rafmacalaba/datause-encoder-data huggingface dataset:
Table A5: Attrition between baseline and follow-up

|_Dep. Var. =1 if interviewed at follow-up_|(1)|(2)|(3)|(4)|(5)|
|---|---|---|---|---|---|
|Treated|-0.004|-0.004|-0.004|-0.004|-0.000|
||(0.005)|(0.005)|(0.005)|(0.005)|(0.007)|
|HIV knowledge|-0.000<br>|||||
||(0.000)|||||
|HIV attitudes||-0.001||||
|||(0.001)||||
|HIV testing|||0.001|||
||||(0.001)|||
|Attitudes towards risky sexual behavior||||-0.000||
|||||(0.001)||
|Risky sexual behavior (for sexually active)|||||-0.001|
||||||(0.001)|
|Female|-0.022***|-0.022***|-0.023***|-0.022***|-0.029***|
||(0.005)|(0.005)|(0.005)|(0.005)|(0.007)|
|Age|0.001|0.001|0.001|0.001|0.002|
||(0.001)|(0.001)|(0.001)|(0.001)|(0.002)|
|Currently attending school|-0.013**|-0.013**|-0.013**|-0.013**|-0.016*|
||(0.006)|(0.006)|(0.006)|(0.006)|(0.008)|
|Years of education|0.001|0.001|0.000|0.000|0.002|
||(0.003)|(0.003)|(0.003)|(0.003)|(0.003)|
|English Spoken|0.002|0.002|0.002|0.002|0.009|
||(0.009)|(0.009)|(0.009)|(0.009)|(0.010)|
|Single|-0.003|-0.003|-0.003|-0.003|0.006|
||(0.005)|(0.005)|(0.005)|(0.005)|(0.009)|
|Does not live with the family|-0.014|-0.014|-0.014|-0.014|-0.021**|
||(0.009)|(0.009)|(0.009)|(0.009)|(0.011)|
|Household size|0.000|0.000|0.000|0.000|0.001|
||(0.001)|(0.001)|(0.001)|(0.001)|(0.002)|
|Wealth index|0.008|0.007|0.007|0.007|0.010|
||(0.005)|(0.005)|(0.005)|(0.005)|(0.006)|
|Home owner|-0.004|-0.004|-0.004|-0.004|-0.015*|
||(0.007)|(0.007)|(0.007)|(0.007)|(0.008)|
|Father’s education _>_ secondary|0.010**|0.009**|0.009**|0.009**|0.009|
||(0.005)|(0.005)|(0.005)|(0.005)|(0.007)|
|Mother’s education _>_ secondary|0.005|0.005|0.004|0.004|0.004|
||(0.004)|(0.004)|(0.004)|(0.004)|(0.007)|
|Muslim|0.012*|0.012**|0.012**|0.012**|0.016**|
||(0.006)|(0.006)|(0.006)|(0.006)|(0.008)|
|Yoruba|0.018|0.018|0.018|0.018|0.006|
||(0.013)|(0.013)|(0.013)|(0.013)|(0.014)|
|Constant|0.948***|0.949***|0.963***|0.956***|0.914***|
||(0.041)|(0.042)|(0.043)|(0.043)|(0.049)|
||5,166|5,166|5,166|5,166|3,246|
|R-squared|0.094|0.094|0.094|0.093|0.080|
|Controls|Yes|Yes|Yes|Yes|Yes|
|City FE|Yes|Yes|Yes|Yes|Yes|
|Mean Dep. Var. (Control)|0.966|0.966|0.966|0.966|0.966|

Notes. Standard errors in parentheses adjusted for clustering at the screening centre level (80 clusters). ***, ** and * denote significance at the 1, 5 and 10 percent level, respectively. The dependent variable is an indicator for whether the individual has been interviewed at follow-up.

xii
Schools were randomly assigned to either (i) a control group that received the “standard” AGE program, consisting of the information intervention with a USD 500-700 grant, or (ii) a treatment group that received the same information intervention but with double (USD 1000-1400) the standard grant amount.<sup>5</sup> The design of the first experiment allows us to estimate the average treatment effect of doubling the grant for parent associations with the parental involvement program. Baseline data for the first experiment were conducted in 2007, with follow-up data collection in 2008, 2009 and 2010.

In practice, the additional grant money in treatment schools is modest. Parents in the 250 public schools in experiment 1 reported spending an average of 9 USD per year per child on school materials, prior to the intervention. With an average of 80 students in these schools, the “double grant” amounts to an additional 7.5 USD per year per student, which covers approximately 83% of the out-of-pocket cost that is typically spent by parents.

The second experiment consists of 180 public schools that had never participated in AGE. Schools were randomly assigned to either (i) no intervention or (ii) the information intervention. The design of the second experiment enables us to estimate the average treatment effect of the information intervention of the parental empowerment program. The duration of the second experiment was only one year, as baseline data were collected in 2009, with follow-up data collection in 2010.

# **3 Data and Descriptive Statistics**

Data for this study come from three sources: the school census data (Estadistica 911), the national standardized exam (ENLACE), and detailed self-reported surveys by students, parents, and teachers in grades 3-5 of the schools participating in the two experiments. The school census data and standardized exam scores were obtained through the National Council for Education Development (CONAFE). The survey data are publicly available on the World Bank Microdata Library.<sup>6</sup> At the school-level, we merge the school census data with parent surveys, which were completed by the head of the parent association. At the student-level, we merge the national standardized exam with

> 5The doubling of the grant was made possible through various donors including: Cinépolis, Deutsche Bank, Fundación Lazos, Fundación Televisa, Gillette Hall, JP Morgan Foundation, Panamerican Development Foundation and Western Union Foundation.

> 6Mejia and Filus (2018) have used the survey data from the double grant experiment to explore which variables predict improvement in student test scores.

7
_Ways Out of Povertv - Diffusing Best Practices and Creating CapabiliIes_ _10_

# **B. Impediments and Traps**

**Types of impediments and traps.** If the provision of capital does not drive growth, if finance, capital goods and know-how are not very hard to obtain, why is it difficult for some countries to grow? One set of arguments has to do with a variety of "traps" or lasting impediments that prevent countries from growing rapidly. They may suffer from being small or landlocked or plagued by inclement climates.<sup>2 6</sup> They may suffer from ethnic strife or civil war.<sup>27</sup> They may have policy and institutional rigidities induced by vested interests.<sup>28</sup> They may suffer from cultural biases or a lack of trust, a critical part of social capital.<sup>29</sup>

**Escapees from traps and impediments.** Whatever the trap, some country has been able to escape from it. All countries that are developed today have over long periods of time emerged from "hopeless" situations. Who would have thought at the time of the thirtyyears war (1618 to 1648) that Germany would ever be an economic powerhouse?

Today many countries have escaped from their trap fairly fast by historical standards.

- Adverse climate conditions can be overcome. Tropical Singapore is a rich country, and Malaysia and Thailand have also performed well as did Venezuela over most of the 20th century until the curse of oil set in during the late 1970s. At the other end of the climate spectrum Scandinavian countries boomed during the 20th century including th~~~~~~~~~~~~~~~3isolated, inhospitable Iceland.<sup>3 0</sup>

- Remote location need not prevent development either. Landlocked, tiny Botswana has consistently been a star performer in recent decades. Landlocked Uganda emerged very rapidly from the days of civil war and dictatorship and experienced a strong growth episode in the 1990s. The tiny island of Mauritius became one of Africa's best performing economies.

- Recovery is possible from civil strife. Civil war ended in El Salvador in 1992. Six years later per capita income had increased by 17 percent. Its human development index was up 8 percent, all without increasing the debt burden. Indeed, El Salvador became the third country in Latin America to receive an investment grade bond rating in 1998 - before Mexico.

- Countries that languish for many decades can turn around. Ireland, which suffered decades of emigration and economic stagnation, overtook the United Kingdom in per capita income in the 1990s, driven by productivity growth from foreign investment.

- Even deeply entrenched dysfunctional institutions may be overcome. Several' former communist countries-constrained by a formidable set of growth

> 26 Gallup et al., 1998.

> 27 Collier, 2000; Collier and Gunning, 1999.

> 28 Acemoglu and Robinson, 2000; Havrylyshyn and Odling-Smee, 2000; Hellman, 1998.

> 29 Collier, 1998; Fukuyama, 2000.

> 30 There was hardly a free-standing house in Iceland a hundred years ago and people lived half underground in grass-covered huts.
**Table A3: Investments by decade of first cash flow.** Realized investments are those with zero holding valuation as of June 30, 2019. Duration is the average years between first and final cash flow or positive valuation.

|Vintage<br>Investments|Realized|Duration|
|---|---|---|
|years<br>inv|estments||
|Panel A) Direct investments|||
|1961-69<br>80|100.0%|17.43|
|1970-79<br>124|100.0%|14.00|
|1980-89<br>231|98.7%|11.54|
|1990-90<br>516|97.1%|8.83|
|2000-09<br>501|82.6%|7.40|
|2010-19<br>577|33.1%|4.53|
|ALL<br>2,029|75.8%|8.22|
|Panel B) Fund investments|||
|1978-89<br>21|100.0 %|11.00|
|1990-99<br>104|98.1 %|11.77|
|2000-09<br>129|45.7 %|11.21|
|2010-19<br>226|7.1 %|4.56|
|ALL<br>480|41.3 %|8.19|

48
8 assesses 15-year-old students in grade seven or higher every three years.<sup>7</sup> Four of the five top places in the 2010 report in both reading and mathematics went to Asian countries: China (Shanghai and Hong Kong SAR, China), S Korea, and Singapore. By contrast, the US ranked 11<sup>th</sup> on reading and 26<sup>th</sup> on mathematics. Discussions of these results, such as OECD (2010), typically focus simply on scores, without factoring in the amounts that countries spend on education. It is perfectly possible in principle that the countries achieving the best outcomes are those that spend the most, and that those who do less well spend relatively little. The more interesting—but rarely asked—question from a policy perspective is how countries vary in their success at translating resources into learning outcomes.<sup>8</sup> We explore this issue using data on knowledge and skills in mathematics, reading and science from the PISA exercise, and secondary education spending data from the World Bank‟s World Development Indicators (WDI). We have an unbalanced panel comprising 118 sets of test scores and secondary education expenditure per pupil for 49 countries for some or all the years 2000, 2003 and 2006.<sup>9</sup> Because information about expenditure per student is missing in the WDI for some countries in some years, 25 of the 118 observations are interpolated or extrapolated. Although in each assessment of PISA, one of the three areas (science, reading and mathematics) is selected as the major domain and is given greater emphasis, while the two minor domains are assessed less thoroughly, differences in test scores across countries and over time are comparable because the tests are linked assessments and the scores are scaled.<sup>10</sup>

# **International health systems—OECD**

It is well known that countries vary considerably in their per capita spending on health care, with the US being among the largest spenders per capita. Inevitably, there has been and continues to be a lot of debate on the question of whether the high spenders (especially the US) achieve sufficiently better results to warrant the extra spending (cf. e.g. Anderson and Frogner 2008), and more generally on how to measure health sector results in international comparisons

> 7 See http://www.pisa.oecd.org/ for further details of the PISA program. For studies examining the cross-country differences in PISA, see Dobert, Klieme, and Sroka (2004), Ammermueller (2007), and Fuchs and Woessmann (2007).

> 8 Afonso and St Aubyn (2005) is an exception. They use DEA to analyze efficiency in education spending in OECD countries using the PISA data.

> 9 We have not included data for 2009 because most countries do not have expenditure data available in the WDI database. The secondary education expenditure per student is generated by multiplying purchasing power parity GDP per capita (2005 constant value) with expenditure per student in secondary education as a percentage of GDP per capita. 10 See PISA 2003 Technical Report (Adams 2005) for details.
45 Figure 9: Relative Log Income of Women Compared to Men After the COVID-19 Shock, and Decomposition by Schooling and Employment Shocks.

<!-- Start of picture text --> HIC<br>3<br>2<br>1<br>0<br>-1<br>-2 Overall<br>Schooling<br>-3<br>0 10 20 30 40 50 60 70 80<br>MIC<br>3<br>2<br>1<br>0<br>-1<br>-2 Overall<br>Schooling<br>-3<br>0 10 20 30 40 50 60 70 80<br>LIC<br>3<br>2<br>1<br>0<br>-1<br>-2 Overall<br>Schooling<br>-3<br>0 10 20 30 40 50 60 70 80<br>Year<br>Relative logGDP, %<br>Relative logGDP, %<br>Relative logGDP, %<br><!-- End of picture text -->
# **Tables**

## **Table 1. Program Characteristics by Estimation Sample**

||Learning<br>Sample|Graduation<br>Sample|Labor<br>Market<br>Sample|Labor Market<br>Sample: SENA|Labor Market<br>Sample: Non-<br>SENA|
|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|
|_Panel A. Program Characteristics_||||||
|Three-year program|0.73|0.69|0.74|0.85|0.71|
|Distance program|0.14|0.13|0.09|0.00|0.11|
|Program age (years)|3.88|4.15|3.76|0.89|4.43|
|Annual tuition (US$)|2,386.00|2,329.82|1,858.78|0.00|2,283.57|
|_Panel B. Institution Characteristics_||||||
|T&T|0.41|0.39|0.34|-|0.41|
|Technological school|0.35|0.35|0.28|-|0.34|
|<br>University|0.24|0.26|0.20|-|0.24|
|Institution size (in thousands of|3.15|2.43|34.00|168.60|3.07|
|students)||||||
|<br>Private HEI|0.66|0.65|0.52|-|0.64|
|Public HEI|0.34|0.35|0.30|-|0.36|
|SENA|0.00|0.00|0.19|1.00|0.00|
|_Panel C. Program and Institution Pra_|_ctices_|||||
|Selective institution|0.54|0.52|0.45|0.00|0.55|
|Institution field specialization|0.48|0.47|0.45|0.35|0.47|
|(index)||||||
|High-quality accreditation|0.11|0.08|0.08|0.00|0.10|
|_Panel D. Local Market Conditions_||||||
|Institution market power (index)|0.21|0.22|0.24|0.39|0.21|
|Market concentration (HHI index)|0.17|0.18|0.17|0.20|0.16|
|<br>Population (in thousands)|3,723.18|3,466.35|3,543.69|2,663.23|3,745.99|
|City size (%)||||||
|Large city|0.58|0.57|0.62|0.75|0.59|
|Medium city|0.25|0.24|0.21|0.04|0.25|
|Small city|0.17|0.18|0.17|0.20|0.16|
|_Panel E. Field of Specialization_||||||
|Agronomy and Veterinary|0.03|0.03|0.04|0.06|0.03|
|<br>Arts|0.09|0.10|0.09|0.09|0.09|
|Health|0.06|0.06|0.06|0.06|0.05|
|Social Sciences|0.05|0.06|0.05|0.03|0.05|
|Economics and Business|0.42|0.40|0.38|0.28|0.41|
|Engineering and Architecture|0.33|0.33|0.37|0.43|0.35|
|Math and Natural Sciences|0.01|0.01|0.02|0.05|0.01|
|Observations|481|621|851|159|692|

_Source_ : Authors’ calculations using administrative data. See Appendix 1 for the definition of the variables. _Notes_ : This table shows program-level statistics for each estimation sample (Columns [1] to [3]) and for the labor market subsamples of SENA programs (Column [4]) and non-SENA programs (Column [5]). All variables are dummies except when the unit of measure is indicated in parentheses. Indices range between 0 and 1. Differences in number of programs are due to the definition of estimation samples and data availability. For example, in the learning sample we exclude programs without students taking the SABER PRO exam in 2011-2. Moreover, the labor market sample is 38
Because we want to test whether the liberalizers have experienced a reduction in urban concentration, we need measures of liberalization and urban concentration. We measure liberalization by the change in tariffs between pre-liberalization and postliberalization periods (i.e., by<sup>ln1 </sup><sup>_tj_</sup> , above). For urban concentration, the previous literature has typically used the population in the largest city (and its share of the urban population). This measure tends to ignore useful information about the dynamics of urban concentration at lower levels of the distribution. Here, we consider an urban concentration measure at the city level to capture the interactions among urban centers. Our (change in) urban concentration measure employs the differences in growth rates of a given number of cities from that of the largest city (i.e.,<sup>ln</sup><sup>_pi_,</sup><sup>_j_</sup> , above). For example, for the US, in our benchmark case, we consider the differences between the rates of growth of populations of all other cities from that of New York City. This is similar to the measure recently used by Redding and Sturm (2008), who study the effects of the loss of trading partners triggered by the division of Germany on urban concentration by focusing on differences in the rates of population growth of border and internal cities.<sup>15</sup> We use the following data for our empirical analysis.<sup>16</sup> _Tariffs._ The country-specific tariff data are from ET, who have compiled data on 15Redding and Sturm (2008) find that loss of trade leads to more urban concentration and a slower rate of population growth for border cities. This finding is similar to ours in that we find that the creation of trade leads to less urban concentration.

16The list of countries is as follows: Algeria, Argentina, Australia, Austria, Bangladesh, Belgium, Bolivia, Brazil, Canada, Chile, China, Colombia, Cote d'Ivoire, Denmark, Ecuador, Finland, France, Germany, Ghana, Hong Kong, Iceland, India, Indonesia, Israel, Italy, Japan, Korea, Malaysia, Mexico, Morocco, Nepal, Netherlands, New Zealand, Pakistan, Paraguay, Peru, Philippines, Spain, Sri Lanka, Sweden, Taiwan, Thailand, Trinidad and Tobago, Turkey, United Kingdom, United States, Uruguay, Venezuela.
# **Cost-benefit estimates**

We use a subsample of 97,050 workers aged 15-65 years who had positive earnings from dependent employment. Annual earnings were converted to $US using the 2017 exchange rate in June, which was equal to 3.52 TL. Table 4 gives their mean earnings by level of education.

|**Table 4: Mean Ann**|**ual Earnings by Educ**|**ational Level**|
|---|---|---|
|Educational level|Mean annual<br>earnings ($US)|Number of<br>observations|
|None|3,714|1,528|
|Primary|5,278|24,850|
|Secondary|5,974|38,567|
|Higher|10,756|32,105|
|All|7342|97,050|

Source: 2017 HLFS We observe a sharp rise in mean annual earnings for those with higher education. This is consistent with the very high demand for university education as well as the restriction on the university admissions by the highly competitive university entrance examination. There is very high demand for university education for several reasons. First, in addition to high income, the university degree confers a prestigious position in Turkish society. Secondly, men with university education serve in the army with a higher rank. These factors are behind the high demand for higher education. The age-earnings profiles by level of education show a very sharp earnings premium for higher education graduates.

**Figure 1: Age-earnings profiles by level of education**

<!-- Start of picture text --> 18000<br>16000<br>14000<br>12000<br>10000<br>8000<br>6000<br>4000<br>2000<br>0<br>15 17 19 21 23 25 27 29 31 33 35 37 39 41 43 45 47 49 51 53 55 57 59 61 63 65<br>None Primary Secondary Higher<br><!-- End of picture text --> The discounting formula presented above was applied to the earnings profiles to estimate the returns assuming a 6-6-4 duration of primary, secondary and higher education, and two years of 9
a plausible range for each of these parameters.

# **Some empirical values of** _τ_

Beegle et al. (2012) consider the cost implications for various types of questionnaire in Tanzania. For example, Beegle et al. (2012, Table 10) report that four households can be interviewed by an interviewer per day. Assuming that each interviewer interviews for eight hours a day, it takes 8 _·_ 60 _/_ 4 = 120 minutes to complete a survey with recall consumption model.

The same study also reports that it takes on average 41 minutes to complete a short consumption module with 17 most important items (Beegle et al., 2012, Figure 1). Therefore, the proportion of time that is spent to collect consumption data is 41 _/_ 120 _≈_ 0 _._ 34. If we assume that the data entry cost is roughly proportionate to the time needed for data collection and that there is no other cost items, we have _τ ≈_ 1 _−_ 0 _._ 34 = 0 _._ 66 in this case.

This estimate may be close to the upper bound of _τ_ . The average time to complete a recall consumption module is longer when a long questionnaire (with 58 items) is used and when a longer recollection period is adopted, even though the difference is modest. When the personal diary format is used, the cost of collecting consumption data can be much higher because it involves frequent visits to the household, As reported in Beegle et al. (2012, Table 10), the number of interviews that can be performed per day may be as low as 0.35. Assuming that the time required to complete the non-consumption component remains the same, the value of _τ_ when personal diary format is used is _τ ≈_ 0 _._ 66 _·_ 0 _._ 35 _/_ 4 = 0 _._ 06.

It should be noted that Beegle et al. (2012) only consider a relatively short questionnaire with a complete consumption module and not a large multi-topic survey. Therefore, when we consider a typical Living Standard Measurement Survey, the value of _τ_ may be higher as the weights of non-consumption modules become more important. On the other hand, if we are only concerned with getting an accurate estimate of poverty, _τ_ could be lower because we typically need a small fraction of non-consumption modules to predict consumption.

In Bangladesh, Ahmed et al. (2014) adopt the assumption that five enumerators have to stay for two weeks to complete the survey in one primary sampling unit (PSU) to estimate the cost of conducting Household Income Expenditure Survey in Bangladesh. They further assume that 38
**<u>Table 7: Trade gap, tariff rates and differentiated products. Specification in first differences.</u>**

||(1)|(2)|(3)|(4)|(5)|(6)|
|---|---|---|---|---|---|---|
||||_Δ Trade_<br>|_Gap_<br>|||
|Δ Tariff|0.001|-0.005|-0.004|0.002|-0.004|-0.002|
||(0.002)|(0.005)|(0.004)|(0.002)|(0.005)|(0.004)|
|Δ Tariff*Conservative dummy||0.012<br>(0.005)**|||0.010<br>(0.005)*||
|Δ Tariff*Liberal dummy|||0.010<br>(0.005)**|||0.008<br>(0.005)*|
|Agriculture|Included|Included|Included|Excluded|Excluded|Excluded|
|Observations|102989|102989|102989|99883|99883|99883|
|Adjusted R-squared|0.0003|0.0003|0.0003|0.0003|0.0003|0.0003|

All regressions include country fixed effects and a constant. Robust standard errors are listed in parentheses. * significant at 10%; ** significant at 5%; *** significant at 1%.

# **5. Channels of tariff evasion**

In the light of the above findings, it is natural to ask how tariff evasion takes place. There are three potential channels through which importers may attempt to avoid or minimize the tariff payment: (i) misrepresenting the price of imported products; (ii) undercounting physical quantities of imported products, and (iii) misclassification of high tariff products as a lower tariff variety. In this section, we explore each of these evasion methods.

## **5.1 Misrepresenting the price of imported products**

To examine the prevalence of misrepresenting the price of imports, we calculate the difference between the unit value of exports reported by Germany and the unit value of imports recorded by the importing country: As before, the gap is calculated at the level of 6-digit HS product for each importing country and each year.

17