rafmacalaba/datause-encoder-data huggingface dataset:
and energy intensity (these latter variables depict a downward trend over time).

**Table 3.** System GMM estimation results for **late period (2005-2015)** .

|Dependent variab<br>|le:<br>2<br>ln_CO_<br>|||||
|---|---|---|---|---|---|
|Variable|Global panel|HI countries|UMI countries|LMI countries|LI countries|
|2<br>1<br>ln<br>_it_<br>_CO_<br>|0.815<sup>***</sup>|0.248<sup>***</sup>|0.456<sup>***</sup>|0.572<sup>***</sup>|0.104<sup>***</sup>|
||(23.035)|(4.804)|(5.915)|(7.350)|(3.804)|
|ln<br>_it_<br>_EC_|0.017<sup>**</sup>|0.793<sup>***</sup>|0.353<sup>***</sup>|0.033<sup>**</sup>|0.293<sup>***</sup>|
||(2.482)|(15.251)|(4.111)|(2.401)|(3.044)|
|ln<br>_it_<br>_ECS_|0.210<sup>***</sup>|0.017<sup>***</sup>|0.151<sup>***</sup>|0.523<sup>***</sup>|1.073<sup>***</sup>|
||(5.038)|(3.606)|(2.376)|(4.938)|(6.783)|
|ln<br>_it_<br>_EI_|0.401<sup>***</sup>|0.814<sup>***</sup>|0.541<sup>***</sup>|0.773<sup>***</sup>|0.170<sup>***</sup>|
||(7.028)|(15.376)|(5.998)|(4.804)|(5.149)|
|ln<br>_it_<br>_PCG_|0.213<sup>***</sup>|2.611<sup>***</sup>|2.789<sup>***</sup>|0.232<sup>**</sup>|0.229<sup>*</sup>|
||(3.686)|(14.150)|(7.060)|(2.041)|(1.955)|
|2<br>ln<br>_it_<br>_PCG_|-0.001|0.258<sup>***</sup>|0.567<sup>***</sup>|0.219|-0.175|
||(-0.080)|(12.143)|(6.562)|(1.186)|(-0.577)|
|ln<br>_it_<sup>_P_</sup>|0.181<sup>***</sup>|0.738<sup>***</sup>|0.857<sup>***</sup>|0.941<sup>***</sup>|0.870<sup>***</sup>|
||(4.941)|(14.232)|(7.214)|(5.404)|(6.607)|
|Constant|0.943<sup>***</sup>|0.963<sup>**</sup>|0.746<sup>**</sup>|1.193<sup>***</sup>|1.283<sup>***</sup>|
||(7.091)|(2.053)|(2.458)|(4.420)|(5.189)|
|Hansen test|0.163|0.158|0.178|0.156|0.606|
|AR(2) test|0.220|0.106|0.110|0.237|0.122|
|Turning point|-|-|-|-|-|

**_Note:_** ***, **, and * denote statistical significance at 1%, 5%, and 10%, respectively; the values in parentheses represent t-statistics; the values of Hansen and AR(2) tests represent p-values; and the unit of turning point is 2010 US$.

# **4.2.2 Varying income levels across countries**

Fig. 9 details the impact of each of the factors. In general, the main factors affecting the emissions for the four income groups did not change much over time. However, it is noteworthy that over time, the impact of changes in energy mix and 21
dummy (if both are introduced simultaneously, the effect of standards harmonization absorbs that of a NS EIA for the exporter).

This result highlights the presence of the type of trade deflection discussed in this paper. Our explanation is as follows. Standards harmonization is costly and raises the price of affected products, possibly pricing them out of other Southern countries. With a price elasticity of demand equal to -2, the ad-valorem equivalent (AVE) of the “average” NS harmonization would be close to 10%.<sup>7</sup> That is, a Southern country signing an EIA with a Northern partner involving the harmonization of technical regulations would suffer a negative competitiveness shock on other Southern markets equivalent to a 10% export tax, which is a substantial effect.

The last three columns of Table 2 investigate the robustness of our results by replicating the estimation of column (4). Column (5) shows that our results are unaffected by the exclusion of agricultural products, which are less subject to TBTs than are manufactured ones. Column (6) controls for the potential bias associated with fixed effects estimation on data pooled over consecutive years (Anderson and Yotov 2011). To do so, only the years 1991, 1994, 1997, 2000, 2003, and 2006 are used. The conclusions remain unchanged. Our third robustness check addresses the lagged trade effects of trade agreements and TBT harmonization. Baier and Bergstrand (2007) showed that the effect of EIAs cannot be fully captured in the year of their formation because most of them have phase-in periods. We therefore allow for gradual phasing-in of EIA and standard harmonization by including one lag for these variables in the estimation. The results are presented in column (7). We report

> 7 The ad valorem equivalent is [exp( _β_ )-1] _/ε_ , where _β_ is the estimated coefficient and _ε_ is the price elasticity of import demand (in algebraic form, i.e., negative). The average price elasticity of imports over all goods and countries is estimated by Kee et al. (2008) as -3.12 at HS6 and -1.1 at ISIC3.

26
results show that direct transfers are more efficient in reducing poverty among the poorest. However, there is still space for improving their effectiveness.

**Table 5.** Effectiveness of direct transfers. Uruguay, 2017

|Indicator|Officialpovertyline|
|---|---|
|Vertical Expenditure Efficiency|0,465|
|Spillover Index|0,213|
|Poverty Reduction Efficiency|0,366|
|PovertyGapEfficiency|0,307|

Source: Authors’ estimates based on ECH (2017), INE and Fiscal Accounts and CEQ Methodology

**C. Incidence and progressivity of taxes and public benefits** This section presents several indicators that help deepen our understanding of the effects on inequality observed in the previous tables. To analyze the incidence of social spending we calculate the ratio of the amount of benefits to Pre-fiscal income by deciles of Pre-fiscal income. We also estimate the concentration coefficient, which assesses the distribution of the benefits when people are ordered by Pre-fiscal income. A benefit is considered progressive when the concentration coefficient is lower than the Gini of Pre-fiscal income. The difference between the concentration index of transfers and the Gini index of Pre-fiscal income is the Kakwani index. Therefore, a negative Kakwani index indicates that the benefit program is progressive. The Kakwani index may be negative even when the concentration coefficient is positive. A negative concentration index suggests that the transfer is more concentrated among the poorest. All of these instruments may be also applied to the study of taxes.

**Table 6** presents the incidence analysis and Kakwani index of the taxes and contributions included in the analysis. The incidence of direct taxes increases with income (or with the household’s relative position in the income distribution): they represent 0.1% of Pre-fiscal income of the poorest 10% of the population and 9,9% of the richest 10%. Besides, since the Kakwani index is positive, direct taxes are progressive. This result follows the analyses from Bucheli et al. (2013) and Arancibia et al. (2019), as they also find direct taxes to be progressive when considering 2009 and 2014 data respectively.

There is some evidence that contributions to the Health System are somewhat neutral: the Kakwani index is close to 0 but negative. Besides, the incidence analysis shows that there is no clear increasing pattern when moving up in the distribution. Though we report these results, it should be noted that the methodology has limited capacity to study the contributions to Health System in isolation. Our estimations of such contributions consider the devolutions that contributors received in 2017 and we do not estimate and subtract the amount that they will receive in 2018. However, we use these estimations to assess contributions and benefits jointly. Starting from pre-fiscal income, we subtract contributions and add contributory health transfers. The Gini index decreases from 0.461 to 0.440. We may therefore conclude that the overall contributory health system has an equalizing effect. However, this is a preliminary result: a 21
numeracy test.<sup>11</sup> Unless otherwise indicated, the following data were collected at each of the four rounds of data collection. Table 3 summarizes the data collected in each wave.

# **4.1. School data**

The data on the school as a whole were obtained through enumerator observation and a comprehensive interview with the head teacher or – in the absence of the head teacher – the teacher in charge of the school at the time. The directly observed information includes the condition of the buildings, the number of classrooms and other facilities, etc. Information from the head teacher included school finances, record keeping, community participation, management practices, etc. To improve the accuracy of the information collected, we requested to see written records to substantiate responses whenever applicable.

# **4.2. Classroom visits**

In each school, we randomly selected two classrooms for observation. The goal of the classroom visit was to gather information about teaching practices, the classroom environment, and student participation. It also served to substantiate the absenteeism data from the administrative records by comparing the student register to the number of students present in the classroom. Each classroom visit lasted fifteen minutes, followed by a five-minute interview with the teacher.

# **4.3. Student written literacy and numeracy test**

Forty students were selected randomly at each school and were given a written numeracy and literacy test. At the baseline, we tested twenty third-grade students and twenty fifthgrade students at each school. Third- and fifth-graders were selected as these are the earliest grades regularly evaluated by the Gambian government. At the first follow up in 2009, we gave the test to students in fourth and sixth grades to allow for tracking of the baseline students. At the second follow-up in 2010, the test was given again to third and fifth grade students because much of the original cohort would have completed primary school. In total, 8,959 students were tested at baseline, roughly evenly distributed across the three treatment groups.

# **4.4. Student interview and oral literacy test**

Of the forty students who took the written test, ten were randomly selected to take an orally administered reading and comprehension test and to participate in an interview about their socio-demographic characteristics, school performance, and other information. These students were tracked in 2009 in the WSD and Control schools, and in 2010 in all the

> 11 The schools were given a range of time during which a team of enumerators would visit them. The actual dates were not disclosed.

10
# **1.  Introduction**

Banking sector outreach varies significantly across countries. In Ethiopia there is less than one branch per 100,000 people, while in Spain there are 96. In Albania, there are four loans per 1,000 people and the average loan size is 15 times GDP per capita, while in Poland there are 774 loans per 1,000 people and the average size of loans is only one-third of GDP per capita. This paper introduces a consistent set of cross-country indicators of banking sector outreach, shows how these can be used to predict household and firm use of banking services, explores their empirical association with other country characteristics, and relates them to firms’ financing obstacles as reported by entrepreneurs. These indicators were collected through a survey of bank regulatory agencies conducted in 2003-4 and complemented with publicly available data. While these are rough indicators of access to and use of banking services, this is the first compilation and analysis of consistent and comparable cross-country data on the outreach or penetration of banking systems.

Although a large literature has established a positive association between financial sector depth and economic growth at the country, industry and firm level,<sup>1</sup> little is known about the breadth of financial systems across countries, the extent to which enterprises and households use financial services, and their relationship to desirable outcomes.<sup>2</sup> This lack of knowledge stems mostly from a dearth of adequate data (see discussion of data issues in Honohan 2004b). While the literature has developed several standard indicators of financial development, with consistent and comparable data available for the vast majority of countries over the past 40 years, to our

> 1 See Levine (2005) for a review of this literature.  Specifically, Beck et al. (2000), Rajan and Zingales (1998), and Demirguc-Kunt and Maksimovic (1998) provide evidence at the cross-country, industry and firm level.  Also see Wurgler (2000) and Love (2003).

> 2 Some exceptions include the following studies that try to measure access to financial services (and in some cases its consequences) at the household and/or firm level: Francisco and Kumar (2004) and Kumar (2005) for Brazil; World Bank (2003b) for Colombia; Wydick (1999) for Guatemala,  Atieno (1999) for Kenya, Aliou and Zeller (2001) for Malawi, Caskey et  al.  (2004) and World Bank (2003a) for Mexico, Basu (2004) for India; Beegle, Dehejia, and Gatti (2003) and Satta (2002) for Tanzania.

2
# **4.2 Forecasting and Nowcasting Models**

To attempt to improve over these baseline models, we first estimate a possibly seasonal Autoregressive integrated moving-average (ARIMA) model for each monthly series.

where _yt_ is the series we wish to forecast, _εt_ follows a white noise process, _L_ is the lag operator _L_<sup>_i_</sup> _yt_ = _yt−i_ , _φ_ ( _L_ )<sup>_p_</sup> = (1 _− φ_ 1 _L −· · · − φpL_<sup>_p_</sup> ) is the non-seasonal polynomial of order _p_ in the lag operator that describes the autoregressive component of the model and _φ_ 12 ( _L_<sup>12</sup> )<sup>_P_</sup> = �1 _− φ_ 12 _,_ 1 _L −· · · − φ_ 12 _,P L_<sup>_P_�</sup> is the seasonal polynomial of order _P_ in the lag operator that describes the seasonal autoregressive component of the model. The polynomial of order _q_ that denotes the non-seasonal MA component of the model is _θ_ ( _L_ )<sup>_q_</sup> , and likewise the seasonal MA component of order _Q_ is denoted _θ_ ( _L_<sup>12</sup> )<sup>_Q_</sup> . The non-seasonal and seasonal orders of differencing are denoted _d_ and _D_ , respectively.

We use the auto.arima function from the forecast package in R<sup>4</sup> for order identification for each series. See Hyndman and Khandakar [2008] for more information on the model identification procedure.<sup>5</sup> The auto.arima automatic model identification procedures allows parameters to be zero, so in principal, for example, the model is only differenced or includes a seasonal component when it is appropriate.

To test whether there is information in the Google Trends data that will help us forecast each series, we use a possibly seasonal ARIMAX model where the Trends data is used as an exogenous variable.<sup>6</sup>

## The seasonal ARIMAX model estimated is specified

> 4We used the 5.4 develpment version obtained from `https://github.com/robjhyndman/forecast/`

> 5We also performed order identification using the AUTOMDL procedure from X-13ARIMA-SEATS [Staff, 2013] as well as using (seasonal) unit root tests to identify the order of (seasonal) differencing and then using the Bayesian information criteria (BIC) to select the best model. None of the procedures used produced identical results, nor did any procedure do unambiguously better than any other. The auto.arima function was the most computationally performant and is thus the basis for the results below. We used the default arguments for this function.

> 6This model is sometimes referred to as a regression model with ARMA errors. Ignoring seasonality, it may be written

This is to contrast it with the ARMAX model which is written 17
- Lee, Ronald D. (1994) “Population Age Structure, Intergenerational Transfer, and Wealth: A New Approach, with Applications to the United States,” _Journal of Human Resources_ 29(4): 1027-63.

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- Lloyd, Cynthia (2005), Editor, Growing up Global: The Changing Transitions to Adulthood in Developing Countries, National Academies Press, Washington, D.C.  .

- Lutz, Wolfang, Warren Sanderson, and Sergei Scherbov (2001) “The End of World Population Growth,” _Nature_ 412: 543-545.

- Martine, George (1996) “Brazil's Fertility Decline, 1965-95: A Fresh Look at Key Factors,” _Population and Development Review_ 22(1): 47-75, March.

- Merrick, Thomas W. and Elza Berquó (1983) _The Determinants of Brazil's Recent Rapid Decline in Fertility_ . Washington, DC: National Academy Press, Report No. 23.

- National Research Council (2000) _Beyond Six Billion: Forecasting the World’s Population_ , Washington, D.C.

- Potter, Joseph, Carl Schmertmann, and Suzanna Cavenaghi (2002) “Fertility and Development: Evidence from Brazil,” _Demography_ 39(4): 739-761.

- UNAIDS Reference Group on Estimates, Modelling and Projections (2002) “Improved methods and assumptions for estimation of the HIV/AIDS epidemic and its impact: Recommendations of the UNAIDS Reference Group on Estimates, Modelling and Projections,” _AIDS_ , vol. 16, pp. W1-W14.

United Nations Population Division (1999) _The World at Six Billion_ , New York: United Nations.

- United Nations, Department of Economic and Social Affairs, Population Division (2005). _World Population Prospects: The 2004 Revision_ . _CD-ROM Edition - Comprehensive Dataset_ (United Nations publications, Sales No. E.05.XIII.11).

- United States Census Bureau (2003) “Historical Estimates of World Population,” retrieved from http://www.census.gov/ipc/www/worldhis.html.

Welch, Finis (1979) “Effects of Cohort Size on Earnings: The Baby Boom Babies’ Financial Bust, _Journal of Political Economy_ . 87(5): S65-97.

- Willis, Robert .J. (1973) “A new approach to the economic theory of fertility behavior,” _Journal of Political Economy_ , March-April 1973, S14-S64.

- World Bank (2001) _World Development Indicators 2001_ , International Bank for Reconstruction and Development: Washington, D.C.

26
Table **5:** Probability **of** having **made land-attached investments between 1990 and 1999**

||Model<br>Probit|Tobit|
|---|---|---|
|Area(mzs.)|0.001***<br>(5.60)<br>|69.1***<br>(7.9)<br>|
|Area squared|-0.0000002***|-0.02***|
||(3.44)|(4.3)|
|Lengthofpossession(years)|-0.001|-66.9|
||(1.56)|(1.0)|
|Receivedagrarianreformtitleafter1990|0.028|1766.6|
||(1.18)|(1.2)|
|Plotwasregisteredbefore1990|0.039*<br>(1.84)|2860.3*<br>(1.8)|
|Plotwasregisteredafter1990|0.088***|7460.1***|
||(4.55)|(5.2)|
|Plotunderperennials|0.025|4290.2*|
||(0.88)|(2.1)|
|Plotunderpasture|0.002<br>(0.13)<br>|-317.3<br>(0.2)<br>|
|Plotunderforest|-0.141***|-13457.5***|
||(3.10)<br>|(2.58)<br>|
|Plot underfallow|-0.122***<br>(2.71)|-12308.1***<br>(2.5)|
|House plot|0.080**|5455.8***|
||(2.33)|(2.3)|
|Plot undulating|0.046**|4220.5***|
||(2.51)|(3.1)|
|Plothilly|0.022|1462.5|
||(1.19)|(1.0)|
|Plotsteep|0.052|3564.6|
||(1.58)|(1.5)|
|Loglikelihood|-1454.76|-7696.6|
|PseudoR2|0.0710|0.01|
|No.ofobservations(plots)|3217|3200|
|**Source:**<br>Owncomputation from2000 WorldBank/U<br>|niversityofWisconsinsurvey.<br>||
|Notes:<br>Dependentvariablesareadummyvariables<br>thevalueofthelandattachedinvestmentfo|for whetherornotinvestmenthad beenmadedu<br>rtheTobitmodel.|ringtheperiodfor theProbitmodeland|

*** significant at 1%; ** significant at 5%; * significant at 10%

For Probit model: coefficients correspond to marginal probabilities at the mean values For Tobit model: coefficients correspond to marginal effects for C$ invested Robust z-statistics in parentheses for Probit, t-statistics for Tobit Omitted categories are no document for type of title; plot under annuals for current use of plot; and flat for plot topography. Regional dummies included but not reported.

27
**TABLE 4.6: Summary Statistics - Public Agency Governance Variables**

|**Dependent Variable**<br>**Obs**|**Mean**|**Std. Dev**|**Min**|**Max**|
|---|---|---|---|---|
|<br>Accessibility<br>161|72|19|18|100|
|**Corruption Indices**|||||
|Overall Corruption 157|22|18|3|91|
|Perceived Corruption160|42|9|18|70|
|Corruption in Personnel Management<br>159|21|19|0|100|
|Corruption in Budget Management159|27|25|0|100|
|Corruption in Public Contracts160|21|24|0|100|
|Administrative Corruption160|20|21|0|100|
|<br>Corruption in Laws and Regulations<br>160|20|21|0|100|
|<br>**Governance Indices**|||||
|160<br>Quality of Rules|69|13|7|96|
|Effectiveness of Rules160|73|15|4|98|
|Audit Mechanisms156|68|16|11|99|
|Meritocracy 159|66|18|10|98|
|Wage Satisfaction 161|37|21|0|94|
|<br>Voice<br>161|71|14|22|96|
|Mission 160|81|8|51|94|
|Service Performance160|66|11|28|91|
|Resources Are Adequate161|58|17|5|94|
|<br>Municipal Dummy<br>161|0.22|0.42|0|1|
|Judicial Dummy<br>161|0.11|0.31|0|1|
|**Regional Variables**|||||
|<br>Languague stratification of the region<br>161|16|20|0|76|
|<br>Average household Income of the region<br>161|12555|4218|4283|18057|
|Regional Dummy - Lima<br>161|0.33|0.47|0|1|
|<br>Regional Dummy - Selva<br>161|0.21|0.41|0|1|
|<br>Regional Dummy - Costa161|0.24|0.43|0|1|
|<br>Regional Dummy - Sierra161|0.22|0.42|0|1|

30
2011 as the base year, is used to analyze the economic implications of the Paris Climate agreement.<sup>4</sup>

# **4. Scenarios**

Two scenarios modeled here are:

**_Scenario 1-_** Implementation of INDCs by all member countries that signed the Paris Accord.

**_Scenario 2_** - Scenario A along with exogenous reduction in the prices of renewables by 25 percent.

Starting from 2011, a baseline model is run until 2030 for GDP, labor force, and population, which would expand the whole global economy, including aspects such as production, trade, and investment. The baseline is based on IIASA's Sustainable Socioeconomic Pathways (SSP). The emission reduction commitment is treated in the model as an endogenous tax on carbon on sectors that use carbon-intensive inputs. It is important to keep in mind that the experiments reported are conducted under the counterfactual or “what if” analysis. The INDCs are unlikely to be implemented at the same time in all the regions reported and even more uncertain is the exact time frame these policies will likely get implemented and therefore a simpler approach is taken for imposing INDC shocks on the global economy for the base year 2011.

The analysis assumes no change in the trade account: thus, the variable representing the current account balance (DBALCAR **)** is set to be exogenous and equal to zero in all countries/regions except one. The slack variable, cgdslack, is made endogenous. These variables are usually exogenous in the standard GTAP closure. The implication here is that investment is calculated as a residue in order to guarantee no change of the trade account. The quantitative restrictions applied to carbon emissions are introduced by making the real carbon tax RCTAX (i.e. the nominal carbon tax deflated by the GDP deflator) endogenous and the emission growth rates gco2t exogenous and equal to the Paris commitments. It is also possible to impose an exogenous real or nominal carbon tax (RCTAX or NCTAX, respectively) and leave the emission growth rates to be determined endogenously.

# **5. Main findings**

All the results are cumulative as happening in the final year of analysis, i.e. 2030 and not annualized or year-by-year. The results from Scenario 1 are discussed below and highlighted in Figures 5 through 8, which show both welfare and macroeconomic impacts of the Paris commitments across countries. Four other hypothetical alternative sub-scenarios were also examined, besides the original INDC commitment scenario. They include: (i) INDC_50MR scenario that looks at the impact of increasing the INDC commitments by 50 percent more than what countries initially intended to achieve. This is an extreme case, however, having such a scenario helps in giving benchmark values to alternative policy measures (ii) INDC20 looks at the impact of achieving just 20 percent of the INDC targets. (iii) INDC50 shows percent

> 4   https://www.gtap.agecon.purdue.edu/ GTAP (1997), T. Hertel Ed., Global Trade Analysis Modeling and Applications, NY, USA.

10