rafmacalaba/data-use-mentions-extended huggingface dataset:
labor regulation, entry and exit, along with the agglomeration environment. They conduct a global analysis while controlling for country fixed effects, offering better control of country heterogeneity. They also explore how the effects of BE variables are different depending on firms’ characteristics (such as age, size) and country level of development.

The literature on the effects of pollution in Africa is less developed. Although the topic has gained attention in recent years, the scarcity of pollution data for the region had limited long-

> 5 There are many studies on the effects of agglomeration economies on long-term growth. See for example Glaeser and Gottlieb (2009); Jones and Romer (2010); Clarke, Li and Xu (2016); Li, Long and Xu (2017)).

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- (iii) Urban area boundaries;

- (iv) Rural settlement boundaries; (v) Security assessment access map.

Pre‐war region boundaries are available as shapefiles from UNDP. The boundaries of urban areas were defined by the urban enumeration areas previously used in UNFPA’s Population Estimation Survey 2014 (PESS). Boundaries of IDP settlements were provided by UNHCR’s Shelter Cluster and PESS. The IDP strata boundaries were subtracted from the urban and rural strata to prevent duplicate sampling. The remaining areas outside of the urban and IDP strata were considered as rural strata. Areas determined too dangerous through the security assessment were removed from the sampling frame.

# _Population sampling frame_

In urban and rural strata, population estimates were derived from the 2015 WorldPop data set, detailed in Linard et al. (2010). This data set uses a combination of data sources and methods, including satellite imagery, to derive highly spatially disaggregated population estimates. First, the starting point are 2005 population estimates at the district‐level from the UN Office for the Coordination of Humanitarian Affairs (OCHA) for 74 districts. Second, an Africover GIS dataset depicting 22 landcover classes was combined with 2005 Landsat satellite imagery depicting settlement outlines. Third, settlement point location data, based on the efforts of various NGOs and UN agencies, with more than 11,000 settlement points along with some population estimates, including urban and rural areas, and IDP settlements. To achieve higher spatial resolution, the OCHA estimates were disaggregated using the information contained in the settlement points data and the landcover class data. The result is a gridded population dataset at 100m‐ by‐100m spatial resolution. For each 100m‐by‐100m cell, the data set contains a population estimate, which, aggregated within the PSU, provides a population estimate for each primary sampling unit (PSU) in urban and rural strata, which was later used for sample selection proportional to size. Due to inadequacies of the population density map for the purpose of creating a sampling
# **2. Related Literature**

The long-term relationship between electricity consumption and economic growth has been well researched. Apergis and Payne (2011) find a causal relationship between electricity consumption and economic growth for 88 countries over 1990–2006. In high-, upper-middle-, and lower-middle-income countries, the causality runs in both directions. In low-income countries, they find only a unidirectional causality from electricity consumption to economic growth.

Due to the strong association of electricity consumption and economic activity, variations in electricity consumption can be used to assess economic fluctuations. Chen et al. (2020) find that electricity usage in Europe declined between 10 and 15 percent during the acute phase of the COVID-19 pandemic. Historically, a 1 percent drop in electricity usage in Europe has been associated with a 1.3 to 1.9 percent drop in output. Cicala (2020) observes a one-to-one relationship between the variables of interest in the United States, including during the global financial crisis, and uses the relationship to assess the economic damage from COVID-19. Similarly, Beyer, Franco-Bedoya, and Galdo (2021) show that energy consumption declined strongly in India after a national lockdown was implemented on March 25, 2020, and confirm that electricity consumption can be a useful indicator in emerging market economies. <mark>Ai, Zhong, and Zhou (2022)</mark> estimate the impact of the COVID-19 pandemic in Hunan province (China) through firm-level electricity consumption data. They employ a difference-indifferences model to show that electricity consumption in Hunan province dropped by 27.8 percent during the early stage of the pandemic.

For Bangladesh, empirical evidence of the relationship is ambiguous. Yıldırım, Sukruoglu, and Aslan (2014) do not find a link between energy consumption and economic growth in Bangladesh. Mozumder and Marathe (2007) find a unidirectional causality from per capita gross domestic product (GDP) to per capita electricity consumption, using annual data from 1971 to 1999. Along the same line, Asghar (2008) examines
Figure 3 shows some examples of the distribution of per capita consumption aggregates based on the “raw” data.<sup>12</sup> All curves display a few features that are common for many variables of interest in welfare analysis (consumption expenditure, income, wealth, but also quantity of items consumed, calorie intake, unit values, and many others): they are skewed and leptokurtic (fat tails), which may be symptoms of the presence of outliers.

12 The “raw” label is used here for convenience: survey microdata shared by National Statistical Offices have almost certainly been edited between the end of fieldwork and the time of dissemination, as is routine – but they are raw, as far as the analyst is concerned. The conversion in per capita (or adult equivalent) terms is necessary in this context, because values that may appear ‘extreme’ on a per-household basis may turn out not to be, once household size is accounted for.

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|**_Sector of economic_**<br>**_activity_**|Primary sector<br>Low-tech manufacturing (food, beverages, tobacco, textiles and clothing)<br>Other manufacturing<br>Construction|
|---|---|
||Retail, restaurants, hotels and repairs<br>Utilities, transport and communications<br>Banking, finance, insurance, professional services<br>Public administration<br>Education, health and personals services<br>Domestic service|

# 3.2. Use of a computer at work

The method applied so far enables detailed insights into country-level data on exposure of occupations to GenAI, with further breakdowns by demographic and socioeconomic characteristics of the affected groups (Table 1). At the same time, the variation in AI exposure across and within countries is only driven by the variation in the occupational structures, because the same occupation in different countries uses the same score of GenAI exposure. To address this limitation, in the next step we introduce cross-country variation of occupation-level scores, by accounting for the variability in the use of computer equipment in the sam ~~e o~~ ccupation located in different national contexts.

We first proceed by imputing the GenAI exposure measures at the 4-digit ISCO08 to the microdata from the Programme for the International Assessment of Adult Competencies (PIAAC) collected by the OECD. These surveys include rich information on detailed tasks carried out by people at work, such as whether workers use a computer (and internet)<sup>17</sup> at work. Using this binary indicator, we split each group of GenAI exposure into those who use a computer at work, and those who do not. Not using a computer at work means that even if the worker is in an occupation that is exposed to GenAI augmentation, such potential productivity gains are unlikely to realize given the lack of access to digital infrastructure. We first implement this exercise for the four countries in the LAC region (
is 5.7 percent (column 3). Adding Addis Ababa fixed e↵ect reduces the elasticity to 3.2 percent, which is not statistically significant. These results imply that the wage premium of large towns is driven by Addis Ababa.

# **5.5 Robustness Check**

## **Excluding Recent Migrants**

Although our analysis has instrumented the town population size, an endogeneity bias due to the sorting of workers is a lingering concern. To assess the bias, we tap into the migration information available in our data.<sup>11</sup> We reestimate our OLS and 2SLS models by excluding recent migrants to towns to highlight agglomeration e↵ects on the stayers. We expect this to reduce the size of the estimated agglomeration e↵ects if sorting has contributed to the e↵ects. Table 9 summarizes the results. As expected, excluding rural-to-urban migrants (columns 2 and 5) and both rural-tourban and urban-to-urban migrants (columns 3 and 6) lower the absolute values of the coefficient estimates for the log of town population. Nevertheless, we still observe significant associations between town population size and the probability of engaging in self-employment jobs.

> 11Unfortunately, only the LFS has such migration information, reducing the sample size for this analysis.

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**Table 9**

**HIES 2016/17 allocation of PSUs by stratum**

|**Stratum no**|**Stratum name**|**No. of PSUs**|
|---|---|---|
|1|Barisal Rural|162|
|2|Barisal Urban|54|
|3|Chittagong Rural|308|
|4|Chittagong Urban|60|
|5|Chittagong City Corporation|28|
|6|Dhaka Rural|262|
|7|Dhaka Urban|178|
|8|Dhaka City Corporation|28|
|9|Khulna Rural|236|
|10|Khulna Urban|98|
|11|Khulna City Corporation|26|
|12|Mymensingh Rural|100|
|13|Mymensingh Urban|44|
|14|Rajshahi Rural|211|
|15|Rajshahi Urban|49|
|16|Rajshahi City Corporation|28|
|17|Rangpur Rural|219|
|18|Rangpur Urban|69|
|19|Sylhet Rural|107|
|20|Sylhet Urban|37|
||**Total:**|2,304|

**Table 10 HIES 2016/17 allocation of PSUs for urban and rural areas**

|**Sector**|**HIES 2016**<br>**No. of PSUs**|**/17 sample**<br>**Percentage**|**Population**<br>**share, Census of**<br>**Population and**<br>**Housing 2011**|
|---|---|---|---|
|Urban|699|30.3%|23.1%|
|Rural|1,605|69.7%|76.9%|
|Total|2,304|100
by adding covariates that help reduce the variance in the outcome of interest. But smaller samples limit the ability to benefit from non-linear machine learning approaches, and also raises questions about how well asymptotic results used to justify the choice of regularization parameters in Lasso work with the sample sizes common in field experiment applications. W ̈uthrich and Zhu (2023) recently showed that PDS Lasso can underselect variables in finite samples when the number of variables is reasonably large, potentially leading to omitted variable bias when these variables have moderate relationships with treatment. Second, the majority of field experiments in developing countries rely on survey data for many outcomes, which can be subject to attrition. In a survey of 96 field experiments, Ghanem et al. (2023) find an average attrition rate of 15 percent. This raises the concern that if the determinants of attrition differ with treatment status, then the sample for which data are available may no longer be comparable across treatment groups. This offers a further potential rationale for double selection methods, which can select covariates that predict treatment status in the attrited data. But the question remains as to whether this makes much difference in practice.

We replicate and re-analyze field experiment papers published in three economic journals between 2017 and 2022 which used PDS Lasso, resulting in 780 treatment estimates. We use this analysis to identify the key practical issues and performance of PDS Lasso, and compare the estimates and their standard errors to those that would be obtained using simple Ancova. We find that while authors typically include a long list of variables to be inputted as potential controls in PDS Lasso (a median of 182 controls), PDS Lasso typically ends up selecting very few control variables. The median is three controls, and in over half the cases, no variables at all are selected in the treatment regression step. When variables are selected for treatment, they are almost never those that are also selected for predicting the outcome of interest. As a result, PDS Lasso leads to minimal changes to treatment estimates and standard
_a_ 15 _/a_ 35, and _a_ 45 _/a_ 35 are less than the upper bound. Since the analysis is at quarterly frequency, I choose 0.077 as an upper bound for the one-quarter oil supply elasticity. This value is a conservative estimate of the upper bound given that it is three times the one-month oil supply elasticity estimate used in Kilian & Murphy (2012). Moreover, in comparison to a small one-month price elasticity of oil supply, evidence from other studies suggest a small price elasticity of oil supply at quarterly frequency as well. For example, Newell & Prest (2019) use microeconomic data to estimate a onequarter oil supply elasticity of 0.017 for conventional crude oil.

However, it can be argued that short-run price elasticity of oil supply would be higher when incorporating shale oil production. Empirical findings suggest a one-quarter supply elasticity that is close to zero even for shale oil production (see Newell & Prest, 2019; Kilian, 2020). This result does not invalidate the common view that, relative to conventional producers, shale oil producers are more agile in responding to movements in oil prices. It only implies that it takes more than three months for both conventional and shale oil producers to respond to changes in oil market conditions (Kilian, 2020). Moreover, based on survey evidence from U.S. shale firms, Golding (2019) states that “the average horizontal well pad in the Permian Basin takes four to six months from the commencement of drilling to production coming online,” and for wells that are drilled but not hydraulically fractured “it may take one to three months to go into production.”

**_Estimation._** The sign restrictions are imposed using the procedure developed by Rubio-Ramirez, Waggoner, & Zha (2010). The procedure involves generating a random orthonormal matrix _Q_ such that _QQ_<sup>_′_</sup> = _I_ by obtaining the _QR_ decomposition of a _K × K_ matrix _X_ of independent _N_
climate variables (Giesbert and Schindler, 2012; Diffenbaugh and Burke, 2019; Azzarri and Signorelli, 2020). In particular, Rentchler _et al._ (2022) combine different data sources on flood, population, and poverty to provide more granular assessment of exposure to flood risks and poverty.

Combining various data sources is especially valuable for providing critical weather information in remote and inaccessible areas where ground-based networks are limited.<sup>9</sup> While satellites can capture a broad spectrum of data simultaneously, satellite data comes with its own set of limitations. For instance, these records typically cover a shorter time span than stationbased data, owing to the relatively recent deployment of satellite technologies. Furthermore, the data require complex processing to account for non-climatic factors, such as variations in satellite orbits and the need for instrument calibration.

 Do studies analyze data that are more disaggregated than the country level? While most existing studies analyze country-level data, using such data has certain drawbacks. Country-level data can mask significant disparities within countries, such as regional variations in weather conditions, exposure to natural disasters, and levels of poverty and inequality. This issue can be particularly pronounced in developing countries, which are often located in tropical areas with diverse weather conditions and disaster risks. Consequently, important intracountry variations can be obscured when data is aggregated at the national level. In response to this limitation, a growing number of studies have begun to exploit subnational data. These studies observe more pronounced effects of climate change on economic growth and poverty at this more granular level (Damania _et al._ , 2020; Kalkuhl and Wenz, 2020; Dang _et al._ , 2023b). This shift towards more localized analyses underscores the need for more disaggregated,

> 9 Temperature and rainfall data are primarily sourced from ground-based weather stations, which are valued for their long-term records and high precision (Jones and Olken, 2010; Dell _et al._ , 2012). However, station-based data often has uneven geographic distribution,
there are issues of implied benefits and guarantees even where none formally exist. This seems to be an issue for mutual insurers as well, with mutual insurance tending to be an afterthought in regulations, as well as an issue with some other faith based organizations offering discretionary benefit or voluntary sharing in medical expenses which are not effectively regulated.

Risk based capital requirements for Takaful insurers are likely to be higher than for conventional insurers as the investment requirements of Takaful insurers push them towards equities, property and property based securities and funds (Tolefat). Ideally an active Sukuk market that provides some greater degree of security will develop however recent setbacks appear to be holding back the rate of issuance.

Reinsurance is another key emerging issue for Takaful, particularly for those that wish to compete in commercial and industrial and liability markets. Strictly speaking any reinsurer used by a Takaful insurer should also satisfy Shariah requirements (Re-takaful) as should any retrocessionaires. Taking this chain to its ultimate reaches creates an almost impossible requirement, and some Takaful insurers have opted for co-insurance, raising the scope for unmeasured accumulations within individual jurisdictions. As at the date of writing there were 8 fully established Re-takaful operators in the Middle East and a number of the major European reinsurers had opened Takaful windows so direct reinsurance capacity is not likely to be an issue.

The remaining major issue in the development of the Takaful sector is the relative powerlessness of the policyholders relative to the TO under all current hybrid structures. This has been considered by an IFSB/ IAIS joint working group, which has pointed to the need for absolute transparency as to the contractual terms and the possible need for enhanced explanation at the time of sale to achieve this. Another recommendation being considered is that a separate board be established consisting of independent directors and a representative of the Shariah Council to oversight consumers‟ interests.

The most recent survey of Takaful insurers carried out by the Middle East Insurance Review show that Malaysia and the GCC region continue to be the main areas of activity. Summary MENA data is shown in
sup> Secondly, Venezuelan migrants are highly geographically mobile, mainly choosing to locate in higher income regions, with the point of entry into Ecuador having very little explanatory power. Specifically, we estimate that _cantons_ with 10% higher income receive 5% more Venezuelan migrants than otherwise similar regions.

Our difference-in-difference estimates do not uncover any effects on the workforce as a whole for the regions with the largest inflows of Venezuelans relative to their population, even when restricting to female workers. However, our estimates clearly indicate a reduction in the quality of employment and earnings among young, low-educated Ecuadoran workers. Compared to similar workers in regions with a small inflow of Venezuelans (relative to population), young, low-educated Ecuadoran workers in high-migration regions have experienced a 6 percentage-point drop in the rate of _adequate_ employment,

> 3Beine et al. (2019) have also used cell phone records in the context of Syrian refugees in Turkey, but their analysis is limited to refugees’ location choices and they do not analyze labor market outcomes.

> 4The cantons containing the cities of Quito and Guayaquil host over 85,000 Venezuelan migrants each.

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**Figure 6:** Debt Relief and Borrower Expectations

<!-- Start of picture text --> Beliefs about self Beliefs about average borrower<br>1[More likely to repay] 22.9% 10.9% 66.2% 1[More likely to repay] 24.8% 11.6% 63.5%<br>1[More likely to apply] 25.0% 11.7% 63.3% 1[More likely to apply] 24.2% 11.0% 64.7%<br>1[Loan approval more likely] 24.6% 17.2% 58.2% 1[Loan approval more likely] 23.0% 15.2% 61.8%<br>1[Future relief more likely] 29.9% 10.7% 59.4% 1[Future relief more likely] 30.1% 10.0% 59.9%<br>Proportion Proportion<br>Less likely No change More likely Less likely No change More likely<br><!-- End of picture text --> _Note:_ The figure summarizes responses to the debt relief survey administered to 1,630 microfinance and bank borrowers with recent exposure to debt relief. Respondents were randomly assigned to answer questions about themselves ( _N_ =813) or the average borrower ( _N_ =817).

impact on their own loan repayment, while 66% believe that debt relief will _improve_ their future loan repayment. Interestingly, the responses are not very different when borrowers are asked whether they expect debt relief to worsen credit discipline in the population. Only 25% of respondents expect the loan repayment of the average borrower to suffer as a result of debt relief, while 64% believe that the average borrower will in fact be more likely to repay their loans. However, in line with the widespread concern that debt relief may generate expectations of future relief
DIALOGIC READING AND STORYBOOK EVALUATION IN KENYA 17 were necessarily administered in the language being assessed – though explanations and prompts were always given in the child’s preferred language. The overwhelming majority (94%) of children preferred to be interviewed in Luo, and the remainder preferred Swahili.

Baseline and follow-up data were collected by trained enumerators, all of whom had university degrees, were from the study areas, were native speakers of the local Luo dialect, and were also fluent in English and Swahili. Enumerators received 15 days of training from members of the research team on child anthropometry and developmental assessments. Enumerators also received extensive training and monitoring from senior research staff at the implementing organization, Innovations for Poverty Action – Kenya. Although not informed of treatment group assignment, enumerators were not blinded to treatment, as it would be immediately obvious when observing the children’s books that were present in the home at endline. It was cost prohibitive to train two separate intervention and enumeration teams, but we conducted high frequency checks during data collection to look for data falsification, and all field teams were led by supervisors who conducted daily drop-in observations of data collection. Baseline data were collected one week before the intervention. Follow-up data were collected four to five weeks after the first modified dialogic reading training session.

# **Measures**

We use data from an adapted version of the Family Care Indicators (FCI) questionnaire to address the first study objective (Bradley & Corwyn, 2002; Hamadani et al., 2010; Kariger et al., 2012). The FCI includes questions about the variety of play materials and children’s books in the home, as well as stimulation activities in the three-day period preceding the interview. From the FCI data, we constructed a child-level indicator for having been read to in the past three days.
period than would have been the case in this counterfactual world. Figure 7 shows how these were spread out over time, with month 1 being January 2017. All of the changes are concentrated in the period after February 2018, getting more severe month by month.

## A similar calculation can be made for export tariff exposure leading to an estimated total

19The agricultural subsidies only have a positive and significant impact for high skilled workers (see column (5)), perhaps contrary to expectations. This could be because the online job postings data captures high skilled agricultural jobs more effectively than the more informal lower skilled agricultural jobs that are less likely to be published online.

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