rafmacalaba/data-use-mentions-extended huggingface dataset:
policy-imposing country by trading partner and by year in a conservative way that does not allow for redundancy.<sup>14</sup> In robustness checks, we also construct this variable using the antidumping policy alone and using all (non-redundant) TTB investigations, even those that did not result in the imposition of new import restrictions.

A second innovation relative to the prior literature is emphasis on a number of bilaterallydefined explanatory variables which enable us to focus on relationships between a policy-imposing economy and its key trading partners.<sup>15</sup> This is empirically relevant for two reasons. First, the temporary trade barriers under study can be imposed bilaterally so as to discriminate across import sources. Second, two of the key macroeconomic determinants of import protection in our model - trading partner _i_ ’s real GDP growth and the bilateral real exchange rate - vary bilaterally. Our dataset with bilateral variation also allows us to examine if countries apply import protection against trading partners facing their own economic shocks.

There are three main differences in variable construction relative to the approach adopted in Bown and Crowley (2013a). The first distinction is this paper’s use of data at the annual frequency, a limitation that the companion paper is able to overcome because data at the quarterly frequency is available for only a smaller set of high income economies. Second, due to data limitations for a number of emerging economies, we generally use domestic real GDP growth to capture the slowdown of the economy, whereas the companion paper used either the change in domestic unemployment rate or real GDP growth. The unemployment rate data series is not sufficiently available for all of the emerging economies in our analysis to use in the baseline estimates; however, we do employ it where 14 At any point in time in the sample period under the Harmonized System, there are roughly 5000 HS-06 imported products that could be imported from any particular trading partner. In terms of policy, governments impose these import restrictions at the 8- or 10-digit product level;
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fourth quarter of 2020’s Quarterly Report of the Sri Lanka Labor Force Survey covering up to 25,750 households, and from the Türkiye Household labor force survey covering 58,560 households for each quarter.</mark><sup>16</sup> <mark>The comparative data are drawn based on whether data are available in the same month as an online survey wave, or within one month before or after. The exception is Türkiye for which the comparative household labor force surveys are conducted two months prior to wave3 of the online survey. The details are presented in panel C in table 3.1.</mark><sup>17</sup>

# **<mark>3.2.  Data Description and Patterns</mark>**

Are the demographics represented in the online data nationally representative, or do they oversample or under-sample specific demographic groups? The answer to this question will indicate whether and how we can use the survey data to make broader conclusions, or if other adjustments will be required in order for them to be nationally representative. We compare the online survey’s wave-2 data with the GMD, and with the Internet-using population from the GMD if Internet usage data are available in that country.<sup>18</sup> The comparison with GMD relies on the assumption that the slightly dated GMD reflects the demographics at the time of the online survey accurately. Indeed, the pandemic itself could

> 13 <mark>The process of compiling the GMD is coordinated by the Data for Goals (D4G) team and supported by the six regional statistics teams in the Poverty and Equity Global Practice.</mark>

> 14 Admittedly, educational attainment might change drastically within a few years, and there have been instances of this in the past (for example, literacy rate changed from 53% in 2014 to 89% in 2019 in Côte d’Ivoire), but these types of drastic changes are minimal in the countries and period
links and 500 TB of raw data. By employing proprietary machine learning algorithms, Semrush analyzes this data to generate estimated metrics such as the number of visits, unique visitors, average session duration, etc. Furthermore, Semrush disaggregates these metrics by economies using users’ IP addresses, providing globally comparable data across both advanced and developing economies. For each website, Semrush also infers user profiles based on patterns of online behaviors. This dataset offers a unique opportunity to monitor a selected sample of generative AI websites’ traffic globally. Despite the inherent limitations of using website traffic as a proxy, it provides an effective means to monitor generative AI usage from a globally comparable perspective.

Utilizing Semrush data complemented with Google Trends data, this paper offers an in-depth analysis of generative AI usage by individuals across countries, focusing on both temporal trends and geographic disparities. We examine the adoption rates of various generative AI tools, identify key socioeconomic factors influencing usage, and explore the early impact of generative AI tools on traffic to other websites, people’s online activities, and broader implications. By highlighting outperforming and underperforming economies and surfacing factors shaping generative AI adoption, this study provides valuable insights into the global diffusion of generative AI technologies and their potential to drive transformative change. The paper is designed to provide descriptive insights that may prompt further questions and exploration. Our analysis seeks not to establish causal relationships but to highlight correlations and trends that could inform subsequent research. Several key findings have emerged from our analysis:

1. Rapid proliferation of generative AI tools and quick adoption. At least hundreds of generative AI tools exist as of March 2024. The top 40 most visited tools have nearly 3 billion monthly visits. Chatbots dominate the generative AI landscape due to their versatility and wider applicability, accounting for 95% of traffic among the top 40 tools. ChatGPT alone commands 82.5% of total traffic and boasts 500 million users per month, representing 12.5% of the global workforce. Impressively, it took only five months for ChatGPT
Heckman approach and Tobit. For the selection and relative concentration, however, Tobit and Poisson regression methods are not possible.

# 3.3 Stocks vs Flows

A critical choice in the investigations of the network/diaspora effect is the appropriate dependent variables. For instance, in their investigation of the determinants of international migration and in particular, the role of the bilateral wage differential, Grogger and Hanson (2010) use stock data (observed in 2000) which allows to focus on the long run effects. Not surprisingly, variables such as colonial links turn out to be strong predictors of stocks in the long run. Colonial links exert two separate effects. First, they allow people to move during colonial times and shortly after independence through special legal rights and arrangements. Part of those migrants are still included in the contemporaneous stock, depending on when the independence was acquired and how long the legal links were sustained. A second more indirect effect is that colonial links create a dependence path for future migrants through the assimilation and family reunification effects. The relationship between migration flows over a specific period and the size of the stocks at the starting point of that period allows to

> 2Santos Silva and Tenreyro, 2006 show that ignoring the zero trade country pairs lead to overestimation of other bilateral factors such as distance.

9
<sup>16</sup> The 1928 census provides a rich set of information on the socio-economic characteristics, e.g., literacy rate by age and professions, of the total Greek population and of the refugees separately. It also tabulates the characteristics of the population in 1920 for comparison. However, these information are available only at the province level level.

According to the refugee census, 778,000 refugees arrived in Greece in April 1923. After the population exchange, their number rose up to 1.07 million in 1928, accounting for 16.6 percent of the total population (out of 6.08 million). 53.9% of refugees settled in urban areas, while only 30% of Greek natives lived in cities in 1928. The three biggest cities in Greece, Athens, Piraeus, and Thessaloniki, hosted about half of the urban refugees. I exclude these three cities from the

> 15See Figure A.2 in the Appendix for the questionnaire (bulletin) and an example of historical table.

> 16This census was taken in April 1923, after the mass inflow of refugees but before the Lausanne Peace Treaty and the agreement on the forced population exchange between the Kingdom of Greece and Turkey. Figure A.1 in Supplementary Appendix presents the timeline of events that are relevant for the settlement and enumeration of refugees.

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38|1.40|0.18|
||**Domestic**<br>**(Private)**|**Foreign**|**t-test**|**p-value**|
|**SME Loans/Total Loans (%)**|11.67|10.56|0.29|0.77|
|**Investment Loans/SME Loans(%)**|22.80|21.48|0.27|0.79|

***** Total loans for banks are used to construct the weighted averages.
br>4.463|742474|70.83692468|
|Partners|6.416674<br>96.21|086|0.141|-<br>699<br>0.945396<br>7.16|868317|98.87093914|
||Number<br>wor|of fem<br>kers|ale|Number of female<br>administrative workers|Number<br>administ|of female non-<br>rative workers|
||Mean|Std|. dev.|Mean<br>Std. dev.|Mean|Std. dev.|
|FDI suppliers|3.057|91|.833|-0.110<br>0.931|5.839|95.029|
|FDI customers|-3.456|58|.424|0.087<br>0.964|-5.583|73.038|
|FDI siblings|3.821|58|.657|0.070<br>0.784|4.464|70.837|
|FDIpartners|6.417|96|.211|-0.142<br>0.945|7.169|98.871|

Notes: this table reports the mean and standard deviation of the bootstrap coefficients on the new indicators for FDI suppliers, FDI customers, FDI siblings and FDI partners from 100 regressions, where domestic firms are randomly assigned to FDI suppliers/customers/siblings/partners status according to industry proportions.

# **5. Conclusion**

This paper studies whether the presence of FDI firms has positive spillover effects on the gender labor practices of domestic firms in developing countries. Based on a unique linked firmto-firm data set of apparel and textiles sectors in Bangladesh, the paper first shows that FDI firms hire more female workers, particularly administrative and production workers. Domestic firms 18
participation in both sectors are not surprising, given the direct and indirect linkages of the cocoa sector to the rest of the economy.

The crop results can be explained to some extent by the relation of the crops to markets, both export and internal. Cash crops such as cocoa, fruits & vegetable, starches, and to a lesser extent oils & pulses had different trajectories over the period and presurnably different linkages to the non-farm sector. The results may be interpreted to show that the crops of cereals, oils & pulses, and starches which did not have strong linkages to the non-farm sector grew in relative importance as determinants of agriculture participation and as an alternative to non-farm work. Cocoa's fall had a negative effect on both sectors, presumably because it had strong linkages to markets. Similarly, fruit and vegetables also having lirkages to non-farm markets show positive impact on agriculture and on non-farm though the impacts on non-farm are not significant. These explanations are only to be considered as possible explanations for the results. Much better data is needed to fully understand the changes in the agricultural sector and how they have impacted labor force participation.

- _6.2 Uganda_

As with the Ghana data, we estimated bivariate models of participation in agriculture and non-farm employment for Uganda for 1992 and 1996 _(Table 17 shows summary statistics and the est;imation results are in Tables 18 and 19)._ We include the same independent variables used in the Ghana base model with the exception of distance to market and agricultural acreage which were not available in the Uganda data.<sup>8</sup> It is

> 8 Agricultural acreage is available in the 1995 Monitoring Survey from Uganda, but we chose to use the 1996 survey for the analysis since it :is most recent and has more information about labor market participation.

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<!-- Start of picture text --> Counting vector CCDF by scenario Difference between counting vector CDF curves<br>First-order stochastic dominance (α=0)<br>0 .2 .4 .6 .8 1 0 .2 .4 .6 .8 1<br>Counting vector (relative scale) Counting vector (relative scale)<br>Baseline Alternative Confidence interval (95 %) Estimated difference<br>C6.3. SOSD: Social Sustainability Index C6.4. Rank correlations<br>Social gaps Rank correlation Cutoff values<br>Social Sustainability Index by cutoff and scenario<br>Second-order stochastic dominance Metrics coefficient k = 33% k = 50% k = 75%<br>Spearman 0.92*** 0.95*** 1***<br>H<br>Kendall tau-b 0.78*** 0.89*** 1***<br>Spearman 0.93*** 0.8*** 0.63*<br>A<br>Kendall tau-b 0.83*** 0.67** 0.5*<br>Spearman 1*** 0.92*** 1***<br>M0<br>Kendall tau-b 1*** 0.78*** 1***<br>0 20 40 60 80 100<br> Cutoff k (relative scale) ***p<0.01, **p<0.05, *p<0.1<br>Baseline Alternative<br>.1<br>1<br>.8<br>0<br>.6<br>CCDF<br>.4 Difference<br>-.1<br>.2<br>0 -.2<br>.4<br>.3<br>.2<
people may also gain from conflict in terms of income and wealth and this may explain why some people do not move. The defining attributes of the alternative choices are very different from any other model and the task of economics is to understand what these defining attributes should be.

In terms of independent variables, “push” factors become more important than “pull” factors in forced displacement models. The intensity of a conflict may be more important than the income opportunities in potential destination areas. In addition to the classic socioeconomic variables, risk aversion, stress, anxiety, other traits of personality and behavioral factors in general have to be well understood and measured. Hence, one could think of four essential blocks of independent variables including individual or household socioeconomic characteristics, “push” factors, “pull” factors and behavioral factors. Also, access to and dissemination of information related to the conflict in the place of origin but also in the potential places of destination may be crucial for people to make choices. This is where social psychology, behavioral economics and neuroeconomics may offer insights into such choices.

Forced displacement data are also unusual in their form. Deciding on whether to flee or not to flee a conflict (the migration choice) can be an individual or household choice and risk coping strategies may include temporary migration, shuttling between places, migration of only selected members of the households or migration of the whole household. This implies that individuals may stay put throughout the period observed, join or leave the household during the period, or have several episodes of out and immigration. Households may decide to leave and come back several times. In econometric terms, this means that longitudinal data may be left and right censored and have spells within. They are therefore the most complex set of panel data possible and require particular treatment of data and modeling. Survival or duration models can usually accommodate many of these complexities but it is very rare to find similar data sets used in published articles. Collecting such type of data is also not
# **Does Maternal Depression Undermine Childhood Cognitive Development? Evidence from the Young Lives Survey in**

# **Peru**<sup>**1**</sup>

Magdalena Bendini<sup>2</sup> Lelys Dinarte<sup>3</sup> _Keywords_ : Child Development, Child Vocabulary, Maternal Mental Health, Peru _JEL classification_ : I15, J13, O15

> 1 We are very grateful to Omar Arias, Monserrat Bustello, Rafael de Hoyos, Deon Filmer, Carol Graham, John Giles, Asif Islam, Kabir Malik, Amparo Palacios, Veronica Schiariti, and Pamela Surkan, whose comments and suggestions improved different drafts of the paper. We also gratefully acknowledge the Young Lives Project for permission to use the data. The findings, interpretations, and conclusions expressed in this paper are entirely ours and not those of the Young Lives Project, the University of Oxford, DFID or any other funders. They also do not represent the views of the World Bank.

> 2 Education Global Practice. The World Bank. email: mbendini@worldbank.org

> 3 Corresponding author. Development Research Group. The World Bank. email: ldinartediaz@worldbank.org

1
number of small commercial banks. However, many of these banks were created to serve particular interest groups for licit and illicit activities and could not and would not be interested in serving the public for savings and credit purposes. The fragility of the systems quickly emerged with the first bankruptcies and governments responded by strengthening banking regulations which resulted in a reduction in the number of banks and in an increase of the average size. In the late 1990s, the typical banking system in these countries included a few tens of banks with two-three banks owning over half of the consolidated capital in the system. To date, financial systems remain very weak and vulnerable, lending rates are high with large interest spreads between credit and lending rates and very low access for small businesses and farms. Sources of capital are also almost exclusively enterprises which are obliged to operate with banks by law while only a very small percentage of households holds bank deposits (Pomfret 2003).

The 1998 Russian financial crisis, while it had some positive externalities on exports, delayed reforms further. Comparing the pre and post crisis period, the EBRD transition indicators deteriorated in most countries<sup>7</sup> with the result that, at the outset of the new millennium, transitional reforms were still largely incomplete and markets were not supported by the necessary institutional infrastructure.

# **Growth and the labor market**

The scale of the general _output_ decline has been faster and deeper in the CIS-7 than elsewhere in transitional economies (figure 1.1). By the end of the recession in 1995, the average GDP stood at 55% below the 1989 level with Armenian and Georgian output having fell by a staggering 70% between 1989 and 1993. As elsewhere in the CIS, the deep recession has been followed by a macroeconomic stabilisation period between 1994 and 1995 that halted the output decline and brought down inflation to manageable levels. Since 1996 and despite the 1998 crisis, the CIS-7 have been able to re-establish growth with an estimated cumulated and cross-country average for the
were no longer credit constrained post-intervention then we would have expected overall borrowing to go up rather than seeing a shift away from education loans. In on-going research we are exploring possible reasons for why subsidy ineligible households appear to remain overall credit constraints in this context.

Our findings for subsidy eligible households are not as conclusive. While Table 10 reveals that the average subsidy eligible MF client household responded to the micro-credit intervention by significantly increasing its overall MFI borrowing, it did so without reducing its uptake of other types of loans (Table 11). This combination of findings does not allow us to identify whether these households decided to take up the sanitation loans in response to the favourable interest rate, and/or because the provision of additional credit relaxed constraints in obtaining sufficient credit to make other types of non-sanitation investments. In the next section we explore further the extent to which micro-credit relaxed credit constraints for subsidy eligible households.

# **5.3 Complementing subsidy support to subsidy eligibles**

The results in the previous sections reveal that, on average, a significant portion of subsidy eligible households took up the sanitation loans, and that these loans were taken in addition to their existing loan portfolio, resulting in an increase in their overall borrowing. And yet, on average, this loan uptake did not translate into a significant number of new toilets. In this section we move beyond average numbers and explore whether micro-credit was able to support particular sub-groups of subsidy eligible households in making sanitation investments, and how. Specifically, we consider the potential use of micro-credit as a means to bridge the time gap between toilet construction and actual subsidy disbursement; before survey over MFI loan amount as recorded in the credit bureau data. Note that here we inevitably assume that the respondent’s misreporting behavior is the same across different types of lending sources.

20
amount of time each municipality spends (as a percentage of fortnights) in yellow, orange, and red (see Table A.1 for descriptive statistics on the warning index and the share of time in each category).

Figure 3. Distribution of warning index and time spent in each color category _Source_ : Authors’ calculations from Guatemalan municipal-level data. _Notes_ : The figure presents the distribution of the warning index and time spent in each color category (as a fraction of the school year) in post-COVID years, 2020 and 2021. The dotted vertical line shows the mean.

The average warning index in 2020 was 5.73, which increased to 6.94 in 2021; with standard deviations of 0.970 and 0.865, respectively. During 2020, municipalities spent 15.4% of the total school year in green (pre-pandemic weeks), 28.1% in yellow, 24.6% in orange, and 31.9% in red. In 2021, these shares shifted to 0, 32.2, 25, and 42.8 percent for green, yellow, orange, and 14