rafmacalaba/datause-encoder-data huggingface dataset:
# **<u>Table 14 Average teacher attendance and activity</u>**

||**UP**|**Attendance**<br>**MP**|<br>**KA**|<br>**UP**|**Activity**<br>**MP**|**KA**|
|---|---|---|---|---|---|---|
|All|0.64|0.67|.88|0.25|0.3|.68|
|Regularteachers|0.61|0.64|.88|0.19|0.25|.68|
|Contract|0.74|0.72|-|0.36|0.36|-|
|Former contract|-|0.71|-|-|0.34|-|

# **<u>Table 15 Average teacher characteristics</u>**

|Percent unless indicated<br>otherwise|<br>Regular|**UP**<br>Contract|Regular<br>|**MP**<br>Contract<br>|Former<br> contract|<br>**KA**<br>All|
|---|---|---|---|---|---|---|
|Age (years)|46|27|45|31|34|.39|
|Non-SC/ST|.83|.76|.75|.65|.65|.76|
|Male|.71|.45|.93|.72|.70|.59|
|Highest education- whether<br>class12|.49|.32|.58|.42|.38|.72|
|whethercollege|.24|.45|.25|.39|.33|.26|
|whetherGraduate|.28|.24|.17|.18|.29|.03|
|Pre service training|.93|.08|.56|.16|.22|.93|
|Days of in service training in<br>last year|5|6|10|11|12|4|
|Years ofteaching experience|17|2|21|5|9|12|
|Distance commute to school<br>(in km)|10|1|9|11|8|11|
|Whether teach multigrades|.76|.89|.86|.90|.86|.91|

29
the “new” exports respectively. Also, notice that the value of the top 50 exports is 40 and 32 times higher than the value of “new” exports for both the low and high productivity categories respectively.

# **Figure 13**

<!-- Start of picture text --> Weighted Average of Exports Value by Productivity Level, 2004<br>Top 50 and "New" Exports<br>2,000,000<br>1,636,803.45<br>1,600,000<br>1,200,000<br>800,000 649,839.31<br>400,000<br>20,568.62 41,265.96<br>0<br>High Low High Low<br>Productivity Productivity Productivity Productivity<br>"NEW" EXPORTS TOP 50 EXPORTS<br>ppp 2000 $<br><!-- End of picture text --> In terms of product income, in Figure 14 shows that the low productivity goods have a product income 49 smaller than the high productivity goods in the case of the top 50 products. For the “new” exports, low productivity goods have a product income 44 smaller than the high productivity goods. Interestingly, when comparing these two rankings, there is not much of a difference within the productivity groups. That is, the ratio of the top 50 exports to the “new” exports is 0.87 for the low productivity goods and 0.95 for the high productivity goods.

# **Figure 14**

<!-- Start of picture text --> Weighted Average of Product Income by Productivity Level, 2004<br>Top 50 and "New" Exports<br>25,000<br>19,925.59<br>20,000 18,918.81<br>15,000<br>11,098.95<br>9,602.61<br>10,000<br>5,000<br>0<br>High Low High Low<br>Productivity Productivity Productivity Productivity<br>"NEW" EXPORTS TOP 50 EXPORTS<br>ppp 2000 $<br><!-- End of picture text --> 19
<!-- Start of picture text --> 300<br>250<br>200<br>150<br>100<br>50<br>0<br>2006 2007 2008 2009 2010 2011 2012<br>Year<br>Benchmark Fixed Entry Costs Fixed Productivity<br>Number of Attacks<br><!-- End of picture text --> Figure 8: Role of learning on the number of attacks Note: The figure plots the total number of attacks in the benchmark model (blue solid line) and two counterfactual scenarios where we shut down private learning (red dash line) and social learning (red solid line).

navy patrols is kept untouched. The results of the counterfactual analysis are presented in Figure 9.

The first order impact of removing private protection is observed in increased success rates, allowing average ransoms to expand in 2011 and 2012 as seen in Figure 9. Without private protection, success rates hovered around 23 percent in 2011 and 2012 as pirates continue to prey on the largest vessels. By contrast, success rates in the benchmark simulations dropped down to 10 percent by 2011, the year when private maritime security contractors were deployed.

With the most valuable vessels left unprotected, the average ransom in 2012 surges to US$4.11 million in the counterfactual, more than doubling the average ransom of the benchmark simulation (US$1.72 million). Expected future profits increase in the absence of private maritime security, indirectly attracting new pirates into the business. Upon observing the absence of armed contractors on board vessels in 2011, pirates expect the same in 2012 as well. As a result, many potential pirates join the business: the total number of attacks skyrockets to 417 by 2012 in the counterfactual, instead of dropping to 31
# Appendix Table A5: Bilateral trade flows, cotton and other plant-based fibres, 2001<sup>a</sup>

# (US$million)

|Importer:<br>Exporter:|<br>United<br>States|Aust-<br>ralia|EU25|High-<br>income<br>East<br>Asia|China|Other<br>East<br>Asia|South<br>Asia|Eastern<br>Europe/<br>Central<br>Asia|Middle<br>East/<br>North<br>Africa|Mexico|Other<br>Latin<br>America|Sub-<br>Saharan<br>Africa|<br>**WORLD**<br>**Exports**|
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
|United States|0|0|109|670|110|559|547|292|23|488|172|1|**3054**|
|Australia|0|0|64|285|28|414|149|0|6|0|3|3|**954**|
|EU25|5|0|422|13|92|12|38|148|29|0|5|2|**779**|
|High-income East Asia|0|0|0|0|1|6|24|0|0|0|0|0|**36**|
|China|0|0|12|39|0|11|1|1|0|0|0|0|**65**|
|Other East Asia|4|0|8|5|9|2|4|0|0|0|0|0|**38**|
|South Asia|3|2|29|22|18|40|84|11|8|0|5|5|**230**|
|E. Europe/Central Asia|14|1|504|88|17|13|87|427|11|0|7|0|**1179**|
|Middle East/N. Africa|13|0|165|25|19|41|121|54|31|0|9|3|**489**|
|Mexico|2|0|5|8|1|3|0|0|0|0|8|0|**29**|
|Other Latin America|8|0|58|13|4|42|75|17|2|4|82|5|**315**|
|Sub-Saharan Africa|0|0|326|89|8|227|95|23|43|4|91|77|**999**|
|**WORLD**|**63**|**4**|**1705**|**1261**|**308**|**1399**|**1249**|**977**|**158**|**497**|**385**|**98**||

a GTAP database Version 6.05, with tariffs updated to 2005 following phase-out of textile and clothing quotas, and assuming a cotton output subsidy in the US of 40 percent in 2001. The inclusion in this table of other plant-based fibres such as flax is important only for a few developing countries such as Bangladesh.

Source: Update of GTAP database Version 6.05 at www.gtap.or
the domestic currency. As a result, a theoretically consistent money-gap model should be able to explain change in both tradable and non-tradable goods prices.<sup>15</sup> A number of studies have applied monetary models to industrial countries. Altmari (2001) is a recent example of such an application. He investigates the performance of a number of monetary models of inflation for the euro area over the period 1998 to 2000. His findings suggest that monetary and credit aggregates contain significant information to forecast inflation in the euro area, particularly at medium-term horizons. Using structural VAR analysis studies by Kasumovich (1996) as well as Fung and Kasumovich (1998) show that a monetary policy shock leads to a persistent money disequlibrium, which is closed as prices adjust over a number of years.<sup>16</sup> Jonsson (1999) and Callen and Chang (1999) are examples of this strand of the empirical literature focusing on emerging market economies. The former study concludes that an increase in the money supply raises domestic prices in South Africa although this effect is somewhat compensated by an increase in domestic interest rates. The latter study considers two models of inflation in India—one based on the monetary approach and the other using the output gap. Their results suggest that monetary aggregates contain the best information about future inflation and that output gap is not a significant explanatory power.

Nevertheless, monetary models should be used with caution since financial innovations can lead to instability in the money-inflation relationship. Furthermore, in the presence of a high short-run interest elasticity of money with respect to interest rates, it may well be the case that the required interest rate increase to bring money back to its

> 15 Moreover, if the exchange rate is partly driven by the evolution of money, the inclusion of both variables would generate estimation problems associated with multicollinearity.

16
Table 3: Number of refugees in 2012 and 2013 in southeastern provinces

||2012|2013|
|---|---|---|
|Adana|0|16,666|
|Kahramanmara ̧s|16,830|28,882|
|Malatya|0|7,205|
|Mardin|0|40,965|
|Osmaniye|7,914|18,046|
|Adıyaman|8,880|10,053|
|Hatay|12,776|85,642|
|Kilis|13,510|63,292|
|Gaziantep|25,512|145,905|
| ̧Sanliurfa|58,558|134,357|

_Source:_ Data from UNHCR.

_Note:_ Data for 2012 only include refugees in camps while 2013 data include refugees in and outside of camps. December values are used for both years.

2012, which is clearly visible also in the sharp drop in the number of new firms.

We have less information about the economic sectors that new firms in host regions are entering. Turkish Statistics (TUIK) reports the number of enterprises by sector in each province but the data are limited to 2013-2014. Similar to the data on sales and profits, TUIK data reports the number of all enterprises including firms and other types of entrepreneurial activity such as sole proprietorships. Sectoral data are therefore aggregated at a higher level than the firm-entry numbers reported by the Chamber of Commerce. Nevertheless, the data set is useful in determining which sectors are the largest in host regions and what the change in sector size was between 2013 and 2014.

# **6 Results**

## **6.1 IV results**

Table 3 presents the results of IV estimates in three panels. Panel A presents our main results from the IV estimations which uses all provinces in Turkey. We find statistically significant and positive effects on all outcomes except for the number of new firms. The number of new firms with foreign capital shares rises by 15% per 10,000 refugees. The share of new firms with foreign capital also rises by approximately one percentage point. For total firm entry, the effects are not statistically significant. Since the number of foreign 15
understand the dynamics of youth unemployment, including where the unemployed come from, how long they spend in that state, how often they enter the state, and where they go upon leaving unemployment. The analysis differentiates between adolescents (15-18 years old) who are at the point of deciding to leave school and enter work and may still be dependent on parents, young adults (19-24 years old) who are in the prime shopping period of Europeans, but are likely to be settling into the Latin American labor force, and prime aged adults (age 25-44), who are used as a counterfactual. We consider the behavior in Brazil, with its moderate unemployment rates, Mexico and its very low unemployment rates, and the high unemployment economy of Argentina.

The main conclusion of the study is that young people’s unemployment in Latin America follows similar patterns to that in Europe. Namely, young people’s unemployment is a reflection of frequent entry to the state, but duration of stay is similar to that of adults. This supports the “shopping around” hypothesis where young people may be trying out different types of jobs in an effort to identify which works best for them.

# **II. Youth Unemployment in Latin America versus Europe**

Youth unemployment rates in Latin America are higher than those in Europe, although the youth to adult unemployment rate is similar. Specifically, unemployment rates of those age 15-24 in Latin America range from 6 to 38 percent (2003 and 2004 data), as opposed to a range of 7 to 26 percent in Europe (Tables 1 and 2).

The unemployment rate alone is not instructive as to whether young people are in a particularly poor state since it does not control for the overall health of the economy. We can use the total unemployment rate as a counterfactual of how easy it should be to get jobs. Deviance from this counterfactual unemployment rate tells us whether young people are in a particularly difficult situation. The youth to adult unemployment rate in Latin America and the Caribbean ranges from 1.5 (Nicaragua) to 2.3 (Chile), suggesting that young people have about twice as much difficulty as adults in entering the labor market (Table 1). A comparison to Europe shows a similar range – 1.2 in Germany, where the large apprenticeship program particularly facilitates school-to-work transitions (Ryan 2001), relative to 2.9 in Italy (Table 2).

public. However, for a discussion of the NEET in Latin America and the Caribbean, and how it compares to youth unemployment rates, see Cunningham (2008).

3
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We test for two forms of strategic manipulation that could explain the mean impacts on school enrollment and exam participation rates. First, program schools may choose to reduce fifth-grade enrollment by limiting promotion to fifth grade or shedding fifth-grade students in order to raise their exam participation rates. We do not find consistent evidence across treatment variants that the program decreases fifth-grade enrollment. Second, program schools may choose to inflate reported enrollment. By constructing pseudo-panel cohorts using grade-specific enrollment information from the yearly ASC surveys, we test whether the program flattens the gradient at which enrollment declines with grade (or, more extremely, inverts it), which would be consistent with enrollment inflation. We do not find evidence suggesting this.

Post-hoc subgroup analysis reveals that the third-year program mean impacts on school enrollment and exam participation rates vary between urban and rural schools. The program mean impact on enrollment in the total sample is mainly due to the urban subsample (which represents 8 percent of the total sample). The program increases enrollment in urban schools in the third year by, on average, 23 percent (the third-year mean impact in rural schools is 2 percent and insignificant). Looking at enrollment separately by grade, we find significant positive mean impacts of similar size in percent terms across first through fourth grade, indicating higher intake as well as higher retention. The program mean impact on exam participation rates in the total sample is fully due to the rural subsample. The program increases exam participation rates in rural schools in the third year by, on average, 3.6 ppts (the third-year mean impact in urban schools is –1.4 ppts and insignificant). Program mean impacts on student exam scores differ between the urban and rural subsamples in the first year but the differences in later years are not significant.

<mark>Given that the program increases exam participation rates, we test whether the absence of significant positive mean impacts on student exam scores is due to negative selection of students into examtaking. We do not find evidence suggesting this. Apart from this test, we are left to speculate on factors that could have blocked impacts.</mark> We conclude the paper with this speculative discussion but, to summarize, <mark>we d</mark> iscount that the absence of positive mean impacts is due to program implementation failures. We also discount that it is due to bonus awards being too low, to the bonus formula being too complex for teachers to comprehend, or (as relevant for the treatment variant) to the group-based nature of the bonuses inducing significant freeriding.

5
From the case studies, we retain a stages approach to ELG industrialization. Export growth first takes place in light manufacturing, followed by successful import substitution (and exports) in heavy industries. In our models, we emphasize the role of externalities as an engine of growth and industrialization during the first stage when light manufacturing is the leading sector. We will consider a single 10-20 year transition period rather than attempt to compare different long-run, steady-state growth paths.

# IV. A Model with an Export Externality

A simple ELG model with an externality linked to exporting is presented in Table 1. To help the transition to the more elaborate model with factor markets and intermediate inputs used in Sections V and VI, we introduce most of the functional forms to be used later. The model starts from de Melo and Robinson (1989).

There is a domestically produced good, D, which is an imperfect substitute in demand with an imported good, M. There is a second domestically produced good, E, which is sold on the export market and is not demanded domestically. The economy can produce combinations of D and E according to a production possibility frontier, or "transformation" function. In equations 1 and 2, the substitution and transformation possibilities are given by CES and CET functions, respectively. Foreign trade takes place at fixed world prices, i.e., we make the small-country assumption (equations 3 and 4). For now, aggregate production, X, is fixed. The balance of trade constraint, equation 10, precludes any free lunch, and equations 6 to 9 specify profit maximization by producers and cost minimization by demanders. Equation 11 is the market-clearing condition.

**-10-**
participation.

The empirical results shown in Table 11 are in line with theoretical predictions. Column (1) suggests that an increase of one standard deviation of the principal component of NTMs is associated, on average, with a decrease of 13% of firms’ probability of participating in exports. Considering the 57% unconditional probability of export participation, the estimated economic impact of NTMs is thus sizable.

Various NTM types influence firms’ probability of export participation. While coefficients of tariffs, quantity controls, TBTs, and SPS are consistently negative and statistically coefficients across all specifications, quantity controls and tariffs undermine firms’ export participation probabilities the most. However, the magnitude of coefficients is low compared to those at the intensive margin. Moreover, our robustness tests in Columns (3)–(5) highlight the relative importance of tariffs and quantity controls in lowering firms’ export participation. In demonstrating consistency with our baseline results, they marshal evidence that reserve causality is not driving our results. Finally, demand conditions at destination positively affect the extensive margin in all specifications. This implies that the likelihood of exporting is larger in bigger destination markets.

# **5.4 Extensive Margin of Trade II**

NTMs may also force Colombian firms to stop exporting to a productdestination market. To explore this hypothesis, we evaluate the roles that tariffs and specific NTM types play for firms’ probability of export exits, the second extensive margin of interest.

We find that, on average, NTMs increase firms’ probability to stop exporting. Column (1) in Table 12 suggests that a one standard deviation increase of the principal component of NTMs is associated, on average, with a increase of 22% in firms’ exit probability. This implies a big economic impact, considering the 55% unconditional probability of stopping exporting.

Our results show that new TBTs in product-destination markets lead to higher exit rates from those markets. Column (2) suggests that the intro26
Policy Research Working Paper 7969

# **Abstract**

The dramatic expansion of mobile phone use in developing countries has given rise to a rich and largely untapped source of information about the characteristics of communities and regions. Call Detail Records (CDRs) obtained from cellular phones provide highly granular real-time data that can be used to assess socio-economic behavior including consumption, mobility, and social patterns. This paper examines the results of a CDR analysis focused on five administrative departments in the south west region of Guatemala, which used mobile phone data to predict observed poverty rates. Its findings indicate that CDR-based research methods have the potential to replicate the poverty estimates obtained from traditional forms of data collection, like household surveys or censuses, at a fraction of the cost. In particular, CDRs were more helpful in predicting urban and total poverty in Guatemala more accurately than rural poverty. Moreover, although the poverty estimates produced by CDR analysis do not perfectly match those generated by surveys and censuses, the results show that more comprehensive data could greatly enhance their predictive power. CDR analysis has especially promising applications in Guatemala and other developing countries, which suffer from high rates of poverty and inequality, and where limited fiscal and budgetary resources complicate the task of data collection and underscore the importance of precisely targeting public expenditures to achieve their maximum antipoverty impact.

This paper is a product of the Macroeconomics and Fiscal Management Global Practice Group. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The authors may be contacted at marcohernandez@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