direct (re)calculations of the quantities reported in the survey reports and online tools. In line with the growing concerns about the use of modeling in global health data sets (AbouZahr et al. 2017; Boerma et al. 2018), we do not predict missing data – we do not produce forecasts for country-years where there is no survey.<sup>1</sup> The downside is that our data set is, as a result, full of gaps. The upside is that, insofar as the surveys we use are reliable, differences over time or across countries ought to reflect reality rather than modeling assumptions; conversely, when real changes occur on the ground, they ought to get reflected in our numbers, rather than being smoothed away by the modeling process. Our data set is freely downloadable, and a data visualization tool is also available.<sup>2</sup> To ensure our data are reproducible, and in line with GATHER (Guidelines for Accurate and Transparent Health Estimates Reporting) (Stevens et al. 2016), we document thoroughly our methods and highlight the differences between our definitions and others’, and provide the essential computer code that ought to make it possible for anyone trying to reproduce our results to do so.<sup>3</sup> The GATHER table showing pages where the various parts of the database construction process are recorded, is included as Table A2 in the Annex.

The HEFPI database can be used to analyze a variety of topics. With the database one can see not just how the population fares but also how different ‘wealth’ or income groups fare on the indicators used in global goals, such as the MDGs and the SDGs: the database allows ‘snapshot’ comparisons as well as comparisons of trends (cf. Wagstaff 2002; Victora et al. 2003; Wagstaff et al. 2014). One can zoom in on a specific topic, such as child malnutrition, and see whether inequalities

> 1 Nor do we replace estimates directly calculated from the survey microdata by modeled estimates. 2 See frontmatter. 3 See frontmatter.
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# **II – CONTEXT: SCHOOL ATTAINMENT, EARNINGS AND ADULT ENROLMENT**

To understand and assess the analysis of this paper, it is helpful to be familiar with the context. This section briefly introduces school attainment, earnings and adult enrolment.

Many adult Colombians have still not attained primary and secondary education. Further, tertiary education remains relatively elitist. Figure 1 presents attained schooling by agecohorts of a representative sample of Colombians living in urban areas. It shows both the progress in education over the last two decades and the large share of the population with little schooling. For example, only 20 percent of Colombians above 60 years have completed secondary education. For the age group 35 – 39, only half of the population has completed secondary education. Among the age cohort of 20 to 24 years, almost 40 percent have primary education or less. Extending this analysis to include the rural population would paint a bleaker picture of the gaps in education. Consequently, the majority of the Colombian labor force has still to attain secondary education.<sup>2</sup>

**Figure 1** – **Attained** **<u>Schooling by Age Group</u>**

<!-- Start of picture text --> 100%<br>90%<br>80%<br>70%<br>60%<br>50%<br>40%<br>30%<br>20%<br>10%<br>0%<br>20-24 25-29 30-34 34-39 40-44 45-49 50-54 55-59% 60-64<br>Less Primary Primary Completed Secondary Completed Tertiary Completed<br><!-- End of picture text --> Source: Encuesta de Hogares, 2000

> 2 Despite the above shortcomings, it should be noted that the Colombian formal education system expanded greatly during the last two decades. According to the World Development Indicators 2002, preschool gross enrollment grew from 11 percent in 1985 to 35 percent in 1998, primary net enrollment from 66 percent to 87 percent, secondary gross enrollment from 44 percent to 53 percent and tertiary gross enrollment grew from 11 percent to 22 percent. However, 87 percent primary enrollment is not impressive in a region where universal primary education is the standard, and the improvements only benefited today’s young generation.

4
<!-- Start of picture text --> Figure 9. Effect of Public Vocational on Formality among Women<br>Old cohort Middle cohort Young cohort<br>0.30<br>0.20<br>0.10<br>0.00<br>-0.10<br>-0.20<br>-0.30<br>-0.40<br>-0.50<br>20 25 30 35 40 45 50 55<br>Average age in years<br>formal employment<br>Estimated effect on probability of<br><!-- End of picture text --> Notes: See notes to figure 4 <!-- Start of picture text --> Figure 10. Effect of Public Vocational on Wage among Men<br>Old cohort Middle cohort Young cohort<br>0.60<br>0.40<br>0.20<br>0.00<br>-0.20<br>-0.40<br>-0.60<br>20 25 30 35 40 45 50 55<br>Average age in years<br>Estimated effect on log wage<br><!-- End of picture text --> Notes: See notes to figure 4 24
The organization of the rest of the paper is as follows. In the next section we describe our data. Section 3 discusses patterns and rates of mobility across states in terms of raw transition matrices. Section 4 describes our estimation and testing framework. We present results of estimation of (reduced form) dynamic sector choice models in Section 5, and those of their structural counterparts in Section 6. We conclude in Section 7.

# **2 Data**

The first wave of the Ethiopia Urban Household Socio Economic Survey took place in 1994. There have been four waves since then, one in each of the years 1995, 1997, 2000 and 2004. Some 9,000 to 10,000 individuals in 1500 to 1600 households were covered in each of these waves. A major point of strength of the dataset generated by the survey is that it has a sizeable panel component. A significant proportion of the individuals covered in the first wave were also tracked by all four subsequent waves. An even higher proportion of them were covered by at least three waves. A weakness is that the survey samples were drawn exclusively from the country’s seven largest urban centers. While these account for the bulk of the urban sector of the economy, we would hesitate to project details of our results to the broader (national) urban labor market. The smaller towns may differ in terms of their labor market structure from the major centers covered by the survey. That said, we would be surprised if the more qualitative aspects of our conclusions did not hold by and large for the urban sector countrywide.

Table 1 provides the breakdown of the survey sample by age groups and labor market states. In it we limit the labor force to those aged 15 to 64 years inclusive. This leads to a labor market participation rate of about 54%, which does not seem to have changed much over the decade spanned by the surveys. The table shows the division of the economically active between the unemployed, on the one hand, and the employed of various categories. The unemployed are defined as those household members of the 1564 age group who were not self-employed, or working for anyone else and were actively looking for paid work. Among the employed, the table distinguishes between the selfemployed and those working for someone else.

Self-employment is understood to include own account workers and owner manages of small businesses. We define formal sector employment as wage or salary employment in a private company, in a state-owned business or in a government agency. The term formal private sector worker refers to wage earners in private companies. We have put 6
- 29 -

Table _5_ shows the fixed-effects panel data regressions using 5-year time intervals data with different groups of added explanatory variables. Since we have seen in all the previous tables that the results from fixed-effects model and random-effects model are very similar, we do not present the results from the random-effects regressions. Again, all the estimated coefficients on foreign capital imports are positive and significant. This result strongly supports our model prediction that foreign technology transfer is one of the most important factors in explaining the different economic growth rates among developing countries.

For comparison, in Table 6, we present the pooled OLS regressions with the same exogenous variables as in Table 5. One can see that the results in Table 6 are very similar in those in Table _5._ Thus we have demonstrated that for practical purposes, pooled regressions produce results similar to results from panel regressions.

# 4. CONCLUSIONS

In summary, we first developed a model specification that links the growth rate of income with that of foreign capital imports' share in GDP and home investment's share in GDP. Then we ran regressions with a sample of around 50 developing counties, using different econometric methods and different time spans. Several conclusions can be drawn from this study. First, our empirical tests confirm our theoretical model prediction that foreign technology transfer has a positive impact on the income growth rate in developing counties. All the results confirm the hypothesis that foreign technology imports are a key element in explaining the differences in the growth rates of income among developing countries. The economic development process in developing countries is different from that in developed countries. More specifically, the increases
———. 2012. _Global Trends 2030: Alternative Worlds_ . Office of the Director of National Intelligence, Washington, DC. http://publicintelligence.net/global-trends-2030/.

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- Nehru, Vikram, Eric W. Swanson, and Ashutosh Dube. 1995. “A New Database on Human Capital Stock: Sources, Methodology, and Results.” _Journal of Development Economics_ 46 (2): 379–401.

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- ———. 2008. _OECD Environmental Outlook to 2030_ . Paris: OECD.

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- Poncet, Sandra. 2006. “The Long Term Growth Prospects for the World Economy: Horizon 2050.” Centre d’Etudes Prospectives et d’Informations Internationales (CEPII), Paris. http://ces.univparis1.fr/membre/Poncet/Perso/The%20Long%20Term%20Growth%20Prospects%20of%20the%20 World%20Economy.pdf.

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72
<mark>deteriorated on government effectiveness and political stability. SSA tends to perform worse than all other world regions – with the exception of the Middle East and North Africa (MENA), which SSA outperforms on the indicators related to voice and accountability and political stability. SSA lags behind all other world regions on measures of government effectiveness and its position appears to have slightly deteriorated since the early 2000s.</mark>

**Figure 4. World Governance Indicators by region, 2000-2020**

<!-- Start of picture text --> Government Effectiveness (percentile rank : 0 to 100)<br>80<br>70<br>60<br>50<br>40<br>30<br>20<br>10<br>0<br>EAP ECA LAC MENA SA SSA<br>2000 2005 2010 2015 2020 2000 2005 2010 2015 2020 2000 2005 2010 2015 2020 2000 2005 2010 2015 2020 2000 2005 2010 2015 2020 2000 2005 2010 2015 2020<br><!-- End of picture text --> _<mark>Source:</mark>_ <mark>Worldwide Governance Indicators (WB).</mark> _<mark>Definitions:</mark>_ <mark>Percentile rank ranges from 0 (lowest) to 100 (highest).</mark> _<mark>Regions:</mark>_ <mark>EAP (East Asia & Pacific); ECA (Europe & Central Asia); LAC (Latin America & Caribbean); MENA (Middle East & North Africa); SA (South Asia); SSA (Sub-Saharan Africa).</mark> <!-- Start of picture text --> Voice and Accountability (percentile rank : 0 to 100)<br>80<br>70<br>60<br>50<br>40<br>30<br>20<br>10<br>0<br>EAP ECA LAC MENA SA SSA<br>2000 2005 2010 2015 2020 2000 2005 2010 2015 2020 2000 2005 2010 2015 2020 2000 2005 2010 2015 2020 2000 2005 2010 2015 2020 2000 2005 2010 2015 2020<br><!-- End of picture text --> _<mark>Source:</mark>_ <mark>Worldwide Governance Indicators (WB).</mark> _<mark>Definitions:</mark>_ <mark>Percentile rank ranges from 0 (lowest) to 100 (highest).</mark> _<mark>Regions:</mark>_ <mark>EAP (East Asia & Pacific); ECA (Europe & Central Asia); LAC (Latin America & Caribbean); MENA (Middle East & North Africa); SA (South Asia); SSA (Sub-Saharan Africa).</mark> 7
Without the adjustment for the disutility of manual labor the pre-NREGS distribution is derived from the post-NREGS distribution simply by subtracting the net gains from the scheme, as given by gross wages less the imputed forgone income as reported by the household. The gain from the scheme is then defined as (in obvious notation) Gi = Cipost −Cipre. The calculation is more complicated for the adjusted consumption, for which the gain is Gi∗ = Ci∗post −Ci∗pre where: We analyze then the relationship between the net gains and the position along the consumption distribution, both original and adjusted, following a similar non-parametric approach described above for participation.

In aggregating these gains we shall use the popular headcount index (H), given by the proportion of the population living in households with mean consumption below the poverty line. However, given that our adjustment for the disutility of work is changing distribution below the line, it is of interest to also look at two “higher-order” measures, for which we use the poverty gap (PG) index, to also reflect the depth of poverty, and the squared poverty gap (SPG) index, which penalizes inequality among the poor, and can thus be interpreted as reflecting the severity of poverty. All three measures are members of the Foster-Greer-Thorbecke (1984) class of additive measures of poverty.

# **5. Results**

Using the NSS data (rural only) and using the median (Q50 Wi ) as our estimator, we � Ci � find a disutility parameter α� of 0.52.<sup>13</sup> We find similar values in the other datasets described above, namely 0.59, 0.52 and 0.57 for respectively NSS Bihar, Bihar Survey sample as a whole and the Bihar Survey for un-rationed households only. Figure 2 shows a kernel estimation of the distribution of Wi⁄Ci and Table 2 gives corresponding summary statistics for the four datasets.

> 13 To help interpret this number, imagine a household that derived its consumption solely from casual manual work. Then a value of 0.5 for Wi/Ci means that 2 days of casual labor were supplied per week per household member, which paid for the weekly consumption of one household member.

10