rafmacalaba/datause-encoder-data huggingface dataset:
|Observations|41,915|31,923|
|---|---|---|
|R-squared|0.4674|0.6314|

Table 2: Regression coefficients from the imputation model. Notes: Standard errors in parentheses.<sup>*</sup> _p<_ 0.05,<sup>**</sup> _p <_ 0.01,<sup>***</sup> _p <_ 0.001. Regressions are weighted by person level weights from respective surveys. Coefficients of harmonized industry codes are suppressed to keep the results tractable. The regression coefficients reported are based on a set of districts common between NSS-2011 and CPHS’ 2015. As CPHS expanded to a few more districts in the following years, the set districts common to the two surveys expanded slightly resulting in slightly different regression coefficients across years.

The error term from the regression model is accounted for when imputing NSS-type consumption into the CPHS. Given the non-normality observed in the NSS, we follow Elbers, et al. (2003) by drawing the errors from the empirical residuals with equal probability (to preserve the empirical distribution for the errors observed in the NSS). Errors terms for households in the CPHS are standardized using the mean and standard deviation, multiplied by the root mean square error term and added to the predictions of the imputation model into CPHS.

Figure 13 compares the mean, variance, and third moments of the imputed (log) NSS-type consumption into the CPHS to the moments of observed (log) consumption from both the NSS and the CPHS. The means of imputed NSS-type consumption and observed CPHS consumption are nearly identical in rural areas. In urban areas, NSStype consumption is on average approximately ten percentage points higher when compared to observed CPHS consumption. This suggests that the CPHS under-estimates consumption in urban India (consistent with observations made in Dhingra and Ghatak, 2021).

The variance of the imputed NSS-type consumption is seen to match the variance of observed NSS-2011 consumption in both rural and urban India, i.e. the use of imputed consumption and adjusted CPHS weights fully closes the gap in variance between the two surveys. Unfortunately, this does not extend to higher moments. While the use of imputed NSS-type consumption in the CPHS helps reduce the gap in third moments (compared to observed log consumption in the NSS), the remaining gap is economically significant and will bias estimates of poverty and inequality if not addressed. This motivates our second approach which is outlined next.

31
However, with these qualifications very much in mind, we have underscored those aspects of business strategy which deserve attention based on our current knowledge. Innovation capability is likely to be central. It will depend upon the strength of the national innovation system of which universities and the training of researchers are key ingredients. It will require openness and flexibility and a willingness to enter into alliances on the part of firms as well as a division of labor between them and other players.<sup>51</sup> Japanese firms, which have stressed research in the fields of electronics, IT and optics (see Table 7)<sup>52</sup> , might want to put more of their resources into the life sciences, nanotechnology and alternative energy sources in view of demographic trends and rising resource constraints. The focus of research in the U.S. (see Table 8) can be a guide.<sup>53</sup> <mark>Charles Lieber ("How to Succeed" 2003, p.89)</mark> has rightly observed, that “Nanotechology can overturn assumptions about how things have to be done. Instead of parts and subassemblies (in manufacturing) you start with building blocks like nanowires, 51 TOTO, for example, was able to diverse a technique for eliminating smells by using the research on photonic catalysis done at the University of Tokyo. But this was made possible by the firm’s own success at developing a simulator for generating smells. Through this and other innovations, Toto now accounts for 60 percent of the Japanese market and is penetrating high end markets overseas, with products such as the Super Bowl (Tilin and Mikami (2004) and personal communication by Professor Fumio Kodama). 52 The major MNCs and also firms such as the Kao Corporation, Nidec, Hoya, Bandai, and NTT DoCoMo have spearheaded Japan’s innovation in these areas (Ng and Yip 2004).

53 Technology Review’s (2004b) annual survey of the technological strength of global companies based on the number of patents, their impact, their science linkage and their technology cycle time, identified the following leaders in each of 8 manufacturing subsectors in 2003: Aerospace, Lockheed Martin; Automotive, Delphi; Biotech and Pharmaceuticals, Pfizer; Chemicals, 3M; Computers, IBM; Electronics Hitachi; Semiconductors, Micron; Telecommunications, Lucent.

35
data can serve as a reliable proxy.

As a cross check, in Appendix Figure A1, we compare the website traffic of ChatGPT against its popularity on the internet, as measured by Google Trend index.<sup>2</sup> Semrush’s estimates of ChatGPT’s monthly website visits closely align with the trends observed in the weekly Google Trends data, a rapid rise following the release of GPT 3.5, a subsequent slowdown after mid-2023, and some short-term fluctuations during holiday seasons. The strong correlation between Google Trends data and Semrush’s traffic estimates substantiates the reliability of the website traffic data. We also compared Semrush data with similar data obtained from other sources, the patterns for popular websites with over 1 million sessions per day are also largely consistent.

While website traffic data is valuable for tracking individuals’ AI usage, it has several limitations. First, individuals’ use of VPNs can obscure the actual geographic origin of traffic. This may bias the usage data for economies with small populations, particularly island territories, though the impact on larger economies is negligible. Second, most of the traffic analytics tools Semrush relies on only work with Google search engine. While traffic estimates from other search engines like Bing, DuckDuckGo, Yandex, and Baidu can be available, the accuracy is less ideal. Results on Baidu is particularly limited, compromising data quality in China. Third, traffic data can inflate user numbers as the same individual with several digital devices could be counted as different users, leading to overestimation of engagement. Fourth, traffic data does not necessarily reflect the depth or quality of engagement with AI tools, what people are using them for, offline AI usage or enterprise-level AI usage. Fifth, as generative AI is increasingly integrated into various new applications, such as search engines or devices like Siri as announced by Apple, the indirect usage of generative AI websites—such as traffic via APIs—cannot be captured by this dataset.

# **3 The Rapid Adoption of Generative AI**

Artificial Intelligence (AI) broadly refers to systems or machines that mimic human intelligence to perform tasks. Generative AI, a specific subset of AI, involves algorithms that create new content—whether text, code, images, audio, videos, and more—drawing from extensive datasets to generate outputs that were not explicitly programmed. The study of AI has been ongoing for

> 2The Google Trends index reflects global weekly searches for the keyword “ChatGPT” from November 2022 to March 2024. Further details are provided in Section 7.

9
Table 1: Potential Applications of Wavelets

||Applications|Countries of Primary<br>Interest|
|---|---|---|
||�Identify acceleration and deceleration phases|All countries|
|Growth Analytics|(hills, plateaus, mountains, and plains)||
||�Identify structural breaks|All countries|
||�Investigate the country resilience by analyzing the<br>persistence of impacts due to diferent types of<br>shocks|All countries|
|Synchronization<br>Analysis|�Analyze the co-movement of growth between two<br>economies|All countries|
||�Investigate the feasibility of economic union<br>formation (monetary union, free trade areas,<br>customs union) by analyzing the cyclical<br>synchronization among a group of economies|Trade/monetary union<br>members or candidates|
|Commodity Price|�Analyze the short-term and long-term behavior of|Commodity traders,|
|Diagnostics|key commodity prices|resource-rich countries|
||�Investigate the co-movement of commodity prices<br>and desired macroeconomic aggregates|Commodity traders,<br>resource-rich countries|

5
**Table 5: Effect of Uncertainty on Bilateralism**

||**(1)**|**(2)**|**(3)**|**(4)**|
|---|---|---|---|---|
|Panel A: F<br>|irst-Stage for I|V Regressions<br>|||
|Uncertainty at sector-region level||0.966***||0.941***|
|||(0.079)||(0.080)|
|Crime, theft and disorder|||0.123*|0.106|
||||(0.070)|(0.069)|
|Customs and trade regulations|||0.051|0.029|
||||(0.051)|(0.051)|
|Transportation|||0.014|0.016|
||||(0.033)|(0.028)|
|Panel B|: OLS and IV R|egressions|||
||**OLS**|**IV**|**IV**|**IV**|
|Uncertainty|-0.202***|-0.253**|-1.294**|-0.392***|
||(0.048)|(0.104)|(0.571)|(0.103)|
|Log of size|-0.020|-0.018|0.034|-0.011|
||(0.016)|(0.017)|(0.034)|(0.017)|
|Firm is part of a larger firm|<br>-0.039|<br>-0.039|<br>-0.047|<br>-0.040|
||(0.063)|(0.060)|(0.079)|(0.055)|
|Firm age (years)|-0.001|-0.001|-0.001|-0.001|
||(0.001)|(0.001)|(0.002)|(0.001)|
|Exporting directly at least 10% of<br>sales|-0.029|-0.017|0.216|0.014|
||(0.057)|(0.059)|(0.176)|(0.062)|
|At least 10% foreign-owned|-0.088|-0.091|-0.159|-0.100|
||(0.070)|(0.068)|(0.109)|(0.066)|
|2-digit sector fixed effects|YES|YES|YES|YES|
|Region fixed effects<br>|YES|YES|YES|YES|
|Number of observations<br>|2,255|2,255|2,255|2,255|
|R<sup>2</sup>|0.125||||
|Cragg-Donald Wald F statistic||176.40|9.91|49.86|
|<br>||14883|172|3671|
|Kleibergen-Paap Wald F statistic<br>||.<br>{0.00}|.<br>{0.25}|.<br>{0.00}|
|HansenJ-statisticp-values<br>Notes: Panel A of this table shows first-stag<br>all exogenous variables included in the seco|e regression estim<br>nd-stage. Panel B|ates. These first-sta<br>shows OLS (colum|{0.72}<br>ge regressions<br>n 1) and IV re|{0.02}<br>also include<br>gression|

Notes: Panel A of this table shows first-stage regression estimates. These first-stage regressions also include all exogenous variables included in the second-stage. Panel B shows OLS (column 1) and IV regression (columns 2-4) estimates. Column (2) uses sector-region averages of uncertainty as the instrument, (3) uses the three variables on obstacles as instrument, (4) uses all four variables. Standard errors are in parentheses. calculated assuming clustering at the level of strata. All regressions use sampling weights. Braces are used for -values. Significance levels: .01 - ***; .05 - **; .1 - *. pp 3
based on fixed-effects estimations.<sup>24</sup> Equation 1 of table 2<sup>25</sup> shows that higher initial development level implies a lower growth rate, while demographic growth tends to reduce growth.<sup>26</sup> The farther away one province is from economic centres, the lower its growth rate is.

**Table 2 – Growth spillover effects of all Chinese provinces (1979-99)**

|Dependent va|riable :<br>ln(<br>,<br>_t_<br>_i_<br>_y_|)<br>ln(<br>)<br>1<br>,<br>−<br>−<br>_t_<br>_i_<br>_y_|||||
|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(3')|(4)|(4')|
|)<br>ln(<br>1<br>, −<br>_t_<br>_iy_|-0.070***|-0.074***|-0.077***|-0.075***|-0.066***|-0.063***|
||(-4.62)|(-4.98)|(-4.01)|(-3.91)|(-4.21)|(-3.98)|
|)<br>ln(<br>,_t_<br>_i_<br>_DP_|-0.169***|-0.157***|-0.150***|-0.161**|-0.181***|-0.185***|
||(-3.44)|(-3.25)|(-2.27)|(-2.45)|(-3.69)|(-3.75)|
|)<br>ln(<br>,_t_<br>_is_|0.001|0.000|-0.005|-0.004|-0.004|-0.004|
||(0.11)|(0.04)|(-0.35)|(-0.30)|(-0.35)|(-0.41)|
|)<br>ln(<br>,_t_<br>_in_|-1.111**|-1.048***|-1.462***|-1.522***|-1.011***|-1.047***|
||(-4.48)|(-4.31)|(-4.23)|(-4.41)|(-4.06)|(-4.18)|
|_t_<br>_i_<br>_GAWL_,||0.330***<br>(4.64)|||||
|_t_<br>_i_<br>_GAWRL_,|||0.138*<br>(1.68)||||
|_t_<br>_i_<br>_GAWPL_,|||||0.152***<br>(2.78)||
|Constant|1.760***|1.637***|1.582***|1.692***|1.829***|1.867***|
||(3.91)|(3.71)|(2.63)|(2.82)|(4.09)|(4.14)|
|Observation<br>number|547|547|395|395|507|507|
|Adjusted R<br>square|0.5707|0.5886|0.5780|0.5739|0.5897|0.5828|

Note: _t_ -students are in parentheses. * significant at 10 percent ; ** significant at 5 percent ; *** significant at 1 percent.

24 The limited number of observations does not allow carrying out the SURE estimations. The results of the two-step GMM estimations with two year-lagged dependent variables as IV matrix do not suggest the superiority of the GMM estimations over the fixed-effects estimations.

25 We have also introduced year dummies to capture the effects of the common shocks that affect the growth performance of all provinces. For the sake of simplicity, the estimations of the dummies are not shown in the tables.

26 Here, the role of investment on regional growth is not significant. Following the Solow growth theory, we do not drop this variable from the estimations.

12
**Table 2. Curricular Areas (by Subject) with Low Levels of “Correct” Response Rates**

|**SUBJECT AREA**|% of correct<br>answers|Curricular areas where teachers display the lowest performance (by<br>teaching**s**ubject)|
|---|---|---|
|Management|30.0|1. Identification of the main elements of labor contracts<br>2. Knowledge of the main elements required for strategicplanning|
|Computers|25.0|1. Characteristics of “data mining”|
|Philosophical thinking|30.0|1. Identification of aesthetic postulates of emblematic exponents<br>2. Identification of main characteristics of the Andean cosmovision|
|Artistic education|35.0|1. Identification of characteristics of technical elements of the arts<br>2. Identification of characteristics of elements of the arts<br>3. Identification of genres or plastic expressions|
|Special education|35.0|1. Identification of characteristics of the components of a curriculum<br>2. Description of weakened psychological processes|
|Physical education|40.0|1. Recognition of adequate postures<br>2. Determination of parameters to assess physical capabilities|
|Early education|30.0|1. Identification of phonological awareness features<br>2. Identification of rights and responsibilities of children|
|Citizenship education<br>|25.0|1. Identification of practices that promote a culture of peace<br>2. Recognition of the characteristics of citizenship|
|Basic general education|25.0|1. Set theory<br>2. Identification of ordered pairs<br>3. Identification of metabolic processes|
|Natural sciences BGE|25.0|1. Identification of characteristics of autotrophic organisms<br>2. Identification of properties of chemical compounds|
|Social studies BGE|20.0|1. Relationship between humanism and cultural changes<br>2. Recognition of laws or actors that regulate markets|
|Language and literature BGE|40.0|1. Recognition of authors’ shortcomings after reading a text|
|Biology UGB|25.0|1. Identification of the main functions of nucleic acids<br>2. Recognition of hormone functions<br>3. Identification of different types of aquatic biomes|
|Mathematics BGE|25.0|1. Geometry<br>2. Set theory|
|Physics UGB|15.0|1. Notions of thermodynamics<br>2. Calculation of physical magnitudes<br>3. Characterizations of energy|
|History and social sciences UGB|20.0|1. Recognition of authors of contemporary philosophy<br>2. Identification of social characteristics from reading texts|
|Language and literature UGB|30.0|1. Recognition of logical connectors from the reading of a text|
|Mathematics UGB|20.0|1. Set theory<br>2. Geometry<br>3. Notions of cardinality|
|ChemistryUGB<br>_Source:_Author using_Ser Maestro_ <br>_Note:_BGE = Basic General Educa<br>|20.0<br>2016 data.<br>tion; UGB = U|1. Identification of types ofphotovoltaic cells<br>nified General Baccalaureate.|

11
internet adoption or continuation –variable taking the value one if the household where a woman or a man lives adopted or continued using internet between 2010 and 2016, and zero otherwise (equation (1)): _Yi_<sup>_g_inequation(1)takesthevalueoneifperson</sup><sup>_i_ofgender</sup><sup>_g_participatesactivelyinthe</sup> labor market and 0 otherwise, and ∆ _Yi_<sup>_g_isthechangeinlaborforceparticipationbetween</sup> 2010 and 2016; _Internet_<sup>_g_</sup> _i_<sup>isanindicatorvariableforwhetherthehouseholdwhereperson</sup> _i_ lives adopted internet or continued having access to it between 2010 and 2016; the vector _X_ includes individual and household level characteristics in 2010 to control for differential trends across age, education, marital status, household size, wealth, and urban-rural areas. The model includes governorate fixed effects.

Even though the use of first-differences controls for any unobserved time-invariant factors, the estimate of _β_ may still be biased if there are unobservable factors that vary over time and that are correlated with _Internet_<sup>_g_</sup> _i_<sup>.Toovercomethischallenge,weuseaninstrumental</sup> variable strategy, where the interaction between the distance to cell towers and the per capita expenditure in communications is used as an instrument for internet use. The first stage equation is: _Distance towers_<sup>_g_in equation (2) is the logarithm of the distance to the nearest 3G cell tower</sup> in the subdistrict _s_ where a person of gender _g_ lives, and _Exp_<sup>_g_</sup> _r_<sup>is the per capita expenditure</sup> in communication services in 2010 in the governorate _r_ where a person of gender _g_ lives.

This is a valid instrument for several reasons. First, it is correlated with internet use since shorter distances to the towers are typically accompanied by better internet access (Klonner & Nolen (2010)), and because the roll-out of 3G towers is expected to lower access costs disproportionately and thereby increase adoption among subdistricts where internet 7
inflation. From this bulk of work emerges the view that the urban poor and near poor are believed to be particularly vulnerable to price spikes, with concerns that it could severely aggravate current malnutrition problems.<sup>9</sup> The poverty impact on rural households is less obvious, depending on the transmission of food prices to farmers’ incomes and their consumption patterns, often less diversified than in urban areas.

A third branch of the literature, more operational, focuses on appropriate policy responses at the macro-economic and social levels. Recommendations<sup>10</sup> emphasize the need to accommodate relative prices shifts – no export restrictions, no attempt to reduce the pass-through of world prices to domestic markets, with a view to foster private sector led agricultural investments in the medium term, while protecting the most vulnerable with targeted transfers and tackling some of the policy-induced factors behind prices spikes – such as bio-fuels subsidies in high-income countries.

Yet, to our knowledge, little effort has been paid so far to consistently integrate these macro-economic, social and time dimensions at the country-wide level, where policy decisions are most generally made. So far, most studies scrutinize one aspect of the problem at the cross-country level. While it is useful to identify vulnerable countries and get a sense of the global magnitude of the crisis, such literature is less useful to understand the various possible interactions between the different effects at play, and assess the efficiency of envisaged policy response at the national level.

Thus, this paper aims at developing a consistent economy-wide analytical framework to assess the impact of commodity price inflation and possible macroeconomic responses on poverty, in the short, medium and long term.

For this purpose, we use a computable general equilibrium (CGE) model to assess the impact of global commodity inflation (agricultural products, fertilizers and oil) on households’ welfare in Tanzania. This country was chosen for its data availability, as well as for the fact that it introduced in 2008 various policies to offset the negative impact of food price inflation, including export bans and the reduction of taxes on food grains.

> accounting matrix multiplier effect approach, and suggest than indirect effects are much more pronounced for oil than food.

> 9 The impact of lower food consumption on malnutrition is not straightforward. Malnutrition is a cumulative process and depends on many other factors (diets) than just the quantity of food consumed. But risks of malnutrition certainly increase as the situation perpetuates. See Ravallion (1997). 10 See World Bank (2008a).

3
**Table 11: Volatility of Financial Variables**

||Exchange<br>Rate<br>Volatility|SENSEX<br>Volatility|Weighted<br>Average Call<br>Rate Volatility|Portfolio<br>equity<br>Volatility|Portfolio<br>debt<br>Volatility|
|---|---|---|---|---|---|
|Inflation Targeting|-0.17<sup>***</sup>|-0.38<sup>***</sup>|-0.31<sup>***</sup>|0.02|-0.01|
||(3.84)|(3.82)|(5.51)|(0.99)|(0.50)|
|Global Financial Crisis|0.58<sup>***</sup>|1.47<sup>***</sup>|-0.59|0.04<sup>*</sup>|0.17<sup>**</sup>|
|Dummy|(4.89)|(3.62)|(0.55)|(1.77)|(2.30)|
|Post Global Financial|0.28<sup>***</sup>|-0.42<sup>***</sup>|-1.84<sup>**</sup>|0.10<sup>***</sup>|0.15<sup>***</sup>|
|Crisis Dummy|(6.36)|(3.58)|(2.12)|(5.58)|(12.33)|
|Constant|0.22<sup>***</sup>|1.54<sup>***</sup>|2.32<sup>***</sup>|0.08<sup>***</sup>|0.02<sup>***</sup>|
||(10.18)|(19.02)|(2.68)|(5.64)|(6.28)|
|Observations|91|91|55|84|84|
|Adjusted_R_<sup>2</sup>|0.45|0.43|0.30|0.33|0.69|

Note: Robust t statistics in parentheses; *, **, ***, indicate significance at 10, 5 and 1 percent respectively.

We do not find changes in the rate of growth of government total, revenue (operating and recurrent), or capital spending under the IT regime. The rate of growth of interest payments is somewhat smaller, which is probably a reflection of lower bond yields.

**Table 12: Industrial production and government expenditure**

||Index of<br>Industrial<br>Production<br>(% change)|Volatility<br>of Index of<br>Industrial<br>Production|Total<br>government<br>Expenditure<br>(% change)|Revenue<br>Expenditure<br>(% change)|Capital<br>Expenditure<br>(% change)|Interest<br>Payments<br>(% change)|
|---|---|---|---|---|---|---|
|Inflation Targeting|-0.88|-0.99<sup>***</sup>|-1.58|-1.43|9.31|-5.45|
||(1.02)|(3.36)|(0.42)|(0.44)|(0.41)|(1.39)|
|Global Financial|-6.98<sup>**</sup>|3.28<sup>***</sup>|23.95<sup>**</sup>|26.23<sup>**</sup>|-6.80|2.52|
|Crisis Dummy|(2.15)|(5.60)|(2.42)|(2.27)|(0.42)|(0.30)|
|Post Global Financial|-3.68<sup>***</sup>|1.22<sup>***</sup>|-0.90|-2.63|-0.55|2.08|
|Crisis Dummy|(4.24)|(4.17)|(0.30)|(1.11)|(0.03)|(0.63)|
|Constant|7.85<sup>***</sup>|2.19<sup>***</sup>|12.54<sup>***</sup>|13.61<sup>***</sup>|22.35|11.60<sup>***</sup>|
||(13.11)|(17.49)|(5.39)|(8.66)|(1.52)|(4.95)|
|Observations|91|91|87|87|87|87|
|Adjusted_R_<sup>2</sup>|0.24|0.33|0.08|0.19|-0.03|-0.02|

Note: Robust t statistics in parentheses; *, **, ***, indicate significance at 10, 5 and 1 percent respectively.

Finally, we looked for evidence that the transmission of policy impulses to banking and financial 20