rafmacalaba/datause-encoder-data huggingface dataset:
baseline level during the second lockdown.<sup>11</sup> The higher mobility during the second lockdown explains the increase in economic activity suggested by our electricity model. When we juxtapose the third and fourth lockdowns with the first one, mobility was also substantially higher during the former. The higher mobility and higher economic activity can be explained by differences in the containment measures and much better adaptation to such measures during later lockdowns.

**Figure 8:** Average Workplace Mobility Trend in Bangladesh

<!-- Start of picture text --> Percent change from baseline<br>0<br>-10<br>-20<br>-30<br>-40<br>-50<br>-60<br>1st Lockdown 2nd Lockdown 3rd Lockdown 4th Lockdown<br><!-- End of picture text -->

# **8. Discussion and Summary**

This paper established a meaningful relationship between electricity consumption and other economic indicators in Bangladesh. The real-time availability of daily electricity consumption data allows for assessment of the impacts of different shocks on overall economic activity when no other data are available yet. Moreover, in contrast to many other economic measures, electricity consumption data are available at subnational levels. Due to its high frequency and spatial granularity, as well as its strong correlation with GDP and other economic variables, electricity consumption is a valuable high-frequency indicator of economic activity.

We presented an electricity consumption model that explains over 90 percent of the daily variation in electricity consumption. Deviations of actual consumption from the model prediction can be used to assess the impacts of economic shocks. We analyzed the impacts of Bangladesh’s lockdowns to contain the spread of COVID-19 on economic activity in Dhaka and showed that the first lockdown came with much higher costs than the subsequent ones. The smaller economic impacts of the later lockdowns can partly be explained by the nature of the lockdowns. For example, while all of them seem to have been similarly stringent according to the OxCGRT Stringency Index, factories were only shut during the first lockdown. In addition,

> 11 The Google mobility trends baseline period is defined as the median value for the corresponding day of the week, during the five-week period from January 3 to February 6, 2020.

16
mining revenues. The third and fourth columns display the second-stage panel IV results for the rural and urban samples, respectively. All specifications include regional and time fixed effects. I control for child’s age, child’s predicted health status, household characteristics (number of children under five years of age, availability of drinking water in the household, household size, and wealth score), community characteristics (rice price, population size, a dummy for living in the highlands, a dummy for living in an urban area, and a poverty index), and regional variables (debt payments)<sup>18</sup> . According to the simple panel data estimate for the whole sample, changes in total public spending appear to have a negative but not statistically significant effect on changes in a child’s nutritional status (the coefficient is equal to -0.052). This result is consistent with the reverse causality hypothesis that the government will spend more in areas where there are pressing social demands. The instrumental variable panel estimation for the whole sample shows a positive but not significant coefficient (0.041)<sup>19</sup> . In contrast, in rural areas, the IV estimation shows a very small, negative (-0.078), and statistically significant impact of public spending. However, in urban areas, conditional on the inclusion of the control variables described above, there is a positive (0.238) and significant impact of changes in public expenditure on changes in a child’s nutritional outcome (almost twice the size of a non-instrumented panel estimation for urban areas<sup>20</sup> ). Furthermore, I verify that the difference in the urban and rural coefficients is statistically significant at a 5% level. For an increase equal to the average annual change in 18 Individual time-invariant variables, such as birth weight, ethnicity, gender, maternal height, and maternal age at birth, were excluded from the estimation because individual fixed effects are included. All of the controls are expressed in changes.

19 I also calculated the instrumental variable pool estimator, which also showed a positive but not significant coefficient (0.535).

20 Simple panel data estimations were performed for urban and rural areas. In rural areas, public spending appears to have a statistically significant at 1% and a negative effect on children’s stunting (with a coefficient of -.115). In urban areas, there is a small, positive, and statistically significant effect at 1% (with a coefficient of 0.141).

15
|P112727|2009|2009||0 Closed|World|
|---|---|---|---|---|---|
|P112727|2009|2010||0 Closed|World|
|P112759|2010|2010||0 Closed|China|
|P112759|2010|2011|6/28/2011|1 Closed|China|
|P112759|2010|2012|3/26/2012|2 Closed|China|
|P112759|2010|2013|12/20/2012|3 Closed|China|
|P112759|2010|2013|6/24/2013|4 Closed|China|
|P112759|2010|2014|12/17/2013|5 Closed|China|
|P112759|2010|2014|6/24/2014|6 Closed|China|
|P112759|2010|2015|12/19/2014|7 Closed|China|
|P112759|2010|2015|6/15/2015|8 Closed|China|
|P112759|2010|2016|12/11/2015|9 Closed|China|
|P112759|2010|2016|6/24/2016|10 Closed|China|
|P112761|2009|2009||0 Closed|Bangladesh|
|P112762|2012|2012||0 Closed|Tanzania|
|P112762|2012|2013|12/31/2012|1 Closed|Tanzania|
|P112765|2009|2009|4/28/2009|1 Closed|Poland|
|P112765|2009|2019|5/9/2019|2 Closed|Poland|
|P112767|2011|2012|11/21/2011|1 Closed|Cabo Verde|
|P112769|2009|2011||0 Closed|Sierra Leone|
|P112777|2009|2010||0 Closed|Europe and Central As|
|P112777|2009|2011||0 Closed|Europe and Central As|
|P112777|2009|2012||0 Closed|Europe and Central As|
|P112777|2009|2019|5/7/2019|1 Closed|Europe and Central As|
|P112778|2009|2011||0 Closed|Europe and Central As|
|P112778|2009|2012||0 Closed|Europe and Central As|
|P112778|2009|2019|5/7/2019|1 Closed|Europe and Central As|
|P112780|2011|2011|6/28/2011|1 Closed|Jamaica|
|P112780|2011|2012|4/14/2012|2 Closed|Jamaica|
|P112780|2011|2013|11/19/2012|3 Closed|Jamaica|
|P112780|2011|2014|8/12/2013|4 Closed|Jamaica|
|P112780|2011|2014|3/18/2014|5 Closed|Jamaica|
|P112780|2011|2015|11/24/2014|6 Closed|Jamaica|
|P112780|2011|2015|6/23/2015|7 Closed|Jamaica|
|P112780|2011|2016|12/17/2015|8 Closed|Jamaica|
|P112780|2011|2016|6/3/2016|9 Closed|Jamaica|
|P112780|2011|2017|12/21/2016|10 Closed|Jamaica|
|P112780|2011|2017|5/31/2017|11 Closed|Jamaica|
|P112817|2010|2010||0 Closed|Lesotho|
|P112817|2010|2011||0 Closed|Lesotho|
|P112817|2010|2012|4/24/2012|1 Closed|Lesotho|
|P112821|2011|2011||0 Closed|Mali|
|P112821|2011|2012|12/17/2011|1 Closed|Mali|
|P112821|2011|2013|7/11/2012|2 Closed|Mali|
|P112821|2011|2013|4/15/2013|3 Closed|Mali|
|P112821|2011|2014|2/12/2014|4 Closed|Mali|
|P112821|2011|2015|10/7/2014|5 Closed|Mali|
Wei (2011) and Claessens, Tong and Wei (2012)). They also find a significant impact of the demand channel, but they only focus on large listed firms.

While the impact of the demand channel is clear, we do not see a clear impact of firms’ credit characteristics on the change in firm sales between July 2008 and July 2009. None of the proxies we use for firms’ dependence on external finance seem to matter much when introduced by itself. Among all proxies for credit sensitivity, only Cash Conversion Cycle is marginally significant, but it is not robust. When both demand sensitivity and the proxies for firm’s credit characteristics are introduced, only demand sensitivity remains robust and significant. None of the three proxies for credit needs is robustly significant (only dependence on external finance becomes significant in one specification, but with the wrong sign). In addition, we interact the small-firm dummy with each demand and credit sensitivity proxy to find potential differential impacts on small firms. We do not find any differential impacts (results not shown here).

As a robustness check, we rerun the regression but replace the change in sales by a dummy variable representing reporting demand as the main factor. The dummy variable equals 1 if the firm reports demand as the main factor to cause damage to the firm, 0 otherwise. We would like to be sure that more demand sensitive firms are more likely to declare that demand channel matters most. Results in Table 2 indeed show this is the case. Reporting demand shocks is strongly correlated with demand sensitivity, and not correlated with other credit sensitivities. Loosely speaking, within a country, if a sector saw an additional 1 percent decline in the U.S. stock index between Sep 10, 2001 and Sep 21, 2001, firms in that sector are 1.15 percent more likely to report demand shocks as the main factor.

12
TABLE 65 Sea (Fod Inisl): OIS Esiat of _Amgmed_ Cobb-l)iPodu.aIor Fuaniions -Deaidn aible - Log of Constant Price ValueAdo

**(Vabues** in pamtheses **are** sndard errors)

||Base<br>in|d on Value-A<br>Domestic Pric|dded<br>es|Bas<br>i|ed on Value-A<br>n World Price|dded<br>s|
|---|---|---|---|---|---|---|
||1|2|3|4|5|6|
|Intercept<br>(% Senegalese Private)|0.394<br>(0.845)|-0.623<br>(0.711)|0.033<br>(0.742)|2.665+<br>(1.546)|1.626<br>(1.531)|1.312<br>(1.381)|
|Ownership:|||||||
|% Senegalese State|1.255'<br>(0.455)|1.164'<br>(0386)|0859<br>(0368)|1.473*<br>(0.581)|1.710<br>(0.569)|1.082!<br>(0.513)|
|% French|0276<br>(0319)|.0.124<br>(0237)|40.069<br>(0228)|0.079<br>(0374)|-0232<br>(0287)|-0.227<br>(0.275)|
|% Syro-Lebanese|0.107<br>(0374)|4.189<br>(0209)|.0.199<br>(0.207)|0.248<br>(0.436)|0.122<br>(0.264)|0.034<br>(0253)|
|% Other Foreign|0.703*<br>(0364)|0.466<br>(0.233)|OA12+<br>(0.231)|0.601<br>(0.439)|0.581+<br>(0.348)|0.351<br>(0326)|
|Trade Variables:<br>Effetive Rate of<br>Protection|0.098!<br>(0.044)|0.118<br>(0.039)|0.109<br>(0.038)|40322<br>(0.058)|-O263'<br>(0.059)|4295<br>(0.054)|
The two main measures of skills we explore in the paper are the share of workforce that is high educated (i.e., share of the workforce with more than secondary education) and the share of skilled occupations (i.e., the share of the workforce that are managers, professionals and non-production workers). The _Enterprise Surveys_ collect detailed information on the occupational composition of the workforce using the following categories: management, professionals, skilled production workers, unskilled production workers and other non-production workers.<sup>5</sup> In particular, we consider the group of skilled workers as being the sum of management, professionals and nonproduction workers while the unskilled workers are the sum of skilled and unskilled production workers. In most of the earlier work on this topic (for example, Pavcnik 2003), this has been the most commonly used measure since it is frequently available.<sup>6</sup> Table A2 in the appendix presents the summary statistics for the share of skilled workers in the workforce. Panel A reports the share of high educated workers (i.e., share of the workforce with more than secondary education) while Panel B reports the share of skilled occupations (i.e., the share of the workforce that are managers, professionals and non-production workers). The countries with the highest share of skilled labor are Indonesia and Republic of Korea, with China and Thailand following behind.<sup>7</sup> For all countries, there is robust evidence that the share of skilled labor in total employment is higher for services than for manufacturing (e.g., Di Gropello and Sakellariou, 2008). In fact, among all the countries with information, the services share is more than double. Within manufacturing, the countries with the smallest shares of skilled labor are Vietnam and Cambodia, where only approximately 10-11 percent of the workforce has more than secondary education.

# **3. Empirical Methodology**

> 5 We have also proxied the use of skilled labor by its share in the total firm compensation (wages and salaries plus bonuses and other benefits). Reassuringly, the findings (not reported) are in line with the ones reported in our main specification. We chose not to report these findings due to the reduced sample size, and the low reliability of the estimates.

> 6 Yet, some of the non-production workers might themselves be engaged in low skilled tasks. Therefore, we will test the robustness of our main findings to an alternative measure of skills that is defined by excluding this group of workers (and considers only manager and professionals).

> 7 Cambodia has a high share of skilled labor in the sample (both in Panel A and B). However, this is explained solely by the larger share of services in the sample for this country.

8
This paper examines the intersectionality of gender and ethnicity in three education outcomes included in the Millennium Development Goals (MDGs): literacy rate, primary school completion, and secondary school completion.<sup>4</sup> It focuses on a select group of Latin American and African countries for which recent census data is available, namely Bolivia, Mexico, Peru, Senegal, and Sierra Leone. Controlling for age, age cohorts and urban/rural residence in a model analogous to difference-in-differences, it compares the education outcomes of men and women in ethnic minority groups with their counterparts in other groups. The analysis finds sizable gaps in literacy and educational attainment based on gender and ethnicity, particularly in Latin American countries. Moreover, the analysis of interaction terms shows that gender-based gaps are generally larger for minority groups compared to others, or alternatively, ethnicity-based gaps are greater for women compared to men. The intersection of gender and ethnicity implies cumulative education disadvantages for ethnic minority women, which goes beyond the individual effects of each of these two identities.

Given the role of education on individuals’ life choices and the wellbeing of their children, cumulative education disadvantages may contribute to the inter-generational reproduction of poverty and deprivation patterns among minority groups.<sup>5</sup> Further, education interventions which focus exclusively on universal coverage and gender parity in schooling— without considering how intersectionality of multiple identities mediates these outcomes—may not successfully address the existing gaps. While this paper captures what can be considered a symptom or outcome of identity-based exclusion, future research must explore the processes that lead to inter-group disparities and move toward multifaceted interventions to redress the overlapping roots of exclusion, especially for men and women in minority groups.

> 4 The education indicators in the Millennium Development Goals (MDGs) include the ratio of boys to girls in primary and secondary school and the ratio of literate men to women aged 15-24. Our use of education measurements (which are related, but not the same as these indicators) is partly due to data availability constraints, as discussed in the data and methodology section.

> 5 See Dubow et al. (2009), Chevalier et al. (2010), and Behrman and Rosenzweig (2002) on the link between mother’s education and children’s wellbeing.

3
reflecting the clandestine nature of much hunting, while projected tourist numbers at 282,000 are within the estimated current range of between 200,000 and 300,000 visitors a year.

Consider first the effects of a decline in _q,_ the carrying capacity, which could occur for a number of reasons (examples include the numerous intrusive structures suggest that would impede the migration, high intensity tourism, mining or other pressures). We begin by considering the effects of a 20% decline in carrying capacity, which is lower than the median prediction of Holdo _et al_ ( _op cit_ ) for current policy pressures.<sup>18</sup> As noted in the previous sections the optimal response to a lower carrying capacity calls for an increase in the allocation of land to wildlife and a reduction in hunting quotas. Despite these policy shifts, wildlife numbers decline by about 22% to 850,000 and tourist numbers fall. The next column explores the effects of a 20% increase in agricultural revenues so that the allocation of land to agriculture is increased. Once again wildlife numbers decline by about 25% to 810,000, with a corresponding fall in tourist numbers and the wildebeest harvest.

The next column considers the combined effects of 20% higher agricultural profits together with a 20% decline in the carrying capacity. This time there is a much more dramatic decline in wildebeest numbers (by about 50%) to 600,000 together with an equally significant reduction in the hunting offtake and tourist numbers by almost 30 percent. The implication is clear. The combined pressures have synergistic effects, with one factor exacerbating the effects of the other, so that the joint effects exceed the sum of the individual impacts.

Could these negative consequences be reversed through improved enforcement? The final column considers the optimistic, though unlikely, case where all fines are doubled. While there is some improvement in wildebeest numbers, the decline in the population is still significant at 32 percent. Evidently though increasing penalties may lead to improved compliance, this does little to address the root cause of the decline in wildlife numbers - a lower carrying capacity resulting from a degraded ecosystem.

These results have two striking implications for policy. First there is a need to be alert to potential synergisms, which may lead to unwelcome surprises when multiple impacts interact. Second the

> 18 The detailed simulations by Holdo et al ( _op cit_ ) based on a spatially explicit model suggest a median population decline of 38%. To guard against exaggerating possible impacts we consider a more modest reduction in carrying capacity

19
worthiness criteria and successful past experience with similar project activities played an important role. No doubt local level connections also played a role.

In common with other development projects, the SWP provided the capital and technical assistance, but it did not provide insurance, and many of the activities are likely to entail non-negligible risk; the income gains will depend on a number of contingencies, including the vagaries of the weather, uncertain demand for the new products and risks associated with out-migration.

The _ex ante_ expectation was that the SWP would virtually eliminate poverty in the selected villages over the longer term. The World Bank’s Implementation Completion Report (ICR) — the final document giving the _ex post_ “self-assessment” of a lending operation by the relevant operational unit — claimed that the SWP had a substantial impact on poverty, citing survey data indicating that the poverty rate had been more than halved in the project areas over 1995-2001 (World Bank, 2003).<sup>5</sup> However, the attribution of these gains to the SWP is questionable. The evaluative claims in the ICR are reflexive comparisons, which only reveal the true impact under the assumption that there would have been no progress against poverty in the absence of the project. That assumption must be deemed highly implausible in this setting.

Ravallion and Chen (2005) studied the impacts of the SWP over the disbursement period, 1995-2000, using survey data for 2,000 randomly sampled households in both SWP and observationally similar non-SWP villages that had first been surveyed in 1995 (at the beginning of the project) and then annually until project completion. On comparing income changes in SWP villages with those in the matched non-SWP villages, they found an average income gain over five years of around 10% of baseline mean income, representing an average rate of return of 9%. The gains are not as dramatic as suggested by the reflexive comparisons in the ICR, but they are still sizeable.

However, Ravallion and Chen found that a large share of the income gain was saved. On comparing the final year of disbursement with the first, Ravallion and Chen found only a modest impact on mean consumption or consumption poverty. The savings rate from the project’s income gains was well above the pre-intervention savings rate.

> 5 This was confirmed by researchers at the Chinese Academy of Social Sciences, who also pointed to a substantial increase in primary school completion rates and a decline in the infant mortality rate which they attributed to the SWP (Guobao et al., 2004).

6
DIALOGIC READING AND STORYBOOK EVALUATION IN KENYA 2

# **Introduction**

Poverty has significant and lasting consequences for children’s cognitive and language development (Bradley & Corwyn, 2002; Brooks-Gunn & Duncan, 1997). In low- and middleincome countries (LMICs), 43 percent of children under age five do not reach their developmental potential (Black et al., 2017). Even in Kenya, one of the best-educated countries in Sub-Saharan Africa, only 30% of third graders can read at the second-grade level (Piper, 2010), and 34% of pre-school children are “on track” for language and numeracy development (Kenya National Bureau of Statistics, 2013). The lack of adequate literacy preparation is a key risk factor for poor performance in primary school worldwide (Behrman et al., 2006).

One reason children living in poverty do not achieve their potential in terms of language development and emergent literacy is that they do not have access to linguistically stimulating home environments (Can & Ginsburg-Block, 2016). The availability of storybooks in the home and the quantity and quality of parents’ reading engagements with their children are consistently associated with children’s cognitive and language development, school readiness, and achievement (Raikes, et al, 2006; Park, 2008; Rodriguez et al., 2009; Zauche, Thul, Mahoney, & Stapel-Wax, 2016). In particular, exposure to storybooks has a direct, positive, causal impact on children’s vocabulary and language skills (Hargrave & Sénéchal, 2000).

In spite of the importance of books and parental engagement, 97% of households in SubSaharan Africa have two or fewer children’s books, and only half of parents report having engaged in any cognitively stimulating activities with their young children in the last three days (United Nations Children’s Fund, 2017). In some African contexts, responsive conversations between parents and their young children are actively discouraged by cultural norms (Weber, Fernald, & Diop, 2017; Jukes et al., 2018). Yet, in order to learn vocabulary and build linguistic