rafmacalaba/datause-encoder-data huggingface dataset:
1. The shock is distributional neutral, and all children lose the same amount. In our intermediate scenario, this increases the share of children below minimum proficiency by 5 percentage points, to 45%. This share is 42% in the optimistic scenario and 49% in the pessimistic scenario.

2. The distribution skews, and the most disadvantaged students lose most. In this scenario, the share of children below minimum proficiency goes up by 10 percentage points (p.p.), to 50%. In our optimistic scenario, this goes up by 7 p.p., while in the pessimistic scenario, this goes up by 13 p.p. Note that the relative impact of the same (pessimistic scenario) is significantly larger when measured as the share of students (+34%) than when measured as the mean score (-6.1%), so a substantial mass of students near PISA Level 2 might fall below this threshold.

3. The distribution flattens, and students at the top pull ahead, while students at the bottom fall behind. Results fall in between the two extreme scenarios already mentioned.

The intermediate scenario of simulation 3 suggests that the share of students below this level will increase by 10 percentage points.<sup>67</sup> We use the PISA distribution database to simulate the effects of COVID-19 in terms of the share of children below this minimum proficiency threshold (figure 10). At present, 40% of learners fall below proficiency level 2 (their scores are lower than 407 PISA points).<sup>68</sup> This will be accompanied by a much larger effect in terms of the learning “gap” – the minimum learning required to secure a basic understanding of the material. A related measure – that of “severity” puts more weight on those farther from the threshold. The latter more than doubles even in the most optimistic scenario.<sup>69</sup> In regions such as ECA prior to COVID-19 31 percent of students were below the level 2 threshold.<sup>70</sup> The optimistic scenario suggests that this will rise to 39% while the pessimistic scenario suggests that this could rise as high as 46%. In LAC and MNA, the baseline levels were already high at 53% and 55% respectively. The optimistic scenario suggests that this number might increase to 60% and 61% respectively, and in the pessimistic scenario these regions may have as many as 68% of student unable to do the basics.

> 67 Examining the impact of COVID-19 in terms of the mean alone runs the risk of understating the true challenge that will face governments when they are ready to reopen schools – far more students will be below the threshold or minimum proficiency than ever before.

> 68 This is the minimum proficiency level (MPL) for lower secondary education as defined in SDG indicator 4.1.1c (407.47 points). To borrow an analogy from poverty analyses, the increase in the share of students below the threshold amounts to a greater headcount of those below the MPL (see UNESCO, 2019a for details).

> 69 The gap is the distance between the threshold and the score of a particular child. The severity is the square of this distance.

> 70 See Annex Table A3.3.

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reach adolescence they are generally expected to spend more time on such household activities as cooking, cleaning, collecting fuel and water, and caring for children. Meanwhile, boys tend to spend more time on farm or wage work. When young children get sick, teenage girls, not boys, tend to increase their time providing care – often at the expense of their schooling (Pitt and Rosenzweig 1990; Ilahi 1999a). Meanwhile, boys are increasingly engaged in market work, preparing to become the main breadwinners of their own household.” (p.152).

These general patterns are supported by the SSA TUS2000 and EMNV98. Table 1 presents weighted means for the allocation of time to System of National Accounts (SNA) work activities<sup>50</sup> and to domestic work and care activities,<sup>51</sup> by all individuals aged under 60 years in the SSA2000 sample. Panels A and B present the figures for those aged 18 to 59 years and 10 to 17 years, respectively, by gender. The results show women aged 18 to 59 years working over 20% more than men in the same age group, and girls aged 10 to 17 working almost 50% longer than boys in the same age group. Table 2 gives the data means for the EMNV98 for the same time use variables and age groups, by gender. Again, the figures show that women work longer than men, and girls longer than boys, although the differences are relatively smaller.

**Table 1:  SSA TUS2000 data means: time allocations by age and gender** (std. dev. in parenthesis)

**–** **<u>Panel A: Individuals aged 18 59 years</u>**

|Minutes per day<br>Female|Std.dev|Male|Std.dev|
|---|---|---|---|
|SNA work<br>170|(241)|281|(295)|
|Domestic work + care<br>289|(199)|97|(125)|
|Totalwork<br>459|(234)|379|(280)|
|Sample size<br>5319||4655||
|**Panel B: Individuals aged 10- 17 years**<br>Minutes per day<br>Female|<br>Std.dev|Male|Std.dev|
|SNA work<br>27|(63)|44|(101)|
|Domestic work + care<br>132|(132)|65|(82)|
|Totalwork<br>159|(152)|109|(125)|
|Sample size<br>1513||1378||

**-** **<u>Panel B: Individuals aged 10 17 years</u>**

> 50 SNA work activities include “Employment for establishments”, “Primary production activities not for establishments” and “Services for income and other production of goods not for establishments”. 51 Domestic work refers to activities classified as “Household maintenance, management and shopping for own household”.  Care activities are those included in the category “Care for children, the sick, elderly and disabled for own households”.

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**Table 10: Techno-economic analysis of a pilot cellulosic ethanol project with capacity as 300 tonne in China**

|Cost category|Unit|Value|Assumptions/remarks|
|---|---|---|---|
|**Feedstock Cost**|Yuan/tonne|**1,550**<sup>a</sup>|1Corn straw price is 220 Yuan/tonne;<br>2Transportation collection radius is about 8 km, and<br>transportation & crush cost of corn Stover is 65-70<br>Yuan/tonne;<br>2Main components of corn straw:<br>Cellulosic: 37.2%;<br>Hemicellulosic: 25.3%;<br>Lingo: 18%<br>4Key indicators of conversion process:<br>Corn straw saccharification rate: 51.6%;<br>Ethanol concentration: 23g/L;<br>Ethanol production rate: 18.1%|
|**Non-feedstock cost**|Yuan/tonne|**5,835**|1Conversion technology is dilute acid pretreatment and<br>enzymatic saccharification and fermentation;<br>2Enzyme cost is most expensive part, which accounting<br>for 31.8% of unit total production cost.|
|**Total production cost**|Yuan/tonne|**7,845**||
|Share of feedstock cost|%|19.8%||
|Investment cost|Yuan/tonne|8,700||
|Designated fuel ethanol<br>wholesale price|Yuan/tonne|See Table 3<br>||

Source: Song, et al (2010) 15
# **Table 3. Lewbel IV Estimates**

|**Dependent variable: SWB**|**WLS Coef.**|**Lewbel Coef.**|**Kleibergen-Paap rk Wald**<br>**F statistic**|**Hansen J test (p-value)**|
|---|---|---|---|---|
|Financial struggle|-0.29**|-0.44*|19.38|0.22|
|Income sufficiency|0.26**|0.52*|48.58|0.44|
|Satisfied with standard of living|0.80**|0.57**|20.50|0.15|
|Personal economic optimism|0.44**|0.43**|50.04|0.77|
|Optimism about economic climate|0.05|-0.10|14.67|0.07|
|Social support|0.27*|0.47*|16.62|0.79|
|Civic engagement|0.07|-0.18|12.30|0.85|
|Acceptance of diversity|0.11|0.52*|13.91|0.72|
|Digital access|0.30**|0.40*|24.17|0.11|
|_Safety_|_0.00_|_-0.04_|_7.33_|_0.70_|
|Satisfied with socializing opportunities|0.09|0.27*|28.23|0.95|
|Satisfied with transportation infrastructure|-0.01|0.18|9.88|0.62|
|Satisfied with educational system|0.07|-0.13|26.69|0.83|
|Satisfied with quality of air|-0.04|0.13|79.42|0.67|
|Satisfied with quality of water|0.07|0.12|16.11|0.21|
|Satisfied with health care|0.18*|0.36*|11.88|0.51|
|_Corruption_|_-0.04_|_0.06_|_4.08_|_0.34_|
|Confidence in national institutions|0.16*|0.22|36.32|0.86|
|Approval of country’s leadership|0.13|0.15|15.50|0.06|
|Satisfied with personal freedom|0.05|0.28*|26.08|0.33|
|Social mobilityispossible|0.04|0.12|629.41|0.18|

_Notes:_ * p<0.05, ** p<0.01. N=10,228. Every estimate in the Lewbel column is based on a separate Lewbel IV model, in which only the variable that is represented in that row was instrumented. The critical value for the Kleibergen-Paap (10% maximal IV relative bias) for the Stock and Yogo test is applied. Estimations with potentially weak instruments are highlighted in _italics_ .

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# **Table 4: Sustainable Surges and Relentless Stops**

||**Su**<br>|**Sustainable Surg**<br>**rges not followed b**<br>|**es:**<br>**y Stops **<br>|**St**<br>|**Relentless Stop**<br>**ops not followed by **<br>|**s:**<br>**Surges**<br>|
|---|---|---|---|---|---|---|
||**All**|**GF-led**|**M&A-led**|**All**|**GF-led**|**M&A-led**|
|**Global Factors**|||||||
|Global Risk|-0.006(.007)|0.002(.017)|0.025(.043)|0.024(.013)|0.015(.014)|0.051(.034)|
|Global Liquidity|0.015(.005)**|0.011(.006)*|0.017(.011)|0.003(.003)|0.002(.003)|0.001(.007)|
|Global Growth|0.215(.088)*|0.195(.092)*|0.710(.180)**|-0.143(.051)**|-0.117(.059)*|-0.237(.118)*|
|**Regional Contagion**|0.023(.007)**|0.020(.009)*|0.027(.024)|0.002(.675)|-0.010(.011)|0.025(.018)|
|**Domestic Factors**|||||||
|Stop previousyear|0.351(.240)|0.444(.281)|0.963(.653)|-0.352(.268)|-0.636(.358)|1.062(.619)|
|Surgepreviousyear|-0.422(.280)|-0.293(.320)|0.176(.596)|1.663(.142)**|1.559(.154)**|4.098(.34)**|
|GDPper Capita(ln)|-0.051(.044)|-0.109(.052)*|0.226(.103)*|-0.027(.038)|-0.091(.038)*|0.084(.079)|
|Natural Resources|0.003(.005)|-0.000(.005)|-0.021(.018)|0.002(.004)|0.006(.003)*|-0.004(.010)|
|Δ Financial Openness|0.166(.190)|0.049(.211)|0.768(.261)**|0.141(.166)|-0.027(.183)|0.801(.241)**|
|Δ Economic and Financial<br>Stability|0.013 (.018)|0.029 (.017)|-0.087 (.037)*|0.002 (.015)|-0.002 (.019)|0.029 (.033)|
|Δ Political Stability|0.002(.023)|-0.002(.023)|-0.031(.057)|-0.033(.023)|-0.038(.023)|0.019(.043)|
|Number of Observations|1768|1768|1768|1768|1768|1768|
|Number of Countries|95|95|95|95|95|95|
|Number of Surges or Stops|152|132|29|228|188|51|
|*p<0.05, **p<0.01; cluster-robust<br>M&A-led surge). The variables Re<br>windsorized at the 1% level.|standard errors in pare<br>gional Contagion, GDP|ntheses. Dependent var<br>per capita and Natural|iable is a 0-1 variable ind<br>Resources are lagged (o|icating if there is a surg<br>ne year). The variables|e or stop episode (eithe<br>GDP per capita and nat|r GF-led surge or an<br>ural resources are|

23
was enormously high. A 1971 Colombian government study estimated that a cut flower grower could expect to earn a profit amounting to 57 percent of the sales value or a 600 percent return per year on the initial investment.' Another source notes that "the cost of a carnation in Colombia could be as lo,w as one cent and the grower could sell it to the wholesaler in the US for 5 cents." (Riemer 1982, 44-45). Still another source notes that some margins were reportedly 100 percent (Morrow 1989).

Although the development of reliable air transportation opened up Colombia's potential as a flower growing area, it was the cow<sup>4</sup> .nation of two additional factors, occurring simultaneously, that led to its development as a major exporter of the cut flowers. The two key elements were the entrepreneurial efforts of a team of four Americans and a major shift in Colombian government policy in 1967. This team of Americans prcvided the right combination of skills to overcome the many obstacles to exporting, while the government's policy shift (consistilng of the removal of government-created burdens) provided an environment in which their efforts would be rewarded. The role played by the Colombian government's policy is outlined later in a separate section.

In 1969, Thomas Kehler, an American businessman, put together a team of entrepreneurs to determine if a flower export business would be feasible. The team consisted of Harmon Brown, the California flower grower concerned with rising land and fuel prices in the United States; Bill Mott, an econotist; and, David Cheever, who as a university student had used computer simulations to identify the Savanah de Bogota as ideally suited for the growing of flowers. Following a feasibility study, each invested $25,000 and formed a company called Floramerica.

The company was a major success. W- 'in a short period of time, it became one of the world's major exporters. By 1986, the company had $50 million in annual sales (Rhee and Belot 1989).

Although Floramerica's success was dramatic, its effect on the Colombian cut flowers industry was even more so. In 1970, Floramerica accounted for 8
the firm site selection issue. Imagine a case where firms update their beliefs about location j’s future weather shocks as the climate scientists are predicting that location j faces medium term extreme weather risk. Forward looking firms will start to migrate to relatively safer locations over time. In this case, an econometrician who estimates equation (1) should be concerned that the firms who continue to locate in location j are not a representative sample of firms. One selection hypothesis would be to posit that better managers might locate in such areas if they have a risk adaptation edge. In this case, they can rent cheap land and still be productive. Another selection hypothesis would posit that worse managers will choose to locate in the riskiest areas because avoiding risk is a normal good.

In estimating equation (1), the researcher does not observe the costs that the firm has incurred to offset extreme weather. Such a regression does estimate the marginal cost to the firm of being exposed to extreme weather. If the firm has incurred upfront costs to adapt, then the econometrician is likely to estimate smaller marginal coefficients in equation (1). In an economy where firms are anticipating more extreme weather, firms will invest more in adaptation and the econometrician will actually estimate a smaller marginal damage coefficient over time.

The climate damage estimate literature relies on observational data as extreme weather provides the variation. Adaptation scholars can rarely implement field experiments here. A field experiment might consist of randomly distributing risk report cards to firms to educate them about the risks their geographic location faces. If firms trust the information source and were unaware of the risk, then this “new news” may affect their adaptation investment. Survey research and observational data on subsequent output can be used to study whether such an information nudge accelerates adaptation investment and lowers the marginal damage (B1 in equation 1) for the treated firms. Another field experiment research design would be to randomly assign different subsidies to firms for purchasing adaptation products. This price variation would trace out the demand curve for adaptation goods and this random variation could again be used to test whether the climate damage is less sensitive to extreme weather when firms have been induced (at random) to invest more in adaptation. As more firms seek to buy adaptation enhancing products, this provides an incentive for entrepreneurs to design these products. This dynamic innovation process will lead to higher quality products that sell for lower prices and this accelerates the global adaptation process Acemoglu and Linn (2004).

20
poor countries were “traditional,” Schultz considered that they were rational decisionmakers and maximized the return from their resources. Their apparent unwillingness to innovate, he argued, was rational because governments of those countries often set artificially low prices on their crops and taxed them heavily. In other words, peasants respond to incentives. Empirical support for this view was overwhelmingly produced (much later) in a multi-country study on the Political Economy of Agricultural Pricing Policy, led by Anne Krueger, Maurice Schiff and Alberto Valdès. The study demonstrated that price policy, trade policy and exchange rate policy in virtually all developing countries have discriminated against agriculture, either directly through food subsidies or taxes on agricultural exports, or indirectly through manufacturing protection and exchange rate overvaluation.<sup>42</sup> The study was recently updated, under the direction of Kym Anderson, and reached the same conclusions.<sup>43</sup> Since the mid-1980s many developing countries have undertaken a great deal of policy reform and opened to trade. The inter-sector bias against agriculture and the antitrade bias have been reduced substantially. Developing countries have benefited proportionately more (relative to GDP) than high-income economies from those trade-related policy reforms, and they would gain nearly twice as much as those richer economies by completing that reform process – with 72 percent of the prospective gains to developing countries coming from agricultural and food policy reforms. In developing countries, net farm income (agricultural value added) is estimated to have been 5 percent higher in 2004 than it would have been without the reforms since the mid-1980s. And if policies remaining in 2004 were removed, that net farm income would rise by another 6 percent (far more than the proportional gain to nonagricultural households). These reforms could further alleviate global inequality and poverty, since three-quarters of the world’s extreme poor are in farm households in developing countries. One way to look at the policy changes of the past 25 years is to say that developing countries follow the example of higher-income countries in moving from anti- to pro-farmer policies as they develop. The Anderson study shows that import-competing farmers in developing countries are being increasingly protected over time.

> 42 Krueger, Schiff and Valdès. 1991-1992.

> 43 Kym Anderson, 2009

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# **7.1 Are government performance, administrative competence and procedural justice related to legitimating beliefs?**

In a wide range of developing societies in Africa, I find considerable evidence of a link between government performance, administrative competence and honesty and procedural justice and citizens’ willingness to defer to governmental tax authority. As illustrated by Figure 3, citizens’ beliefs about how fairly their government is treating members of their ethnic group has the largest relative effect on deference to the tax department followed by perceptions of corruption within the tax administration. The relationship between perceptions of the state’s enforcement capacity among ordinary citizens and the willingness to defer to the tax department is negative.

I argue that perceptions of government effectiveness are correlated with the acceptance of a government’s right to make people pay taxes. There are three components to government effectiveness: government performance and administrative competence and honesty. The objective indicators of — government performance the presence of a sewage system and a school in respondents’ neighborhoods — are not significant at the p _<_ 0.05.<sup>10</sup> There are three possible explanations for the lack of association between the objective indicators of service delivery and the willingness to defer to the tax department. First, there is reason to believe that better service delivery may affect citizens’ willingness to defer to the tax department only through its effect on improved outcomes that matter for citizens’ livelihoods. Unless improved services and infrastructure have a positive impact on citizens’ welfare, individuals are unlikely to credit the government for these outputs (Sacks and Levi, 2010). The Afrobarometer’s objective measures of service delivery only denote the presence or absence of infrastructure and services. The data do not indicate the condition of the services and infrastructure.

Citizens may perceive and reward relative improvements or sanction deteriorations in services, rather than the absolute level of service quality they receive. If services deteriorate or improve, taxpayers may alter their beliefs about governments’ performance and should attempt to adjust their terms

> 10I also tested whether there is a relationship between the presence of a concrete road, health clinic, post office and electricity grid in the enumeration areas and respondents’ willingness to pay taxes. None of these objective indicators except for the presence of an electricity grid were significant at the p _<_ 0.05 level. The presence of an electricity grid is negatively associated with the willingness to defer to the tax department.

17
Thus the power of difference-in-differences estimation is the same with a single baseline and follow-up and _n=100_ , as it is with two pre-treatment rounds and two post-treatment rounds and _n=50_ . Intuitively, with difference-in-differences, every observation counts equally in calculating the pre-treatment and post-treatment means which form the basis of estimation.

This result of course depends on the assumption of equal correlation being a reasonable one. So this implication is likely to make sense when choosing between one or two follow-up surveys, or even between one and five follow-up surveys, but should not be taken to the extreme of saying we should expect the same power from _T=100_ and _n=2_ as we would from _T=2_ and _n=100_ .

**_Implication 9:_** With ANCOVA estimation, when the autocorrelation is high it is better to do a larger cross-section and fewer survey rounds, whereas when the autocorrelation is low, it is better to do relatively more survey rounds and a smaller cross-section. Holding nT fixed, with a single baseline: (i) the optimal value of _r_ holding _nT_ fixed is ; (ii) two follow-up surveys will offer more power than a single follow-up survey for ρ<0.5; (iii) three follow-up surveys will offer more power than one or two follow-up surveys for ρ<1/6.

_Proof:_ Set _m=1_ and _nT=K_ in (11), and then take the derivative with respect to _r_ to show (i). Compare the variances with r=1, 2, and 3, to show (ii) and (iii).

Therefore when the total sample size is fixed at _K_ , power will typically be greater using a baseline and 2 post-treatment surveys with a cross-sectional sample of K/3 than a single baseline and follow-up for many types of economic data. But it will be rare in practice for more than two post-treatment surveys to be optimal with a fixed total sample size. However, the standard practice of a single baseline and single follow-up used in so many economics experiments is unlikely to be the optimal choice for all.

In practice the cost of adding another cross-sectional unit versus surveying the same unit a second time can differ. In many experiments there can be large fixed costs of adding additional units to the experiment. These fixed costs can include the costs of screening and enrolling more subjects in the study, the cost of the intervention itself (many times this is by far the largest cost), and potentially the cost of more handheld units for surveying them. Denote the fixed costs of another cross-sectional unit by _a_ . There can also be fixed costs of conducting another survey round, such as the need to pay field managers for more months of work. Denote the fixed cost of 16