rafmacalaba/datause-encoder-data huggingface dataset:
We further expand our database with recent rounds of PASEC (2014), SACMEQ (2013) as well as PIRLS (2016). The size of our database has a few ramifications. Since the methodology we use to link assessments hinges on overlap in countries which take both an RSAT and an ISAT, the larger the database, the more overlap, and thus all scores are more robust. In addition, we include the largest number of developing countries to date. These countries have the most potential to benefit from educational progress. Thus, a more expansive database enables the inclusion of developing countries as well as enhances the robustness of the methodology used to include them, making each update significant.

In addition to the size of the database, this is the most current database with data as recent as 2017. Given a series of recent global initiatives which focus on education quality highlighted in the 2018 World Development Report, there is significant demand for current, credible and globally comparable measures of learning. This database provides the largest, most comparable, current learning data. Moreover, in this paper we provide a foundation for systematic and continual updates on a periodic basis going forward, enabling tracking of learning outcomes progress and longitudinal analysis.

We introduce a series of methodological improvements. First, we include single years, rather than 5-year intervals. Second, we use all available data to create conversion factors between assessments. Third, we construct a fixed conversion factor for data since 2000. This enables us to deduce changes in scores over time as a function of learning outcomes progress rather than changing conversion factors. Fourth, we provide measures of uncertainty in the form of plausible bounds for our estimates. Fifth, all data are derived directly from test score data. In contrast to black-box and complex imputation, this enables methodological transparency. Moreover, it produces a clear policy lever: when countries perform better on RSATs and ISATs they can be certain the HLO will improve.

# 2. Data

Each assessment included in prior databases is documented in depth in Altinok, Angrist and Patrinos (2018). The ISATs include old IEA assessments, PIRLS, TIMSS and PISA. The RSATs include SACMEQ, PASEC, LLECE and MLA. In this paper, we elaborate mostly on the inclusion of new data, including EGRA, as well as recent PASEC, SACMEQ and PIRLS data. Annex Table 1.0 shows all data included in this database.

# 2.1. EGRA

The Early Grade Reading Assessment (EGRA) is a basic literacy assessment. The assessment is conducted most often in grades 2-4. Since 2006, EGRA has been conducted in over 65 countries.

EGRA is focused on early grade proficiency on basic literacy skills. The EGRA testing framework is based on reading assessments such as DIBELS (Dynamic Measurement Group, 2008), CTOPP (Wagner et al. 1999), and a series of other assessments that measure literacy skills. The EGRA toolkit outlines the EGRA tool in depth (RTI 3
provides descriptive statistics of labor characteristics by the category of city size: 1) population up to 15,000; 2) between 15,001 and 40,000; 3) between 40,001 and 150,000; 4) between 150,000 and 500,000; and 5) Addis Ababa. Compared to smaller towns, labor force participation rates are lower in larger towns, while their unemployment rates are higher. Based on the UEUS/LFS data, around 75 percent of working-age urban populations are active labor forces, 83 percent of whom are employed and 17 percent are unemployed. The labor force participation rates are 72 percent in Addis Ababa (64.1 percent for female and 81 percent for male), while the rates in small towns (town size less than 15,000) are 80 percent. By contrast, unemployment rates tend to be higher in larger towns, and it is 20.6 percent for women and 12.6 percent for men in Addis Ababa.<sup>10</sup> In terms of employment types, wage employment (private and public) is common in larger towns, whereas the majority of workers are self-employed in smaller towns. The data show that 48 percent of urban workers are engaged in self-employment, 21 percent are private employees, and 23 percent are public employees. Unskilled workers tend to engage in self-employment jobs (commonly in the sectors of agriculture, manufacturing, and construction).

A third of urban workers are engaged in informal jobs. The UEUS/LFS allows to define informal jobs in two ways: whether the job has a book account or whether the job has a license. According to the former definition, 23 percent of private employees and 90 percent of self-employed workers are in the informal sector. Based on the license definition, only 9 percent of wage workers in the private sectors are informal, while nearly 60 percent of self-employed workers are informal.

The average hours worked per week is only 35 hours in small towns, in contrast to 50 hours in Addis Ababa. A sizable portion of urban workers are potentially under- employed. Underemployment is defined as workers who work fewer than 30 hours per week and are willing to work more in the analysis. About 13 percent of workers are underemployed. Underemployment is prevalent among less-educated workers and/or those engaged in self-employment jobs. In small towns, nearly 30 percent of workers can be classified as underemployed.

Nominal wages are higher among public employees (relative to private employees) and/or in larger towns. The UEUS/LFS reports the amount of income and frequency of payments for those who are private and public employees. Importantly, there is no income information available in

> 10In the UEUS/LFS, an individual is classified as unemployed if she or he is not engaged in a productive activity and is available for work in the next month if the opportunity arises.

11
# **8.  Summary and Conclusions**

This paper has identified high-priority sites for methane emissions reduction and introduced a global monitoring approach that employs data from the ESA’s Sentinel-5P (S5P) satellite platform. Drawing on geolocated S5P observations that typically number over 200,000 per day, we compute monthly mean methane concentration anomalies for each cell of a 5-km global grid. Our data span the period from January 2019 to November 2022. To highlight local accountability, our monitoring approach estimates trends in S5P atmospheric concentration data for districtlevel (GADM2) administrative units and scale-equivalent areas within national exclusive economic zones (EEZs) where offshore oil and gas extraction occur. These global geographic units (GGUs) comprise 51,976 areas. The spatial distribution of methane emissions is so highly skewed that 2.2% of these areas account for 50% of anthropogenic methane emissions in “bottom-up” estimates provided the Emissions Database for Global Atmospheric Research (EDGAR). We focus on these 1,131 high-priority GGUs, drawing on EDGAR to identify their dominant sources of emissions from seven sectors (Agricultural Soils, Livestock, Gas, Oil, Coal, Landfills and Wastewater).

We illustrate our monitoring approach with three GGU case studies: landfills in Spain (Madrid); irrigated rice production in India (Karnal district, Haryana state); and oil production in Iraq (Al Amarah district, Maysan governorate). Each case employs S5P data from the GGU subregion that includes all EDGAR emissions points in the point set that accounts for 50% of global emissions. These points are often spatially clustered, and our approach ensures that our trend models focus on areas where emissions are most highly concentrated.

In each case, we estimate two trend models by fixed effects. The first model estimates the monthly trend for the period January 2019 – November 2022, while the second focuses on recent history by estimating the difference between mean concentrations in 2022 and the previous three years.

We extend this approach to 775 high-priority GGUs that have sufficient S5P observations for robust trend estimation. For each GGU, we provide a prototype monitoring report that incorporates the direction and significance of parameter estimates for the two models, along with the dominant source sector from EDGAR. We aggregate the econometric results into four categories: No Trend (statistically-insignificant results for both models); Mixed (statisticallyinsignificant results for one model, or significant but opposite-signed results for the two models); Decreasing (significant negative results for both models); and Increasing (significant positive results for both).

Overall, we find that about 30% of the GGUs have No Trend or Mixed results, about 13% are Decreasing and 57% are Increasing. These results hold for both “raw” estimates and estimates weighted by EDGAR-estimated emissions. We find striking differences across sectors, with Increasing emissions dominant in five sectors and Decreasing emissions in two (Gas and Oil). For
the aim of assessing the impact of Argentina’s 2001 crisis. Fieldwork for the RHS was conducted at the end of 2002 and the beginning of 2003.<sup>11</sup> The survey was collected in the midst of a crisis and data therefore reflect the specific and peculiar situation in the rural population at that time. Hence, we do not make predictions or extrapolate the future or the past from the series. Due to the small size of provincial samples, disaggregated information from the sample should be analyzed cautiously.

# **Methodology**

Income–poverty measures are designed to count the poor and to diagnose the extent and distribution of poverty. Income–poverty measures proposed by Foster, Geer, and Thorbecke (1984) are used throughout the paper. These are the headcount rate (P0), poverty gap (P1), and squared poverty gap (P2) measures. The former measures the magnitude of poverty and the latter two poverty measures assess both poverty magnitude and intensity.

The headcount rate is defined as the proportion of people below the poverty line. One concern about applying the P0 measure is that each individual below the poverty line is weighted equally and therefore the principle of transfers is violated. A limitation of the measure is illustrated by the fact that it would be possible to reduce the P0 measure of poverty by transferring money from the very poor to lift some richer poor out of poverty, thereby increasing social welfare according to the measure. P0 takes no account of the degree of poverty and is unaltered by policies that lead to the poor becoming even poorer.

One measure of poverty that takes this latter point into account (at least in weak form) is the poverty gap measure (P1). P1 is the product of incidence and average distance between the incomes of the poor and the poverty line. It may be interpreted as a per capita measure of the total economic shortfall relative to population. P1 distinguishes the poor from the not so poor and corresponds to the average distance to the poverty line of the poor. One problem with the poverty gap, as an indicator of welfare, is that poverty will increase by transfers of money from the extreme poor to the less poor (who become nonpoor), and from the poor to the nonpoor. Furthermore, transfers among the poor have no effect on the poverty gap measure.

The P2 measure of poverty is sensitive to the distribution among the poor because more weight is given to the poorest below the poverty line. P2 corresponds to the squared distance of income of the poor to the poverty line. Thus, moving from P0 toward P2 gives more weight to the poorest in the population.

> 11 In Mendoza information was gathered between December 5 and 30, in Santiago del Estero between December 7 and 19, in Chaco between December 27 and January 15, and in Santa Fe between December 7 and 30.

14
# **Figure 2b. Ineffective risk financing instruments**

<!-- Start of picture text --> Expected<br>return<br>Efficient frontier without<br>risk financing instruments<br>B<br>Efficient frontier with risk<br>A financing instruments<br>Hedging costs<br>C<br>Return variability<br>Source: Authors.<br><!-- End of picture text -->

## **Box 3.  Risk financing instruments as market-enhancing solutions**

Detrended data on cocoa yields from 1961 to 2002 in a South American country are used for the sake of illustration. The hedging costs represent the costs of self-retaining risks and/or the insurance costs, beyond the fair premium.

The catastrophe coverage is assumed to be sold at twice its fair price. The catastrophe insurance costs are thus equal to 0.72% of the average yield for the production of cocoa and 0.29% for banana. The non-catastrophe coverage faces insurance costs equal to 50% of the fair premium (i.e., 50% loading factor), for the sake of simplicity.

If the crop yield risk is self-retained, the costs of holding the minimum capital requirements (reserve load) represent 15% of the minimum capital (see Box 2).

The cost of self-retention (reserve load) is higher than the cost of non-catastrophe insurance in the case of the production of cocoa, whatever the average operating profit margin. The costs of hedging against cocoa yield risks are thus equal to the sum of the catastrophe insurance cost and the non-catastrophe insurance costs. On the contrary, self-retaining yield risk is less expensive than transferring the non-catastrophic component of this risk in the case of banana production. The costs of hedging against banana yield risks are thus equal to the sum of the catastrophe insurance cost and the reserve load.

10
The role of women in developing country governance has received much attention in recent years. Today, 57 countries have quotas for women specified in their constitution or in national legislation; this figure increases to 94 when including countries with voluntary political party quotas.<sup>1</sup> Gender equality is also one of the eight Millennium Development Goals, and the World Bank has put gender empowerment high on their agenda, stating that gender inequality hinders development and poverty reduction (World Bank 2001).

One key motivation for the reservation of government seats for women is to address the exceptionally poor status of women in developing countries. Among the poorest individuals in the developing world, women are particularly worse off in terms of health, income, job opportunities, rights, and political representation. Political reservations are one way to attempt to improve the status of women in society and ensure their participation in government. Women’s well-being also affects others, especially their children. Political reservations thus also aim to lead to more pro-women policies, which may include pro-child, pro-family, and pro-health policies. Understanding the performance of women who hold reserved seats, therefore, becomes of utmost importance. To this end, we investigate women’s political reservations in village governments in India’s Tamil Nadu state and whether the gender of political leaders influences public goods provision.

The impact of women’s reservation policies has been hotly contested in both policy and academic arenas, largely because of a lack of counterfactuals. India’s large-scale political reservations provide an excellent setting for evaluating the impact of reservation policies; the

> 1 From the database at http://www.quotaproject.org.  These statistics include countries with quotas at national or sub-national levels.  Political party quotas are only used in places with a proportional representation system, as opposed to first-past-the-post.  These figures do not account for non-compliance issues.

2
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**Table 7. Selected profitability and performance ratios of the banking sector (%)**

|Ratio|2002|2003|2004|2005|2006|2007|2008|2009|2010|
|---|---|---|---|---|---|---|---|---|---|
|ROA|0.5|0.5|1.4|1.6|1.8|1.7|1.6|0.9|1.05|
|ROE|5.8|5.8|17.2|20.6|22.4|22.5|21.1|11.3|12.7|
|CAR|13.8|13.7|15.5|14.5|13.2|12.1|11.2|13.3|13.8|
|NIM|3.4|3.2|3.3|3.3|3.3|3.1|3.2|2.6|2.8|
|NPL|21.1|21.2|14.9|11.0|7.4|4.1|3.5|7.5|8.5|

ROA – Return of Assets; ROE – Return of Equity; CAR – Capital Adequacy Ratio (minimum requirement is 8 percent and this level is binding); NIM – Net Interest Margin, NPL – Nonperforming Loans ratio. Source: NBP, KNF.

**Table 8. Structure of large credit exposures by section of economic activity classification**

|Section|2004|2005|2006|2007|2008|2009|2010|
|---|---|---|---|---|---|---|---|
|A|2.0|1.7|1.6|1.7|1.5|1.5|2.7|
|B|0.8|0.5|0.6|0.7|0.8|0.9|1.0|
|C|36.2|36.5|38.4|34.7|31.8|31.2|31.1|
|F|7.0|7.5|8.1|9.7|10.8|10.9|16.9|
|Market<br>services|54.1|53.8|51.3|53.2|55.1|55.4|49.1|
|**Total**|**100.0**|**100.0**|**100.0**|**100.0**|**100.0**|**100.0**|**100.0**|

Notes: A – Agriculture, B – Mining, C – Manufacturing, F – Construction; Market services – Retail trade and repairs; transportation and storage; hotels and restaurants; information and communication; real estate activities; professional activities. Data for 2008–2010 are presented at the end of September, other years—at the end of December. Large exposures: for a bank in the form of a joint stock company, state-run bank, and a nonassociated cooperative bank, an exposure toward one enterprise in excess of PLN 500,000; for an associated cooperative bank an exposure toward one client in excess of PLN 50,000. Source: NBP.

**Table 9. Number of banks failing to meet supervisory liquidity standards**

||03.2009|06.2009|09.2009|12.2009|03.2010|06.2010|09.2010|12.2010|
|---|---|---|---|---|---|---|---|---|
|Commercial<br>banks|4|1|3|1|0|0|0|0|
|Cooperative<br>banks|5|11|10|10|7|10|6|4|
|Branches of<br>credit<br>institution|1|1|1|0|0|0|0|1|
|Share in<br>banking<br>sector’s<br>asset(%)|4.7|0.3|0.4|0.3|0.2|0.2|0.2|0.1|

Source: KNF.

29
7 term unemployment has been substantially reduced through the application of the Restart Programme. Other OECD countries have, consequently, adopted similar measures to fight long-term unemployment (e.g. Germany and Ireland). In transition economies large programmes similar to Restart do not exist yet, even though longterm unemployment is clearly one of the major problems. Whether the more conventional forms of ALMP (b, c and d) which are applied in transition economies are, on the other hand, effective might be doubtful in the light of the above presented stylised facts.

# **III Active Labour Market Policies in the Czech Republic, Hungary and Poland**

Tables 1 and 2 show public expenditure and participant inflows in labour market programmes for Hungary and the Czech Republic. In these tables the classification of the OECD is used when describing labour market policy measures. The Polish data will be presented and discussed separately below. When inspecting the tables one should keep in mind the differences in the unemployment rates for Hungary and the Czech Republic for the years 1992 and 1993. (12.3 and 12.1 for Hungary versus 2.9 and 3.1 for the Czech Republic).

Looking at the expenditure patterns of OECD countries (cf. OECD (1993), Table 2.B.1 and Chart 2.1) two points stick out. With the exception of Sweden all countries with a substantial unemployment rate spend more on passive than active policies. With this being the case expenditures are higher the higher the unemployment rate. It, therefore, comes as no surprise that Hungary with its relatively high unemployment rate has expenditures on labour market policies which are (in terms of percent of GDP) comparable to many OECD countries. Among those countries which in 1992 had similar unemployment rates as Hungary only Belgium spent decisively more on labour market programmes in general and on ALMPs. Britain, on the other hand, having an average unemployment rate of 10% in that year spent somewhat less on total labour market policies and a lot less on ALMPs than Hungary (!), while Canada spent exactly the same percentage of GDP. This international comparison certainly leads to the conclusion that as far as expenditures on the labour market are concerned Hungary does not perform worse than most OECD countries.

The Czech Republic, on the other hand , has for the reported period spent less on labour market policies than virtually all OECD countries if our GDP measure is
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instrument becomes where ∆ _<u>pBSTATEdr</u>_<sup>_,j_istheproductionshareweightedmeanchangeinthelog-priceofcrop</sup> _j_ across states _excluding those in BSTATEd_<sup>_r._</sup> Here, again, the logic is that the price instrument should not directly, or, in this case, even indirectly, be driven by local shocks that also determine _differential_ wage growth across districts (and states).

The choice of _r_ , the radius of “influence” of local wage shocks on prices in bordering states may seem arbitrary. Since, on average, districts are 57 kilometers apart (centroid-tocentroid), at _r_ = 100 kilometers, the sets _BSTATEd_<sup>_r_and</sup><sup>_STATEd_differonlyfordistricts</sup> relatively close to their state’s border with another Indian state. Indeed, _IV_ 3<sup>100</sup> _d_ = _IV_ 2 _d_ for half of the 462 districts in my estimation sample (those in the deep interior of states or along the coasts or international borders). By contrast, _IV_ 3<sup>200</sup> _d_ = _IV_ 2 _d_ for fewer than 10 percent of sample districts. This suggests a strategy of comparing alternative estimates of _γ_ from equation (8) based on _IV_ 3<sup>_r_</sup> _d_<sup>with successively higher values of</sup><sup>_r_to determine at what</sup> point increasing the radius of influence ceases to matter.

Finally, it should be evident from equation (11) that differences in price trends _across_ crops is key to identification; if the ∆ _<u>pBSTATEdr</u>_<sup>_,j_are the same for all</sup><sup>_j_, then</sup><sup>_IV_3</sup><sup>_r_</sup> _d_<sup>collapses to</sup> <u>∆</u> _<u>pBSTATEdr</u>_<sup>, essentially a constant.Given the inclusion of the constant term</sup><sup>_c_,</sup><sup>_γ_is virtually</sup> nonidentified in this scenario. Equally as important is variation in crop composition across districts (see Table 2). If _ad,j_ = _aj_ for all _d_ , then even if the ∆ _<u>pBSTATEdr</u>_<sup>_,j_arenotall</sup> equal, _IV_ 3<sup>_r_</sup> _d_<sup>againessentiallycollapsestoaconstant.</sup> Figure 1 illustrates the considerable variation in<sup>�</sup> _j_<sup>_sd,j_∆</sup><sup>_pSTATE_</sup> _d_<sup>_,j_acrossthe462sampledistricts(</sup><sup>_CV_=0</sup><sup>_._130),theobviously</sup> more limited variation in _IV_ 3<sup>200</sup> _d_ ( _CV_ = 0 _._ 073), and the regression of the former variable on the latter–i.e., my first-stage. The adjusted _R_<sup>2</sup> _s_ of the first-stage regressions using _IV_ 1 _d_ , _IV_ 2 _d_ , _IV_ 3<sup>100</sup> _d_<sup>,and</sup><sup>_IV_3200</sup> _d_ are, respectively, 0 _._ 788, 0 _._ 121, 0 _._ 103, and 0 _._ 091 _._ 16