rafmacalaba/datause-encoder-data huggingface dataset:
Another way of illustrating the heterogeneity of changes in test scores along different parts of the distribution of learning outcomes is through a growth incidence curve (GIC). Originally developed for incidence analysis in the poverty and inequality literature, the GIC in the case of learning outcomes computes changes in average test scores by percentiles of the test scores distribution (for methodological details see Ravallion and Chen, 2003). Therefore, a GIC with a positive slope would indicate a regressive change in test scores, that is, students with initial lower scores improve less (or decrease more) than those with higher ones. The GIC summarizing the changes in reading test scores for the period 2000-12 is presented in Figure 2.<sup>6</sup> Between 2000 and 2006, average test scores declined along the entire distribution of learning outcomes. However, the decline in reading test scores was substantially larger among students in the bottom 20 percent of the distribution of scores in 2006 vis-à-vis their peers in 2000. The decline in learning outcomes is lower as one moves to better-performing percentiles. The reverse is true for the period 2006-12. Except for students around the 95th percentile of the test score distribution, everybody did better in 2012 than in 2006. Not only did test scores increase during this period, but those that gained the most were the most disadvantaged students in the bottom part of the test score distribution.

_Figure 2: Growth Incidence Curve for the Changes in PISA (Reading Test Scores)_ <!-- Start of picture text --> Change between 2006<br>and 2012<br>Change between 2000 and<br>2006<br>0 20 40 60 80 100<br>Percentiles of PISA Distribution<br>Source: authors' own computation with data from PISA<br>40<br>20<br>0<br>-20<br>Change in Reading Test Scores<br>-40<br>-60<br><!-- End of picture text -->

# **IV. Methodology**

Since the pioneering work of Coleman et al. (1966), researchers have used several methodological approaches to estimate what has been called the education production function. The aim of this approach is to link learning outcomes (outputs) with observable characteristics 6 Since PISA results are normalized, the y-axis in Figure 2 depicts the absolute change in PISA test scores as opposed to the percentage change as would be the case in a conventional GIC.

10
# Figure 13a:

# Delhi

# Figure 13b:

<!-- Start of picture text --> Delhi Delhi<br>Predicted Revenue Growth Revenue Drivers<br>vs. Actual Revenue Growth and Stamp Tax<br>100.00 100.00<br>80.00<br>80.00<br>60.00<br>60.00 40.00<br>prop val grow th<br>40.00 20.00<br>taxrevgrow 0.00 real gdp grow th<br>20.00 stamprategrow1 -20.00<br>0.00 predictedrevgrowth -40.00<br>-20.00 -60.00<br>1992-931994-951996-971998-992000-01 1992-93 1994-95 1996-97 1998-99 2000-01<br><!-- End of picture text --> Maharashtra Figure 14a: <!-- Start of picture text --> Maharashtra<br>Predicted Revenue Growth<br>vs. Actual Revenue Growth and Stamp Tax<br>100<br>80<br>60<br>40 taxrevgrow<br>stamprategrow1<br>20<br>predictedrevgrowth<br>0<br>-20<br>-40<br>1992-931994-951996-971998-992000-01<br>Growth (%)<br><!-- End of picture text --> Figure 14b: <!-- Start of picture text --> Maharashtra<br>All growth except Predicted Revenues<br>100<br>80<br>60<br>40 prop val grow th<br>20 real gdp grow th<br>0<br>-20<br>-40<br>1992-93 1994-95 1996-97 1998-99 2000-01<br>Growth (%)<br><!-- End of picture text --> 79
_Engineering Graduates_ : To the degree possible, calculations are done with actual graduates of engineering colleges and universities within the country. Clearly, many engineers even in the US acquired valuable training on the ground, or may have had partial degrees from some type of technical program. However, such skills are difficult to capture with any degree of commonality across geographical units. As a consistent metric across countries, we take the number of degrees awarded.<sup>10</sup> Though most countries also employed foreign engineers, we are interested in indigenous technical capacity and the institutional structure to generate it, so we focus on domestically trained engineers. Some nationals studied abroad, however the historical evidence from Argentina, Chile, Colombia and Mexico suggests the numbers are small and difficult to document. Since the working life of an engineer is roughly 40 years, we begin accumulating the stock in 1860, discounting the stock in each period by .983 as the rate of death/attrition in each year. In some cases, we have a long series of graduation records which make this procedure straightforward. In Bolivia, Denmark, Mexico, New York, Per ́u, Spain, Sweden, Venezuela, or Mexico, the flow of graduated engineers is available for the 1860-1900 period. We refer to Ahlstr ̈om (1982)’s estimates for Germany and France, the frontier countries, which are allegedly generated in a virtually identical manner, yet we do not have access to his data and these are not our calculations. In general, the larger and more advanced the country, the greater the number and types of institutions and hence the more difficult guaranteeing comparability and this also influenced our decision to limit our geographical focus.<sup>11</sup> In other cases, for instance, Argentina, Brazil, Chile, Colombia, Ecuador, and the US as a whole, the information is less complete and we bring other sources of data to bear to fill in the gaps in the series.

> _Membership in Engineering Societies_ : Data on membership in Engineering societies

> 10Fox & Guagnini (1993) tabulate for several advanced countries the number of students enrolled. However, we find often that the difference between students enrolled and eventual graduates can differ greatly so enrollment rates are not as reliable.

> 11In fact, he calculates the stock for France, Germany as well as Sweden for which we do have the raw underlying data and hence can verify that we are doing the calculations in a virtually identical fashion.

6
score data are matched at the district level for use in the enrollment model below.

# **II Enrollment and price changes since FPE**

In this section, we document the effect of FPE on school enrollment and prices (e.g. school fees). Because the FPE reform was instituted simultaneously nationwide, we base our inferences about the effect of FPE on a comparison of the trajectories of enrollment and prices across two dimensions: public versus private primary schools, and primary versus secondary schools.

The public versus private school comparison allows us to distinguish the effects of an exogenous policy change on the supply side (fee abolition) from trends in the demand for education which would arguably push enrollment in public and private schooling in the same direction.

The comparison of trends in primary enrollment to secondary enrollment provides a second, completely independent control group that allows us to address concerns about causal inference that may not be answered using data on public and private primary schools alone. For instance, one could argue that the increased demand for (higher quality) private schooling in the wake of FPE was the result of rising incomes and a general increase in demand for quality education. If so, one would expect to observe similar patterns at both the primary and secondary level. Alternatively, one could argue that the emergence of private schooling under FPE was a consequence of the end of the Moi regime and the advent of competitive multi-party democracy which contributed (by hypothesis) to a more permissive environment for private schools to compete with state schools. Once again, one would expect this new _perestroika_ to have had similar effects at both the primary and secondary level.

## **Enrollment patterns**

Did the abolition of fees lead to an increase in primary public enrollment? Table 2 shows net enrollment rates for public and private primary schools before and after FPE. Contrary to expectations, FPE was not associated with an increase in enrollment in public primary schools. In fact, net enrollment in public primary schools was essentially unchanged over the ten-year period 10
_35_

# TABLE 5

# DISTRIBUTION OF PUBLIC UT!LITY SERVICES IN TWO COUNTRIES: MALAYSIA, COLOMBIA

|_<br>|__________<br>|Electricity||
|---|---|---|---|
|Author, Year of Data, Country|Urban|Rural|Country|
|Meerman, 1974, Malaysab|-'|_||
|Selowsky, 1974, Colombiab|_|_||
|Maddock and Castano, 1986, Medellin,<br>Colombia <sup>0</sup>|+<br>|NA<br>I_<br>I|NA|
||H|ousehold Piped Wat|er|
|Meerman, 1974, Malaysiad|_<br>__|||
|Selowsky, 1974, Colombiad|_|_||
|<br>|Pub|lk or Private Sewer|age|
|Meerman, 1974, Malaysiae|_|||
|Selowsky, 1974, Colombia||_||
|'<br>=pro-poor,- =pro-icbh 0= neutal<br>bPerceage of households with connect<br>'Compenated vaiation for users of ele<br>dPercentage of households with water su<br>"Perountage of households with sewerag|; ?=no clear pat<br>ions by income qu<br>ctricity.<br>pply by income q<br>e comnections by i|tern; NA=not avail<br>itile.<br>uindle.<br>ncome quintle.|able.|

SOURCES: Meeuman (1979), pp. 182, 202, 213; Selowsky (1979), Tables SA-28, SA-30, SA32; and Maddock and Castano (nd.).
country is fairly constant across time.<sup>17</sup> The importing countries with MRL regulations with the widest product coverage are the US, Canada, and Australia. However, importing countries such as Japan, Canada, and the EU which have default MRLs are the countries with the widest coverage of products once default MRLs are accounted for. The same is true when we analyze the breadth of MRL regulations regarding the number of pesticides in Appendix Table A.2. In total, the importing countries in our sample regulated 863 pesticides in 2006 whereas by 2012 this number had increased to 992. Japan, the Republic of Korea, Switzerland, and the EU have the most extensive coverage of specific pesticides.

There are three challenges when working with MRL data. First, there are two dimensions of restrictiveness that need to be considered: the number of regulations per product and how strict those regulations are. Second, the heterogeneity of pesticides regulated across products and countries makes it difficult to compare how restrictive one country is relative to another. For example, the 16 pesticides regulated for oranges in the Russian Federation in 2011 might not be included among the 102 pesticides regulated for oranges in Brazil. Thus the question arises: how do we compare Russia’s and Brazil’s restrictiveness for oranges? Averaging MRLs across pesticides for each country-product pair would generate a misleading measure. Pesticides differ on their degree of toxicity and thus highly toxic pesticides always tend to have lower MRLs than less toxic pesticides. The group of pesticides a country chooses to regulate for a specific product will determine if the average MRL is high or low without providing any information on the restrictiveness of those measures relative to other countries. Consequently, it is imperative to normalize MRLs at the product-pesticide level to get a true measure of how restrictive a country’s MRL is, relative to how all other countries are regulating that same pesticide for a given product. Third, it is not clear how to interpret the missing values that originate from a pesticide being regulated in one country but not the other. We cannot replace these missing values with zeros, as is commonly done with missing trade values, because an MRL set to zero is equivalent to banning that pesticide entirely. We choose to fill in these missing values with the maximum MRL (the least restrictive measure) across all importing countries for each productpesticide pair.

> 17 Only 35 products have pesticide limits regulated by all countries in the sample, among which we find: potatoes, tomatoes, peas, beans, apples, oranges, wheat, maize, sorghum, and ground nuts.

9
the evidence as supportive of the model.

Figure D.1: Self-Employment in Model and Data <!-- Start of picture text --> Fraction of Entrepreneurs Chile Fraction of Entrepreneurs China: Model vs Data<br>Model<br>Data<br>Model<br>Data Self−Empl<br>1985 1990 1995 2000 2005 2010 1995 2000 2005 2010<br>time Year<br>1.2 2.0<br>1.1 1.8<br>1.0<br>1.6<br>0.9<br>1.4<br>0.8<br>Relative to 1991 Relative to 1998<br>1.2<br>0.7<br>0.6 1.0<br>0.5 0.8<br><!-- End of picture text --> Note: Chile's data on Self-Employment is drawn from the International Labor Organization's ILOstat database. Both the model and the data are normalized to be equal to one in 1991, which is the rst data point. Self-Employment in China is drawn from China's Statistical Yearbooks of 2018, and is dened as the ratio of Self-Employed individuals in urban areas over the total number Urban Employed Persons. The data is measured relative to its value in 1998, and the model is measured relative to the initial steady state, which is calibrated to the distortions measured for 1998.

To complement the above, we turn now to discussing the implications of adopting the number of rms as the empirical counterpart for rms in the model. We can see in gure D.2 that a similar validation for the model's mechanisms emerges under this metric, albeit with dierent quantitative t. In particular, the model falls short of capturing the spike in the number of rms in China between 2003 and 2005, while it over-predicts the decline in the number of rms in the early years of Chile's acceleration, and under predicts it towards the end.

# E Decomposition of TFPR into Capital and Output Distortions

The paper adopts _TFPR_ as the summary of idiosyncratic distortions in the data, and uses the properties of the distribution of _TFPR_ to discipline the distribution of revenue taxes in the model. However, _TFPR_ is dened by a combination of output distortions and capital distortions, as labeled in Hsieh and Klenow (2009). To assess the extent to which each ingredient is contributing to the overall dynamics of _TFPR_ , we provide a decomposition in the gures that follow.

As a quick reminder, _TFPR_ is proportional to capital and output distortions in 53
# Table 7 continued.

|Fruits/Vegetables|AEZ6<br>|AEZ7<br>|AEZ8<br>|AEZ9<br>|AEZ10<br>|AEZ11<br>|
|---|---|---|---|---|---|---|
|and Maize|0.2922|0.0775|0.3461|0.1678|0.1296|0.2344|
|T (<sup>◦</sup>C)|0.0221|0.0073|0.0365|0.0138|0.0166|0.0258|
|P (mm/mo)|0.0029|0.0003|0.0012|0.0012|0.0004|0.0017|
|Fruits/Vegetables|0.0849|0.0607|0.1601|0.0520|0.2424|0.1048|
|T (<sup>◦</sup>C)|0.0087|0.0098|0.0223|0.0095|0.0196|0.0237|
|P (mm/mo)|0.0003|-0.0001|0.0005|0.0005|-0.0015|0.0006|
|Ground nuts and<br>millet|0.0010|0.2901|0.0013|0.1121|0.0418|0.0371|
|T (<sup>◦</sup>C)|0.0011|-0.0091|0.0016|-0.0193|-0.0022|-0.0044|
|P (mm/mo)|0.0000|-0.0026|-0.0003|-0.0050|-0.0004|-0.0028|
|Maize|0.3777|0.2849|0.3479|0.3070|0.0910|0.3874|
|T (<sup>◦</sup>C)|-0.0143|-0.0198|-0.0517|-0.0259|-0.0153|-0.0478|
|P (mm/mo)|-0.0034|-0.0027|-0.0023|-0.0026|-0.0009|-0.0020|
|Maize and ground|||||||
|nuts|0.0311|0.1310|0.1164|0.2040|0.0013|0.1715|
|T (<sup>◦</sup>C)|0.0036|0.0144|-0.0030|0.0320|-0.0004|0.0148|
|P (mm/mo)|0.0002|0.0022|0.0014|0.0049|0.0000|0.0028|
|Millet|0.0146|0.0894|0.0028|0.0625|0.0877|0.0167|
|T (<sup>◦</sup>C)|0.0033|0.0042|0.0005|-0.0025|0.0002|-0.0025|
|P (mm/mo)|0.0008|0.0029|-0.0004|0.0006|0.0025|0.0000|
|Millet and|||||||
|Sorghum|0.0947|0.0357|0.0185|0.0546|0.0354|0.0398|
|T (<sup>◦</sup>C)|0.0047|-0.0001|-0.0050|0.0006|-0.0083|-0.0074|
|P (mm/mo)|0.0003|0.0000|0.0000|0.0004|-0.0003|-0.0001|
|Wheat|0.1038|0.0306|0.0070|0.0399|0.3708|0.0083|
|T (<sup>◦</sup>C)|-0.0293|-0.0067|-0.0013|-0.0081|-0.0102|-0.0021|
|P (mm/mo)|-0.0011|0.0000|-0.0002|0.0000|0.0002|-0.0002|

27
16 empirical studies, a typical solution is to use the shortest air distance between laborexporting and labor–receiving countries as a proxy variable.<sup>25</sup> This is the solution that will be adopted here: the costs of migrating will be measured by the air distance from the labor-exporting country to one of three labor-receiving regions (United States, OECD (Europe) or the Arab Gulf).

In addition to the three explanatory variables listed in equation (2) – population, income and migration costs – recent empirical work has suggested that other economic, demographic and political variables may also influence the decision to migrate.<sup>26</sup> From an economic standpoint, it is useful to enter both an income variable and its square in the equation to see if the propensity to migrate rises and then declines with level of country income (development). Some studies have also hypothesized that other economic variables – such as higher rates of income inequality, inflation and unemployment – tend to encourage migration from labor-exporting countries.<sup>27</sup> With respect to demographic factors, human capital theory argues that more educated people are more likely to migrate because they enjoy higher wage-earning opportunities in labor-receiving countries.<sup>28</sup> Finally, policy variables – such as the level of government stability and a country’s credit worthiness – may have an effect on migration.<sup>29</sup> The reasoning here is that people will be more likely to migrate from countries that are politically unstable or that have poor economic management as manifested by low international credit ratings.

Combining all of these variables together, the empirical version of the migration model to be estimated can be written as: Log _M_ ij = λ0 + λ1 log( _d_ ij) + _λ2_ log( _g_ i) + λ3 log( _yi) + λ4_ log( _yi)_<sup>_2_</sup> _+ λ5_ log( _rfi)_
and technology. Having physical and financial capital as well as access to technology is not enough: there must be the skilled manpower to make use of these resources. For example, the successes of South Korea and Taiwan in developing their resnective economies and the failure of Thailand, until recently, to develop hers, may be partially attributed to the relatively lower level of educational development in Thailand in the 1960's and 1970's. By the late 1980's, however, Thailand has finally caught up with the level of educational development achieved by South Korea and Taiwan in the early 1960's and is now well on her way to becoming the fifth "Newly Industrialized Economy' (NIE).

Despite the abundance of microeconomic level empirical evidence on the positive effect of , .. 1;aon on productivity, at the aggregate, macroeconomic level, the effect of education on real output has not been well documented.' In particular, there are very few published studies of aggregate production functions that succeed in establishing a statistically significant direct link between real GDP and the educational attainment of the labor force. One reason why the effect of education is frequently found to be statistically insignificant in aggregate production function studies for individual countries is multicollinearity of the data on the inputs: capital stock, labor force (less so for employment), and educational attainment of the labor force. (The land input generally does not vary much.) The capital stock and labor force are usually heavily trended. Similarly, the average level of educational attainment of the working-age population typically shows slow and smooth increases over time. The consequence is that it is difficult if not impossible to identify the effects of capital and labor, let alone those of education and technical progress, separately, using time-series data from a single country. Moreover, the effects of scale are often confounded with those of technical change, especially if all the inputs have been increasing over time. Thus, additional rastrictive assumptions (such as constant returns to scale in the physical inputs) frequently have to be made in order to obtain meaningful estimates.

'Aggregate production function studies for the agricultural sector have found statistically significant positive effects of education on output. See, for example, Lau and Yotopoulos (1989).

3
Do Bilateral Labor Agreements Increase Migration? Global Evidence from 1960 to 2020<sup>∗</sup> Samik Adhikari, Narcisse Cha’ngom, Heidi Kaila, and Maheshwor Shrestha _Keywords_ : Bilateral Labor Agreements, BLAs, migration, development _JEL_ : F22, J08, O15

> ∗This paper serves as a background paper for the Africa Regional Report on Migration (Abdel Jelil et al., 2024). The authors would like to thank Andrew Dabalen for guidance and Quy-Toan Do, David McKenzie, Mauro Testaverde, and Soonhwa Yi for helpful discussions and feedback.
of personal data. Developing states might also engage regionally and bilaterally with other jurisdictions to effectively distribute the costs of enforcement through systems of mutual recognition.

As the above review demonstrates, the cost of data privacy compliance can be quite high, so high that companies avoid certain jurisdictions entirely or simply ignore the laws. The GDPR can be so difficult to comply with that more than half of the EU privacy professionals surveyed in the IAPP/EY study said that their organizations were not “fully” or even “very” compliant.<sup>234</sup> At the same time, the cost of not having data privacy protection can be quite high as well, and results in consumers and other counterparties avoiding beneficial transactions because of the risks that the information they share will be misused. Concerns over costs of compliance or costs of enforcement might be ameliorated if stronger data protection laws make it easier for local businesses to participate in global value chains.<sup>235</sup> The digital economy depends on a proper legal framework that protects privacy.

The study also reveals the need for further inquiry. Private companies are reluctant to publish information about the costs of compliance, which might be perceived as either too little (by consumers) or too much (by shareholders). Might particular data privacy obligations such as the right to data access, to redress, to reasonable cybersecurity, for example, offer particularly cost-effective privacy? Governments should review their own enforcement efforts, including whether the resources they deploy are sufficient to regulate the growing digital economy. How effective are different types of government enforcement efforts (such as audits, sanctions, or guidance regarding best practices)? Governments could gather more data from companies on their compliance expenditures. Understanding costs is a critical step towards achieving privacy.

> 234 Hughes & Saverice-Rohan, _supra_ note 74.

> 235 The World Bank Grp., WORLD DEVELOPMENT REPORT 2020: TRADING FOR DEVELOPMENT IN THE AGE OF GLOBAL VALUE CHAINS (Caroline Freund, Aaditya Mattoo & Pol Antras eds., World Bank Publications 2020).

42