rafmacalaba/datause-encoder-data huggingface dataset:
Figure 6: Bank of Ghana’s financing of government, 2007 to mid-2014 <!-- Start of picture text --> 9000<br>8000<br>7000<br>6000<br>5000<br>4000<br>3000<br>2000<br>1000<br>0<br><!-- End of picture text --> Note: * Figure for 2014 is a projection.

Source: Data from Bank of Ghana.

Figure 7: Consumer price inflation (per cent), 2000 to mid-2014 <!-- Start of picture text --> 45<br>40<br>35<br>30<br>25<br>20<br>15<br>10<br>5<br>0<br>2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014<br><!-- End of picture text --> Source: Data from Bank of Ghana.

# **8 A deteriorating external payments position**

Ghana’s external payments position has also deteriorated since oil was discovered, amid a growing current account deficit and fragile foreign exchange reserves. The current account of the balance 19
# **Table A7: The effects of temperature on poverty – Grid-level analysis**

|Dependent variable:|Panel|model|Long differ|ences model|
|---|---|---|---|---|
|Poverty rate at $1.90|Baseline<br>(1)|Extension<br>(2)|Baseline<br>(3)|Extension<br>(4)|
|Temperature|0.102***|-2.046***||-0.0006***|
||(0.022)|(0.060)||(0.0001)|
|∆Tempearture||0.870***|0.009***|0.005***|
|||(0.033)|(0.001)|(0.001)|
|Temperature squared||0.092***<br>(0.002)||0.0001***<br>(0.00004)|
|Controlling for rainfall|Yes|Yes|Yes|Yes|
|Country FE|Yes|Yes|No|No|
|Year FE|Yes|Yes|No|No|
|Number of countries|82|82|82|82|
|Observations|1,115,478|1,072,575|42,903|42,903|
|R-squared|0.929|0.555|0.001|0.007|

_Notes:_ Robust standard errors in parentheses. Standard errors are clustered at the country level. Poverty incidence is calculated using subnational GDP from Kummu _et al._ (2018) and the poverty line from WDI. The long differences estimation is based on cross-sectional data with a smaller sample size compared with panel data. *** p<0.01, ** p<0.05, * p<0.1 55
# **I. INTRODUCTION**

Although there is an extensive literature on the effects of climate on agriculture (Reilly et al 1996; McCarthy et al 2001), there are very few studies that have measured adaptation. Studies that compare the impacts of climate change that include adaptation, such as Ricardian studies (Mendelsohn, Nordhaus, and Shaw 1994, Mendelsohn and Dinar 1999; Mendelsohn et al. 2001; Mendelsohn and Dinar 2003; Seo et al. 2005; Kurukulasuriya et al. 2006; Fleischer et al 2007; Seo and Mendelsohn 2007, Wang et al. 2008), tend to find much lower damages than studies that do not include adaptation, such as agronomic analyses (Rosenzweig and Parry 2004; Reilly et al. 1996; McCarthy et al 2001; Parry et al. 2004). It is clear from this empirical evidence that it is very important to include adaptation in any impact analysis of long term climate change. Of course, adaptation is likely to be less important with respect to year to year weather fluctuations as farmers may have fewer options to adapt to sudden or abrupt changes.

Adaptations are actions that people and firms take in response to climate change to reduce damages or increase benefits (IPCC 2007).<sup>1</sup> What specifically do farmers do to adapt to climate? How have they adjusted to the climates that they live in today? A new series of studies have begun to examine this question. By comparing what farmers do in one climate zone versus another, the studies quantify how farmers have made long term adjustments to climate. For example, studies have examined how climate affects the choice of irrigation in Africa (Kurukulasuriya and Mendelsohn 2006a) and South America (Mendelsohn and Seo 2007). Studies have explored how climate affects livestock choice in Africa (Seo and Mendelsohn 2006) and South America (Seo and Mendelsohn 2007a). Previous studies have explored how climate alters crop choice in Africa (Kurukulasuriya and Mendelsohn 2006) and South America (Seo and Mendelsohn 2007b). All of the above mentioned adaptation studies find that farmers adjust irrigation practices, crop varieties, and livestock species to both temperature and precipitation levels.

> 1 One should distinguish between adaptation to climate change, which spans over a long period, which differs from adaptation to climate variance, the changes in weather from year to year (Leary et al. 2006). Adapting to weather is important but it should not be confused with adapting to climate.

3
economies, about 60% of SMEs that need a loan are unable to obtain one due to various constraints (Dalberg, 2011).<sup>2</sup> A number of studies have investigated the determinants of firms’ access to banking finance.<sup>3</sup> These studies reveal that the human capital of a firm’s management (e.g. education, age, work experience, social background) critically impacts the probability of obtaining credit from banks (e.g. Storey, 2004; Kumar and Francisco, 2005; Irwin and Scott, 2010). The size of the firm also influences credit-worthiness by affecting the availability and transparency of financial information,<sup>4</sup> the ability to offer collateral (e.g. Berger and Udell, 1995) and the risk perception of the lender (e.g. Schiffer and Weder, 2001). All these factors make it more difficult for smaller firms to borrow. Multiple studies in developing economies show a strong and positive association between a firm’s age and access to credit, as survival works to lower uncertainty, boost information, and increase accumulated assets that could potentially be offered as collateral (e.g. Aryeetey et al., 1994; Levy, 1993). Further, research confirms that ownership and legal structure also influence access to external credit in the sense that listed firms and foreign-owned firms face weaker financial constraints (e.g. Harrison and McMillan, 2003; Beck et al, 2006b; Storey, 1994). Among other firm characteristics, studies reveal that firm productivity is positively associated with greater access to credit (e.g. Bigsten et al., 2000; Topalova, 2004).

The industry sector of a firm can also be important in affecting access to finance. For instance, finance-dependent or capital-intensive industries such as textiles seem to grow more rapidly in countries with greater financial development or that are otherwise constrained (e.g. Rajan and Zingales, 1998; Levine, 2005; Kumar and Francisco, 2005; Byiers et al., 2010). By contrast, firms in service industries may find it harder to access finance because they do not have enough assets (capital, machinery, land) to pledge as collateral against the loan (e.g. Silva and Carreira, 2010) due to the human capital-intensive nature of these firms.

> 2 In this regard, credit information sharing systems and reforms relating to credit bureaus and credit registries could be helpful. See Peria and Singh (2014) for one such study.

> 3 For a cross-country study on households’ access to financial services see Beck et al. (2008)

> 4 Sacerdoti (2005) and Allee and Yohn (2009) assert that firms with audited financial statements have a higher probability of getting credit. Uchida (2008) finds that firms that are audited regularly have a higher probability of obtaining loans in the United States, although not so in Japan.

7
# **Additional information on selection of studies and extraction of effect sizes estimates and study descriptors.**

**Selection of studies.** We follow the established meta-analytical protocol (cf. Lipsey and Wilson 2001, p.23; Stanley 2001, p.143; Stanley and Doucouliagos 2012; Stanley et al. 2013). This starts with systematically searching the relevant databases for the most common keywords in order to aggregate a large sample of potentially eligible studies to be included in our metaanalysis. Keywords are (i) financial literacy; (ii) financial knowledge; (iii) financial education; (iv) financial capability; and (v) combinations of these keywords with “intervention.” To minimize publication bias and capture the broadest sample of studies possible, we systematically search not only the relevant databases for published records (e.g. ISI, Business Source Premier via EBSCO Host, JStor) but also for registered trials, working papers, and informal research reports (e.g. AEA RCT-registry, SSRN, Fin. Lit. E-Journal, RePEC, NBER, Worldbank eLibrary). All records from recent systematic accounts of the literature (Fernandes et al. 2014; Miller et al. 2015) are included in our initial pool of studies. In addition, we screen the references of narrative literature reviews (Fox et al. 2005; Collins and O’Rourke 2010; Willis 2011; Xu and Zia 2012; Hastings et al. 2013; Blue et al. 2014; Lusardi and Mitchell 2014).

This search resulted in over 500 potentially relevant published journal-articles and over 600 results from working paper databases with some apparent overlap. We stopped collecting articles from these databases in October 2016.

From this collection, we drop studies that do not meet our three criteria of inclusion: (i) Reporting on impacts of an exogenous educational intervention designed to strengthen the participants’ financial literacy and/or leading to behavioral change in the area of personal finance; (ii) providing a quantitative assessment of intervention impact that allows coding an effect size statistic (�) and its standard error; and (iii) relying on an observed counterfactual in 35
where u's and v's are replaced with p's and a's. Using the duality property of this linear programming problem, one can derive an equivalent envelopment form: minO1 **_O,A_** _st.-Y,ik_ +YA20 (3)

**_°xim_** kR 2 0 A., Ž0

where X is an N by 1 vector of constants and 0, a scalar, is the efficiency score for the<sup>**_e4h_**</sup> DMU.<sup>6</sup> Note that O • 1, with O0 = 1 implying a DMU which is located on the efficiency frontier. Due<sup>to</sup> fewer number of constraints, this formulation is usually used for computations.

However, the above approach is somewhat simplified as it assumes a constant return to scale. The CRTS assumption is only appropriate when all banks are operating at an optimal scale. Factors that may cause banks not to operate at an optimal scale include imperfect competition, leverage concerns, certain prudential requirements, etc. The fact that banks face non-constant retums to scale has been documented empirically by, among others, McAllister<sup>and</sup> McManus (1993), and Wheelock and Wilson (1997). This phenomenon led Banker _et al._ (1984) to suggest an extension of the CRTS DEA model to account for a variable return to scale (VRTS). They added a convexity constraint Ni ' = 1 to problem 3 above (where Ni is a N by 1 vector of ones). This condition ensures that an inefficient bank is "benchmarked" against similar sized banks. As a result, VRTS technology envelops the data more closely than CRTS technology, and consequently, VRTS technical efficiency scores are greater than or equal to CRTS technical efficiency scores. The advantages of the VRTS model outweigh the increase<sup>in</sup> computational power necessary to solve the model, which allowed the VRTS to gain popularity over the CRTS method (Fried _et al._ (1993), Coelli _et al._ (1998), Cooper _et al._ (2000)).

Data Envelopment Analysis was used extensively in studies of the banking industry in developed market economies.<sup>7</sup> On U.S. data alone the method was used in more than 30 published articles. The method was also applied for cases in Norway, Spain, U.K., and several _6_ X _=_ [Xl,...,XN] is an K by N input matrix with columns x, and Y [yl,...,yN] is an M by N output matrix with columns y,.

> 7 Various versions of the Data Envelopment Analysis are used for monitoring and/or early warning systems used by bank regulatory agencies (see Barr _et al._ (1994), Brockett _et al._ (1997), and Simak (1997)).

8
# **6 Appendix: Survey Questions**

Below are the relevant questions that respondents answered in the survey. Other questions referring to respondents demographics, education, family background, and future income and employment expectations are not shown below (but available upon request). We highlight a few words below to emphasize differences between the control and treatment surveys. The original surveys were not highlighted. We also give a title to each question for the reader’s convenience; the original survey questions did not have a title.

## **Question 1: Choosing between two financially equivalent contracts**

Suppose you need $10,000 to finance a one year program. In one year you will join the workforce. How do you prefer to finance your education?

**Control Treatment** 60 monthly payments of **Loan: $200** . If in any month your 60 monthly payments of **$200** . income is below **$2,000** , If in any month your income is then you only have to pay below **$2,000** , then you only 10% of your income in have to pay 10% of your that month. income in that month. 60 monthly payments **Human Capital Contract:** equal to 10% of your 60 monthly payments equal to income. If in any month 10% of your income. If in your income is larger than any month your income is $2,000, then you only larger than $2,000, then have to pay $200 in that you only have to pay month. $200 in that month.

16
because income shocks may pose a unique challenge to particular skill groups. Past work has found, for example, that recessions diminish labor market prospects of less-qualified youth and greatly increase their vulnerability to long-term unemployment (World Bank, 2006, World Development Report, 2007). Third, household head‘s ethnicity and religion, and the location of the home are included. Although these factors may affect job prospects and hence, household income, ex-ante, we are not certain about the correlation between these household characteristics and income shocks. De Wall (1990), however, suggests that different religious and ethnic backgrounds lead households to react differently during crises. The covariates are balanced to test the hypothesis that ̳covariates are jointly insignificant‘. Table 5 presents the results for this hypothesis test.

Selection of an appropriate model is equally important for good matching. Since we have a binary treatment (meaning households are either affected or not affected by income shocks), we chose a probit model of the following form: where, _xi_ represents demographic characteristics of the household, _i_ ; _zi_ represents educational attainment; _wi_ is a dummy variable for ethnicity; _mi_ is a dummy variable for religion; and _di_ is a location dummy. Table 4 reports the contribution of these covariates to a household‘s predicted vulnerability to income fluctuations. The key question to be answered here is whether households affected by income shocks respond differently to households not affected by income shocks. The predicted value or the propensity score derived from this model converts the multidimensional relation to single dimension and makes it easier to select the _similar_ households in the comparison group. Based on propensity scores, Figure 1 presents the area of common support for each country and provides evidence that the criterion on sufficient common support for PSM is satisfied.

# 4.1 Sensitivity analysis

The semi-parametric approach employed in this paper eliminates potential bias from observable variables. However, this approach is not robust to hidden bias from unobserved variables which 10
20 tested.18 When other combinations of explanatory variables were added the relationship improves to the point that over 50 per cent of the variation in developing countries' RCA indices are accounted for. In these runs the natural resource, consumer good ratio, and human capital (per capita wage) variables appear to make the. strongest contribution toward improving the regressicn's explanatory power.

When the regressions shown in Table 4 were repeated for the two Asian developing country sub-groups rather different results (not shown) were achieved. The pattern for Indonesia, Malaysia, Thailand and the Philippines corresponded closely to that for all developing countries, but there were marked differences for the Asian exporters of manufactures (Rep. of Korea, Hong Kong, Singapore, and Taiwan, Province of China). Here, there was a deterioration in the explanatory power of the labor intensity and physical and human capital variables.19 The weaker results suggest that these countries have advanced to a stage where lower labor costs are no longer providing a major stimulus to 18 When a double log form was used the results improved slightly (R<sup>2</sup> 0.28) -- about the same level of explanatory power was achieved using a quadratic form (see equation 3). Given that several of the independent variables take negative values or are zero-one dummys, it was not possible to utilize a standard nonlinear form for the multiple regressions although several variables appeared not to take a linear form.

> 19 For example, the regression between the Asian NICs revealed comparative advantage indices and the labor intensity variable took the form:

which wa; much weaker than the corresponding equation for all developing countries shown in Table 4. Also, the explanatory variables shown in the table were only able to account for 33 per cent of the variation in the NTCs results while they "explained" over half of the variation in all developing countries RCA industries.
<!-- Start of picture text --> Dias Xerxenevsky De Aquino Almeida et al<br>(2008, 2009) (2007, 2008, 2009) (2007, 2008) (2007-2011)<br>35 ° fi_ o 8 ° é= °<br>3e‘252£2<br>=3=5=3=3<br>|| (No effect) | (No effect)<br><!-- End of picture text -->

EEE Significant and negative (ES Significant and positive ME Insignificant
enable researchers to explore a wide range of issues. Most importantly, surveys are the most reliable data sources in origin countries.

There are several potential drawbacks of survey data, including the relative variance of the difficulties faced in implementing household surveys in terms of data collection and processing, and their comparatively small sample sizes. These limit their potential for assessing global trends and performing comparisons. Ethnic and other minorities, including migrants, might be underrepresented in the data and stratified sampling can only be meaningfully applied should other nationwide data with proper sampling, such as a census, become available. The variation in surveying practices across countries and their relatively small sample sizes imply that they fail to capture migrants from smaller corridors and are seldom used in the construction of databases of bilateral migration stocks. But surveys remain an indispensable tool for studying migration, its determinants and impacts. Where migration modules have been successfully integrated in household surveys using appropriate sampling techniques, they have often succeeded in capturing sufficiently large numbers of migrants for meaningful analyses. Depending on the resources devoted, use of surveys will and should increase in the future (Center for Global Development, 2009).

# **Data Challenges**

As discussed above, countries use a range of statistical methods and tools – censuses, registers, administrative records and surveys – to document and profile their populations, including migrants. Each source has advantages and disadvantages with respect to 9
<!-- Start of picture text --> Age HH op ° ° Significant coefficient<br>Age squared HH +' +° Insignificant coefficientCross-country median value<br>Female HH co edfeam oo<br>Married HH © conene = °<br>Children and no elderly o camper @ ° 4<br>Children and elderly ° Oo +090 000 $<br>Only elderly o e400 woo + «<br>Urban ° oe seco e<br>Has car 10 © dame<br>Detached house secefel o °<br>Lower secondary HH ° oo og cates 0 0 °<br>Upper secondary HH . oe ote Pew +0 °<br>Post-secondary HH . cpoofo 0m 0 0 °<br>Tertiary HH ° 00 0 0 Oh OMe oo 0<br>Manager and professional HH ° + eee mee 6<br>Support and service worker HH ° 00 @ omee ooo<br>Trades and elementary occup. HH $0.c0e Mie eee 0<br>Plant‘machine operator HH 4 ao eate cone @<br>Agricultural worker HH od o 00 @ © boo © 00¢0 om<br>-0.80 -060 -040 -0.20 0.00 020 040 060 £0.80<br>Value of the regression coefficient<br><!-- End of picture text --> <!-- Start of picture text --> Age HH odes * Significant coefficient<br>Age squared HH +' +° Cross-countryInsignificant coefficientmedian value<br>Female HH © weber o<br>Married HH + ¢ ejeceose<br>Children and no elderly onememe 46 0 |<br>Children and elderly 6 © 0000-6 0 comm 0 $0<br>Only elderly ° odo open + °<br>Urban conten +<br>Has car [coche +<br>Detached house o ewocheen<br>Lower secondary HH °o 0 do 4o e0om ¢<br>Upper secondary HH ° ° ee eget + °<br>Post-secondary HH ©} @e of H0e ° °<br>Tertiary HH ° ' © 0 MOO Home o ey<br>Manager and professional HH $ © 0 Seelewee ooo<br>Support and service worker HH : © dmteloeoeereoo<br>Trades and elementary occup. HH fo Co;e pee &<br>Plant/machine operator HH $ we oo oo<br>Agricultural worker HH ° eo} ookee-fow oom of<br>0.80 -060 -040 -020 000 020 040 060 0.80<br>Value of the regression coefficient<br><!-- End of picture text -->
metrics for individual websites and subpages, our study offers rich, timely insights into how emerging technologies are diffused and adopted globally, user characteristics and usage patterns.

Second, this study contributes to a strand of literature on the geography of technology diffusion, deepening the understanding of the country level factors that influence the adoption of generative AI. Previous research has broadly identified economic and infrastructural factors as influencers of technology adoption at the country level (Keller 2004; Comin and Hobijn 2010; Czernich, Falck, Kretschmer, and Woessmann 2011; Delera, Pietrobelli, Calza, and Lavopa 2022), but lacks specific analysis related to generative AI. This paper uses regression analysis to explore how demographics, economic development, industry structure, human capital, and digital infrastructure affect the adoption of various generative AI tools. Our findings highlight the risks of AI exacerbating the already growing digital divide and call for collaborate efforts to ensure the technology is accessible to all.

Finally, by combining website traffic data with Google Trends data, this study also sheds light on early impacts of generative AI tools on individuals’ online activities, and discusses the implications for public preferences, digital firms’ business model and competition dynamics.

The remainder of the paper is organized as follows. Section 2 introduces the website traffic data. Section 3 identifies a list of the most popular generative AI tools and shows the rapid adoption of generative AI by individuals. Section 4 analyzes user characteristics of different types of generative AI tools and usage patterns. Section 5 demonstrates the geographical disparities in generative AI usage and highlights the record-breaking global diffusion of generative AI. Section 6 delves into the socioeconomic and infrastructural factors that influence these geographic disparities in adoption. Section 7 briefly explores early impacts of generative AI tools on individuals’ online activities, and discusses the broader implications. Section 8 concludes and discusses avenues for future research.

# **2 Website Traffic Data to Monitor Generative AI Usage**

Thanks to advancements in digital analytics technologies, we are now able to monitor generative AI usage in real-time through website traffic data. Website traffic data typically include metrics such as number of visits, time spent on site, bounce rates, and user demographics. This data has been widely used by website owners, digital marketers, business analysts, and researchers to gain insights into 7
<!-- Start of picture text --> 30.0<br>35.5 35.3 35.4<br>35.0<br>34.5<br>34.1<br>% 34.0<br>==c 33.5<br>© 33.0<br>32.5<br>32.0<br>315 317 sis<br>:<br>31.0 313<br>2010 2016 2022<br><!-- End of picture text --> <!-- Start of picture text --> Rural @Urban<br>45<br>40<br>35 | I i<br>30<br>25<br>15<br>10<br>5<br>0<br>2010 2016 2022 2010 2016 2022<br>Gini Theil, alpha = 1<br><!-- End of picture text -->