rafmacalaba/datause-encoder-data huggingface dataset:
is not very clear just from observation on the figures, and there are many reasons other than the release of ChatGPT that affect the popularity of the keywords on Google. To investigate the correlation of ChatGPT usage and the Google Trend of each keyword more rigorously, we collect the Google Trends of each keyword searched in each economy and run panel data regression. We first take the monthly average of the Google Trend index to match Semrush monthly traffic of ChatGPT. For each keyword, we run the following regression: In the equation, the dependent variable GoogleTrend<sup>(keyword</sup> _it_<sup>_k_)</sup> is the Google Trend index of keyword _k_ of economy _i_ in month _t_ . The independent variable of interest is the intensity of ChatGPT usage. Specifically, ln(ChatGPT _it−_ 1) stands for the log of ChatGPT’s monthly visits per internet user, of economy _i_ in the previous month _t −_ 1. We use the one-month lag of ChatGPT usage to avoid the simultaneity problem due to the reverse impacts from Google Trend to ChatGPT. Control variables _Xit_ include monthly traffic of Google of economy _i_ in month _t_ , as well as an economyspecific trend. Google traffic controls for the overall Google search activity, and the economyspecific trend controls for the underlying trend of a certain keyword happening in the country, which may not be impacted by the usage of ChatGPT. We also control for month fixed effects _δt_ and economy fixed effects _θi_ . It is worth noting that as the dependent variable is normalized within an economy and country fixed effects are added in the regressions, the coefficient of interest, _α_ , can be interpreted as the within-economy change of popularity of a keyword searched on Google.

Figure 20 presents the regression results, showing point estimates of the effect of ChatGPT usage intensity on Google Trends for various keyword. For most keywords, the correlations are insignificant, which is expected given the early stage of generative AI adoption. However, three keywords stand out with significant correlations: “Chat” and “AI” are positively correlated with ChatGPT usage intensity. Unsurprisingly, as they directly relate to ChatGPT and generative AI, Google searches for these terms increase as ChatGPT usage intensifies.

Notably, the keyword “Learn” shows a strong negative and significant correlation with ChatGPT usage intensity. The point estimate of -2.4 indicates that a one percent increase in ChatGPT’s monthly visits per internet user in the previous month is associated with a 0.024 percentage point 42
distinguish between accepted and rejected loan applicants. That is, with respect to portfolio risk, the EFL tool does not successfully replace credit history information, but it does well at complementing this information.

For unbanked entrepreneurs—i.e., those with no formal credit history—our results suggest that the EFL tool can be used to make additional loans to applicants rejected based on the traditional screening method without increasing portfolio risk. In line with these results, we also found evidence that the EFL tool increases unbanked entrepreneurs’ access to credit.

Our findings clearly show the importance of information in assessing credit risk, making accurate credit decisions, and expanding credit supply. They highlight the power of traditional screening methods, based mainly on applicants’ credit history, to screen out loan applicants with poor loan repayment behavior. Increasing the quality of the information that credit bureaus can access—for example, including data from retailers and utility companies in addition to banks and financial institutions, and allowing positive information (payment history on accounts in good standing) in addition to negative information (late payments, number and amount of defaults and arrears, and bankruptcies)—could improve credit-scoring models and increase credit markets’ confidence in their credit scores, even for entrepreneurs who have not previously borrowed from formal financial institutions. In the meantime, EFL offers a practical solution to financial institutions in countries where well-developed credit bureaus are in the process of consolidation.

25
-34 -

# Joint Exchange Rate and Intereat Rate Policy Actiop and Monetization

4.11 The simulation results in Table VI are from joint exchange rate and interest<sup>rate policy</sup> action and monedzation of the residual public sector borrowing requirement.<sup>Viewed</sup> appropriately, this scenario is one of exchange rate and interest rate policy action<sup>when the</sup> domestic inflation target is not of primary concern. In &ict, under this scenario infladon<sup>is</sup> allowed to reach 8.5 percent in 1992 and _6.5_ percent in 1993 compared with 7.0 pecent<sup>and 6.0</sup> percent respectively in the base projections. Real domestic interest rates also behave differently: 11.1 percent in 1991, 9.7 percent In 1993 and 5.3 percent in 1996 when the exchange<sup>rate,</sup> interest rate and monetization are used; 11.2 percent in 1991, 1O.S percent in 1993<sup>and 10.3</sup> percent in the base projections; 11.2 percent in 1991, 10.9 percent in 1993 and 13.0 percent<sup>in</sup> 1996 when the interest rate is used alone; and 11.1 percent in 1991, 10.1 percent<sup>in 1993 and</sup> 8.3 percent in 1996 when joint exchange rate and interest rate policy action is taken (Figure Two). Clearly, the interest rates are lowest when all three policy options<sup>are activated</sup> simultaneously. Then private investment reaches 36.0 percent of GDP in 1996<sup>compared with</sup> 27.7 percent of GDP in 1996 when the interest rate policy is used aloae,<sup>and 33.1 percent when</sup> interest rate policy is supported by exchange rate policy action. In sum, the<sup>economy reaches</sup> a higher equilibrium path but experiences slightly higher inflation than all scenarios<sup>except when</sup> the fiscal deficit is monetized.

# Summary of Simulation Results

4.12 Para. 1.2 identified domestic macroeconomic imbalances as threats to recent<sup>economic</sup> and stabilization gains In Kenya. The prevailing threats arise,<sup>in part, from high fiscal deficits,</sup> monetary expansion and external shocks. The gains originated, in part,<sup>in adjustment programs</sup> which reduced the fiscal deficit and lowered its domestic financing, maintained positive<sup>real</sup> borrowing prudently. Other reforms in key sectors such as agriculture,<sup>industry and interest</sup>
industrialized world during the 1960s. On the other hand, it could also be due to the fact that all 1965 scores consist of a single math test from the First International Math Study on secondary level (FIMS), whereas the test score of 1970 is exclusively averaged over Science and Reading scores (from FISS and SISS in 1970), even including scores from primary school Reading. Longer lasting trends, especially for the Latin American and Caribbean countries require inclusion of assessments carried out after 2003 (PISA 2009, TIMMS 2007, PISA 2006, PIRLS 2006 and especially SERCE).

It is important to note that given the scarcity of previous data on test scores, our extension of the Altinok and Murseli (2007) data set is significant, especially by allowing for the inclusion of more developing countries.

In addition, since there was an error in the 2006 PISA survey in the United States, and the United States is our reference point for all countries, no reading scores since 2003 from any country were internationally comparable until the inclusion of 2009 United States PISA reading scores in our dataset.

Another key contribution of this data set is the inclusion of more _doubloon countries_ , since two new Latin American countries (Panama and Peru) participated in the 2009 PISA Survey. The inclusion of these two new countries expands our sample of doubloon countries by 33 percent up to 8 counties. Since we use the average test scores of all _doubloon countries_ within a region to calculate our test score adjustment index (described in section 2), this addition improves the accuracy of our adjusted test scores for developing countries.

Finally, whereas Altinok and Museli (2007) include data from 2003 and in a recent updated paper (Altinok and De Meuleester 2010) for 2007, we intentionally group our most recent test score data into 5-year steps. We average 2003 and 2007 results into 2005 test scores, and group 2009 data into the year 2010. This approach allows us to align recent adjusted test scores to previous test score intervals in the data set. As discussed in section 2, this generates the most accurate dependent variable of educational outcomes since if our test score steps are uneven we might pick up differences in years of schooling instead of determinants of achievement in our final analysis.

16"
**Industrial Organization Implications of QR Trade Regimess Evidence and Welfare Costs**

# 1. Introduction

Quantitative restrictions (QRs) are the most common form of protection in many developing countries. Often this type of protection emerges during balance of payments crises but, once in place, is not removed. Students of developing countries' foreign exchange regimes have long noted that QRs have deleterious effects beyond those that would emerge from calculations relying strictly on the 'tariff equivalent' of quotas. So far most analysis has concentrated on quantifying the cost of rent-seeking activities which allegedly accompany QRs. 1, The purpose of this paper is to extend this analysis by parametrizing two stylized observations that have often been noted about the manufacturing sector of QR-ridden foreign trade regimes: (1) unrealized economies of scale; (2) lack of competition among domestic firms. The first arises because of the small size of the domestic market; the second arises because of the made-tomeasure protection of QR trade regimes. In sum, the paper builds on the stylized observation that in most QR regimes too many firms operate at too small a scale and often make above normal profits.

The interaction between trade policies and industrial organization has received theoretical and empirical attention in industrial organization studies of structure-conduct-performance in developed countries where it is known as the 'import-discipline' hypothesis: the threat of entry by foreign competitors constrains domestic firms to adopt entry-forestalling prices that more _losely approximate competitive prices. In addition to receiving empirical support in cross-section econometric analyses of structure-performance relationships, the import-discipline hypothesis has also been recently included in general equilibrium calculations of the costs of protection in Canada (Harris 1985, Cox and Harris 1985). 2/ The analysis here is also in a general equilibrium setting
- 31-

9.15. The central bank's balance sheet, accordingly, has the following structure: 9.16. Let the respective nominal rates of return on the assets and liabilities be given by f', h', a*, r', and u' (there - is presumably no return on B' R'). (The notation convention is that an apostrophe indicates a nominal domestic-currency rate and an asterisk a nominal U.S.-dollar rate.) Over a given short time period -- taken here to be one accounting accrual period -- the central bank's operatina profit in nominal terms is given by (The central bank's own operating costs -- wages, supplies, etc. -- are ignored.) 9.17. (The asset and liability stocks and their corresponding interest rates may be thought of as vectors. Thus, for example, f' and F' would be vectors incorporating rates of return on and stocks of different kinds of government obligation held by the central bank.) 9.18. The central bank's nan-cperatLng profit, again in nominal terms, is given by N' - (e - e-1) A* - d H'1_, [2] where "d" represents the proportion written off the stock outstanding at the end of the preceding period of commercial-bank obligations to the central bank. The first term in this expression represents the appreciation in local-currency terms of the net international-reserve position as a result of currency devaluation.

9.19. For simplicity, assume that the central bank receives no additional capitalization nor pays out any dividends, so the central bank's capital position simply increases by the overall profit:
Where ten years of historical data is not available at the current insurance unit level, Pure Premium Rates must be set with reference to aggregated data from the old insurance unit. If the yield distribution varies across the old insurance unit, use of data from the old insurance unit may lead to an underestimate of the expected cost of providing the product in some current insurance units. To protect against adverse selection the Pure Premium Rate therefore includes a Heterogeneity Load whenever a sufficient history of appropriate data is not available at the current insurance unit level.

Fourth the Commercial Premium Rate is calculated, giving the premium and premium subsidy rates payable by farmers and government (Section 0). The Commercial Premium Rate is higher than the Pure Premium Rate both to account for costs such as administrative and reinsurance costs, and to allow a margin against future contingencies.

# **2. Data Cleaning and De-trending**

An actuarial approach to design and ratemaking requires stability in the process for collecting data. Before the actuarial calculations described in future sections, data must be checked for any obvious errors, and trends or structural breaks caused by changes to farming practices or the recording of data. The de-trending step, in particular, requires actuarial judgment in addition to statistical analysis.

This section focuses on yield data, but the principles equally apply to other datasets. A related procedure for cleaning and de-trending weather data was suggested in Syroka (2007).

Sections 2, 3 and 4 of this paper illustrate the suggested procedure using worked examples of groundnut products to be sold across India in 2010 and cotton products to be sold in Gujarat in 2008. These examples use real yield data but district and subdistrict names are not disclosed for reasons of confidentiality.

# _Data Cleaning_

# <u>Description of step</u>

All historical yield data to be used for design and ratemaking should be collated into a standard format for use in actuarial calculations. Ten years of historical yield data is used in the procedure suggested in this paper.

Missing data should be recorded as distinct from a zero yield. At this stage missing yield data should _not_ be `filled in’, but recorded as missing.

Where the insurance unit has changed in the last ten years two historical yield series may be recorded for each crop. First, the series for data from the new insurance unit only; and second the series for data from the old insurance unit only. The choice of data format to be used when the insurance unit has changed in the last ten years will depend on the ultimate use of the data. Section 0 provides an overview of one possible framework for using this data in actuarial calculations.

# <u>Actuarial justification</u>

Data should be cleaned and collated into a standard format to enable efficient checking and scrutiny of actuarial calculations. International best practice for robust risk management requires that each actuarial calculation should be performed by one individual, checked by another and scrutinized by a suitably qualified actuary.

6
# **Figure 1a: Service imports as percent of GDP by income groups, 1994**

<!-- Start of picture text --> 6 all countries high income upper middle income middle income lower middle income low income<br>5<br>4<br>3<br>2<br>1<br>0<br>Figure 1b: Service imports as percent of GDP by income group, 2004<br>5 all countries high income upper middle income middle income lower middle income low income<br>4<br>3<br>2<br>1<br>0<br>in per cent of GDP<br>Transport Travel Communication Construction Insurance Financial services Computer services Royalties and license fees Other business services Personal, recreational, etc. services Government services<br>in per cent of GDP<br>Transport Travel Communication Construction Insurance Financial services Computer services Royalties and license fees Other business services Personal, recreational, etc. services Government services<br><!-- End of picture text -->

**Figure 1b: Service imports as percent of GDP by income group, 2004**

_Source:_ IMF BOP statistics.

Technological changes have supported rapid growth in international exchange of business services. The business services exports of developing countries have grown nearly four-fold in the last decade. The average annual growth rate of business services exports for Brazil and China during 1995-2005 was 15 percent, for India it was 25 percent (Figure 2). In large part this reflects growth in so-called business process outsourcing (BPO) services. This activity arises from the outsourcing (and out-location through FDI) of non-core business processes throughout the value chains of both manufacturing and services industries. Within BPO activities, the more advanced 9
between 1940 and 1995, whereas from 1940 through 1960 there was a relatively rapid process of regional convergence in Mexico, and from 1960 through 1995 this process suddenly stopped and it even began to revert itself. Sanchez-Reaza and Rodriguez-Pose (2002) show that the final stages of the import substitution period were dominated by convergence trends, whereas the period of trade liberalization (GATT) and economic integration (NAFTA) led to divergence. Using techniques from spatial economics literature, Aroca, Bosch and Maloney (2005) find that divergence has emerged in the form of several income clusters that only partially correspond to traditional geographic regions. They argue that the increased divergence lies not on the border but in the sustained underperformance of the southern states and to a lesser extent in the superior performance of an emerging convergence club in the North-center of the country.

Some authors studied the regional dynamics of the manufacturing sector in Mexico in recent decades. Krugman and Livas Elizondo (1996) observe a relation between trade policy and urban development. They argue that closed domestic markets have been a key factor in the emergence of the huge metropolises in the developing world, as a result of a self-reinforcing process of agglomeration. Firms manufacturing for the Mexican domestic market chose production sites with good access to consumers. Moreover the variety of goods produced near Mexico City ensured that it offered the best access to such inputs, generating powerful backward and forward linkages. The authors conclude that significant economies of scale and industrialization oriented primarily toward the domestic market, which were leading to concentration of manufacturing employment in Mexico City, were the result of the import substitution policy applied from the mid-1940s to the mid-1980s. Once the economy opened up in the 1980s, firms started selling a larger fraction of their output to foreigners and purchasing inputs from abroad and it began a dramatic shift of manufacturing away from Mexico City, especially to northern States (Figure 1). Hanson (1998) finds that trade liberalization has contributed to the decomposition of the manufacturing belt in and around Mexico City and the formation of broadly specialized industry centers located in northern Mexico, relatively close to the United States, and attributes as a main reason the economies of transport cost.

**Figure 1: Manufacturing value-added of Mexico City’s Metropolitan area and of the northern Border States, in percentage of Mexico’s total Manufacturing industry.**

<!-- Start of picture text --> 50%<br>40%<br>30% Metropolitan<br>Mexico City<br>20% Border States<br>10%<br>0%<br>1970 1985 1995 2006<br><!-- End of picture text --> Source: Calculations based on information from INEGI 5
**<u>Table 7: Double difference regression to assess impact of participation on casual labor income</u>**

||**All In**|**dividuals**|**Fe**|**males**|**M**|**ales**|
|---|---|---|---|---|---|---|
||**2006/08**|**2004/06**|**2006/08**|**2004/06**|**2006/08**|**2004/06**|
|Household participated|3,304.4***|-47.68|1,796.9***|56.16|1,522.0**|-212.9|
|in NREGS in 2008|(4.53)|(-0.11)|(5.71)|(0.29)|(2.55)|(-0.55)|
|Household located in|-683.3|631.8|-209.4|90.09|-748.5|655.9|
|hamlet|(-0.99)|(0.96)|(-0.71)|(0.36)|(-1.24)|(1.10)|
|Being very poor|4,167.5***|-1,141.1**|1,265.4***|79.56|3,213.7***|-1,241.2***|
||(5.18)|(-2.03)|(3.72)|(0.34)|(4.70)|(-2.60)|
|Being poor|<br>2,731.3***|<br>-248.9|<br>967.9***|<br>263.7|<br>1,950.8***|<br>-582.5|
||(3.72)|(-0.48)|(2.94)|(1.24)|(3.11)|(-1.31)|
|Scheduled tribe|<br>79.91|<br>1,835.3***|<br>164.1|<br>624.0**|<br>165.3|<br>1,194.0**|
||(0.09)|(3.04)|(0.39)|(2.25)|(0.21)|(2.44)|
|Scheduled caste|<br>267.4|<br>-1,098.4|<br>67.10|<br>-280.8|<br>98.24|<br>-983.0*|
||(0.25)|(-1.55)|(0.16)|(-0.90)|(0.11)|(-1.79)|
|Non backward caste|-2,693.2***|-318.5|-1,166.7***|32.48|-2,010.8***|-305.6|
||(-3.33)|(-0.59)|(-3.31)|(0.13)|(-2.89)|(-0.67)|
|If any member can write|<br>915.2|<br>-1,187.3**|<br>-226.3|<br>-456.5**|<br>945.0|<br>-1,013.2**|
||(1.05)|(-2.30)|(-0.56)|(-1.98)|(1.33)|(-2.32)|
|Female headed|<br>-2,151.3**|<br>1,400.2*|<br>-556.3|<br>506.9|<br>738.6|<br>1,543.5*|
||(-2.48)|(1.92)|(-1.27)|(1.46)|(0.71)|(1.78)|
|Number of female adults|<br>392.1|<br>-1,215.1***|<br>658.8**|<br>-468.4**|<br>-108.7|<br>-820.6**|
||(0.70)|(-2.79)|(2.19)|(-2.31)|(-0.22)|(-2.22)|
|Number of male adults|<br>-752.0|<br>527.3|<br>-417.2**|<br>132.8|<br>-511.8|<br>299.0|
||(-1.48)|(1.42)|(-2.04)|(1.00)|(-1.07)|(0.91)|
|Household size|<br>2,251.1***|<br>1,932.4***|<br>706.7***|<br>810.8***|<br>1,369.1*|<br>1,036.3**|
||(2.64)|(3.49)|(2.65)|(3.37)|(1.69)|(2.34)|
||<br>-178.4**|<br>-164.6***|<br>-52.28**|<br>-70.06***|<br>-112.2|<br>-87.02*|
|Household size squared|(-2.10)|(-2.86)|(-2.31)|(-2.83)|(-1.40)|(-1.95)|
|Amount of irrigated land|-386.3**|176.8|-283.4***|104.1**|-133.3|88.61|
|owned (ac.)|(-2.58)|(1.47)|(-4.57)|(2.16)|(-1.04)|(0.88)|
|<br>Amount of rainfed land|<br>-346.0***|<br>45.82|<br>-133.5**|<br>4.388|<br>-227.4**|<br>42.64|
|owned (ac.)|(-2.61)|(0.32)|(-2.25)|(0.08)|(-2.14)|(0.38)|
||3.965***|-3.279|1.710***|-0.899|2.449**|-2.424|
|Total land owned square|(2.82)|(-0.96)|(2.75)|(-0.64)|(2.18)|(-0.89)|
|District dummies|YES|YES|YES|YES|YES|YES|
|Number of observations|3,621|3,711|3,468|3,632|3,195|3,358|

Source: Authors’ computation from household survey and administrative data. _Notes: t_ statistics are in parentheses. Significance level: *: 10%, **: 5%, ***: 1%.

22
**Table 3 Trends in Real Wages: 2003 = 100**

||2000-04|2005-08|2009-11|2012-14|2015-17|
|---|---|---|---|---|---|
|**High Remittance Receipients**||||||
|Albania|92|124|153|162|191|
|Armenia|90|167|212|269|312|
|Bosnia andHerzegovina|92|110|130|132|129|
|Georgia|91|200|283|321|346|
|Kyrgyz Republic|91|142|190|216|234|
|Moldova|89|146|153|148|164|
|Montenegro|103|130|162|164|156|
|Serbia|86|143|149|156|154|
|Tajikistan|92|174|246|361|470|
|Ukraine|87|139|145|159|142|
|Uzbekistan|97|na|290|311|333|
|**Mean**|92|148|192|218|239|
|**Median**|91|143|162|164|191|
|**Fuel Exporters**||||||
|Azerbaijan|89|149|209|215|243|
|Kazakhstan|95|111|115|120|123|
|Russian Federation|86|125|143|144|149|
|Turkmenistan|64|69|75|87|118|
|**Mean**|84|113|135|141|158|
|**Median**|88|118|129|132|136|
|**Other Transition Economies**||||||
|Belarus|97|145|169|199|202|
|Bulgaria|96|115|152|174|203|
|Croatia|100|107|108|107|105|
|Czech Republic|95|115|122|125|132|
|Estonia|95|120|125|131|148|
|Hungary|95|113|113|112|109|
|Latvia|95|123|144|146|170|
|Lithuania|96|126|136|137|159|
|North Macedonia|98|107|124|117|115|
|Poland|97|98|106|110|117|
|Romania|98|122|140|145|173|
|Slovak Republic|97|120|139|146|160|
|Slovenia|99|114|121|121|124|
|**Mean**|97|117|131|136|147|
|**Median**|97|115|125|131|148|

Sources: UNECE, ILO and SEE jobs Gateway for wages, WDI for GDP deflators 16