rafmacalaba/datause-encoder-data huggingface dataset:
5

**Table 1.  Mean years** **<u>of schooling and illiteracy rates (%), 1870-2000</u>**

||Africa|Japan<br>Korea<br>Taiwa<br>n|Latin<br>Ameri<br>ca|Easter<br>n<br>Europ<br>e|Europ<br>e &<br>offsho<br>ots|China|<sup>South</sup><br>Asia|Other<br>Asia|World<br>mean|
|---|---|---|---|---|---|---|---|---|---|
|1870||||||||||
|School<br>years|0.13|0.63|0.59|0.83|3.02|1.00|0.03|0.25|<br>1.0|
|<br>Illiteracy|96.5|82.9|86.0|79.1|37.2|82.3|99.3|94.6|78.9|
|<br>1910||||||||||
|School<br>years|0.27|2.41|1.28|1.61|5.00|1.26|0.27|0.50|<br>1.8|
|<br>Illiteracy|93.8|46.2|71.8|67.8|16.1|78.6|94.6|89.6|67.0|
|1950||||||||||
|School<br>years|0.79|6.31|2.81|5.05|7.22|1.65|0.88|1.43|<br>3.2|
|<br>Illiteracy|85.2|13.2|44.7|12.1|4.5|73.8|83.9|73.4|51.2|
|1970||||||||||
|School<br>years|1.71|8.78|4.13|7.65|9.06|3.33|2.25|3.35|<br>4.7|
|<br>Illiteracy|70.5|4.1|32.7|7.0|1.7|50.9|66.9|46.4|39.1|
|1980||||||||||
|School<br>years|2.37|9.58|4.89|8.02|9.83|4.55|2.91|4.35|<br>5.3|
|Illiteracy|60.4|2.0|26.6|6.6|1.6|37.1|60.1|34.5|33.3|
|1990||||||||||
|School<br>years|3.18|10.29|6.22|9.47|10.50|5.66|3.31|5.57|<br>6.1|
|<br>Illiteracy|50.2|1.2|18.4|6.9|1.7|26.0|54.3|21.1|27.7|
|2000||||||||||
|School<br>years<br>Illiteracy|4.02<br>39.5|10.98<br>1.3|6.94<br>14.5|9.86<br>5.5|10.99<br>1.8|6.64<br>19.7|4.02<br>48.3|6.52<br>15.3|<br>6.7<br>23.6|

Source: Based on Morrisson and Murtin (2007: Table 2) The pattern in Table 1 shows that between 1970 and 2000, average years of schooling rose by similar amounts across all regions at about 2-3 years of schooling. The biggest increases are in China and the smallest in South Asia. Other than China and Other Asia, there is only modest convergence in average years of schooling between developed and developing worlds. The pace of world growth in human capital accelerated in the second half of the 20th century. Since 1950, world years of schooling have increased relatively steadily at a rate of 0.6 to 0.7 years per decade. Before 1950, only Europe, North America and the OECD countries of Asia maintained that pace of schooling increase. That means that the current developing countries are adding human capital at a pace comparable to that of the current developed countries. The recent acceleration of world human capital development is due to developing countries investing at the pace of developed economies. The developed economies have not slowed their pace, adding about 0.7 years of schooling per decade for the last 130 years.
substantial merit-scholarships for solid first year grades. A third group combined both interventions. The authors find that service take-up rates were much higher for students offered both services and scholarships than for those offered services alone. They also find that females used services more than males. No program had an effect on grades for males. First-term grades were significantly higher for females in the two scholarship treatment groups. These effects faded somewhat by year's end, but remain significant for females who planned to take enough courses to qualify for a scholarship. There also appears to have been an effect on retention for females offered both scholarships and services. This effect is large enough to generate an overall increase in retention. The authors conclude that the results suggest that a combination of services and incentives is more promising than either alone, especially for females. Fall term grades for females in the combined group were 0.35 of a standard deviation higher than in the control group.

Leuven _et al_ . (2005) conducted a randomized field experiment where first-year university students could earn financial rewards for passing all first year requirements within one year. They find small and non-significant average effects of financial incentives on the pass rate and the numbers of collected credit points. There is, however, evidence that high ability students collect significantly more credit points when assigned to (higher) reward groups. Low ability students collect less credit points when assigned to higher reward groups. After three years these effects have increased, suggesting dynamic spillovers. The small average effect in the population is, therefore, the sum of a positive effect for high ability students and an (partly) offsetting negative effect for low ability students. A negative effect of financial incentives for less able individuals is in line with research from psychology and recent economic laboratory 26
49 108. Analysing the same 1960 census data, Reyes Osorbo<sup>and assoclates</sup> (1974) derive opposite concluslons: an examination<sup>of yield levels by state,</sup> controlling for differences In the access to<sup>Irrigation between tenure sector,</sup> reveals that, except In the case of wheat, ejidos<sup>have higher land productivity</sup> than large private farms. A study by Pillet (1973)<sup>found that, according to the</sup> 1960 census, crop yields were roughly the same<sup>In both tenure sectors; moreover,</sup> the hcrease In yields between the 1950 and 1960<sup>census was greater for the</sup> ejidos than for large private farms, In spite of the<sup>latter's higher consumption of</sup> purchased Inputs.

109. Studies comparing the 1960 and 1970 censuses<sup>reach equally varied</sup> conclusions. Nguyen and Martinez (1979) find that,<sup>In the 1960s, the growth rate</sup> of output was considerably greater for the land<sup>reform sector than for the</sup> private sector; they attribute this, In large part,<sup>to the substantial transfer of</sup> land from the private to the ejido sector In the 1960s.<sup>These authors find that</sup> ejidos had lower crop yields than private farms<sup>In 1960 and 1970. However, both</sup> average and marginal returns to expenditure on<sup>farm Inputs were found to be</sup> considerably higher for ejidos than for private<sup>farms. Referring to the 1970</sup> census data for the state of Puebla, Soberon-Ferrer<sup>(1986:165) reaches a</sup> different conclusion: he shows that small prIvate<sup>farms obtained the highest</sup> average and marginal product from land, followed<sup>In descending order' by large</sup> private farms and ejidos.

110. In terms of the efficlency of resource use, studies<sup>based on the census</sup> data up to 1970 tend to agree that small farms<sup>(ejido and private) are more</sup> efficient than large farms. Estimates of the relative<sup>efficiency of small private</sup> farms and Individual ejidos vary considerably between<sup>studies. Eckstein and others</sup> (1978:Appendix C,p.1), Wecksteln (1970:405) and Van<sup>Ginneken (1980:189-192) find</sup>
A Duration Analysis of CONALEP 1991-1992 by the introduction of the modular program, which was the forerunner of competency-based education and training (CBET) (Hobart, 1999).

In 1998, as part of the Education Modernization Project (PMETyC), CONALEP introduced CBET for nine areas of training, to bring the CONALEP education program closer to the needs of industry. The initial pilot project to introduce competency-based education and training effectively in CONALEP demonstrated the challenges of this new way of teaching. This project helped the institution to understand the complexities of its significant role as a player in the forthcoming standards-based approach to education and training, and the need for major reforms to its administration and educational practices (CONALEP, 1994 and 1999).

This paper attempts to analyze the impact of modular courses implemented by CONALEP in 1991-1992 on CONALEP graduates’ labor market outcomes. The intention of the paper is to answer the following four questions: 1) Did the graduates from the 1998 reform program find jobs faster than those from the cohorts of the 1994 survey from the pre-reform program? 2) Did the reformed program increase graduate probability of continuing their education after completing CONALEP? 3) Did the reformed program give CONALEP graduates more status in the job market? 4) Did the reforms of the CONALEP program increase the earnings that it graduates received?

This paper is organized as follows: Section two presents a description of CONALEP’s students and graduates. Section three discusses CONALEP’s past evaluations. Section four introduces the CONALEP graduate tracer surveys used in this paper. Section five describes the methodology used in this paper. Section six presents the results of the duration analysis of CONALEP. Finally, section seven offers conclusions.

# **2. The Evaluation of the CONALEP System: Students and Graduates**

The socioeconomic and academic level of CONALEP students varies according to location. Data from the National Evaluation Center (Centro Nacional de Evaluación, CENEVAL) suggest that CONALEP most frequently serves low-income students at the uppersecondary school level in Mexico City. The results of a random sample of those who took the entrance examination to upper-secondary school in the metropolitan area of Mexico City in 1999 suggest that CONALEP students come from families with the lowest average income and the lowest parental education (Table 1). The parents of an average CONALEP student have about 4
41

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- Tombe, T. (2015). The missing food problem: Trade, agriculture, and international productivity differences. _American Economic Journal: Macroeconomics , 7(3): 7_ (3), 226—-258.

- UNCTAD (2019). _International Classification of Non-tariff Measures - 2019 version_ . Geneva, Switzerland: United Nations.

- U.S. Department of Agriculture, Foreign Agricultural Service (2022, June). Impacts and repercussions of price increases on the global fertilizer market. Technical report, U.S. Department of Agriculture, Foreign Agricultural Service. International Agricultural Trade Report.

- Weerahewa, J., A. Senaratne, and S. Babu (2021). Reforming fertilizer import policies for sustainable intensification of agricultural systems in sri lanka: Is there a policy failure?

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-  ̈Ozdo ̆gan, M., S. Wang, D. Ghose, E. Fraga, A. Fernandes, and G. Varela (2025). Field-scale rice area and yield mapping in sri lanka with optical remote sensing and limited training data. _Remote Sensing 17_ (17: 3065).
In this specification, the indicator for high interest rate HIi (low interest rate LIi) denotes low (high) competition because a credit at lower cost is more attractive. As before, the regression also includes dummies for each type of financial institution (Lj).

# **6. Empirical Results**

Columns 1 and 2 of Table 2A report, respectively, the average waiting time and the interview time or face to face time of savings auditors with the staff. For auditors dressed informally with low experience and a preference for a checking account (dubbed “control” auditors because all treatment dummies take value zero), each visit lasted on average 18.5 minutes, including 10.2 minutes of face to face interaction with the staff. Auditors visiting commercial banks had to wait on average18.5 minutes while the wait time in the other institutions was approximately 7 minutes. Commercial banks and SOFIPOs had the shortest face to face time (approximately 11.5 minutes), while low income banks and SCAPs had longer interview times (14.5 and 25 minutes, respectively). Experienced auditors reported significantly longer interview times relative to neophyte auditors by 6.3 and 8.3 minutes depending on the interest rate quoted from other institutions. This difference is however not significant suggesting that the extent of competition does not affect how long staff spends with the auditor. We also do not find differences in interview time between auditors seeking an investment account compared to those interested in a checking account. Column 3 reports the number of different savings products offered by the staff. Commercial banks and SOFIPOs offered the least number of products. Similarly, auditors seeking an investment account are offered fewer products, simply because the institutions have less distinct investment products. According to the CONDUSEF dataset, of the 125 savings products, only 35 percent are investment products. Experienced auditors are not provided with more choices.

[TABLE 2A HERE] We note that the number of observations drops from 112 to 107 because all the auditors that were assigned to visit SOFOMs were turned away empty-handed, suggesting that SOFOMs do comply with the law. In addition, 92 percent of the visits the auditor was offered a product that aligned with his or her preferences (column 4). In sum, experienced auditors spent more time with the staff but were not offered more products.

# [TABLE 2B HERE]

Table 2B reports similar results to those of Table 2A for credit audits. Since an observation is now an auditor-branch pair that may involve more than one visit, columns 1 and 2 report the sum of each visit’s length in minutes. For control auditors, now defined as informally dressed, with low experience and low indebtedness, the average wait time was around 6 minutes and the total time in face to face interactions 15
# **The Use of Random Geographic Cluster Sampling to Survey Pastoralists**

Kristen Himelein, Stephanie Eckman, and Siobhan Murray<sup>1</sup> _Keywords_ : GIS, cluster sampling, pastoralists, livestock surveys _JEL Classifications_ : C83, C90 _Sector Boards_ : Agriculture and Rural Development (ARD), Poverty Reduction (POV) 1 Author affiliations: Kristen Himelein (AFTPM) and Siobhan Murray (DECRG) of the World Bank, Stephanie Eckman of the Institute for Employment Research (IAB).

Acknowledgements: The authors would like to thank their partners in the Ethiopia Central Statistics Agency, in particular Samia Zekaria, Biratu Yigezu, Habekiristos Beyene, Abate Sidelel, Jemal Ali, Abdulaziz Shifa, and the other CSA staff that supported this project. We would also like to thank Alemayehu Ambel and Jon Kastelic of the World Bank for their facilitation and technical assistance, as well as Sarah Walker, Svenja Wippich, Ruben Bach, Angus Cameron, Mike Brick, Keith Rust, the participants at the 2012 International Conference on Methods for Surveying and Enumerating Hard–to-Reach Populations conference, and four anonymous reviewers for their comments on earlier concept notes and drafts. Finally, we would like to thank Asmelash Haile Tsegay for his critical work on all levels of the project. Funding for this project was provided by the Bill and Melinda Gates Foundation Trust Fund for Improving the Quality and Policy Relevance of Household-Level Data on Agriculture in Sub-Saharan Africa and the Knowledge for Change programs at the World Bank. All views are those of the authors and do not reflect the views of the World Bank or its member countries.
Figure 5. Concentration curves for under-five mortality, Vietnam <!-- Start of picture text --> 100%.-.. . . .<br>80%<br>60 j equality<br>c 40% / / . _~~~~~~~~~~~~a ~~~~~~~~~~~~~~1998 1997 DHSVLSS<br>75<br>2 0%<br>20%I<br>0% 20% 40% 60% 80% 100%<br>cumul % births, ranked by HH living standards<br><!-- End of picture text --> 10
# **Figure 3: Magnitude of Decline in Government Expenditure under Reform Scenario 2 (Libyan Dinars)**

<!-- Start of picture text --> 4.00e+08<br>Flour<br>Flour-bread<br>Semolina<br>Rice<br>3.00e+08<br>Sugar<br>Government Tea<br>Savings Macaroni<br>Vegetable Oil<br>2.00e+08<br>Paste tomatoes<br>Milk for children<br>Milk (concentrated)<br>1.00e+08<br>0<br>0 20 40 60 80 100<br>Percentage decrease in subsidies<br><!-- End of picture text --> Source: Libyan Household Consumption Survey 2007/8; Libyan authorities; and Bank staff calculations.

Removing subsidies on food products would have a significant negative impact on poverty (Table 14). We estimate poverty in Libya based on both the international poverty line ($1.25 per day)<sup>16</sup> and an updated national poverty line (966.26 LYD per person per year).<sup>17</sup> Using the national poverty line, poverty is estimated at about 14.4 percent of the population. If food subsidies were eliminated, poverty would rise by about 2.8 percentage points under scenario 1 and by 9.6 percentage points under scenario 2. Flour (for bread), rice, and vegetable oil are the products whose price increases would contribute the most to a rise in poverty. Using the international poverty line would lead to a pre-reform poverty rate of 8.5% and a reform impact of 2.0 percentage points for scenario 1 and 8.1 percentage points for scenario 2.

Along with greater poverty, inequality would rise, from 30.2% to 33.2% in case of a complete elimination of food subsidies. This is consistent with the finding that food subsidies are pro-poor. It is interesting to note that inequality in Libya is very low: at 30.2 percent, the Gini coefficient is one of the lowest values

> 16 We convert $1.25 to Libyan dinars using 2009 PPP exchange rate data (1 LYD = 0.74 $PPP; latest available data) and inflation for the period 2009-13. We find the equivalent universal poverty line for 2013 to be 821.42 LYD per person per year. This is lower than the national poverty line of 966.3 LYD per person per year leading to lower poverty rates.

> 17 To estimate the national poverty line, we use the 2003 poverty line—which was estimated at 593.6 LYD by staff of Libya’s Office of Statistics but was not necessarily endorsed officially—and CPI inflation between 2003 and 2013. This national poverty line estimate corresponds to 2.65 LYD per day, or about $2 at the actual exchange rate. The national poverty line estimate represents 49% of the average per-capita expenditure of households (1,967 LYD).

17
# **C Pre-trend checks**

The key identifying assumption of our difference-in-differences strategy is that, in the absence of the ULGSP, outcomes in treated and reference units would have evolved similarly. While this parallel trends assumption is not directly testable, in this section we use nighttime light (NTL) data as proxy for economic activity and assess whether treatment and reference units presented similar trends before our first round of interviews.

In order to cover a longer time period before the intervention, we use the recently harmonized NTL dataset developed by Li et al. (2020), covering the 1992-2018 period. This is a synthetic dataset that uses the original Defense Meteorological Satellite Program (DMSP) data for 19922013 and calibrate data from the Visible Infrared Imaging Radiometer Suite (VIIRS) for the period 2012-2018. Following recommendations from the authors, in our main specification we drop any pixels with NTL value lower than 7.<sup>30</sup> We present results in Figure C1. In panel A we present average NTL, with 95% CI, for ULGSP and comparison wards included in our survey sample. Consistent with the fact that ULGSP was targeted at more urban districts, the average nighlights are higher for those wards when compared to comparison. The trends before the first assessment, however, do not suggest differential pre-trends. We test this formally using a difference-in-differences model of the following form: where ntlwy are measures of nighlight in ward _w_ in year _y_ , γw and θy are ward and year fixedeffects, and the coefficients βy measure the differential nighlights in ULGSP wards in each period. We allow for differential linear trends by region, included in the time-varying vector Xwy. Since treatment is defined at the LGA(district)-level, we cluster standard errors at that level. We use the entire sample period 1992-2018, but pool all years before 2000 in one coefficient.

We present the βy coefficients in panel B of Figure C1. While results are somewhat noisy, they do not suggest a differential trend between ULGSP and comparison before the 1st ULGSP assessment in 2013. While there is a temporary decrease in nighlights in ULGSP wards relative to comparison ones in 2014, that differential is temporary and quickly disappears in the following years.

We also present results using not only wards included in our survey, but measuring average nighlight at the entire district in Figure C3. Here we observe some degree of convergence between 2000 and 2013: the difference in mean nighlights in comparison and treatment districts falls by about 2 NTL points in the period (in 2011 the standard deviation (s.d.) of NTL across the approximately 800 wards included in study districts was 6.2, meaning that the estimated convergence in NTL was smaller than 0.3 s.d.). This is reflected in the DiD coefficients in Panel B. A

> 30In Figure C2 we present the original series and the adjusted series, as well as raw VIIRS data for the period 2012-2018.

61
**Figure 1: Median Values of the Sources of Vulnerability**

<!-- Start of picture text --> Private Sector Credit Growth (%) REER Overvaluation<br>60 .04<br>50 .02<br>40 0<br>30 -.02<br>20 -.04<br>10 -.06<br>0 -.08<br>2000 2002 2004 2006 2008 2000 2002 2004 2006 2008<br>Fiscal Deficit (% of GDP) External Debt (% of GDP)<br>10<br>100<br>5 80<br>0 60<br>-5 40<br>-10 20<br>2000 2002 2004 2006 2008 2000 2002 2004 2006 2008<br>Loan to Deposit Ratio (%) Short-Term Debt (% of Foreign Reserves)<br>220 400<br>200<br>180 300<br>160<br>200<br>140<br>120 100<br>100<br>80 0<br>2000 2002 2004 2006 2008 2000 2002 2004 2006 2008<br>Cluster L Cluster V Cluster G Cluster H<br><!-- End of picture text --> Sources: IMF’s World Economic Outlook database and authors’ calculations. Notes: The lower growth and lower vulnerability is identified by L, the lower growth and higher vulnerability is identified by V, the higher growth and lower vulnerability is identified by G, and the higher growth and higher vulnerability is identified by H. The absence of higher growth and higher vulnerability countries in 2000 and 2008 reflects the lack of data in such cluster for those years.

Is there a correlation between the growth and vulnerability dimensions? As Figure 2 shows, high credit growth rates and loan-to-deposit ratios are typically associated with higher growth rates. In contrast, higher fiscal deficits, debt-to-GDP ratios, real exchange rate overvaluation, and short-term debt (on a remaining maturity basis) to foreign exchange reserves ratios are associated with lower rates of economic growth. A more detailed understanding of these growth-vulnerability combinations requires relying on econometric evidence, which is what is pursued in the next section.

# **2. Estimation Results**

Table 5 presents the descriptive statistics for each of the regressors and the components of the growth and vulnerability clusters; Appendix 1.A and 1.B describe in greater detail the descriptive statistics of the cluster methodology. The next step is to estimate the multinomial logit. The paper chooses the worst of all possible outcomes—lower growth and higher vulnerability (V)—as the reference cluster. The estimated coefficients show the likelihood of being in one group relative to this 6