rafmacalaba/datause-encoder-data huggingface dataset:
# 5 References

Allen, R. C. (2017). Absolute poverty: When necessity displaces desire. _American Economic Review_ , _107_ (12), 3690-3721.

Alpizar, F., Carlsson, F., & Johansson-Stenman, O. (2005). How much do we care about absolute versus relative income and consumption?. _Journal of Economic Behavior & Organization_ , _56_ (3), 405-421.

Anderson, E., & Esposito, L. (2014). On the joint evaluation of absolute and relative deprivation. _The Journal of Economic Inequality_ , _12_ (3), 411-428.

Atkinson, A. (2016). _Monitoring Global Poverty: Report of the commission on global poverty_ . The World Bank, Washigton, DC.

Atkinson, A. B. (2019). _Measuring poverty around the world_ . Princeton University Press.

Atkinson, A. B., & Bourguignon, F. (2001). Poverty and inclusion from a world perspective.

Atkinson, T., Cantillon, B., Marlier, E., & Nolan, B. (2002). _Social indicators: The EU and social inclusion_ . Oup Oxford.

Blanco, M., Engelmann, D., & Normann, H. T. (2011). A within-subject analysis of otherregarding preferences. _Games and Economic Behavior_ , _72_ (2), 321-338.

Bourguignon, F. (1999). Absolute poverty, relative deprivation and social exclusion. In _Villa Borsig Workshop Series_ (pp. 1-4).

Bowles, S., & Gintis, H. (2002). Social capital and community governance. _The economic journal_ , _112_ (483), F419-F436.

Brandolini, A., & Rosolia, A. (2019). The distribution of well-being among Europeans. _Bank of Italy Occasional Paper_ , (496).

Browning, M., Chiappori, P. A., & Lewbel, A. (2013). Estimating consumption economies of scale, adult equivalence scales, and household bargaining power. _Review of Economic Studies_ , _80_ (4), 1267-1303.

Chakravarty, S. R., Chattopadhyay, N., Deutsch, J., Nissanov, Z., & Silber, J. (2016, November). Reference groups and the poverty line: An axiomatic approach with an empirical illustration. In _Inequality after the 20th Century: Papers from the Sixth ECINEQ Meeting_ . Emerald Group Publishing Limited.

Cherchye, L., De Rock, B., & Vermeulen, F. (2011). The revealed preference approach to collective consumption behaviour: Testing and sharing rule recovery. _The Review of Economic Studies_ , _78_ (1), 176-198.

Citro, C., Michael, R. (1995). Measuring Poverty : A New Approach. Washington, DC : Natl. Acad.

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Table 2: State-level urbanization rates for India's manufacturing sector

||Tot|al establishm<br>counts|ent|Urb<br>es|anization<br>tablishme|rate<br>nts|Urb<br>e|anization<br>mployme|rate<br>nt|Ur|banization<br>output|rate|
|---|---|---|---|---|---|---|---|---|---|---|---|---|
|State|1994|2000|2005|1994|2000|2005|1994|2000|2005|1994|2000|2005|
|Andhra Pradesh|748,360|1,469,307|1,437,720|0.25|0.27|0.30|0.40|0.38|0.38|0.56|0.46|0.43|
|Bihar|1,053,839|1,250,688|1,307,078|0.11|0.13|0.12|0.18|0.17|0.16|0.56|0.52|0.56|
|Chandigarh|3,906|6,046|1,534|0.93|0.89|0.54|0.95|0.94|0.85|0.96|0.97|0.98|
|Delhi|131,842|230,598|99,712|0.81|0.95|0.96|0.87|0.97|0.97|0.98|0.97|0.94|
|Gujarat|551,254|545,122|647,845|0.63|0.55|0.54|0.70|0.62|0.61|0.68|0.58|0.56|
|Haryana|90,898|189,548|227,445|0.41|0.49|0.49|0.62|0.66|0.65|0.71|0.74|0.67|
|Himachal Pradesh|80,096|94,708|102,682|0.10|0.08|0.07|0.16|0.17|0.18|0.29|0.30|0.35|
|Karnataka|526,997|1,032,334|942,142|0.25|0.34|0.31|0.36|0.42|0.41|0.70|0.64|0.66|
|Kerala|237,391|481,157|572,320|0.18|0.18|0.23|0.27|0.23|0.27|0.41|0.39|0.41|
|Madhya Pradesh|463,195|957,108|1,019,022|0.25|0.28|0.32|0.41|0.36|0.38|0.68|0.55|0.60|
|Maharashtra|629,357|1,223,468|1,116,648|0.50|0.46|0.51|0.66|0.61|0.64|0.71|0.68|0.60|
|Orissa|1,078,820|955,560|831,287|0.04|0.06|0.10|0.07|0.09|0.13|0.37|0.43|0.38|
|Punjab|151,520|331,683|290,789|0.54|0.46|0.50|0.72|0.65|0.64|0.80|0.71|0.68|
|Rajasthan|343,140|599,027|608,407|0.32|0.37|0.36|0.44|0.46|0.51|0.64|0.58|0.65|
|Tamil Nadu|943,138|1,463,466|1,465,806|0.42|0.45|0.42|0.49|0.51|0.48|0.57|0.52|0.47|
|Uttar Pradesh|1,894,046|2,302,322|2,257,163|0.24|0.29|0.29|0.30|0.37|0.39|0.53|0.59|0.59|
|West Bengal|1,628,708|2,733,376|2,694,214|0.17|0.23|0.20|0.30|0.30|0.29|0.69|0.63|0.63|
|Totals and wtd averages|10,928,081|16,517,785|16,307,628|0.26|0.30|0.30|0.40|0.41|0.41|0.64|0.59|0.56|
|Unweighted averages|620,971|933,266|918,930|0.36|0.38|0.37|0.47|0.47|0.47|0.64|0.60|0.60|

Notes: Indian descriptive statistics taken from Annual Survey of Industries and National Sample Statistics. Totals and weighted averages at the bottom of the table are for the full manufacturing sector, including states and industries not in the final sample. Urbanization rate in establishments is measured as the share of establishments in urban areas. Urbanization rates for employment and output are similarly defined.
and, from 1980 on, crude oil has generally accounted for 40 percent to 60 percent of Cameroon’s export revenues. The rapid decline in oil prices in 1985 and 1986 is generally identified as the source of Cameroon's economic crisis.

Cameroon's lack of export diversification is illustrated by the fact that its five principal exports account for 74 to 81 percent of total export revenue over the last five years. All these products are either agricultural (broad-bean cocoa, and raw cotton) or natural resource-based (crude oil, wood processing, and refined petrol).<sup>6</sup> The top five imports in 2003 were hydrocarbons, machinery, chemicals, transport equipment, and flour. During the last five years, the top five product clusters have accounted for 65 to 68 percent of the total value of imports.

The European Union (EU) is by far the most important trading partner of Cameroon. In 2003, 64.5 percent of Cameroon’s exports and 54.6 percent of Cameroon’s imports were exchanged with the EU.<sup>7</sup> The EU is followed by Africa (13.6 percent of exports and 22.5 percent of imports), Asia (10.3 percent of exports and 15.6 percent of imports), North America (7.6 percent of exports and 5.5 percent of imports) and Latin America (1.4 percent of exports and 2.1 percent of imports).

# **2. Modeling Features and Data**

Our CGE microsimulation model involves 10,992 households, as compiled from the “ECAM II” household survey undertaken in Cameroon in 2001 (CNIS 2002a; 2002b; 2003). Other data were processed from the Cameroon’s 2001 Supply and Use Tables (SUT) of Cameroon, and from the underlying Integrated Economic Account Tables (IEAT). The general

> 6 Other products that have been among the top five exports for at least one year during the last decade are: crude wood, raw aluminum, coffee and bananas.

> 7 The EU is considered here as the 15 member-countries of 2003, before its enlargement to 25 members in 2004.

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considerable concern.<sup>13</sup> In this extreme example, we would in reality only have one data source, not two, and inferences about governance based on the two data sources would be no more informative than inferences based on just one of them.

This example is of course contrived because it makes the implausible assumption that the two data sources make perfectly correlated measurement errors when they assess governance across countries. However, even if the errors made by the two data sources are highly, but not perfectly, correlated, there will be benefits to relying on both of the data sources. The important empirical question is whether this hypothetical correlation of errors across sources is large or not. Empirically identifying correlations in errors across sources is difficult. Simply observing that two data sources provide assessments that are highly correlated is not enough, since the high correlation could reflect either (i) the fact that both sources are measuring governance accurately and so are highly correlated, or (ii) the fact that both sources are making correlated measurement errors in their assessments of countries.

In order to make progress we need to make identifying assumptions. In Kaufmann, Kraay and Mastruzzi (2006) we detail two sets of assumptions that allow us to disentangle potential sources of correlation in the errors. One assumption is that surveys of firms or individuals are less likely to make errors that are correlated with other data sources than, for example, the assessments of commercial risk rating agencies. If this is the case, however, we would expect that the assessments of commercial risk rating agencies be very highly correlated with each other, but less so with surveys. This turns out not to be the case. For example, the average correlation among our five major commercial risk rating agencies for corruption in 2002-2005 was 0.80. The correlation of each of these with a large cross-country survey of firms was actually slightly higher at 0.81, in contrast with what one would expect if the rating agencies had correlated errors. We do this exercise for components of all six of our aggregate governance indicators, and find at most quite modest evidence of error correlation. While this is unlikely to be the final word on this important question, we do think it is a useful step forward to

> 13 In fact, in our very first methodological paper on the aggregate governance indicators (Kaufmann, Kraay and Zoido-Lobatón 1999a) we devoted an entire section of the paper to this possibility, and showed how the estimated margins of error of our aggregate governance indicators would increase if we assumed that the error terms made by individual data sources were correlated with each other.  Recently this critique has been raised again by Svensson (2005),  Knack (2006) and Arndt and Oman (2006), although largely without the benefit of systematic evidence.  Kaufmann, Kraay, and Mastruzzi (2007) provide a detailed response.

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**Table 3b:  Characteristics of Home-based Workers in Ecuador (1999), in percent**

||**Home-based**|**Workers**|**Rest of the**<br>**Economy* **|
|---|---|---|---|
||_Informal_<br>_Entrepreneurs_|_Contract_||
|_Sample size _|_464_|_13_|_2228_|
|Female|74.1|61.5|34.9|
|Age 15-25|7.1|38.5|29.5|
|Age 26-45|50.2|15.4|47.6|
|Age 46+|42.7|46.2|22.9|
|No school|3.9|0.0|3.3|
|Primaryschool|44.3|53.9|38.9|
|Secondaryschool|51.8|46.2|57.8|
|Household head|42.5|23.1|47.0|
|Husband|15.3|15.4|25.9|
|Wife|31.7|23.1|13.2|
|Single Mother|22.6|15.4|12.7|
|Unmarried Female,no children|0.4|0.0|0.2|
|Unmarried Male|4.7|23.1|21.7|
|Child age 0-5 in household|25.4|30.8|30.1|
|Child age 6-12 in household|35.8|15.4|39.01|
|Child age 13-18 in household|40.1|76.9|38.9|
|Indigenous**|N/A|N/A|N/A|
|% Household income from thisjob?|51.0|35.0|56.0|

*Rest of the economy are those who are working, but not in their homes

** Ethnicity was not reported in the survey

28
**Table 5:  Mother’s education, household asset ownership and child animal-source food intake probability**<sup>**a**</sup>

|**Household**<br>**Asset**<br>**Quintile**|**0**|**Mothe**<br>**Primary**<br>**(1-6)**|**r’s Educat**<br>**Middle**<br>**(7-9)**|**ion (Years)**<br>**Secondary**<br>**(10-12)**|**Post-**<br>**Secondary**|
|---|---|---|---|---|---|
|**1**|**0.38**|**0.44**|**0.55**|**0.58**|**0.63**|
|**2**|**0.44**|**0.50**|**0.61**|**0.63**|**0.69**|
|**3**|**0.53**|**0.59**|**0.69**|**0.72**|**0.76**|
|**4**|**0.55**|**0.60**|**0.70**|**0.73**|**0.77**|
|**5**|**0.55**|**0.61**|**0.71**|**0.73**|**0.78**|

# **a Predictions computed for January**

The second striking feature of the results is the seasonal impact of fish supply on child nutrition. Table 6 displays predicted animal-source food intake probabilities by education and asset group for September, a peak catch month, and February, after the floods have receded. For mothers with no education, children’s September animal-source food intake probability in the lowest asset quintile (53%) matches the February probability for children in the highest asset quintile. In the lowest asset quintile, children’s September intake probability for mothers with no education (53%) matches the February probability for mothers with middle school education (7-9 years). Taken together, the results in Table 5 highlight the critical importance of expanded fish supply for children’s animal-source food intake in the poorest, least-educated households.

**Table 6: Economic and education status vs** animal-source food **intake probability: Seasonal comparison**

|||**Mother**<br>**Hous**<br>|**’s Educati**<br>**ehold Asse**<br>|**on: 0 Years**<br>**t Quintile**<br>||
|---|---|---|---|---|---|
|**Month**|**1**|**2**|**3**|**4**|**5**|
|**February**|**0.37**|**0.42**|**0.51**|**0.53**|**0.53**|
|**September**|**0.53**|**0.58**|**0.67**|**0.68**|**0.69**|
||**Mo**|**ther’s Hous**<br>**Mothe**<br>|**ehold Asse**<br>**r’s Educat**<br>|**t Status: Qui**<br>**ion (Years)**<br>|**ntile 1**|
|**Month**|**0**|**Primary**<br>**(1-6)**|**Middle**<br>**(7-9)**|**Secondary**<br>**(10-12)**|**Post-**<br>**Secondary**|

12
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For each indicator, there is a threshold and continuous variable option. In theory, the threshold option ‘percent zero’ on either reading comprehension or oral reading fluency is most plausibly comparable. This measures the most basic foundational skills attained which should be relatively invariant to details which would affect performance higher up the learning margin. It is also easily interpretable and meaningful across contexts. However, since this variable is a threshold rather than a continuous measure of learning it is difficult to convert this to a score we can place on an international scale.

Instead we produce mean scores using a continuous variable that is plausibly comparable, the ‘percent of answers correct’ on reading comprehension. We use the percent correct since the number of test items varies slightly for each EGRA. If students did not attempt any question this is recorded as a zero. We further confirm that the threshold and continues variables are tightly correlated, with an inverse correlation of -0.9 (see Figure 2.0). We compute a standardized Z-score from the continuous reading comprehension metric and scale it with a mean of 500 and standard deviation of 100, akin to RSAT and ISAT scales.

EGRA data is designed for grades 2-4, although certain countries will participate out of this range. We restrict data used for our database to grades 2-4 to be consistent with the design of EGRA. This range further optimizes the coverage-bias tradeoff. If we include additional grade levels to expand coverage, the data starts to skew due to availability of data for certain countries coming from a particularly low or high grade (grades 1 or 6). If we restrict our interval to reduce grade bias, we start to lose countries. In the grade 2-4 range, we optimize this trade-off.

Figure 2.0: Correlation between Percent Zero Reading & Percent Reading Comprehension <!-- Start of picture text --> Pearson Correlation Coefficient: -.8991<br>NIC<br>MKD<br>IDN IDNTJKKGZAFGKGZKGZIDNKGZHNDPHL<br>MKD LBR<br>BDI BDIPNGTONTZAAFGTJKJORAFGPNGRWAJORLAOIRQGMBPNGEGYETHRWABGDTUVRWALBRKIRMMRSDNMMRKENGUYMMRSENTONKENTLSGMBTLSEGYLBRHTILBRVUTMARBGDCODRWAYEMGMBNGABGDHTINGAMWIHTICODSLE NGAMLIMLIZMBGHAGHAUGAMW<br>0 .2 .4 .6 .8 1<br>% Reading Zero<br>100<br>75<br>50<br>Reading %<br>25<br>0<br><!-- End of picture text --> Figure 3.0 demonstrates sensitivity to grade within this interval. We run a regression with and without grade-fixed effects comparing mean scores relative to a given country which participates across all three grade levels 2-4. This gives us an indication of sensitivity to 6
(Hale et al., 2020).<sup>6</sup> Figure A. 6 shows the changes in the stringency indices of major countries since the first wave of the pandemic. The stringency index averaged 25; at its highest, the index was 96. Figure A. 7 shows differences in maximum lockdown severity among selected countries.

Figure A. 8 plots three indicators of COVID-19 severity: new cases per million, new deaths per million, and the lockdown stringency index developed by Oxford University. The graphs on the left-hand side in each row display the new cases per million (scale shown on the left axis) and the new deaths per million (scale shown on the right axis). The scales of the two indicators are adjusted to allow the line graphs to have a similar height. The graphs on the right-hand side show the change in policy stringency throughout the study period. We define the start of the pandemic as March 15, 2020 (marked by the red vertical line).<sup>7</sup> As shown in the plots, the new cases per million population and the new deaths per million population mirror each other very well, with the death indicator lagging several days compared to the case indicator. The stringency index, however, does not necessarily follow either the cases or the deaths closely. For example, it rose promptly at the start of the pandemic when the new cases and deaths per million were relatively low compared with the rest of the post-pandemic era. The stringency of the policies then gradually decreases as economies reopen in the summer of 2020. With the arrival of colder weather (around October 2020), the caseload significantly increased. The stringency index rose slightly again while governments were re-imposing restrictions to mitigate the new COVID wave. The Delta variant was first detected in India in December 2020. It spread to other countries and generated another wave of cases in the summer and fall of 2021.

Different regions also show very different COVID-19 severity patterns. At the beginning of the pandemic, the European countries and the United States saw a relatively fast spread of COVID-19 in March 2020, whereas Brazil and India did not reach their first peak until July 2020. In the winter of 2020, the European countries were ravaged by the so-called “second wave,” whereas Brazil and India did not see a significant winter spike. Later, in May 2021, India’s case

> 6 For instance, Italy was the first European country to impose a full lockdown measure; we can see that the stringency index for Italy rose before that of other countries. Another good example is India: during the Delta surge, India’s stringency index rose again. Note that even within the same continent, European countries often had stringency profiles different from those of other countries. We exploit this variation as an identification strategy to examine the impact of COVID-19 on electricity generation.

> 7 The World Health Organization declared COVID a pandemic on March 11, 2020.

8