rafmacalaba/datause-encoder-data huggingface dataset:
The first of these is associated with the movement that workers make between economic sectors. As an example, a migration of the average worker from an economic sector such as agriculture, traditionally intensive in manual tasks, to the service sector which is intensive in cognitive tasks, results in a change in the profile of the tasks performed in the average workplace of the countries. This movement of workers between economic sectors can be seen as motivated by different causes such as the change in the terms of trade that affect the sector as a whole and puts them at a disadvantage in the face of international competition, the changes to global business centers and the appearance of other countries with greater comparative advantages in the sector, the urbanization processes which occur as people leave their jobs in rural areas to migrate to the big cities and join the industrial, service or business sectors, among others. However, technological change plays no minor role in this process. The incorporation of new production technology in the sector which displaces the labor force traditionally employed in the performance of manual tasks is exactly what forces this same labor force to seek opportunities in other lines of work.

The second channel of transmission of changes in the content of tasks performed by workers is the movement of workers between occupations within the same branch of activity. As an example, in the extreme, we could use the case of a worker who leaves his job as a bank cashier, a job primarily routine and cognitive, and begins to work in a restaurant or as a taxi driver (nonroutine manual). This example highlights the importance technological change can have on the average job profile encouraging workers to move between occupations.

The third channel through which technological change affects changes in the average content of the tasks performed by workers are the changes within the specific occupations. In other words, the incorporation of new production technology in each occupation forces workers to reassign their roles within the workplace. The adoption of assembly machinery which is automated and administered from a computer program requires those tasks previously performed by the labor force to be reassigned. For example, dedicating the majority of their time to tasks relating to sales and merchandising.

In order to examine in detail the importance these transmission channels have had in the changes observed on the content of the different types of tasks performed in the average workplace in both countries, there follows below a factorial decomposition exercise. In order to do this, we took as reference the total changes in the intensity of the tasks between 1998 and 2015 for Argentina and 1995 and 2015 for Uruguay identifying three possible separate effects, namely: (i) Structural change or effect between sectors (ES). The hypothesis behind this effect is that part of the change in the intensities of the tasks which the labor force performs

- 15 -
clusters, and upgrade the sophistication of clusters to more advanced activities.”<sup>25</sup> For countries such as Latvia, “Cluster linkages, especially the quality of local suppliers and the presence of specialized local research and training providers” are critical determinants of national competitiveness.

The index and resulting benchmarking exercise presents a mixed picture for Latvia. On the one hand, Latvia ranks 45<sup>th</sup> out of 80 countries participating in this year’s survey.<sup>26</sup> Latvia’s ranking is more or less equal to that of Poland, but behind every cc except Romania and Bulgaria. Latvia is two notches behind Greece, the weakest EU member in terms of most science and technology indicators. Slovenia, Hungary and Estonia, by comparison, are only a few notches behind Norway, New Zealand, Korea and Spain, countries that generally score relatively well on most innovation indicators. Seen from this perspective, Latvia has no room for complacency. Much work remains to be done.

**Table 3 Global Competitiveness Indexes for Latvia and Selected Economies**

|Country|MICI Ranking|Company<br>Operations|Quality of the National<br>Business Environment|
|---|---|---|---|
|||And Strategy<br>Ranking|Ranking|
|Slovenia|27|26|27|
|Hungary|28|29|29|
|Estonia|30|36|28|
|Czech Republic|34|34|34|
|Lithuania|40|39|39|
|Slovak Republic|42|43|40|
|**LATVIA**|**45**|**48**|**42**|
|Poland|46|46|45|
|Croatia|52|53|54|
|Turkey|54|56|55|
|Russian Federation|58|62|56|
|Romania|67|69|64|
|Bulgaria|68|72|63|
|Ukraine|69|66|69|

_Source_ : Global Competitiveness Report, 2002-2003 On the other hand, it is important to emphasize that the picture is by no means bleak. On the contrary, Latvia is one of the few middle income countries (along with Lithuania, Estonia, and Hungary among the NMS’s) that is considered to have a potentially bright future because “the platform is in place to support higher GDP per capita if macro,

> 25    Ibid., p. 28

> 26  It is important to stress at the outset of this benchmarking discussion that a country’s current rankings must not be confused with its destiny.  Many formerly high ranking countries tumbled in the rankings over time and, more importantly, many of today’s high ranking countries started from a much lower ranking.  Good policies, hard work and political commitment and consensus, much more than current rankings, are the major determinants of future rankings and prosperity.

27
statistics (p. 110). This suggests that small and medium size ...rms are routinely omitted from the IO table samples, and that the industry import intensities inferred are often based on data of mostly large ...rms. For China, according to the National Input-Output Survey Methods of China (2007) published by the National Bureau of Statistics of the People’s Republic of China, the sample used to construct the IO tables consists of all large ...rms that have at least 300 million yuan in revenue (about 38 million USD during the sample period), along with some small- and medium-sized ...rms sampled with unknown proportions (see item 5 on p. 3 about sample selection and item 4 on p. 27 about size cuto¤s). In other words, the sampling method behind the construction of Chinese IO tables is heavily biased towards the very large ...rms.

Second, recent research by Amiti, Itskhoki and Konings (2014) and Blaum, Lelarge and Peters (2014) shows that large ...rms tend to have a higher import-to-sales ratio. This is also con...rmed by our sample of Chinese ...rms. When we regress a ...rm’s import intensity on ...rm size (measured by log(sales)), controlling for industry-year ...xed e¤ects, we ...nd that doubling ...rm sales is associated with a 0.5 percentage-point increase in the ...rm’s import intensity (signi...cant at the 1% level).<sup>29</sup> Given that ...rms’import intensity and DVAR are negatively correlated, by omitting the smaller ...rms, IO tables by construction tend to include ...rms with a lower DVAR. Such sample selection criteria could cause the aggregate DVAR estimates to be signi...cantly biased downward.

To demonstrate the signi...cant aggregation bias driven by sample selection when the underlying population of ...rms are heterogeneous in size and import intensity, we conduct the following decomposition exercise relying on China’s 2004 ...rm census data, which covers the universe of all manufacturing ...rms and is therefore much larger than our original ...rm survey data set which only includes manufacturing ...rms with a minimum 5 million RMB

> 29 Results are available upon request.

15
A Duration Analysis of CONALEP Approximately 35 to 40% of the parents of CONALEP students have higher levels of education than the parents of an average student at the Colegio de Bachilleres. Nonetheless, on average, CONALEP students come from the lower socioeconomic categories and generally have lower scores in the CENEVAL examination than students in the other streams of uppersecondary education. Only students attending other technical-professional schools (DGETI) are comparably low on these indicators.

# **3. CONALEP’s Past Evaluations**

The CONALEP system as a whole has been evaluated several times in the past. The first evaluation was done by CONALEP (1994) and CONALEP (1999) using graduate tracer surveys. These data sets are described in the next section. The other evaluations were done by Lane and Tan (1996) and by Lee (1998). CONALEP also hired international consultants (Carnoy and others 2000) to assess the evolution of the CONALEP system. For this purpose the consultants used a different data set, as is explained below.

The CONALEP 1994 and 1999 tracer studies had several problems, one of the most important being the lack of a well defined control group. A control group was expected to be added later, using data from the National Urban Employment Survey (ENEU). However, the studies neither include in-depth information on how the analysis was performed nor provide useful information on how CONALEP graduates perform relative to a control group.

Lane and Tan (1996) also encountered several problems in their evaluation. The first was the construction of a non-arbitrary control group. The ENEU sample is representative of metropolitan areas while the CONALEP graduate tracer survey is representative nationally. The difference in geographical coverage of the two groups makes comparison difficult. Second, the control groups were constructed ad hoc. The control groups included individuals between the ages of 17 and 30: (A) those who have completed lower-secondary education; (B) those who have completed non-professional, elementary vocational training (CECATI), and (C) those who have completed one to three years of general academic (non-vocational) high school. Some doubts remain with respect to the second group, since the ENEU survey does not distinguish between formal and informal training/technical courses.

Lee (1998) compares the individuals from the Encuesta de Egresados 1994 (the treatment group) with two other groups. One group comprises all 1991 graduates from upper-secondary 6
that inequality a ff ects subjective well-being negatively. If inequality is stronger in urban areas, this could generate an omitted variable bias.

To capture the impact of product variety N, we construct for each ward indices of variety for food, non-food, and durables. For each household, the survey collected consumption expenditure information on 67 separate food items, 58 non-food items and 16 durable goods items. Based on this information, we compute the total expenditures si by all surveyed households in a ward on item i. Not all items are consumed in any given ward. For instance, of the 67 food items listed in the questionnaire, some wards consume 63 items while others only consume 33. Based on this information, we compute, for each of our three groupings J (food, non-food, and durables), a Her ndahl concentration index de ned as:<sup>12</sup> This index gives a rough idea of what is available for sale in the ward: the higher its value, the more concentrated spending is on a small number of categories, and the less diversi fi ed ward consumption is.<sup>13</sup> N<sup>J</sup> does not, however, measure product diversity within each subcategory and is thus an imperfect measure of variety. Index values reported in Table 3 show more concentration in durable expenditures and less in foodstu ff s.

Two sets of dummies control for climatic and economic factors: ecological belt dummies and regional dummies. Ecological belt dummies divide the country into three North-South zones based on elevation. The mountain zone is the part of the country located at 4000 meters (12000 feet) of elevation and above. The Terai is the narrow plain bordering India. The Hills is the

> 12 To the extent that richer consumers buy a greater variety of products, our variety indices are correlated with consumption expenditures. This is not a cause for concern, however, since we control for consumption expenditures directly in the regression analysis.

> 13 N J = 1 corresponds to complete concentration in a single item while N J = 1/J corresponds to equal expenditure shares for all J items in a category.

14
**Table 3** – Determinants of long-term growth: 2SLS estimations using cross-sectional data

||(1)<br>OLS|(2)<br>OLS|(3)<br>2SLS|(4)<br>2SLS|(5)<br>2SLS|(6)<br>2SLS|(7)<br>2SLS|
|---|---|---|---|---|---|---|---|
|Rule of law||0.0217<sup>_∗∗_</sup><br>(2.40)|0.0857<sup>_∗∗∗_</sup><br>(2.65)|0.0871<sup>_∗∗_</sup><br>(2.53)|0.0717<sup>_∗∗_</sup><br>(2.15)|0.0850<sup>_∗∗_</sup><br>(2.21)|0.0844<sup>_∗∗_</sup><br>(2.21)|
|Log(GDP per capita)|-0.684<sup>_∗∗∗_</sup>|-0.800<sup>_∗∗∗_</sup>|-1.142<sup>_∗∗∗_</sup>|-1.100<sup>_∗∗∗_</sup>|-1.348<sup>_∗∗∗_</sup>|-1.596<sup>_∗∗∗_</sup>|-1.593<sup>_∗∗∗_</sup>|
||(-4.18)|(-4.91)|(-3.83)|(-3.54)|(-4.37)|(-3.60)|(-3.61)|
|Natural res.|0.0132|0.0210<sup>_∗∗_</sup>|0.0443<sup>_∗∗∗_</sup>|0.0419<sup>_∗∗∗_</sup>|0.0418<sup>_∗∗∗_</sup>|||
||(1.28)|(2.23)|(2.89)|(2.61)|(2.85)|||
|Oil rents||||||0.0586<sup>_∗∗_</sup><br>(2.43)||
|Oil rents _<_ median|||||||0.145<br>(0.64)|
|Oil rents _>_ median|||||||0.0586<sup>_∗∗_</sup><br>(2.44)|
|Non-oil rents||||||0.0180|0.0179|
|||||||(1.22)|(1.21)|
|Observations|113|113|113|113|113|113|113|
|Hansen J|||1.879|1.932|1.802|1.248|1.274|
|p-value|||0.170|0.165|0.180|0.264|0.259|
|Kleibergen-Paap|||8.592|7.157|7.404|6.758|6.865|
|p-value|||0.014|0.028|0.025|0.034|0.032|
|Corruption||-0.0280<sup>_∗∗∗_</sup>|-0.0496<sup>_∗∗∗_</sup>|-0.0469<sup>_∗∗∗_</sup>|-0.0380<sup>_∗∗∗_</sup>|-0.0369<sup>_∗∗∗_</sup>|-0.0363<sup>_∗∗∗_</sup>|
|||(-4.17)|(-2.80)|(-3.81)|(-3.19)|(-2.91)|(-2.93)|
|Log(GDP per capita)|-0.684<sup>_∗∗∗_</sup>|-0.822<sup>_∗∗∗_</sup>|-0.929<sup>_∗∗∗_</sup>|-0.852<sup>_∗∗∗_</sup>|-1.156<sup>_∗∗∗_</sup>|-1.231<sup>_∗∗∗_</sup>|-1.228<sup>_∗∗∗_</sup>|
||(-4.18)|(-5.14)|(-5.06)|(-5.09)|(-5.92)|(-5.63)|(-5.63)|
|Natural res.|0.0132|0.0185<sup>_∗_</sup>|0.0227<sup>_∗∗_</sup>|0.0197<sup>_∗∗_</sup>|0.0234<sup>_∗∗∗_</sup>|||
||(1.28)|(1.83)|(2.28)|(2.00)|(2.96)|||
|Oil rents||||||0.0286<sup>_∗∗∗_</sup><br>(2.77)||
|Oil rents _<_ median|||||||0.114<br>(1.00)|
|Oil rents _>_ median|||||||0.0286<sup>_∗∗∗_</sup><br>(2.77)|
|Non-oil rents||||||0.0109|0.0109|
|||||||(0.87)|(0.87)|
|Observations|113|113|113|113|113|113|113|
|Hansen J|||2.243|4.016|5.465|5.719|5.855|
|p-value|||0.134|0.260|0.141|0.126|0.119|
|Kleibergen-Paap|||10.112|11.076|11.084|11.185|11.494|
|p-value|||0.006|0.026|0.026|0.025|0.022|

_p_ -values in parentheses. _∗ p <_ 0 _._ 1, _∗∗ p <_ 0 _._ 05, _∗∗∗ p <_ 0 _._ 01. All specifications include education, the logarithm of total population, an openness measure, investment, and a measure of ethnic fractionalization. In Column (4), inflation and its standard deviation, as well as a measure of undervaluation, in Column (5) life expectancy and latitude are added.

Overall, the results of the long-term cross-sectional analysis confirm the results drawn from the panel setting, and most importantly, even after instrumentation, the rule of law measure and the 17
Policy ReseaRch WoRking PaPeR 4847

# **Abstract**

Expanding and improving basic education in developing countries requires, at a minimum, teachers who are present in the classroom and motivated to teach, but this essential input is often missing. This paper describes the findings of a series of recent World Bank and other studies on teacher absence and incentives for performance. Surprise school visits reveal that teachers are absent at high rates in countries such as India, Indonesia, Uganda, Ecuador, and Zambia, reducing the quality of schooling for children, especially in rural, remote, and poor areas. More broadly, poor teacher management and low levels of teacher accountability afflict many developing-country education systems. The paper presents evidence on these shortcomings, but also on the types of incentives, management, and support structures that can improve motivation and performance and reduce avoidable absenteeism. It concludes with policy options for developing countries to explore as they work to meet Education for All goals and improve quality.

This paper—a product of the Human Development and Public Services Team of the Development Research Department and the Education Team of the Human Development Network—is part of a larger effort in the department to improve education outcomes by raising teacher performance. Policy Research Working Papers are also posted on the Web at http:// econ.worldbank.org. The authors may be contacted at hrogers@worldbank.org and evegas@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
# Figure 1. Debt developments during COVID

# A. Total debt

# B. Government debt

<!-- Start of picture text --> Percent of GDP Percent of GDP<br>320 150<br>World World<br>Advanced economies Advanced economies<br>EMDEs EMDEs<br>240<br>100<br>160<br>50<br>80<br>0 0<br>1970 1980 1990 2000 2010 2020 1970 1980 1990 2000 2010 2020<br>C. Private debt D. Debt in LICs<br>Percent of GDP Percent of GDP<br>200 World 150 Total<br>Advanced economies Government<br>EMDEs Private<br>150<br>100<br>100<br>50<br>50<br>0 0<br>1970 1980 1990 2000 2010 2020 1970 1980 1990 2000 2010 2020<br><!-- End of picture text --> Source: Kose et al. (2021).

Note: Data are available for up to 192 countries, including 39 advanced economies, 153 EMDEs, and 24 LICs. Nominal GDP weighted averages.

21
<!-- Start of picture text --> Volume in Monitored Outlets: Beef<br>(01/21192--08/25/92)<br>60 . ,. .,w............ ........... .............. .. ............. .<br>S. .. 50 S<br>S S'<br>e .. .. . ;:;.;s 7-..32<br>-30 ~~~~~~~~. 1..>,. . . 't .. .......... ........ .......... ......... ... ... ........ ... ..<br>30 .}.. .................... ............... ................ ........... ... ...................<br>° Xt ,@\ ,.e~Mrket; Voum V;olume{'s "tt<br>10' l I I I I I I I I I I I I I I I I I J1 I l I I I!<br>01/21 02/04 MM/1 03/17 04/07 04/21 05/05 05t19 06/09 06/23 07/07 07/21 Oa 04 08/18<br>Date<br>| M arket V olu me .... State V olum e |<br><!-- End of picture text -->

32
However, it must be noted that the above numbers based on GLSS are likely to be underestimates of the actual size of informal sector, as many activities are small and fall outside the standards definitions of economic activity and will not be considered as important by households and hence not divulged to enumerators during household or even enterprise surveys. It is an accepted fact that non-farn enterprises always underreport profits as their concept of profits mostly does not take subsistence consumption into account which tend to form a large component of revenue in most households level activities. In the absence of any other meaningful tracking of informal sector activity it can be argued that GLSS type household data can be used to construct reasonable estimates of informal activities in agricultural and nonagricultural sector.

In Ghana's case under household agricultural activity food crops predominate export crops as the main item of consideration in informal sector activities. In certain agro-climatic regions like, rural forest and rural Savannah a large majority of output and value added in crop production comes from infornal activities. In crops maize and cassava are followed by grains and starches, from all of which large proportion is consumed by households. Cocoa production accounts for a very low proportion of total household informal activity income. In cases such as livestock production more than two thirds of income is non-cash and also a large portion is self consumed.

**Table4.4: Source of Household Income by Quintile**

|**Source**|**First**|**Second**|**Third**|**Fourth**|**Highest**|**Ghana**|
|---|---|---|---|---|---|---|
|wage|7.2|9.6|12.1|14.0|17.2|13.6|
|NF self emp income|1.7|0.2|0.5|1.6|1.7|1.3|
|**_Formal_**|**_8.9_**|**_9.9_**|**_12.7_**|**_15.6_**|**_18.9_**|**_14.9_**|
|wage|4.3|4.0|3.2|2.3|1.5|2.6|
|Agric income|58.7|56.4|52.6|46.2|44.9|49.4|
|NF self emp income|19.1|21.9|25.1|26.1|25.5|24.5|
|**_Informal_**|**_82.1_**|**_82.4_**|**_80.9_**|**_74.6_**|**_71.9_**_-_|**_76.6_**|
|**_Other_**|**_9.0_**|**_7.8_**|**_6.5_**|**_9.8_**|**_9.2_**|**_8.7_**|
|All|100|100|100|100|100|100|

_Source: GLSS, 1987/88_ In non-farm agricultural household enterprises a large share is in urban areas and two main sectors are trading and manufacturing. The latter was analyzed using RPED data, while the former is least understood. It is clear that in trading there is low capital involved and very seldom hired labor utilized, making it a substantially household enterprise activity, as opposed to most manufacturing which compared to trading has more capital and more hired labor. Other activities such as transport, construction and services also employ a large share of non household members, while transport by far has the highest capital component of any informal sector activity.

Informal sector activities play an important role in the lives of poor people. It can be easily seen from the following table that income derived from informal sector 28
|**Appendix Table 3: Prices and Shocks**<br>(1)<br>Dependent Var|(2)<br>iable: Price Paid|
|---|---|
|Somebody in Household Sick<br>25.81<br>||
|(6283)||
|.<br>Respondent Sick<br>74.36<br>(6006)||
|.<br>Death of a Friend or Relative|-72.38|
||(107.87)|
|Vaginal Sex<br>69.41|43.55|
|(30.66)**|(35.19)|
|Vaginal Sex * Somebody in HH<br>-35.37||
|Sick<br>(42.80)||
|Vaginal Sex * Respondent Sick<br>-62.05<br>(39.64)||
|Vaginal Sex * Funeral|33.50<br>(83.67)|
|Anal Sex<br>89.49|147.49|
|(26.77)***|(37.47)***|
|Anal Sex * Somebody in HH<br>-5.06<br>Sick<br>(33.85)||
|Anal Sex * Respondent Sick<br>-2318||
|<br>.<br>(37.11)||
|Anal Sex * Funeral|50.74|
||(71.00)|
|Oral Sex<br>26.66|47.76|
|(19.16)|(22.49)**|
|Oral Sex * Somebody in HH<br>-6.22||
|<br><br>Sick<br>(23.57)||
|Oral Sex * Respondent Sick<br>3.92<br>1960||
|(.)<br>Oral Sex * Funeral|128.87|
||(74.59)*|
|Had Unprotected Sex<br>43.83<br>(2211)**|75.14<br>2509***|
|.<br>Had Unprotected Sex * Somebody in HH<br>7.65|(.)|
|<br>Sick<br>(27.84)||
|Had Unprotected Sex * Respondent<br>-9.16||
|<br>Sick<br>(2218)||
|<br>.<br>Had Unprotected Sex * Funeral|-98.05|
||(9307)|
|Regular<br>-18.29|.<br>-13.06|
|(1844)|(2001)|
|.<br>Regular * Somebody in HH<br>-5.18|.|
|<br>Sick<br>(25.50)||
|<br><br>Regular * Respondent<br>12.19||
|Sick<br>(25.20)||
|<br><br>Regular * Funeral|-52.19<br>(4041)|
|_p_-value for joint sig., HH sickness interaction terms<br>0.37|.|
|_p_-value for joint sig., own sickness interaction terms<br>0.13<br>||
|_p_-value for joint sig., funeral interaction terms|0.54|
|Mean of Dependent Variable<br>453.71|531.66|
|<br>Observations<br>18504|10331|
|Number of women<br>192|190|
|R-squared<br>0.05|0.05|
|Note: All regressions are fixed effects regressions with controls for the mo|nth and the day of|
|<br>the week. Clustered standard errors (at the individual level) in parenthes<br>include controls for the round of data collection. The dependent variable i|<br>es. All regressions<br>s Kenyan shillings.|
|<br>The exchange rate was approximately 70 Kenyan shillings to $1 US durin<br>collection period. Regressions include several other measures of sexual|<br>g the data<br>behavior, but the|
|<br>coefficients are omitted for space. These include providing company, ma<br>sex in thighs, manual stimulation, and other activities. The p-values listed|<br>ssage, stripping,<br>at the bottom of|
|the table test for the joint significance of all the interaction terms (includin<br>|g those that are|
|omitted for space).<br>* significant at 10%; ** significant at 5%; *** significant at 1%||