rafmacalaba/datause-encoder-data huggingface dataset:
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25
<!-- Start of picture text --> Figure 2<br>Intra-IndustryTrade with the EU.<br>0.55<br>0.53<br>0.51-.<br>0.49<br>0.47<br>0.45 OAI -- ", - - _, - _,._--<br>0.39<br>0.37<br>i0 35r<br>'O0.41<br>T0.31<br>r0 029-<br>0E27-<br>025- ___ __<br>0.17-<br>0.15 I III<br>1987 1988 1989 1990 1991 1992 1993 1994<br>Year<br>-o-CEEC-m -MENA - - -Israel<br><!-- End of picture text --> costs and risks associated with development of export markets, while at the same time obtaining know-how from suppliers. While OPT is frequently restricted, at least in the initial stages, to labor intensive, low value added activities, these can create significant employment.

In the period following the implementation of the agreements with the CEECs, exports after outward processing accounted for about 18 percent of total CEEC exports to the EU in 1993, up from 10 percent in 1989. For Romania, processing activities generated 30 percent of exports to the EU in 1993. In contrast, exports to the EU of processed goods represented only 1.7 percent of total MENA exports in 1993, a share that has remained constant since 1989 (Table 2). Most of the processing occurs in leather/footwear, clothing, electrical machinery, precision instruments and furniture. OPT accounts for a substantial share (about one quarter) of the growth in exports from the CEECs to the EU (Hoekman, 1995a). In contrast, it plays a very minor role in MENA, the two exceptions being Morocco and Tunisia.<sup>7</sup> 7 The welfare implications of OPT may be ambiguous, insofar as the policy regime induces EU sourcing of inputs that are more costly than what is available on the world market.

10
|**Table 3. Trad**|**e Flows (in pe**|Imports<br>**rcentage or billion**|**s of Kenyan shillin**|**gs, BKS)**|Exports|
|---|---|---|---|---|---|
||BKS|% Total<br>|% Supply|BKS|% Total<br>|
|||_a/_|_b/_|||
|**IRTS Goods and Services**|0.3|86.0|31.0|0.1|46.0|
|**CRTS Goods and Services**|0.0|14.0|5.6|0.1|54.0|
|**Business Services**|0.0|2.3|2.6|0.0|1.3|
|Communication||||0.0|0.7|
|Insurance|0.0|0.7|7.4|0.0|0.2|
|Banking and other financial s|0.0|1.5|7.4|0.0|0.4|
|Other services||||||
|**Transportation**|0.1|16.0|29.7|0.0|14.3|
|Road services|0.0|10.0|29.7|0.0|9.0|
|Railway transport|0.0|0.3|29.7|0.0|0.3|
|Maritime transport|0.0|1.1|29.7|0.0|1.0|
|Pipeline transport|0.0|0.5|29.7|0.0|0.5|
|Airline transport|0.0|4.0|29.7|0.0|3.6|
|**Dixit-Stiglitz Goods**|0.2|67.7|46.9|0.1|30.4|
|Beverages & tobacco|0.0|0.5|5.2|0.0|4.6|
|Other manufactured food|0.0|5.2|60.2|0.0|1.1|
|Printing and publishing|0.0|2.5|26.2|||
|Petroleum|0.1|18.2|56.4|0.0|6.0|
|Chemicals|0.1|15.7|66.6|0.0|5.8|
|Metals and machines|0.1|16.8|63.8|0.0|5.9|
|Non metallic products|0.0|0.9|8.7|0.0|1.4|
|<br>Other manufactures|0.0|8.0|34.7|0.0|5.5|
|**Agriculture**|0.0|5.7|11.1|0.1|39.3|
|Maize|0.0|0.2|2.0|0.0|0.1|
|Wheat|0.0|3.3|96.1|0.0|0.0|
|Rice|0.0|1.2|53.7|||
|Barley||||0.0|0.0|
|Cotton||||0.0|0.0|
|Other cereals||||0.0|0.0|
|Sugarcane|0.0|0.4|42.5|0.0|0.6|
|Coffee||||0.0|4.4|
|Tea|0.0|0.1|9.0|0.0|17.7|
|Roots & tubers||||||
|Pulses & oil seeds|0.0|0.1|3.4|0.0|3.1|
|Fruits||||0.0|0.8|
|Vegetables|0.0|0.1|2.7|0.0|3.0|
|Cut flowers||||0.0|8.0|
|Others crops|0.0|0.2|6.0|0.0|1.7|
|Beef||||||
|Dairy||||||
|Poultry||||||
|Sheep goat and lamb for slaugh|ter|||||
|Other livestock<br>a<br>/Percentage of total is the percentage of economy-wide i<br>b<br>/Total use includes final consumption, intermediate use,|mports.<br>investment and|government dema|nd.|||

32
# **1. Teacher absence**

In trying to achieve Education for All goals, few inputs are more essential than having a teacher present in the classroom. It is obvious why the teacher’s absence will affect educational quality: if students end up doing ‘busy work’ or playing in the schoolyard, little learning is likely to take place. But teacher absence can also affect educational access and school completion rates, if poor quality discourages parents from making the sacrifices necessary to send their children to school. More important, high rates of teacher absence often signal deeper problems of accountability and governance that are themselves barriers to educational progress.

This paper reviews the issue of teacher absence for education policymakers and researchers. First, it explores what is known about the extent of the problem and its causes; and second, it lays out some possible approaches to reducing teacher absenteeism by improving incentives and support for teachers. We should note at the outset that in both cases – teacher absence and incentives – rigorous evidence has only recently begun to become available. Still, there is already enough evidence to show that teacher absence is a serious problem in some countries and regions, but also that governments can tackle it through their policies and programs.

## **How prevalent is the problem of teacher absence?**

This straightforward question is not easy to answer. One difficulty in studying teacher absence is that administrative records of teachers’ attendance may not be accurate. Inaccuracies are possible even in well‐run school systems, of course. A teacher may come to school but have to leave early to deal with a family emergency, for example, and even if a colleague covers, the absence may not make it into the log book. But in the countries with the highest absence rates, administrative records may be an especially poor guide to actual teacher attendance. If poor governance and low levels of accountability undermine teachers’ incentives to attend school, those same factors are likely to reduce the accuracy of official attendance records. In environments with weak institutional capacity and accountability, the head teachers who keep such records may know that there will be few spot checks of their accuracy, and that even if inspectors were to find discrepancies, there would likely be no consequences. Administrative records may be poor even if head teachers are not trying to cover for themselves and/or their subordinates: when accountability mechanisms are weak, keeping such records is not likely to be a priority.
purpose, the proxy used in the paper is the share of self-employed in the labor force, as reported in the surveys collected by the International Labor Organization. The final sample consists of 471 observations in 42 countries when we consider all countries, and of 182 observations in 18 countries when we consider developing countries only.

The rest of the paper proceeds as follows. Section 2 presents a stochastic model of employment and production in a dual (formal/informal) economy. Section 3 presents the empirical analysis, introducing first the data and sample and then discussing the estimation results for, respectively, the long- and short-run relationships. The empirical section includes several robustness checks on both the criteria to obtain the sample and the of outliers.

# **2 A Basic Model of Informal Activity**

The economy consists of a measure one of workers, and of two sectors, a formal and an informal one. Each worker is the owner of her own firm, hence, in what follows we shall use equivalently the concept of workers and firms. In each sector workers have different productivities depending on their skill level _s_ , which has a distribution _ξ_ ( _s_ ) over _s ∈_ [0 _,_ 1] . More precisely, productivity in the formal sector is equal to _θR_<sup>_α_</sup> _· F_ ( _s_ ) _− R_ , where _θ_ represents the overall productivity level of the economy, _s_ the workers’ skills, _R_ the level of regulation, 0 _< α <_ 1, and _R_<sup>_α_</sup> represents the efficiency with which the government uses regulation (i.e. countries with a higher parameter _α_ make a better use of regulation). We also assume that workers’ productivity increases with their skills in a concave manner, so that _F_<sup>_′_</sup> ( _s_ ) _>_ 0 _, F_<sup>_′′_</sup> ( _s_ ) _<_ 0.

The way we introduce regulation _R_ wants to capture its dual role. Some degree of regulation can increase firms’ productivity by allowing the government to provide better services to firms, such as the ability to solve disputes through an efficient judicial system, standardized procedures, and the ability to solve moral hazard and adverse selection 5
welfare measure (e.g. consumption for the second period), the actual welfare distributions conditional on observable characteristics should be identical. This (somewhat strong) assumption implies that the distributions (for both the welfare measure and the observable characteristics) should be drawn from the same population. It also implies that the variables, especially the welfare measure, must have been collected in the same way—that is, the survey design should be the same. If the two distributions are not comparable, the counterfactual distributions would not be deemed to come from the same data generating process or model; the resultant mobility estimates would be incorrect as a result.

Between 1990 and 2012, at least 148 multi-topic surveys that collected consumption data were completed across countries in Sub-Saharan Africa. More recently, more than half of the countries have conducted a consumption survey between 2011 and 2015 (see Beegle, Christiaensen, Dabalen and Gaddis, 2016). However, only 27 of the 48 countries in Sub-Saharan Africa had at least two comparable household surveys for the period between 1990 and 2012. Among these 27 countries, we are able to use two survey rounds for each of 21 countries to create synthetic panels. These countries and their surveys, which are listed in Table 2, represent around two-thirds of the population of the region and an even higher fraction of its poor population. Nearly all the surveys were conducted in the 2000s, and the two survey rounds in each pair are, on average, separated by about 6 years. Notably, this was also a period of sustained economic growth for the region.

Table 2 shows the estimated poverty rate for each country for each of the two periods, and the net changes in poverty between the two periods. Most of the countries witnessed a downward trend in poverty. More than three-fourths (i.e., 16 out of 21) of the countries saw poverty reduction that ranges from around one percentage point (e.g., Nigeria and Togo) to 12 percentage points (e.g., Botswana, Mozambique, and Uganda). Almost half of the countries – 10 of 21 – had a poverty 12
V. **Discussion and conclusion** This paper presents empirical evidence on the magnitude of liberal grade promotion in public schools in Nepal. The pass rate in the year-end exam whose scores determine grade transition is 256 percent higher than the pass rate in internally administered trimester exams. This suggests that as many as seven out of 10 students who transition from grade eight to nine may be doing so due to the policy.

The study has a number of limitations. First, due to the unavailability of data, the estimates are not conclusive. In particular, the study cannot positively rule out the possibility that other inputs like teacher attendance, time spent on classroom instruction, and remedial classes for the end-of-theyear exams were responsible for the observed leap in pass rates. Second, the small number of schools in the study were selected purposively from semi-urban areas of Kathmandu for a randomized controlled trial of student incentives, so these schools are not representative of all the schools in the district or the country. Further research is necessary to establish the external validity of the results.

Although not a causal attribution, liberal promotion policy appears to have been successful in reducing the system’s inefficiency: between 1999 and 2014, the repetition rate at the primary level dropped from 23 percent to 10 percent, the transition rate from primary to lower secondary level climbed from 80 percent to 87 percent, and the survival rate to grade five, the last grade of the primary school cycle, rose from 59 percent to 70 percent (World Development Indicators).<sup>11</sup> There is, however, little evidence of improvement in the quality of education over the same period. The pass rate in School Leaving Certificate (SLC) exams, the terminal exam taken in grade 10, continues to remain low; in 2014, it was 34 percent in community schools compared to 89 percent in institutional schools.<sup>12</sup> According to the national assessment of grade 10 students in 2011, the average achievement level in the five core subjects was only 41 percent (Educational and Developmental Service Centre, 2011). An assessment by Independent Evaluation Group (IEG) of BPEP, the project that introduced the liberal promotion policy, concluded that the policy encouraged children to stay longer in school but “...no means were developed to help the children who were falling behind learn the required material and pass grades or stay in school” (Independent Evaluation Group, 2009).

> 11 Accessed March 15, 2016.

> 12 <u>http://myrepublica.com/society/story/23078/slc-results-2015-declared-with-47-43-pc-pass.html</u> Accessed March 15, 2016.

10
59 age group will decline by 0.7% _annually_ during this period, while the 60+ age group will increase by 0.9% _annually_ . The dependency ratio will increase from 46.5 to 74.2 in 2050, when the elderly will form 65% of the dependents, up from 50% in 2005. The demographic trends in the newly-acceded countries are no different.

Meanwhile, developing and least developed countries’ share in world population will rapidly increase with the fastest population growth countries being concentrated in Africa and the Middle East. The total population of Northern Africa and Western Asia (which includes Armenia, Azerbaijan, Georgia, and Cyprus but excludes Iran) will increase from 400 million in 2005 to 700 million in 2050 under the medium projection. The size of the 15-64 age group will increase from 250 million to 450 million while the 60+ age group will grow from 26 to 128 million.

**Figure 1**

<!-- Start of picture text --> Population Distribution by Age Group<br>40%<br>30%<br>20%<br>10%<br>0%<br>0-20 20-40 40-60 60-80 80+<br>EU in 2005 EU in 2020 Maghreb in 2005 Maghreb in 2020<br><!-- End of picture text --> _Source:_ UN (2009) Figure 1 presents the current (2005) and projected (2020) population distribution in the EU-15 and the three North African countries with significant migration flows to the EU – Algeria, Morocco and Tunisia. The first two bars for each age group represent the EU, the second set of bars the average for the three Maghreb countries. The dark bars are for 2005, while the lighter bars are 2020 figures projected by the UN. The current population in the 0-20 age group is significantly larger in the three Maghreb countries – 4
**Do Poor Countries Really Need More IT? The Role of Relative Prices and Industrial Composition**

JEL: O14, O33, O57, E22 Keywords: ICT adoption, industrial composition, ICT capital stocks

**Maya Eden**<sup>1</sup> **Paul Gaggl**<sup>1</sup> The World Bank University of North Carolina at Charlotte Development Research Group Belk College of Business Macroeconomics and Growth Team Department of Economics 1818 H St. NW 9201 University City Blvd Washington, DC 20433 Charlotte, NC 28223-0001 Email: meden@worldbank.org Email: pgaggl@uncc.edu

> 1This paper was prepared as background work for the World Bank’s World Development Report 2016 on the topic of the Internet. We thank Asad Zaman for helpful research assistance and Robert Inklaar for assistance with technical details in the Penn World Table 8.1. We are further grateful for helpful comments by Uwe Deichmann, Aart Kray, Deepak Mishra, Indhira Santos, Marc Schiffbauer, as well as Luis Serven and for generous financial support from a World Bank RSB grant.
# **<mark>6.4 Transmission Mechanisms</mark>**

<mark>While the association between the refugee shock and nutrition outcomes for children of mothers who lived under the shock in early age has proven to be robust, the mechanisms that turned the shock into these results remain unexplored. These mechanisms can be many, are difficult to capture with retrospective data and would require a separate study. However, our data contains information on grandmothers of the children we found in 2015-2016 and this allows us to explore the hypothesis that these are children of mothers who, because of the refugee shock, may have received reduced home care during early childhood. Studies on early childhood development have largely documented the importance of the first five years of life for health and nutrition, cognitive behavior, earnings, motherhood and other outcomes in adulthood and it is possible that the refugee shock has altered the behavior of grandmothers in a way that affected mothers later in life.</mark> <mark>What we know from previous studies on refugees in Tanzania is that household well-being increased for households living close to the refugee camps. Most importantly, the sudden arrival of refugees created temporary secondary employment opportunities for mothers and fathers who, as a consequence, should have been expected to dedicate less time to intrahousehold domestic chores including child care. Our hypothesis is that an increase in female labor market participation and the decrease in health and educational services already documented has led to a decrease in child care. In Table 6, we first use the treatment group</mark> _<mark>q</mark>_ <mark>=1 to see whether grandmothers</mark><sup>21</sup> <mark>who lived under the refugee crisis in 1996 (during the crisis) had a higher labor market participation than those in 1991(before the crisis). We find that grandmothers who lived under the refugee shock in 1996 had a higher labor market participation than grandmothers in 1991 whereas we find no statistically significant increase of children’s grandfathers’ labor market participation as a result of the refugee arrival. This confirms previous findings that showed an increase in female labor force participation during the refugee crisis.</mark> <mark>We also investigate the long-term impacts of refugees’ arrival on contemporary socio-economic outcomes of mothers and fathers. We explore the potential mechanisms by focusing on parental endowments in terms of assets, level of education and labor market participation. We estimate the following econometric model for mothers and fathers:</mark>

> 21Note that these are mothers in 1991 and 1996. We call them grandmothers, as our analysis focuses on 2015-2016 children outcomes.

20
characteristics, income and demographic information. The large number of observations combined with substantial rate changes under the price reform help identify both price and nonprice segments of the demand function.

A common issue of using survey data for statistical analysis is that we only selectively observe those who choose to report. In the Turkish household budget survey, about 28 percent of households with access to electricity did not report electricity expenditure. To address potential sample selection bias, I apply Heckman’s selection model (Heckman, 1979) but do not find evidence of selection on unobservables, which suggests that sample selection bias is not a concern in this study. I then use the model to estimate the distributional impact of the 2008 tariff increase in Turkey. To lend credence to the specification and results, I also conduct out-ofsample tests of the model that show how well it predicts consumption responses to new price changes in 2009.

Overall, I find that a 10 percent increase in electricity price will cause an average household in the bottom income quintile to reduce electricity consumption by 1.8 percent, and an average household in the top income quintile to reduce electricity consumption by 5 percent, _ceteris paribus._ The poor are less flexible in adjusting electricity consumption most likely because they are close to their minimum electricity consumption level and have fewer options for switching to other types of fuel.

Not surprisingly, lower income households experienced a greater welfare loss as a percentage of income. The welfare loss from the 2008 price increases, approximated by consumer surplus change, is about 164 Turkish Lira (TL) for a bottom income quintile household, or 2.16 percent of household disposable income, compared with 330 TL, or 0.75 percent of income for the top quintile. However, because expenditures on electricity have been a moderate component of household budget in Turkey - they represented 3.5 and 4 percent of the household’s disposable income in 2008 and 2009 - the impact of the 2008 tariff increase on consumer welfare has been limited. These results are robust to alternative model specifications, modeling techniques and sample data.

The rest of the paper is structured as follows. Section II provides the background of Turkish electricity price reform and a literature review. Section III discusses the empirical strategy. Section IV describes data and descriptive analysis. Section V presents the estimated price elasticities, consumer welfare change, and out-of-sample validation tests. Section VI concludes.

# **II. Background and Literature Review**

# _Turkish Electricity Market Reform_

Since 2001, the Turkish Government has embarked on a comprehensive electricity reform program. The reform aims to establish a competitive electricity market so as to increase private investment, improve the supply- and demand-side efficiency and ultimately strengthen Turkey’s 3