rafmacalaba/data-use-mentions-extended huggingface dataset:
income or consumption from surveys. Interestingly, we do not see as much narrowing of the gap among the richest countries for GDP, as with HFCE. This results from a widening of the gap between HFCE and GDP over time. Upper-middle-income countries have the largest gap, on average, but it is not much larger than that of the high-income countries in our sample. Additional estimates for gaps in levels with different weighting schemes are available in Appendix 2, Table A2.1.

The larger gaps between national accounts and surveys observed in middle-income countries may reflect the difficulty of measuring consumption in household surveys in the context of rapid growth and associated changes in consumption patterns. The experience of revisions to Vietnam’s consumption survey in 2010 is pertinent to this (see Badiani et al., 2013). From the early 1990s to 2010, a period of rapid economic growth in Vietnam, the national statistics office made few changes to the consumption survey instrument and methodology, resulting in a failure at capturing new durable consumption items, such as cellphone and computers. By 2010, the country’s much greater affluence necessitated the use of a revised consumption questionnaire and survey methodology. Consumption measures from the old survey methodology, which reflected the consumption patterns in Vietnam in 1992, was just 78 percent of what was measured as the average household budget in 2010 using the new survey instrument and the new methodology for measuring household consumption. Household consumption in the surveys leading up to 2010 were falling further and further below the consumption captured in the revised survey instrument. The example of Vietnam gives some context as to how the surveys fail to capture consumption as countries get richer. At the same time, one should recognize the importance of maintaining consistent series of consumption data over time. One approach to combine consistency and revise methodology would be to generate several consumption aggregates chaining new and old survey methods, as commonly done when revising national accounts.

# **_HHS – NAS gaps, growth rates_**

We now turn to assessing how growth rates
imports. This process would continue until the balance of payments surplus is eliminated. The opposite would happen in the case of a balance of payments deficit.

> 9 Individual production activities do not respond to changes in relative prices for exports and domestic sales if their output only has one destination, either exported in full or sold domestically in full. In the Congo database, construction and all types of government services are only sold domestically. By the same token, domestic demanders do not have a choice between imports and domestic output for commodities that only have one source; in the Congo database, this is the case for the forestry, oil and government service sectors. Such structural features reduce the flexibility of Congo’s economy.

18
Care Policy to facilitate coordination and implementation of care policies at the national level. This coordination can lead to more efficient resource utilization and better outcomes.

4. Recognize the importance of local leadership in narrowing gender gaps. Local and regional knowledge and engagement are crucial for effectively challenging culturally entrenched discriminatory norms and driving change<sup>11</sup> from within communities. Consequently, local leaders can have a significant role in promoting change.

5. Enhance public information systems with gender breakdowns to enable better diagnosis and design of public policies.

The findings also emphasize the vital role of education in improving working conditions for women. Higher levels of education are associated with reduced income gaps, increased labor

> 11 Lachapelle (2020) provides empirical evidence, based on self-reported data, which demonstrates the significance of training programs for leaders in enhancing their engagement with their community and objectives. Furthermore, Nwaze and Mohammed (2013) present both quantitative and qualitative evidence supporting the crucial role of traditional and religious leaders in promoting initiatives towards the eradication of polio in Nigeria.
) or “Universal Education Campaign.” Partly financed by a special education tax, this national program sought to correct historical inattention to primary education and led to a substantial increase in annual spending on primary education across several major categories of inputs including school infrastructure, teacher quality, studentteacher ratios, and school feeding programs.<sup>1</sup> However, the public education system in India also faces substantial governance challenges that may limit the extent to which this additional spending translates into improved education outcomes. Our indicator of systemic inefficiency - teacher absence - presents a particularly striking indicator of weak governance. A nationally-representative study of over

> 1Total spending on education more than doubled between 2004 and 2009. In the year 2004-2005, India’s education budget was Rs.1,528 billion ($25 billion) and it increased to Rs.3,783 billion ($60 billion) in 20092010 (Pratham, 2010).

1
# I. Introduction

The collapse of Lehman Brothers in September 2008 had tremendous effects on financial markets across the globe. What started as a US crisis spread rapidly across both advanced and developing countries. It was transmitted worldwide through financial markets, international banks and trade links, and affected many economic sectors (Imbs, 2010; Ahn, Amiti and Weinstein, 2011; Cetorrelli and Goldberg, 2011; Chudik and Fratzscher, 2012; IMF, 2013).

These events offer an important opportunity to study how financial and macroeconomic instability affect the capital structures of firms. How did firms’ capital structures evolve after the onset of the Global Financial Crisis in 2008? Were all firms affected equally or were privately held firms, especially small and medium‐sized enterprises (SMEs), especially hit? Did capital structures change only in the countries that experienced a banking crisis, or did they change in many countries, depending on their specific institutional, financial, and macroeconomic characteristics? We particularly focus on two characteristics of firms: market access and size. It is important to distinguish between firms that are publicly‐listed and privately‐held, because the former are likely to enjoy better access to capital market financing than the latter. Importantly, greater information availability in being a listed firm may also mitigate the reduction of bank credit during a crisis. In contrast, an unlisted SME’s access to external finance is more likely to depend on specific banking relationships relative to other firms, and, therefore, may be more severely affected by negative bank credit supply shocks due to their intrinsic opaqueness.

This paper examines these questions by relying on a unique firm‐level data set covering more than 270,000 firms operating in 79 countries during the period 2004‐2011. We study how 2
Financial Regulation and Development Figure 5: Policy Change and Trends: NBE Bills and Safe Assets <!-- Start of picture text --> 0 5 10<br>Quarters<br>NBE Bill − Small B. NBE Bill − Big B.<br>Safe Assets − Small B. Safe Assets − Big B.<br>1500<br>1000<br>500<br>Million Birr − Real 2010<br>0<br><!-- End of picture text --> _Note:_ This figure plots the coefficients of the overall trend exhibited by small and big banks for NBE bills and safe assets over all quarters available in the data (NBE bills in black and safe assets in green). Big banks are reported using a solid line, while small banks are reported with a dashed line. The policy is announced in mid-March 2011 and implemented in April 2011 (shown by the vertical red line). As is evident, there occurs an important discontinuity around the policy introduction (Quarter 5) and larger banks respond substantially more than smaller banks by purchasing more NBE bills and purchasing overall more safe assets, though their composition changes. As is evident from Table 1, the pre-policy trends are not statistically different from zero, while post-policy all of them are. Appendix F reports the same picture with 95% confidence intervals, also exploiting two-month dummies rather than quarters.

# **2.2.2 Evidence from a Branch Map of Ethiopia**

In this section, we present further evidence on a key feature of Propositions 1 and 2, where we show that the 27% rule is associated with greater branch expansion. In order to test this hypothesis, we construct a map of all branches in Ethiopia, where for each bank we know all the branches installed, their region and city of introduction with the month and year of opening. Our map covers 2,023 branches, installed by all 14 banks registered until 2013 and opened between
consumption levels, we construct a measure capturing the distance in terms of consumption from the national poverty line. This distance, reported as a fraction of the consumption level and set to zero for households above the poverty threshold, is an indicator of the resources needed to bring an average poor household to the level of the poverty line. As it is unlikely that migration _per se_ is able to bring households out of poverty, the distance from the poverty line is meant to estimates the extent to which migration can improve the livelihood of those living below the poverty line. Our estimates, reported in Table 8, indicate that overall migration reduces the gap by about 11 percentage points, with no economically significant differential impact by year of migration. The coefficients is also similar among migrant households from Coastal and Inland Ghana: on average it is about 18 percentage points among the first, and 16 percentage points among the latter.

# 5 Concluding Remarks

This paper investigates the impact of internal migration on household welfare in Ghana. We make use of the most recent nationally representative household survey conducted in 2012/13 and controlling for region of origin and adopting an inter-regional definition of migration, we identify the impact of migration on the consumption level of households that migrated relative to

> 17 Results are available upon request.

16
ing, such as “Optimize”, “Diagnose”, “Analyze”, and “Code”. These complementary effects may indicate ChatGPT’s current limitations in these areas, prompting users to verify information on Google. On the contrary, words related to language processing and simpler tasks like “Translate”, “Write”, and “Edit” show negative correlations, highlighting ChatGPT’s strengths in these areas.

While acknowledging the imperfections in our measures and results, these trends provide valuable direction for further exploration as more comprehensive data becomes available. Moreover, this analysis reinforces our confidence in the website traffic data and the insights derived from our previous sections. The observed patterns suggest a nuanced impact of ChatGPT on information-seeking behaviors, with varying effects across different types of tasks and knowledge domains.

# **8 Conclusion**

This paper leverages unconventional data, including website traffic data and Google Trends, to shed light on generative AI adoption patterns by individuals across countries. Our analysis yielded several key findings: First, we documented a proliferation of generative AI tools and their rapid adoption, noting the dominance of chatbots in the generative AI landscape, signs of market saturation and intensifying competition. As of March 2024, There are at least hundreds of generative AI tools around the world. The top 40 most visited new generative AI tools collectively have nearly 3 billion visits per month and hundreds of millions of unique users. Among the top 40 tools, chatbots occupy the top seven positions and account for a staggering 95% of the traffic, with ChatGPT alone commanding 82.5% of total traffic. Nonetheless, traffic to some generative AI tools has plateaued since mid-2023, hinting at market saturation, intensifying competition, and potential challenges for future growth.

Second, users of generative AI tools heavily skew toward young, highly educated males compared to general internet and Google users. The youth and male bias is particularly pronounced for video generation tools. While users of image and video generation tools have similar education backgrounds as Google users
# **INTRODUCTION**

It is argued that carbon pricing allows reaching the internationally accepted temperature targets at minimal cost (Gollier and Tirole 2015; Borissov, Brausmann and Bretschger 2019). The report of the Commission on Carbon Pricing explains the role of education and skilled labor for the transition to green growth (Stiglitz et al. 2017). It explains that production using clean technologies is human capital intensive so that education decisions become closely interlinked with the energy transition. At the same time, shortages of skilled labor may constrain the decarbonization process of an economy (Faye 2012). The OECD (2017) argues that the path to a green economy can only be made by developing the right skills. Carbon pricing - by its impact on human capital accumulation - may not only be the cornerstone of climate policy but may also act as a development policy, fostering technology switches and education of the labor force (Borissov, Brausmann and Bretschger 2019). Yet, one mitigating factor that has received less attention is education quality. If technological change that reduces the reliance of production on emissions is skill-biased, then carbon pricing may increase the skill premium of earnings and subsequent wage inequality; however, a more elastic skill supply through better education quality may mitigate adverse economic outcomes including wage inequality and enhance the effect of carbon pricing on technological change and subsequently emissions. This paper explores the role of skills as measured through cognitive achievement on emissions through carbon pricing in European Union countries.

Many governments are enacting policies to place a price on greenhouse gas emissions, including the establishment of carbon taxes and cap-and-trade systems, to reduce emissions. For example, 31 high income OECD countries have one or more carbon pricing schemes, including 14 countries with carbon or emissions taxes and 25 countries with an emissions trading system (Yunis and Aliakbari 2020). According to the World Bank’s Carbon Pricing Dashboard (World Bank 2021), 27 countries have implemented a carbon tax nationally, and nine countries have implemented an emissions trading system; these figures exclude
**(7)** Excelsior Latin America, **(8)** Govett Latin America, **(9)** Ivy South America, **(10)** Scudder Latin America, **(11)** T. Rowe Price Latin America, **(12)** Merrill Lynch Latin America, and **(13)** Templeton Latin America. Not all of these funds existed from the beginning of our sample; on average we have about **10** quarters of data (out of a possible 24) per fund.

Our third source of data is Bloomberg and the International Finance Corporation **(IFC).** Bloomberg provides monthly price series for all equities held **by** the **13** funds, including ADRs. (The need for monthly price data arises in our analysis of lag-one momentum trading.) These price series are corrected for splits and dividends. The **IFC** provides information on stock market indexes, which we need for our contagion trading analysis. Our contagion trading analysis uses the **IFC** Latin America Stock Market index, the **IFC** Asia Stock Market index, and several **IFC** country stock market indexes. The **U.S.** equity return is the **S&P 500** return. **All** return data are expressed in percent 14
sup>2</sup>|0.201|0.287|0.240|-|
|Endogeneity test statistics|χ<sup>2</sup>(1)=1.950|χ<sup>2</sup>(1)=7.810|χ<sup>2</sup>(1)=1.119|χ<sup>2</sup>(1)=3.934|
||p=0.162|p=0.005|p=0.290|p=0.047|
|N|11,200|11,200|11,200|11,200|

Note: The FE unit is thana times year. A household’s adoption of microenterprise activity is considered endogenous and so is instrumented using village-level placement of banks and microcredit programs and their interactions with householdlevel exogenous variables as instruments. For the estimation, data from 2000, 2005 and 2010 rounds of HIES are combined and then households from only those thanas that are common to all three rounds are kept. Figures in parentheses are t-statistics. Regressions additionally include community prices of consumer goods, daily wage, etc., and agroclimate characteristics (land elevation, average number of sunny months, and excess rain amount per month). Sources: HIES surveys, 2000, 2005 and 2010.

40
called the _propensity matrix_ . The second is the rate of transition, and is referred to as the _rate of separation matrix_ . By decomposing the transition matrix into the propensity matrix and the rate of separation matrix, we can determine if movements to employment states observed in the transition matrix are reflecting greater entry of certain age groups into certain employment states or if the observed transitions are simply due to greater turnover by certain age groups in general.

Finally, elements of the propensity matrix allow us to estimate the duration of each state of employment.

All three methodologies take advantage of the panel nature of the three data sets utilized in this paper. In particular, we can follow the labor market status of young people at discrete moments in time over a one month (Brazil), three month (Mexico), and six month (Argentina) period.

# _Transition Matrices_

Transition matrices are generated using panel data, where each cell denotes the probability of moving between an initial labor market state i to a final labor market state j. Each cell of the transition matrix is a simple probability where: Where pij is the probability that a person moved from some initial state i into a final state j for i=1, ..., K and j=1, ..., K. The term nij is the number of people who were in state i and moved to state j between periods t and t+1 and ni is the number of people who were in state i in period t. The transition matrix is denoted by: This matrix will be used in three ways. First, we are interested in the share of people transition into employment; i.e. transition from any out of employment state i into any 6
between 2007 and 2018 (see Figure 14). This means that in both 1987 and 1997 sectors that had a relatively high labor productivity subsequently increased their share of overall employment. In 2007, the opposite was the case and relatively unproductive sectors increased their employment share in the ensuing ten years. Also, despite the overall positive correlation between initial labor productivity and subsequent labor reallocation for the periods from 1987 to 1997 and from 1997 to 2007, the employment share of manufacturing as a prominent sector with a relatively high level of labor productivity declined by about 4.6 percentage points between 1997 and 2007. In parallel, the share of employment in the trade services sector, which had a below average level of labor productivity in 1997, increased by about 5.1 percentage points in the ensuing ten years.

**Figure 12: Relationship between initial productivity and changes in employment share, 1987-1997**

<!-- Start of picture text --> 0.8<br>0.6<br>trn<br>0.4 bus<br>0.2 man<br>0<br>agr oth con<br>-0.2<br>-0.4 trd<br>-0.6<br>-0.8<br>-1<br>-20 -15 -10 -5 0 5 10<br>Change in share of employment (%), 1987-1997<br>aggregate productivity in 1987<br>Log of ratio of sectoral productivity to<br><!-- End of picture text --> Source: Authors’ calculations based on data from DOSM Note: Sectors depicted are agriculture, business services, construction, manufacturing, trade services, transport services, and other services.

**Figure 13: Relationship between initial productivity and changes in employment share, 1997-2007**

<!-- Start of picture text --> 0.8<br>bus<br>
savings histories.<sup>5</sup> Eligible clients receive an SMS message letting them know how much they may borrow and that they can obtain the loan through a Baobab agent. If a client pays back the loan within 15 days, she becomes eligible for a larger loan in the next cycle. If a client repays the loan within 16-30 days, she is eligible for the same amount in the next cycle. However, if the client does not repay the loan within 30 days, she is not eligible for another Taka loan in the near future. Because the convenience of agent networks facilitates loan repayment, and because the loan amounts offered are rooted in analysis of account activity, this “accelerator” approach has led to substantial credit growth for BSN (and BMG).<sup>6</sup> Digital credit, sometimes referred to as mobile credit or nano-credit, is easy to access and can be approved and disbursed very quickly and thus has shown dramatic growth in countries such as Kenya. Using data from Kenya, Bharadwaj, Jack, and Suri (2019) show that digital credit makes households more resilient to negative shocks. But digital credit is also characterized by high default and delinquency rates. Using data from a digital lender in Mexico, Burlando, Kuhn and Prina (2021) exploit quasi-experimental variation in the speed of disbursement to study its impact on repayment. They find that by doubling the delivery time from ten to twenty hours, the likelihood of default is reduced by 21 percent. Besides the speed of delivery, default in digital credit may be brought about by poor business practices and weaknesses in credit reporting.<sup>7</sup> Digital lenders, which encompass a range of institutions beyond MFIs, such as Fintech companies and cellular providers, do not consult nor report to credit reporting bureaus fully or regularly. In large part, this is because credit reporting bureaus were set up for a pre-digital era. They are not inclusive of all types of lenders and face
an area-level model, predictions from a household “unit-context” model specified with area-level predictors were equally accurate in sample, but markedly more accurate out of sample. In sum, there is a clear ranking in accuracy across the different methods for incorporating the geospatial auxiliary data: The household model was usually more accurate, and never noticeably less accurate, than the two other methods. This was generally followed by the sub-area model estimates, and finally the area-level model estimates. There is no evidence in any of the settings considered that area-level models give estimates that are more accurate and efficient, or less biased, than household-level unit-context models.

All geospatial small area estimates, however, remain substantially less accurate than those generated using survey and household census data from 2010. This suggests that municipal poverty patterns changed slowly in Mexico during this time, although the methodology used to generate the 2015 benchmark estimates may also not fully capture transient changes in welfare.<sup>24</sup> The correlation between the 2010 estimates and the 2015 estimates is very high: 0.91 in sample and 0.90 out of sample. Thus, if forced to use municipal poverty estimates to inform a pro-poor budget allocation in 2015, the most accurate source would be the 2010 census-based estimates. Analysts should therefore exercise healthy caution before jettisoning moderately older census-based estimates in favor of newer small area estimates based on geospatial data.

The high accuracy of older census-based estimates, however, may not generalize to other contexts, and more research is needed to better understand under what circumstances older census-based estimates give more accurate predictions than recent geospatial poverty estimates. This will depend on the extent to which regional poverty patterns have changed in the country since the last census, as well as the size of the sample survey and the predictive power of the satellite-based auxiliary data, among other factors. It is also likely that the predictive performance of geospatial and other big data will continue to improve as more and better indicators become available.

In addition, surveys can also be fielded