rafmacalaba/datause-encoder-data huggingface dataset:
subjective welfare on standard covariates, we have compared them with a model that explicitly allows for the heterogeneity in scales. For this purpose, the thresholds were modeled as functions of covariates, assuming consistency between own-welfare scoring and scoring of the vignettes. While some differences are notable, when taken overall, our results suggest quite similar factors influencing subjective welfare when comparing the standard regressions with those augmented to allow for systematic scale heterogeneity. This holds in the data studied for all three countries.

Our findings suggest that scale heterogeneity is a serious concern when using subjective welfare data for making inter-personal comparisons of welfare. The meaning of widely-used subjective poverty measures appears highly questionable. However, more encouragingly, scale heterogeneity is not as great a concern as some observers have claimed for using subjective poverty and welfare measures as dependent variables in situations in which there is no option but to assume constant scales. It seems that, despite scale heterogeneity, one can learn something that is reasonably robust about trade-offs from such data—trade-offs that are otherwise hard to identify.

It should be emphasized that we have deliberately focused here on subjective economic welfare in developing countries. Applications in rich countries or on broader concepts of welfare such as “happiness” or “satisfaction with life” may well entail greater latent heterogeneity in scales, although for happiness it is possibly less obvious that this heterogeneity would be correlated with standard regression covariates. One might try to develop vignettes for richcountry settings or for broader welfare concepts. However, even aside from the difficulties of having long vignettes in surveys, adding many more dimensions into the vignettes will make it less likely that an unambiguous welfare ranking of the vignettes is possible. So it might reasonably be argued that we have picked the “low-lying fruit” of the scale-heterogeneity problem in subjective welfare questions. How best to reach the higher fruit remains as a topic for future research.

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Figure 9: Different types of employment and income in Roraima.

<!-- Start of picture text --> Formal Informal<br>−20 −10 0 10 −20 −10 0 10<br>Employer Employee<br>−20 −10 0 10 −20 −10 0 10<br>Own−account Monthly Income<br>−20 −10 0 10 −20 −10 0 10<br>55 30<br>50<br>25<br>45<br>20<br>40<br>35 15<br>12 145<br>10 140<br>86 135130125<br>4<br>120<br>70 55<br>60<br>50<br>50<br>45<br>40<br>30 40<br><!-- End of picture text --> Note: The y-axis is in thousand and the x-axis the distance in quarters from the first quarter of 2018. The data comes from PNADC and solid line shows the local linear fit while the dashed lines show the 95% confidence interval. The monthly gross income is in real Brazilian reais and is adjusted for inflation by IPCA.

Figure 10 reveals that the total employment in agriculture seems to have incurred an uptick in the first quarter of 2018, but has a downward trend after 2018. Total employment in industry fell sharply in the first quarter of 2018 and has remained lower than pre 2018 level. Total employment in construction and transport seems to have an increasing trend after 2018. Employment in hospitality exhibit a positive jump in the first quarter of 2018, however, hospitality seems to have incurred the highest loss in employment due the COVID-19 pandemic as the large drop in the second quarter of 2020 exhibits.

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6 Figure 2: Macroeconomic channels of transmission from global food prices

# (A) Share of agriculture in economy

<!-- Start of picture text --> Percent of total<br>70 Value added<br>Employment<br>60<br>50<br>40<br>30<br>20<br>10<br>0<br>EMDEs LICs<br><!-- End of picture text -->

# (B) Net food imports and exports

<!-- Start of picture text --> Percent Share of countries Percent<br>Share in consumption (RHS)<br>80 8<br>60 6<br>40 4<br>20 2<br>0 0<br>EMDEs LICs EMDEs LICs<br><!-- End of picture text -->

- (C) Inflation in LICs

<!-- Start of picture text --> Percent Food Energy Other<br>16<br>12<br>8<br>4<br>0<br>2000 2002 2004 2006 2008 2010 2012 2014 2016<br><!-- End of picture text -->

- (E) Terms of trade in LICs

<!-- Start of picture text --> Index, 100=2000 Index, 100=2010<br>130 Terms of trade 150<br>Food prices (RHS)<br>120 120<br>110 90<br>100 60<br>90 30<br>80 0<br>2000 2002 2004 2006 2008 2010 2012 2014 2016<br><!-- End of picture text -->

# (D) Contribution of food prices to inflation

<!-- Start of picture text --> Percent 2007-08 2010-11<br>70<br>60<br>50<br>40<br>30<br>20<br>10<br>0<br>EMDEs LICs<br>(F) Fiscal balance in LICs<br>Percent of GDP Index, 100=2010<br>0.0 140<br>-1.0 110<br>-2.0 80<br>-3.0 50<br>Fiscal balance<br>-4.0 20<br>Food prices (RHS)<br>-5.0 -10<br>2000 2002 2004 2006 2008 2010 2012 2014 2016<br><!-- End of picture text --> Source: Kose et al. (2017), World Bank.

A. Based on a sample of 93 EMDEs, and 21 LICs. Averages for 2010-2016. C. Average inflation based on a sample of 12 LICs.

D. Share of inflation accounted for by food price inflation. Yellow line indicates half. E. Net barter terms of trade index.

- F. Median based on a sample of 26 LICs.
<!-- Start of picture text --> 100% |<br>90% |<br>e 60% | # Rudimentary<br>S 50% | Informality<br><!-- End of picture text -->

<!-- Start of picture text --> & 40% TM Modern<br>© 30% | Informality<br>S 20% | TMFormality<br>10% |<br>0% |<br>2010 2020 2030 2010 2020 2030 2010 2020 2030<br><!-- End of picture text -->

<!-- Start of picture text --> 100%<br>90%<br>=oO 60% | # Rudimentary*<br>S 50% | informality<br><!-- End of picture text -->

<!-- Start of picture text --> 2 40% TM Modern<br>2 30% | informality<br>S 20% | TMFormality<br>10%<br>0% |<br>2010 2020 2030 2010 2020 2030 2010 2020 2030<br><!-- End of picture text -->
**Figure 1:** Sovereign bond yields following Mozambique’s hidden borrowing

<!-- Start of picture text --> 2,800 Mozambique<br>Restructuring re-enters<br>of market debt debt distress<br>2,400<br>WSJ reports<br>on and IMF<br>confirms<br>2,000 existence<br>of hidden debt<br>1,600 Mozambique<br>signals intent<br>to restructure<br>market debt<br>1,200 Issuance<br>of market<br>and hidden<br>800 debt in 2013<br>400<br>0<br>Dec 2013 Apr 2014 Aug 2014 Dec 2014 Apr 2015 Aug 2015 Dec 2015 Apr 2016 Aug 2016 Nov 2016<br>(in basis points)<br>Sovereign bond yield spread<br><!-- End of picture text --> _Sources:_ Wall Street Journal (2016, April 3) _“Tuna and Gunships: How $850 Million in Bonds Went Bad in Mozambique”_ , Reuters (2016, April 23) _“IMF says Mozambique has over $1 bln of hidden debt”_ , J.P. Morgan (2024). _Notes:_ The figure plots the EMBI+ spread for Mozambique from December 2013 until December 2016. EMBI+ spread in basis points.

racy of the country’s debt statistics (see Figure 3).<sup>8</sup> At the beginning of 2020, the onset of COVID-19 and the accompanying capital outflows from emerging markets exacerbated the country’s debt challenges. In March 2020, Zambia’s ministry of finance sought advice from banks on the restructuring of USD 11.2 billion of foreign debt.<sup>9</sup> The news sent Zambia’s bond spread surging and ultimately pushed the country into default when the government missed a USD 42.5 million coupon payment on one of its Eurobonds in October 2020.

The uncertainty over the true scale of Zambia’s debt stock caused long delays in the ensuing debt resolution process. Creditor committees declined Zambian requests for a debt standstill and a restructuring proposal over concerns about the “lack of transparency regarding the country’s other external debts’’.<sup>10</sup> After several months of data reconciliation, Zambian authorities eventually published revised debt statistics that confirmed investor concerns and led Zambia’s new president to admit that the “hole is bigger than expected”.<sup>11</sup> Four years later, as of June 2024, the restructuring had not yet concluded.<sup>12</sup>

> 8See Africa Confidential (2018, April 6) _“Into the valley of debt”_ and Bloomberg (2018, April 8) _“Zambia’s bonds plummet on concern it’s pulling a Mozambique”._

> 9See Financial Times (2020, April 1) _“Zambia’s bonds drop on expected restructuring”._

> 10See Financial Times (2020, November 13) _“Zambia on brink of default after lenders reject debt relief request”_ and Financial Times (2020, September 30) _“Bondholders balk at Zambia’s plan to delay debt payments”_ .

> 11BBC (2021, September 1) _“Zambian President Hichilema inherits ‘empty treasury’”_ .

> 12Negotiations are still ongoing with non-bonded commercial creditors representing a quarter of the country’s external debt.

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While this methodology generates comparable scores, like all other adjustment methods, this method has its limitations. First, our transformation of regional scores into an internationally comparable value is more accurate the more _doubloon countries_ are available. If our Index relies on just one _doubloon country_ (just because it is the only country participating in both surveys), it is quite ambitious to convert all other regional scores using this quotient.

Second, this approach refrains from adjusting a joint standard deviation over all tests. So, although anchoring test scores allows us to match our results across surveys and over time, we cannot say by exactly how much each country improved. For example, we might know that _country a_ outperforms _country b_ by 20 adjusted points in year x, and that it has increased its average test score level by about 40 adjusted points in year x+3. Now it outperforms _country b_ by 30 points. So, _country a_ has done better in both years than _country b_ and has also improved over time. However, we cannot specify the scale of the improvement. Adjusted points do not necessarily map one-to-one to any existing achievement scale and depend on which countries participated in the survey as well as the contents of each test.

Our final database consists of 128 countries, over 40 of which are developing countries, and spans the period 1965-2010.

# **3. The Database of Adjusted Test Scores**

Our database, which aggregates test scores across regions and tests over time, is constructed as a quasi-panel in five-year steps. While it would be ideal to have a test score for every year since 1960, test frequency is too low. Following Altinok and Murseli (2007), we provide a subject (Math, Reading and Science) and grade level-specific (Primary or Secondary) test score for every five-year interval. If countries participated in several comparable tests in or around a specific year, we build the average over the respective tests. For example, a country’s adjusted math score in secondary school in the year 2000 follows from its adjusted PISA score in 2000 and its adjusted TIMSS score in 1999, if the country took part in both surveys. If just one adjusted test score is available for the country (either from TIMSS 1999 or PISA 2000), this single result is used as the country’s secondary math score in the year 2000.

We group test scores into five-year steps for a few reasons. First, we often have test scores that are comparable by subject and grade level, yet were administered one or two years apart.

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Table 5: Variations on Table 4's estimation design and sample composition

||<br>DV is|<br>logcount of new|<br>women-owned|<br>establishments|<br>in unorganized s|<br>ector bystate-in|<br>dustry|
|---|---|---|---|---|---|---|---|
||Baseline|Using the|Dropping|Dropping all|Dropping the|Clustering|Dropping state|
||estimation<br>from Column 3<br>of Table 4<br>|<br>coding scheme<br>of Iyer et al.<br>(2012)<br>|<br>Andhra<br>Pradesh and<br>Karnataka<br>|<br>non-adopting<br>states<br>|<br>two industry<br>groups with<br>the largest<br>women-<br>ownership<br>shares<br>|<br>standard errors<br>at the state<br>level<br>|<br> <br>x year fixed<br>effects and<br>controlling for<br>unreported<br>main effect<br>|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|
||||A|. Base estimatio|n|||
|(0,1) state has political reservations|0.399+++|0.397+++|0.335++|0.443+++|0.378+++|0.399+|0.388+++|
|x log women HH share, 1994|(0.135)|(0.129)|(0.140)|(0.154)|(0.142)|(0.202)|(0.135)|
|Observations|3606|3606|3300|2834|3358|3606|3606|
|Adjusted R-squared|0.738|0.738|0.724|0.738|0.723|0.738|0.735|
|<br>Controls||State x|industry, State|x year, and Indus|try x year fixed|effects||
|||<br>B. Panel A|<br>including male|<br>analogue of act|<br>ivity in state-ind|<br>ustry-year||
|(0,1) state has political reservations|0.390+++|0.388+++|0.327++|0.428+++|0.374+++|0.390+|0.378+++|
|x log women HH share, 1994|(0.134)|(0.127)|(0.139)|(0.153)|(0.143)|(0.197)|(0.133)|
|DV analogue for male-owned|0.096+++|0.096+++|0.106+++|0.094+++|0.078++|0.096+++|0.116+++|
|establishments in state-industry-year|(0.031)|(0.031)|(0.033)|(0.035)|(0.032)|(0.027)|(0.031)|
|Observations|3606|3606|3300|2834|3358|3606|3606|
|Adjusted R-squared|0.740|0.740|0.728|0.740|0.725|0.740|0.739|
|<br>Controls||State x|industry, State|x year, and Indus|try x year fixed|effects||

Notes: See Table 4.
Figure 11. Average annual GDP growth per capita by country group <!-- Start of picture text --> 5<br>4<br>3<br>2<br>1<br>0<br>RR Non-RR Rest of the World RR<br>-1<br>Pre boom Boom Post boom<br>%<br><!-- End of picture text --> Note: RR= Sub-Saharan African resource-rich countries listed in IMF (2012), nonRR= rest of Sub-Saharan African countries, RR RoW= rest of the world resource-rich countries listed in IMF (2012) Source: author’s calculations based on World Bank data.

This result is striking. While the boom period represented a prosperous period of growth for resource-rich Africa, the subsequent slump in growth rates underscore how resourcedependent this was and how little economic transformation had occurred, to develop other sources of growth during the boom. The same period saw a slowing of growth across the entire region, reflecting the overall higher level of regional resource-dependency compared to before the boom.

# **What about the rest of the economy?**

It is important to consider not just total GDP growth performance but also how nonresource GDP has fared. This is because the resource sector is such a large driver of growth in most of the region, and particularly during the boom, that a focus on GDP growth numbers is largely reflecting the price-driven increase in resource sector activity in this period. To appreciate the broader growth experience, and particularly the sustainability of economic growth we need to look beyond the resource sector.

Figure 12 separates the average GDP growth rate (left panel) and compares it to the nonresource GDP boom – using sectoral GDP data that subtracts GDP attributable to resource extraction. It shows that resource-rich Africa saw a boom in both GDP and non-resource GDP during the period of higher prices - lying above the non-resource-rich countries for much of this period. During the boom, by the end of 2003 and until 2014, 13.3% of the 20
A most interesting feature of the _Ser Maestro_ assessment, as mentioned, is that teachers’ knowledge is tested against a series of “curricular areas” within each subject. Such a level of granularity of information enables the identification of very specific knowledge areas where teachers display cognitive gaps. Data from the _Ser Maestro_ identify the share of correct and incorrect teacher responses in the assessments. The specificity of this information enables personalized teacher development programs to be designed based on actual needs and knowledge gaps (table 2).

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Das, J., S. Dercon, J. Habyarimana, P. Krishnan, K. Muralidharan, and V. Sundararaman. 2013. “When Can School Inputs Improve Test Scores?” American Economic Journal: Applied Economics 5 (2): 29–57.

Davis, E. A., F. J. Janssen, and J. H. Van Driel. 2016. “Teachers and Science Curriculum Materials: Where We Are and Where We Need to Go.” Studies in Science Education 52 (2): 127–60.

De Hoyos, N. R., A. J. Ganimian, and P. Holland. 2017. “Teaching with the Test: Experimental Evidence on Diagnostic Feedback and Capacity Building for Public Schools in Argentina.” Policy Research Working Paper No. 8261, World Bank, Washington, DC.

De Hoyos, R., P. A. Holland, and S. Troiano. 2015. “Understanding the Trends in Learning Outcomes in Argentina, 2000 to 2012.” Policy Research Working Paper No. 7518, World Bank, Washington, DC.

De Philippis, M. 2016. “STEM Graduates and Secondary School Curriculum: Does Early Exposure to Science Matter?” CEP Discussion Paper No 1443, Center for Economic Performance, London School of Economics and Political Science.

DiNIECE. 2004. Programme for International Student Assessment. Informe Nacional República Argentina. Buenos Aires: DiNIECE. http://repositorio.educacion.gov.ar/dspace/handle/123456789/55289.

DiNIECE. 2015. Anuario Estadístico Educativo. Relevamientos anuales 2007–2014. Buenos Aires: DiNIECE. http://portales.educacion.gov.ar/diniece/.

Duflo, E., R. Hanna, and S. Ryan. 2012. “Incentives Work: Getting Teachers to Come to School.” American Economic Review 102 (4): 1241–78.

Duflo, E., P. Dupas, and M. Kremer. 2011. “Peer Effects, Teacher Incentives, and the Impact of Tracking: Evidence from a Randomized Evaluation in Kenya.” American Economic Review 101 (5): 1739–74.

Educ.ar. 2005. Anuario-En cifras. Buenos Aires: Educ.ar http://portal.educ.ar/acercade/anuarios/2006/cifras.html.

Ercan, O., E. Ural, and D. Ateş. 2016. “The Effect of Educational Software Based on Ausubel’s Expository Learning on Students' Academic Achievement, Science and Computer Attitudes: ‘Human and Environment’ Unit Example.” British Journal of Education, Society & Behavioural Science 14 (1): 1–10.

Forbes, C. T., and E. A. Davis. 2010. “Curriculum Design for Inquiry: Preservice Elementary Teachers' Mobilization and Adaptation of Science Curriculum Materials.” Journal of Research in Science Teaching 47 (7): 820–39.

Fredriksson, P., B. Ockert, and H. Oosterbeek. 2012. “Long Term Effects of Class Size.” Quarterly Journal of Economics 128 (1) 249–85.

Furman, M., Luzuriaga, M., Taylor, I., Anauati, M. V., & Podestá M. E. 2018. « Abriendo la «caja negra» del aula de ciencias: un estudio sobre la relación entre las prácticas de enseñanza sobre el cuerpo humano y las capacidades de pensamiento que se promueven en los alumnos de séptimo grado. » Enseñanza de las ciencias, 36(2), 81-103 [Opening the “Black Box” of the Science Classroom: A Study on the Relationship 34
<!-- Start of picture text --> Gender Work status<br>225 e<br>8 —<br>Ss © 5<br>éS85. a25.<br>Income Education<br>2 © 75-<br>& 7s- 3<br>Fa8a 58Ss<br>5 so- 3<br>£25§ 254<br><!-- End of picture text --> <!-- Start of picture text --> =°33<br>Age<br><!-- End of picture text -->
evidence, the Wikipedia entry on the Penn effect describes it as “a consistent econometric result for at least fifty years.” The economic mechanism postulated by the Balassa-Samuelson model should also hold over time, as long as the data are in reasonable accord with the assumptions of the model. As a poor country develops, its productivity in the traded goods sector will then rise, as will the real wage rate, and so its PPP will move closer to its exchange rate. There is some supportive evidence in time series data for specific developed countries, notably Japan, but not others (Rogoff, 1996). Past tests of whether the implications of the Balassa-Samuelson model (including the Penn effect) hold over time have been largely confined to developed countries.<sup>11</sup> Arguments can be made for and against the Balassa-Samuelson assumptions. A key assumption is that richer countries have higher relative productivity in traded goods. Balassa (1964) presented (influential) evidence supporting that assumption. But technology has changed considerably since 1964, entailing greater potential for productivity growth in the services sector. Take, for example, India’s booming business services sector. This sector has seen very high growth since the early 1990s, facilitated by the availability of skilled labor and changes in information technology.<sup>12</sup> Superficially this does not sound much like the Balassa-Samuelson model. However, it should be noted that this change has also come with a transformation of many business services into internationally traded commodities. So it can be argued that India’s rising productivity in services is in fact consistent with Balassa-Samuelson. By this view, it is the presumption that services are non-traded that is now questionable, given technological change.

However, even if growth does come with rising productivity for traded goods, the way labor markets work in reality may not pass this effect fully onto wage rates in the non-traded goods sector. This could happen if there are impediments to labor mobility. For example, labor hiring in the traded-goods sector may be subject to (explicit or implicit) contracts favoring incumbents, leaving the services sector as a residual employer. Or there may be specialized skill requirements, which effectively restrict entry to the traded-goods sector in poor countries with limited human capital. A wedge between wage rates in the two sectors could also arise if the traded-goods sector is the “formal” sector, which is taxed, while services are informal, and untaxed. Then we may find a persistent wage gap (with _W_ T> _W_ N), creating a potential disconnect

> 11 The only exception I know of is Choudhri and Khan (2005) who find evidence consistent with Balassa-Samuelson effects in panel data for 16 developing countries.

> 12 See Kotwal et al. (2009) for evidence on this point.

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important for the baby that both parents use the parental leave'. However, we also find unintended effects by increasing the level of agreement with the statement `Women should take care of their baby during breastfeeding'. Mentioning breastfeeding in the planning message seems to backfire. The hypothesis is that this message produced a strong association between parental leave and breastfeeding; thus, mothers are identified as the main caregivers during this period.

Table 7. Effects on gender norms attitudes, experiment 2

||||**Panel A: Men**<br>|||
|---|---|---|---|---|---|
||Both<br>parents|Father<br>involvement|During<br>breastfeeding|First<br>months|Who earn<br>less|
|Role model (M2)|-0.001|-0.002|0.031|0.014|-0.034|
||(0.049)|(0.030)|(0.052)|(0.075)|(0.070)|
|Planning (M3)|0.081**|0.016|0.133***|-0.081|0.000|
||(0.041)|(0.030)|(0.046)|(0.077)|(0.075)|
|Observations|300|303|302|301|291|
|R-squared|0.155|0.264|0.153|0.172|0.229|
|Control mean|0.883|0.936|0.830|0.596|0.402|
|Diff M2-M3|-0.0822|-0.0181|-0.102|0.0953|-0.0341|
|p-val|0.0327|0.542|0.0257|0.188|0.621|
|||**P**|**anel B: Women**|||
||Both<br>parents|Father<br>involvement|During<br>breastfeeding|First<br>months|Who earn<br>less|
|Role model (M2)|-0.023|0.002|-0.047|-0.018|-0.022|
||(0.024)|(0.019)|(0.030)|(0.040)|(0.034)|
|Planning (M3)|0.013|-0.003|-0.038|-0.065|-0.034|
||(0.023)|(0.019)|(0.029)|(0.040)|(0.033)|
|Observations|925|935|929|924|890|
|R-squared|0.057|0.024|0.041|0.104|0.116|
|Control mean|0.913|0.945|0.869|0.481|0.229|
|Diff M2-M3|-0.0360|0.00430|-0.00944|0.0472|0.0122|
|p-val|0.138|0.816|0.765|0.241|0.709|

Notes: The table shows the results of regressing the variables regarding agreement with gender norm attitudes on individual control variables (age, nationality, geographic region, cohabiting status, maximum educational level), and sample strata fixed effects. Robust standard errors reported in parenthesis. ***significant at the 1% level, **5% level, *10% level. The estimation sample consists of 1,242 individuals in experiment 2 (304 men and 938 women). Source: Post-intervention survey.

# _Parental leave usage_

Finally, we report the effects of the intervention on parental leave usage by sex in experiment 2. Table 8 shows the results on the outcome variables indicating whether the couple talked or is going to talk about how to use the parental leave (Planning), decided or will decide to use it both mother and father (Use both), use it only him(her)self (Use only me), or only his(her) partner (Use only partner), as well as a variable indicating whether the person effectively applied and became beneficiary of the parental leave after the intervention (Effective use).

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