rafmacalaba/datause-encoder-data huggingface dataset:
<!-- Start of picture text --> 600<br>Vietnam<br>500<br>Brazil<br>400<br>Tanzania<br>India<br>300<br>Nigeria<br>Ghana<br>200<br>4 6 8 10 12 14<br>Expected Years of School<br>FIGURE II<br>Schooling versus Learning<br>Learning<br><!-- End of picture text --> _Notes:_ We compare the average country learning in the latest available year from our database to expected years of schooling.

_Source:_ Expected years of schooling are from the World Bank Human Capital Index based on data compiled by UNESCO; average learning outcomes are from our database. Both measures take the latest data available.

Figure III explores the contrast between changes in schooling and learning over time. We measure schooling using primary school enrollment rates. We compare this to our measure of learning in primary school for the years 2000-2015. We use data this period since it has the most overlap of schooling and learning measures. We restrict our comparison to countries with at least two data points over this time period for both enrollment and learning data in primary school to maximize comparability and minimize bias due to changing country coverage over the time period.

We see a clear trend towards increased schooling, while learning progress is inconsistent and in some cases stagnated. We observe limited learning progress even in regions where enrollments are relatively constant, such as Latin America and the Caribbean (LAC). We explicitly condition average learning on enrollment across countries and over time using multivariate regression and show results in Figure IV. We find learning has stagnated on average, even when conditioned on enrollment and when including country-fixed effects. Together, these data reveal a striking human capital gap: students are increasingly in school but learning progress is inconsistent and limited. This trend has been referred to as ‘the learning crisis’ (UNESCO 2017; World Bank 2018).

8
|**Table 7.**GEE parameter estimates of a fractional Probit forP3(Participation)<sup>+</sup><br>|
|---|
|Variable<br>MENA<br>OECD<br>|
|P2(Provision)<br>−1.45<sup>∗∗</sup><br>−0.27<br>|
|(0.70)<br>(0.39)<br>|
|P1(Protection)<br>1.19<sup>∗∗∗</sup><br>3.10<sup>∗∗∗</sup><br>|
|(0.33)<br>(0.32)<br>|
|rf_rp<br>−0.01<br>−0.97<sup>∗∗∗</sup><br>|
|(0.23)<br>(0.21)<br>|
|rf_iu<br>0.36<br>0.01<br>|
|(0.34)<br>(0.15)<br>|
|rf_hdi<br>1.03<sup>∗∗</sup><br>0.48|
|(0.51)<br>(0.56)|
|Constant<br>−1.65<sup>∗∗∗</sup><br>−1.70<sup>∗∗∗</sup>|
|(0.44)<br>(0.35)|
|Wald statistic<br>97.61<sup>∗∗∗</sup><br>453.68<sup>∗∗∗</sup><br>|
|Obs.<br>172<br>372<br>|
|+Dependent variable:P3; Robust standard errors in parentheses;*:p< 0.10,**:p< 0.05,***:p< 0.01.<br>|
|<br>**Table 8.**QMLE parameter estimates of a fractional Probit forP3(Participation) with IV<sup>+</sup><br>|
|Variable<br>MENA<br>OECD<br>|
|P2(Provision)<br>−2.42<sup>∗∗∗</sup><br>3.06<sup>∗∗∗</sup><br>|
|(0.51)<br>(0.48)<br>|
|P1(Protection)<br>2.64<sup>∗∗∗</sup><br>3.62<sup>∗∗∗</sup><br>|
|(0.27)<br>(0.25)<br>|
|rf_rp<br>−0.64<sup>∗∗∗</sup><br>−0.84<sup>∗∗∗</sup><br>|
|(0.12)<br>(0.08)<br>|
|rf_iu<br>0.64<sup>∗∗∗</sup><br>−0.19<sup>∗∗</sup><br>|
|(0.19)<br>(0.08)<br>|
|rf_hdi<br>−0.93<sup>∗∗∗</sup><br>−1.85<sup>∗∗∗</sup><br>|
|(0.31)<br>(0.31)<br>|
|p�21<br>1.69<sup>∗</sup><br>−4.24<sup>∗∗∗</sup><br>|
|(0.89)<br>(0.47)<br>|
|p�22<br>−2.21<sup>∗∗∗</sup><br>−0.84<sup>∗∗∗</sup>|
|(0.32)<br>(0.28)|
|Constant<br>−0.87<sup>∗∗∗</sup><br>−1.07<sup>∗∗∗</sup>|
|(0.30)<br>(0.14)|
|Wald statistic<br>210.52<sup>∗∗∗</sup><br>3503.65<sup>∗∗∗</sup><br>|
|Obs.<br>122<br>350<br>|
|+Dependent variable:P3; Standard errors in parentheses obtained using 500 bootstrap replications; Instruments|
|<br>for MENA data: Domestic credit to private sector (% of GDP), Exports of goods and services (% of GDP), Imports<br>of goods and services (annual growth rate), FDI (net outflows, % of GDP), and real GDP; Instruments for OECD<br>data: Tax revenues (% of GDP), Military expenditures (% of total government expenditures), and Real GDP per<br>capita;<sup>*</sup>:p< 0.10,<sup>**</sup>:p< 0.05,<sup>***</sup>:p< 0.01.|

26
include in the analysis;<sup>13</sup> (3) prediction of a “normal” return during the event window in the absence of the event; (4) estimation of the abnormal return within the event window, where the abnormal return is defined as the difference between the actual and predicted returns; and (5) testing whether the abnormal return is statistically different from zero. The market model is of interest here to estimate abnormal returns.

The market model assumes a linear relationship between the return of any security to the return of the market portfolio: where _t_ is the time index, _i_ = 1 2,,..., _N_ stands for security, _Rit and Rmt_<sup>are the returns</sup> on security _i_ and the market portfolio respectively during period _t_ , and _eit_ is the error term for security _i_ .

Equation (1) is generally estimated over a period which runs between 120 and 210 days prior to the event up to some days prior to the event. The event window is defined as the period from some days prior to the event to some days after the event. The size of the event window is really an empirical matter. With the estimates of α _i and_ β _i_ from equation (1), one can predict a “normal” return during the days covered by the event window. The prediction error (the difference between the actual return and the predicted

> 13 Firms may be excluded if simultaneous events are occurring within the event window.

13
somewhat from the economist’s usual rational expectations specification, it does not seem highly unrealistic in this case.

The first graph in Figure 7 shows the baseline scenario our prior analysis has assumed in terms of the distribution of the covariant component of the stochastic mortality or asset depreciation shocks faced by dynasties. A discretized approximation to the livestock mortality data from northern Kenya used in the analysis Chantarat et al. (2012), this baseline risk scenario is meant to represent the extent and severity of risk faced by pastoralist households circa the year 2000. While we do not have down-scaled climate change projections for this precise region, the other three graphs in Figure 7 show what we assume happens over a time span of four generations to the distribution of risk faced by these households. As can be seen, while in baseline, we assume that droughts occur only one year in five, at the end of our climate change scenario that probability has increased to one year in three. In addition, we assume that shocks become increasingly severe, with proportionately larger increases in the probability of severe shocks that may destroy as much as 60% of a dynasties’ productive assets.

In results available from the authors, we show that increasing risk of this sort shifts the Micawber Frontier to the northeast and causes a deterioration of the chronic poverty map. Rather than focus on these maps for each policy scenario, Figure 8 traces out the impact of climate change using the consumption-based poverty measures introduced earlier. The first generation results are as in Figure 4 above, climate change kicks in beginning with the second generation (year 26 and beyond). The budget is held fixed for comparison with the baseline scenario<sup>12</sup> . As can be seen, without social protection, both the poverty headcount and poverty gap measures worsen substantially in this stylized system in which risk is a major driver of poverty.

Importantly, under the climate change scenario, in-kind transfers become much less e↵ective than the insurance-based subsidy scheme. Whereas without climate change, the insurance-based scheme modestly outperformed in-kind transfers, under climate change the insurance-based scheme has poverty rates and gaps that are roughly half those of the budget neutral in-kind transfer scheme. Interestingly, the insurance-based scheme holds up quite well to climate change over generations 2 and 3<sup>13</sup> . However, by generation 4, the insurance scheme begins to feel the pressure of climate change and poverty rates begin to climb higher.

> 12An alternative experiment is to keep “performance” in terms of poverty similar and calculate the requisite budget as climate change increases.

> 13Note also that the VSP asset replacement program also begins to perform relatively more favoably following moderate climate change.

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# b. Conceptual Frame

The paper draws on the analytical framework developed in _Inclusion Matters: The Foundation for Shared Prosperity_ (World Bank 2013) and adapts it to the assessment of SCDs.

_Inclusion Matters_ defines social inclusion as: _The process of improving the terms for individuals and groups to take part in society_ Or more specifically as: _The process of improving the ability, opportunity and dignity of people disadvantaged on the basis of their identity to take part in society (World Bank 2013, 50)._ World Bank (2013) links the idea of social inclusion to socially constructed notions of identity. It defines exclusion or inclusion on the basis of affiliation with a group. As referenced in the last section, among the most common group-based identities that can result in social exclusion are gender, race, caste, ethnicity, religion, and disability status. Such exclusion based on group attributes can lead to lower social standing, often accompanied by more visible disadvantages in terms of lower income, human capital endowments, poorer access to employment and services, and low voice in both national and local decision making. The report goes on further to highlight **markets, services and spaces** as domains across which exclusion plays out (Figure 2.1). Just as the dimensions of an individual’s life intersect, so do the three domains, representing both barriers to, and opportunities for, social inclusion. Markets can comprise land, housing, labor, and credit. Services can include inter alia, social protection, information, electricity, transport, education, health, and water. Spaces may be physical as well as symbolic. In the most blatant cases of exclusion, excluded groups may be banned from entering certain physical spaces, such as clubs, educational or religious institutions. But often exclusion can take place covertly and groups may be underrepresented in, or left out entirely from, decision making processes, or face discrimination in social interactions in their daily lives. Finally, World Bank (2013) considers three interrelated channels through which inclusion can be enhanced: **ability, opportunity, and dignity** . All three channels act in tandem to produce the conditions for individuals and groups to take part in society.

11
SUPPLEMENT TABLE IV REVIEW OF STUDENT ACHIEVEMENT TESTS

|Organization|Abbr.|Year|Subject|Countries/ Areas|Grade/Age|
|---|---|---|---|---|---|
|IEA|TIMSS|Every four years since 2003 (latest round is 2015)|M,S|38, 26, 48, 66, 65|4,8|
|UNESCO|LLECE|2006, 2013|M,S,R|13, 16 (only 6 for science)|3,6|
|UNESCO|SACMEQ|2000, 2003, 2007, 2013|M,R|7, 15, 16|6<br>6|
|CONFEMEN|PASEC|2006, 2014|M,R|22 (before 2014), 10|Until 2014: 2,5<br>After 2014: 3, 6|
|IEA|PIRLS|Every five years since 2001 (latest round is 2016)|R|35, 41, 55|4|
|OECD|PISA|Every three years since 2000 (latest round is 2015)|M,S,R|43, 41, 57, 74, 65, 71|Age 15|
|RTI/USAID|EGRA|2007-2017|R|65|2,3,4|

_Notes:_ When denoting subjects, M=math; S=science; and R=reading.

SUPPLEMENT TABLE V DISTRIBUTION OF SOURCE TEST FOR HLO

||DIS<br>Tot|TRIBUTION<br>al|OFSOURCET<br>Prim|EST FORH<br>ary|LO<br>Secon|dary|
|---|---|---|---|---|---|---|
|Test|Frequency|Percent|Frequency|Percent|Frequency|Percent|
|EGRA|72|0.03|72|0.10|0|0.00|
|LLECE|86|0.04|86|0.11|0|0.00|
|PASEC|60|0.03|60|0.08|0|0.00|
|PIRLS|160|0.07|160|0.21|0|0.00|
|PISA|1034|0.48|0|0.00|1034|0.75|
|SACMEQ|78|0.04|78|0.10|0|0.00|
|TIMSS|644|0.30|298|0.40|346|0.25|

_Notes:_ We include country-year-level observations by source test based on the metadata.

# III. ADDITIONAL METHODOLOGICAL PARAMETERS

_Over-time Comparability.—_ ISATs and RSATs have been designed to be comparable since the late 1990s and early 2000s. Thus, the use of these modern assessments enables comparability over time from this time period onwards.

_Time Intervals.—_ While this is one of the largest and most comprehensive comparable learning outcomes databases produced to date, it is still sparse given limited test frequency. In other databases, the data is often disaggregated over 5-year periods. This produces continuously spaced intervals, is designed to reduce noise by averaging results within these intervals, and is comparable to the Barro-Lee approach for years of schooling. In this database, we have moved away from this approach. We now provide the year of test as documented in official reports. This enables greater granularity and precision of the data and enables the users of the database to make trade-offs at their discretion.

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# **3.1.2 Estimating the impacts of mask manufacturing exposure**

We estimate the impacts of exposure to licensed mask manufacturing on availability and prices of domestically manufactured masks and the spread of COVID-19 using an event-study design. For outcome _ydt_ measured in sub-District _d_ in month _t_ , we estimate the following two-way fixed effects specification: _θd_ and _γt_ are a set of sub-District fixed effects and month fixed effects, respectively. _Xd_<sup>_′δt_allowstime-varyingcoefficientsonsub-Districtcharacteristics,tocontrolfor</sup> sub-District characteristics that may be correlated with both mask manufacturing exposure and time trends (Duflo, 2001). Our coefficients of interest are _βt_ , the impact of mask manufacturing exposure in month _t_ . We use March 2020, one month prior to the initial licensing of textile manufacturers to produce multi-layer masks, as the reference month. Coefficients _βt_ should therefore be interpreted as impacts on changes in outcomes relative to March 2020. For inference, we cluster standard errors at the sub-District-level, level at which mask manufacturing exposure varies.

**Choice of supply shifter** We use exposure to licensed mask manufacturing as our source of exogenous variation in mask supply. We do so, rather than mask manufacturing intensity, for two reasons. First, variation in mask manufacturing intensity is unlikely to be exogenous—one potential concern is that sub-Districts which manufacture more masks are likely to have more productive garment manufacturers, and this may be correlated with determinants of demand for masks and factors influencing the spread of COVID-19. Second, licensed mask manufacturing in a given sub-District poorly predicts the exposure of consumers in that sub-District to licensed mask man-

15
respondents who say they were affected “a great deal” or “a fair amount” by the crisis received at least one of the four types of benefits. Differences in the coverage of poor households that were affected by the crisis can be explained in part by differences in the proportion of the population that already received universal benefits (regardless of the crisis) and the policy response. However, because the proportion of poor households among those that report being affected by the crisis varies by country, the subjective coverage of public safety nets (as measured in LiTS II) is not equivalent to objective measures of coverage of the poor (as measured with administrative and household data).

The four type of benefits considered in the LiTS II are generally targeted towards the poor, but provide little insurance against unemployment in non-EU countries. In most countries the poorest third of the population were more likely to have received these benefits than other people. However, targeting benefits at those most affected by the crisis is constrained by the fact that only a small proportion of the population in non-EU member states is covered by unemployment insurance. In some countries, this is because a large portion of the workforce is employed in the informal sector. Only in EU member states did the proportion of households applying for and receiving unemployment benefits in the previous 12 months (among households in which at least one member lost his or her job due to the crisis) exceed 10 percent.

# **_Private safety nets_**

Households mostly sought informal insurance mechanisms when attempting to raise a loan in the crisis. Over 60 percent of households tried to obtain loans from relatives and 48 percent did so from friends. Only 28 percent went to a bank. As expected, poorer households are even more likely to seek loans from relatives or friends, whereas those in the higher-consumption bracket are substantially more likely to borrow from a bank.

The crisis also had a negative impact on informal insurance networks. Thirteen percent of respondents from affected households and 9 percent of all respondents had to stop or reduce help that they were previously giving friends or relatives.

14
Even though these comparisons are comforting for those wishing to use tariffs as a rough indicator of the overall restrictiveness of a trade regime, it still remains that the strongest justification for their use is their widespread availability and the likelihood that error measurements will be less than with other proposed measures.<sup>11</sup> The main weakness in the data set is the absence of a measure of financial openness. Miniane (2004) provides a summary of available indices of financial market integration. It turns out that even for the WYD data set which only covers the 1988-98 period, about 2/3 of the countries in our data set would not have a measure of financial market integration. We have therefore decided not to tackle the issue of financial market integration (using FDI as in e.g. Milanovic (2005), would not be appropriate since it is largely an outcome variable).

# **`3. Trade Liberalization and Inequality: Endowments matter`**

We start exploring the basic HO prediction that trade liberalization should reduce inequality in low-income countries and increase it in high-income countries. Next, we bring in factor endowments which we interact with the tariff variable to isolate the effects of differing endowments on inequality. Throughout this section, the data cover the period 1980-2000 and the Gini coefficient is the inequality measure.

# 3.1 Openness, Income and Inequality

We start with the traditional specification:

> 11 Because tariffs do not take into account NTBS, we also correlated several frequency indices of NTBs with our tariff measure at the HS-6 level using Jon Haveman’s treatment of TRAINS data. Correlations (available upon request) for different tariff ranges and the overall NTB frequency index ranges between 0.20 and 0.30 confirming high tariffs barriers are effectively correlated with high indices of NTBs.

11
**Table 9. Quality** **<u>Change Bias and inflation inequality by population quintile</u>**

|**Pid**|**iil**|**iil Ifli**|**CB**|**Corrected**|**Inflation**|
|---|---|---|---|---|---|
|**ero**|**Qunte**|**Qunte naton**|**Q**|**Inflation**|**Differential**|
|**1988-1996**|**1**|725.47|0.501|721.33|4.12|
|**il**|**2**<br>|726.43<br>|0.523<br>|722.11<br>|3.35<br>|
|**az**|**3**|727.59|0.543|723.09|2.36|
|**Br**|**4**|729.06|0.558|724.43|1.02|
||**5**|732.02|0.541|727.53|-2.07|
||**All**|729.95|0.541|725.45||
|**1997-2003**|**1**<br>|10.53<br>|0.399<br>|10.09<br>|0.30<br>|
|**ia**|**2**|10.50|0.407|10.05|0.34|
|**mb**|**3**|10.61|0.420|10.15|0.24|
|**olo**|**4**|10.76|0.446|10.26|0.13|
|**C**|**5**|11.20|0.482|10.66|-0.27|
||**All**|10.90|0.450|10.39||
|**1984-1989**|**1**|77.58|0.387|76.90|0.24|
||**2**|77.60|0.365|76.95|0.19|
||**3**|77.65|0.395|76.95|0.19|
||**4**|77.73|0.406|77.00|0.13|
||**5**|78.15|0.448|77.35|-0.21|
||**All**|77.88|0.418|77.14||
|**1989-1994**|**1**|15.94|0.385|15.50|0.76|
||**2**|16.17|0.389|15.72|0.54|
||**3**|16.42|0.407|15.95|0.31|
||**4**|16.51|0.425|16.02|0.24|
|**o**|**5**|17.26|0.466|16.71|-0.46|
|**xic**|**All**|16.77|0.435|16.26||
|**1994-1996**<br>**Me**|**1**|35.12|0.354|34.64|-1.38|
||**2**|34.92|0.371|34.42|-1.16|
||**3**|34.47|0.387|33.95|-0.69|
||**4**|34.17|0.404|33.63|-0.37|
||**5**|32.92|0.450|32.32|0.94|
||**All**|33.78|0.414|33.26||
|**1996-2002**|**1**|12.08|0.365|11.67|0.32|
||**2**|12.17|0.379|11.74|0.25|
||**3**|12.30|0.399|11.85|0.14|
||**4**|12.38|0.413|11.92|0.07|
||**5**|12.69|0.442|12.20|-0.21|
||**All**|12.47|0.418|11.99||
|**1995-1999**|**1**|7.79|0.406|7.35|0.41|
||**2**|7.86|0.363|7.47|0.30|
||**3**|7.98|0.376|7.58|0.19|
||**4**|8.13|0.384|7.72|0.05|
||**5**|8.41|0.418|7.96|-0.19|
||**All**|8.16|0.397|7.77||
|**1999-2001**|**1**|3.75|0.383|3.35|0.79|
||**2**|3.89|0.374|3.51|0.64|
|**ru**|**3**|4.16|0.370|3.77|0.37|
|**Pe**|**4**|4.52|0.385|4.12|0.03|
||**5**|5.24|0.422|4.79|-0.65|
||**All**|4.62|0.396|4.14||
|**2001-2003**|**1**|1.28|0.408|0.87|0.32|
||**2**|1.35|0.403|0.94|0.24|
||**3**|1.46|0.409|1.04|0.14|
||**4**|1.58|0.425|1.15|0.04|
||**5**|1.81|0.461|1.34|-0.15|
||**All**|1.60|0.433|1.19||

_Note:_ The table reports the inflation rate by population quintile of the expenditure distribution, the quality change bias (QCB), the inflation rate by quintile once the QCB is taken into account (corrected inflation), and the difference between the corrected inflation rate corresponding to each quintile and the average corrected inflation rate.

25
<!-- Start of picture text --> Generation Transmission Distribution Consumers<br>NPC NPC :<br>IPPs kad Industrial<br>pee Distribution<br>+) — Utilities JN Commercial<br>NPC Kes 2<br>Gencos G:-0oun 7)<br>an vy<br>Residential<br>SEE<br>DU's kKe4 rs Others<br>IPPs Sn<br>Legend: ——— Energygy Transaction<br>——P Power Flow<br><!-- End of picture text --> DUs = distribution utilities and electricity cooperatives, Gencos = generation company, IPP = independent power producer, NPC = National Power Corporation, Source: KPMG. 2013. The Energy Report Philippines, 2013-2014. Manila
persons are somewhat more attentive to situations where their voice is being trampled in view of the fragility of their situation. The story is different for the OECD that mostly includes developed countries. To some extent, unemployed persons in OECD may be more under pressure to become active as the cost of living (housing, heating, various insurances, etc.) is generally higher in OECD relative to MENA countries. The expenses are actually diverse and the costs often significant. Unemployment benefits are rarely sufficient to allow them to face these costs and have a decent life. In this context, unemployed persons in OECD may be more focused on finding a job given the greater level of job opportunities, and thus be somehow less attentive to some situations where their voice is violated.

At this stage, it is important to check the reliability and the strength of the instruments used. To this end, Table 9 reports the results of the Anderson and Rubin (1950) test of overidentifying restrictions (OIDR) that checks the validity of the instruments and the Stock and Yogo (2005) test that checks the weakness of the instruments. The latter is based on the Cragg and Donald (1993) minimum eigenvalue (CDME) statistic, the value of which is compared to the critical values relating to the Limited Information Maximum Likelihood (LIML) size of a nominal 5% Wald test.<sup>44</sup> This testing approach corresponds to the Stock and Yogo (2005) second characterization of a weak instrument. We do not consider the first characterization due to both the estimation method and the number of instruments.<sup>45</sup> We see from Table 9 that the null hypotheses of validity of the instruments are not rejected, saying that the instruments used with both the MENA and OECD datasets are valid. As to the strength of these instruments, we see that the values of the CDME statistics are higher than the respective critical values, thus rejecting the null hypothesis that the instruments used with both datasets are weak.

> 44 For details on the LIML method, see Rivers and Vuong (1988).

> 45 For more details on these characterizations, interested readers may refer to Stock and Yogo (2005).

25