rafmacalaba/datause-encoder-data huggingface dataset:
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, chatbot users are more highly educated, likely due to frequent usage by college-educated white-collar workers for professional purposes. In contrast, usage of image and video generation tools tend to be more experimental and recreational. Generative AI tools are also primarily accessed via desktop computers and used during weekdays, indicating that they are 44
the heterogeneity in firm formation we study the formal sector and informal sectors separately in this paper and do find differing life-cycle patterns. Our findings are also consistent with Ayyagari, Demirguc-Kunt, and Maksimovic (2013) who find the upward sloping size-age profile to be pervasive across a vast majority of developing countries.

The remainder of the paper is organized as follows. In section II we describe the data. In section III we discuss in detail the relationship between firm size and age in India. In this section we also reconcile our findings on the financial sector in India with the broader finance and growth literature. Section IV concludes.

# **II Data**

We use data on formal manufacturing plants in India from the Annual Survey of Industries (ASI), which is conducted by the Indian Ministry of Statistics and Program Implementation. The ASI sampling frame consists of all registered factories employing 10 or more workers using power or 20 or more workers without using power. The sampling frame consists of the “Census” sector which are surveyed every year and the “Sample” sector where plants are sampled randomly and unit multipliers are provided to take into account sampling probabilities. The definitions of the Census and Sample sectors have changed over the years as described in the table in Appendix A.<sup>8</sup> In robustness checks, we find all our results to hold when we just analyze the Census sample. We use data from five years: 1983/84; 1989/90; 1994/95; 2000/01, and 2004/05.

> 8 The ASI also contains some establishments outside of manufacturing. Thus while the primary unit of enumeration in the survey is a factory in the case of manufacturing industries, it could also be a workshop (for repair services), an undertaking or a licensee (electricity, gas & water supply undertakings) or an establishment (bidi & cigar industries). According to the Ministry of Statistics, “the owner of two or more establishments located in the same State and pertaining to the same industry group and belonging to census scheme is, however, permitted to furnish a single consolidated return. Such consolidated returns are common  feature mostly in the case of bidi and cigar establishments, electricity and certain public sector undertakings”

8
Edmonds, E. and N. Pavcnik 2005. “The effect of trade liberalization on child labor.” Journal of International Economics. 65:401-19.

Evenson, R. 2001. “Economic impacts of agricultural research and extension” Ch 11 in Evenson, R. and Pingali, P. eds. Handbook of Agricultural Economics, 1(1):573-626, North Holland, Amsterdam .

Friedman, J. and Levinsohn, J. 2002. "The distributional impacts of Indonesia's financial crisis on household welfare: a "Rapid Response" methodology," World Bank Economic Review 16(3):397-423.

Hertel, T. and Winters, L. A. eds. 2006. Poverty and the WTO: Impacts of the Doha Development Agenda. Basingstoke and Washington DC: Palgrave-Macmillan and the World Bank.

Hertel, T., Ivanic, M., Preckel, P. and Cranfield, J. 2004. “The earnings effects of multilateral trade liberalization: implications for poverty.” World Bank Economic Review 18(2):205-36.

Ianchovichina, E. and Martin, W. 2004. “Economic impacts of China’s Accession to the World Trade Organization” World Bank Economic Review 18(1):3-28.

Klytchnikova and Diop 2006. “Trade Reforms, Farm Productivity, and Poverty in Bangladesh.” Policy Research Working Paper 3980, World Bank.

Malawi, Malawi Government Statistical Office. 2004. Second Integrated Household Survey 2004 Martin, W. and Mitra, D. 2001. Productivity growth and convergence in agriculture versus manufacturing. Economic Development and Cultural Change, 49(2):403-22.

Minot, N. and Goletti, F. 2000. Rice Market Liberalization and Poverty in Viet Nam.

Research Report 114, International Food Policy Research Institute, Washington DC.

23
which we ignore the data collected from previous waves (pseudo-panel data) as it could entail some recall bias (we are asking for information about the first round in subsequent rounds). Though there is some small variation in terms of the magnitudes of the coefficients (between one and two percentage points in the case of change in sales), the qualitative interpretation and the statistical significance of the results do not change.

Table 6: Probability of receiving support

|Dependent variable=1 if received support|Conditional|Cont. initial<br>productivity|Conditional|Cont. initial<br>productivity|
|---|---|---|---|---|
|Informality|-0.0977**|-0.125**|||
||(0.0176)|(0.0219)|||
|Necessity|||-0.0462**|-0.0622**|
||||(0.0133)|(0.0149)|
|De Soto|||-0.0370**|-0.0450**|
||||(0.0110)|(0.0123)|
|Parasite|||-0.0314**|0.00729|
||||(0.0113)|(0.0163)|
|% change in sales during past 30 days|0.000586**|0.000697**|0.000574**|0.000675**|
||(0.0000937)|(0.000110)|(0.0000925)|(0.000109)|
|Intial productivity||-0.000408||-0.00198*|
|||(0.00104)||(0.00109)|
|Panel controls|Yes|Yes|Yes|Yes|
|Other controls|Yes|Yes|Yes|Yes|
|Observations|14078|11086|14368|11227|

**N** otes: All continuous variables are winsorized at the 1% and 99%. Recovery is calculated as the difference in the changes in sales across rounds of data collection. Robust standard errors in parentheses * p<0.10, ** p<0.05, ** p<0.01 Source: Authors’ calculations using data from the World Bank Business Pulse Survey (BPS).

_Initial thresholds_ : A third exercise related to our _productivity for classifying informal firms_ classification of firms according to the views of informality is related to the threshold in terms of the level of pre-pandemic productivity of formal firms (quartiles). We tested with alternative thresholds, for example, top and bottom 30% or top and bottom 20%. In all cases, our results remain robust, with the magnitude of the coefficients being within the one percentage point range of the base model.

: _Alternative definition of informality_ A final robustness check is related to using a sharper definition of informality following Ulyssea (2018) and Ulyssea (2020). To do this, we define an extensive margin as those firms that do not have a registration and do not pay taxes. We use an intensive margin definition identifying among those that are formal on the extensive margin, which ones do not provide social security for their workers or do not use a bank account. Our results using such dichotomous variables (instead of the ones from continuous measures or types of informality) show very similar magnitudes.

25
Table H.2. Heterogeneous Effects on Indices by Social Desirability Score

||Prosociality<br>Index<br>(1)|Attitudes<br>Index<br>(2)|Beliefs<br>Index<br>(3)|
|---|---|---|---|
|Game [=1]|0.250***|0.331***|0.158**|
||(0.080)|(0.079)|(0.081)|
|Video [=1]|0.194**|0.331***|0.020|
||(0.090)|(0.085)|(0.087)|
|Social Desirability Index (SDS)|-0.083|-0.127*|-0.174***|
||(0.067)|(0.066)|(0.056)|
|Game x SDS|0.132|0.056|0.119|
||(0.083)|(0.081)|(0.080)|
|Video x SDS|0.022|0.109|0.208**|
||(0.093)|(0.087)|(0.083)|
|P-value: Game + Game x SDS = 0|0.001|0.000|0.009|
|FDR q-value: Game + Game x SDS= 0|0.002|0.001|0.007|
|P-value: Video + Video x SDS = 0|0.119|0.001|0.070|
|FDR q-value: Video + Video x SDS = 0|0.042|0.002|0.029|
|Av. Dep. Var. (Control group)|0.000|0.000|0.000|
|Observations|823|822|822|

_Notes:_ This table presents OLS estimates derived from Equation 1 with interactions between the treatment exposure indicator variables and the standardized variable for the Social Desirability Score (SDS). SDS is a measure of an individual’s propensity to report socially desirable answers. It represents a standardized variable of a measurement scale ranging from 0 to 4, where 0 indicates that the respondent does not exhibit social desirability bias, and 4 represents the highest level of this measure. The dependent variables are constructed following the methodology outlined by Kling, Liebman and Katz (2007) for each group of outcome variables: (i) Prosocial Behavior (detailed in Table 2), (ii) Attitudes (detailed in Table 4), and (iii) Beliefs (detailed in Table 5). All columns control for student status and gender, the stratification variables. Robust standard errors are reported in parentheses. The reported p-values, such as Game + Game x SDS = 0 and Video + Video x SDS = 0, represent the p-value of a Wald test for each linear combination. Additionally, the False Discovery Rate (FDR) q-values for Game + Game x SDS = 0 and Video + Video x SDS = 0 are provided. Significance levels are denoted as<sup>_∗∗∗_</sup> at 1%,<sup>_∗∗_</sup> at 5%, and<sup>_∗_</sup> at 10%.

58
19 severity. Also, the Northern belt has a 9.1 percentage points decline in headcount, 2.1 percentage points decline in depth and 0.4 percentage points increase in severity.

**Table 4.8: Rural/Urban Poverty Incidence in Nigeria by Geographical Belts, 1985-93 (Poverty Line-N395.41)**

||||**_RURAL_**||||
|---|---|---|---|---|---|---|
|||**1985/86**|||**1992/93**||
|**Region**|**P0**|**P1**|**P2**|**P0**|**P,**|P2|
|Northern|0.555|0.219|0.112|0.464|0.198|0.116|
|Middle Belt|0.521|0.219|0.120|0.385|0.179|0.112|
|South Belt|0.420|0.144|0.066|0.254|0.102|0.057|
|_All Rural_|_0.495_|_0.169_|_0.098_|_0.364_|_0.167_|_0.098_|

_URBAN_

|||**1985/86**|||**1992/93**||
|---|---|---|---|---|---|---|
|**Region**|**Pr**|**P1**|**P2**|**P0**|**P,**|**P2**|
|Northern|0.331|0.105|0.047|0.417|0.180|0.095|
|MiddleBelt|0.381|0.117|0.049|0.371|0.153|0.086|
|South|0.289|0.075|0.028|0.221|0.093|0.050|
|_All Urban_|_0.317_|_0.124_|_0.051_|_0.304_|_0.140_|_0.076_|

Note: Northern belt comprises: Bauchi, Borno, Sokoto, and Kano. Middle belt comprises: Kaduna, Benue, Kwara, FCT, Niger and Plateau states. Southern belt comprises: Anambra, Cross River, Gongola, Imo, Bendel, Lagos, Ogun, Ondo, Rivers and Oyo States.

Source: NCS, 1985/86 and 1992/93.

Looking at the urban areas in Table 4.8, the headcount in northern belt has increased by 8.1 points and the depth of poverty by 7.5 points. Also, the southern belt shows a higher decline of 6.8 points in headcount compared to 1.0 point in middle belt. Table 4.9 also shows that despite the decline in headcount in the middle belt, its contribution to urban headcount increased by 1 percentage point. In the south, which had displayed a larger decline in headcount there was a decline of 8.3 percentage points in its contribution to urban headcount from 46.7 points in 1986 to 38.4 points in 1992.
shocks may be considered as exogenous to the firms, households, and banks, i.e., not significantly caused by or easily anticipated in advance by any of these economic agents. A number of measures of these shocks are readily available at the national and state levels from several publicly available sources. These are quite useful both for research and for us to discuss the crisis.<sup>5</sup> We focus on these shocks during the first half of 2020, which is when most of the economic damage of the crisis occurred. The public health shocks in the U.S. commenced when the first COVID-19 case was identified on January 21, 2020, and the first government activity restrictions shock in the country started on February 29, 2020, when the Governor of the State of Washington declared a state of emergency.

Berger, Bouwman, Norden, Roman, Udell, and Wang (2021a) provide an illustration of how these shocks are employed in research, and their data help us navigate the first months of the crisis. They construct 14 different COVID-19 shocks from the raw data. Their public health shocks include new and total COVID-19 cases and deaths at the national and state levels, and their government activity restrictions indices include totals of stay-at-home orders, business shutdown orders or limitations, quarantines, etc. at the national and state levels. The rich variation in COVID-19 shocks across the different measures allows us to see how the crisis unfolded at different rates across the nation and helps enable the precision and robustness of research on the COVID-19 crisis. Studies of the GFC and earlier crises often just compare effects during a crisis versus normal times and do not have access to such detailed exogenous shock data.

We first consider here one of the public health shocks, new disease cases per 100,000 (100K) population. National new cases per 100K population intensified considerably over time

> 5 Examples include the Economic Tracker of Chetty, Friedman, Hendren, and Stepner (2020); the Johns Hopkins Coronavirus Resource Center; and the University of Washington COVID-19 Policies Database from Adolph, Amano, Bang-Jensen, Fullman, and Wilkerson (2021).

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# **4. Results**

The effects of the program can be summarized as follows. The program generated significant effects on the behavior it most directly targeted: villagers’ savings and their associations in SHGs. Both increased significantly in the treated communities. These effects were particularly profound among the poorest members of those villages as was the intention of the program. Indeed the program had no significant impact on the savings on the non-poor in treated communities. There is also some evidence of enhanced livelihoods: Respondents in treated communities reported significantly greater production of and income from meat and fish. There were no impacts on other sorts of production or income though.

There was no evidence for broader social impacts of the program however. Looking across six sets of indicators of social capital (behavior in the laboratory, a survey of economic networks, a survey of social networks, retrospective self-reported group membership and retrospective self-reported community voluntary activity) the program produced significant increases in only one of these areas—retrospectively self-reported group membership.

# **_4.1 Savings outcomes_**

The effects of the program on savings outcomes and membership in savings groups are exhibited in Tables 1 and 2. Table 1 reports the effect of the program on log savings for the full sample and the sample split into the poor (IDPoor 1 and 2) and non-poor. The results show that the log of savings among the poor in treated communities was about 3.27 larger. This means that ID-poor respondents in treated communities had savings of about 30,320 riels more than those in control areas. This amounts to roughly 7.6 USD and corresponds to 1.2 times the savings of poor people in control communities. Put differently, this is 30% of the overall sample standard deviation in savings of the IDPoor. Furthermore, these savings were almost entirely concentrated in SHGs as shown in the second column of Table 1. The estimated increment in savings is virtually identical for both savings in general and savings in SHGs. The effect of the program on the log savings of the non-poor 13
Policy Research Working Paper 7454

# **Abstract**

Using a new and representative data set of Chinese household finance, this paper documents household access to and costs of finance, along with their correlates. As in most developing countries, informal finance is a crucial element of household finance, and wealth tends to be associated with better access to formal and informal finance. Better financial knowledge shifts loan portfolios toward formal sources relative to informal ones. Connections to the Communist Party are associated with significantly better access to finance in rural areas but not in urban areas. A larger social network is positively associated with access to informal finance. Controlling for household characteristics, rural residents pay interest rates on loans similar to urban residents. Younger residents pay higher rates, while households on firmer economic footing face lower rates. Taking financial classes and college education is associated with higher interest rates for urban residents, suggesting perhaps that financial knowledge coincides with greater demand for credit in areas with more economic opportunity. Overall, the findings suggest that Chinese residents face dual credit markets, with the poor, young, those with poor financial knowledge, and those with larger family sizes relying much more on informal finance, while others are better able to access formal finance.

This paper is a product of the Finance and Private Sector Development Team, Development Research Group. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The authors may be contacted at lxu1@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
difference in the historical performance of the high-, middle- and low-income countries in terms of licensing Balance of Payments demonstrates this and suggests there is a growing IP commercialization divide.

<!-- Start of picture text --> High-income states: licensing & royalties<br>Figure 4. High-income States: Licensing and Royalties Balance of Payments, 1980-2009<br>balance of payments, 1980-2009<br>$65<br>$55<br>$45<br>USA<br>$35<br>Japan<br>France<br>$25<br>Germany<br>$15 Italy<br>$5<br>($5)<br>Billions<br><!-- End of picture text --> (Source: International Monetary Fund, Balance of Payments Statistics Yearbook and data files) Source: International Monetary Fund, Balance of Payments Statistics Yearbook and data files Figure 4 shows that high-income countries all exhibit slow growth or a static Balance of Payments – hovering between + $5 billion and - $5 billion - between 1980 and 2007 (the most recent available year). The USA is the obvious exception in this group. With a phenomenal increase in its Balance of Payments from + $5 billion to + $65 billion, it is the clear historical winner.

The passing of the Bayh-Dole Act in 1980, which enabled US universities, SMEs and non-profit organizations to appropriate control of IP resulting from federally funded research, is often regarded as an important factor in the increase of patenting and licensing activity by these actors. This has led many other OECD and developing nations to consider enacting similar policy measures to stimulate licensing revenue. Clarifying IP ownership in public-private partnerships is a useful policy measure to reduce operating uncertainty and stimulate the commercialization of IP. However, critics remark that it is difficult to disaggregate the effects of the Bayh-Dole Act from other factors that influenced the growth of licensing revenue in US universities post-1980 (David C. Mowery and Ziedonis 2002; D.C. Mowery et al. 1999; Henderson, Jaffe, and Trajtenberg 1998). The highly interactive relationship between US research universities and industrial research groups that existed prior to the Act as well as the structure of US universities, ‘which blended financial autonomy, public funding from state and local sources with federal research support’ on a large scale, resulted in research that focused on solving agricultural and industrial problems rather than pursuing purely scientific principles (Mowery and Sampat 2005).

10
26% by ischemic heart dieses and 22% by COPD. Mortality caused by HAP accounts for almost 8% of global mortality, which is higher than that from malaria, tuberculosis and HIV/AIDS combined (WHO, 2016). WHO (2016) also reports, in terms of morbidity, that HAP poses the biggest environmental health risk factor worldwide, more critical compared to lack of access to clean water and sanitation.

WHO (2016) also reports that HAP doubles the risk of child mortality (under 5-year age) due to pneumonia, which is the single biggest killer of children aged under five years worldwide. The same report suggests that more than half of deaths of children below Age 5 due to acute lower respiratory infections (ALRI), is caused by HAP, and almost one-quarter of all premature deaths from stroke can be attributed to the chronic exposure to HAP. Similarly, approximately 15% of all deaths due to ischemic heart disease (or over a million premature deaths annually), can be attributed to HAP and more than one-third of premature deaths from COPD in adults in low- and middle-income countries are due to HAP exposure. Furthermore, approximately 17% of annual premature lung cancer deaths in adults are attributable to HAP. Besides, HAP causes other health risks such as such as low birth weight and stillbirths, cervical cancer, tuberculosis, asthma, ear and upper respiratory infections, and with nasopharyngeal and laryngeal cancers.

**Figure 6: Mortality from Household Indoor Air Pollution in 2012**

<!-- Start of picture text --> Total deaths due to HAP in 2012 in the world<br>34%<br>4.3 Million<br>26%<br>22%<br>12%<br>6%<br>Pneumonia Stroke Ischaemic heart Chronic obstructive Lung cancer<br>disease pulmonary disease<br>(COPD)<br><!-- End of picture text --> Source: WHO (2016) 21