rafmacalaba/datause-encoder-data huggingface dataset:
these prices is illustrated in Figure 1. On average over the period studied, the price of electricity is very high relative to that of the other fuels (and is thus represented on a separate axis). In 2004 the price of electricity per TOE is approximately ten times higher than coal, and three times greater than natural gas and oil. In general electricity prices in Ireland are expensive relative to other European countries. This is largely due to high dependency on imported fossil fuels. Ireland also has high transmission and distribution costs due to the dispersed nature of the population.

Figure 1: Price indices <!-- Start of picture text --> 450 1120<br>400<br>1100<br>350<br>300 1080<br>250<br>1060<br>200<br>150 1040<br>100<br>1020<br>50<br>0 1000<br>2004 2005 2006 2007 2008 2009<br>Gas Oil Coal Capital costs Electricity<br>Price index<br><!-- End of picture text --> For the majority of fuels, prices are trending upwards until 2008, at which point there is a relative decline. For firms in our data, the average oil price declines after 2006 - this is driven by decreases in the price of the heavy fuel oil component of the oil price (the price of the light fuel oil component continued to trend upwards until 2008). The price of electricity increases significantly from 2005 to 2008 - this is driven largely by increasing natural gas prices, as the vast majority of electricity generated in Ireland comes from natural-gas-fired power plants. In recent years, the need to invest in the network to bring renewable generation sources (generally located far from load centers) on stream has further added to electricity costs.

From 2004 to 2005 there was a small decline in the cost of capital for Irish manufacturing firms, which was largely reversed by 2006. This variable then followed a modest upward trend to 2009 driven by changes in the interest rate and a modest increase in the depreciation rate for the machinery-and-equipment component of capital.

12
We observe that the level of the NIM is consistently lower than the SELIC rate (Figure 5b). This means that the returns of banks are lower than by just lending to the government, which is theoretically risk free. This is counter-intuitive because theory suggests that by taking risk, lending to the individuals and corporations, banks should theoretically earn a higher return than by lending to the government. A possible explanation for this is that banks underestimate default risk and the realized default rates are higher than the anticipated default rates. However, the fact that the SELIC rate is consistently higher than NIM is of concern. A risk-averse bank would choose to lend risk free to the government and get higher returns. There is an arbitrage opportunity that is not being exploited by the banking sector.<sup>11</sup> Even when we take out the competition factors from the pure spread, in our Step 2 regression, we see that macro-factors are still a very small part of the NIM in 2009 (Figure 6a and 6b). This supports our argument that macro-factors only play a small part in the determination of spreads in Brazil and microfactors are the most significant (Figure 6b).

**Figure 6a.  Breakdown of NIM Spread in Brazil** **<u>(2009)</u>**

<!-- Start of picture text --> Figure 6b. Breakdown of NIM Spread as % in<br>Brazil (2009)<br><!-- End of picture text --> <!-- Start of picture text --> 10<br>9<br>4%<br>8<br>17%<br>7<br>6<br>5<br>4<br>3 79%<br>2<br>1<br>0<br>NIM Pure Spread Macro Micro Competition Macro<br>Percentage points<br><!-- End of picture text --> _Source_ : Authors’ calculations based on BankScope data _Source_ : Authors’ calculations based on BankScope data

# **_5.2 Brazil's Bank Spreads in an International Context_**

Many studies have concluded that spreads in Brazil are the highest in the world (generally as measured by the data provided by IMF’s IFS). We find that Brazil’s IFS spreads are the highest among the countries we investigate in the paper (Table 2) for 2009. However, when we further investigate the measurement of these spreads we find they are not comparable across countries. The IFS spreads cannot reliably be used for cross-country comparison because of the disparate rates reported by countries. Some countries report the difference between lending and borrowing prime rates, while others report interest rates of different maturities.

> 11 Another possibility would be that the banks believe that the government would not repay its debts. Nevertheless, this rationale is improbable given that the Brazilian government’s debt ratings have improved significantly and the debt to GDP ratio has decreased.

17
# **Table 2:  Rankings of Asian Utility Regulation Frameworks, 1998**

|**Country and Sector**||**Categorisati**|**on by Criteria**||||
|---|---|---|---|---|---|---|
|||Formal Attrib|utes||Informal Attribut|es|
||**Clarity**|**Autonomy **|**Accountability**|**Participation **|**Transparency **|**Predictability**|
|**Bangladesh**|||||||
|<br>Electricity|2|2|2|1|1|1|
|**India**|||||||
|Electricity, Federal|2|3|2|1|3|1|
|<br>Electricity, Orissa|4|5|4|5|5|2|
|<br>Natural Gas|1|1|1|1|3|1|
|Telecoms|3|4|4|5|5|3|
|**Indonesia**|||||||
|Natural Gas|1|1|1|1|1|1|
|Transport|1|1|1|1|1|1|
|**Malaysia**|||||||
|Telecoms|3|3|2|3|1|1|
|Transport|3|3|2|2|1|2|
|Water|3|2|2|2|1|2|
|**Pakistan **|||||||
|Electricity|3|4|3|3|3|2|
|**Philippines**|||||||
|Electricity|3|3|2|3|2|3|
|Water|3|3|3|3|3|2|

**_Scoring_** :  Each sector was ranked on a 1-5 scale where 1 indicates no merit with regard to the criteria for effective and independent utility regulation and 5 represents best OECD practice.

**_Source_ :** “Governance and Regulatory Regimes for Private Sector Infrastructure Development” A report by NERA for the ADB, ADB RETA 5758-REG, Manila, 1998, Table 3.8 also Stern and Holder (1999) Table 2.

# **3.2 The Prayas Energy Group’s 2003 Report on Indian State Electricity Commissions**

The Prayas Report<sup>24</sup> (2003) is a fascinating document. Since the study was carried out when there had been a reasonably long period under which Indian State Electricity Regulatory Agencies had been in place, it was able to go much further than the 1998 NERA study in providing substantive and systematic analysis of the performance of regulatory agencies in practice.

> 24 “A Good Beginning But Challenges Galore:  A Survey Based Study of Resources, Transparency and Public Participation in Electricity Regulatory Commissions in India”, Prayas Energy Group, Pune, February 2003.

16
A strand of the literature studies spillovers from protests. Several papers have documented that protests in Tunisia and Egypt have inspired protesters elsewhere (Bamert et al, 2015; Hale, 2013; Lynch, 2013; Saideman, 2012). Similar spillovers were observed from other revolutionary periods, such as the Eastern European democratization in the 1990s and the revolutionary wave of 1848 in Europe (Hale, 2013; Weyland, 2010, 2012).

The remainder of the paper is organized as follows. Section II presents the data and the empirical strategy. Section III shows the results. Section IV presents robustness checks. Section V concludes.

# **II. Data and Empirical Strategy**

Data on protests are from the Armed Conflict Location & Event Data (ACLED) Project. Protests are defined in the data set as _“non-violent demonstrations, involving typically unorganized action by members of society.”_ This category covers peaceful protests, protests with interventions (e.g. arrests), and protests in which excessive force was used against protesters (e.g. causing injuries and deaths). Note that this definition does not include violent demonstrations and mob violence. The data coverage starts in 1997 but is severely imbalanced, rendering difficult the use of earlier years to conduct the spatial analysis. We aggregate the number of protests at the monthly (and quarterly) level.

Figure 1 shows the evolution of the number of protests at a monthly frequency as recorded by ACLED—in absolute number (Panel A) and in per million capita (Panel B). Note that starting in 2010 and subsequent years more countries were added to the data set—see Panel A of Appendix Table A1 for the list of countries included in the data set. That explains the big jump in the series in the year 2010. We hence restrict our sample to start in January 2010 when using the ACLED data set.<sup>2</sup>

**Figure 1: Evolution of the Total Number of Protests**

**Panel A: Protests in levels Panel B: Protests in per capita**

<!-- Start of picture text --> 1995m1 2000m1 2005m1 2010m1 2015m1 2020m1 1995m1 2000m1 2005m1 2010m1 2015m1 2020m1<br>Sources: Authors' calculation using ACLED dataset Sources: Authors' calculation using ACLED dataset<br>1.2<br>6000<br>1<br>.8<br>4000<br>.6<br>.4<br>2000<br>Monthly number of protests<br>.2<br>Monthly number of protests (per million persons)<br>0 0<br><!-- End of picture text --> 2 Note that for countries added to the data set after 2010, we consider these country-year pairs as missing observations (and not zeros) before they are added to the data set. However, our results remain robust when using data from ACLED starting in January 1997.

3
we do not have exporter-level data on that chapter for important oil exporting countries such as Burkina Faso, Cameroon, Iran, Kuwait, or Yemen.<sup>23</sup>

**f) Value:** The unit of measurement of export values in the cross-country raw dataset is the US Dollar (USD). The export values in the raw data were already in USD for many countries. For countries for which export values were provided in local currency, they were converted to USD using an annual official exchange rate series.<sup>24</sup> The exchange rates used differ slightly from those used in the United Nations COMTRADE database that are weighted average annual exchange rates based on monthly exchange rates and monthly trade volumes (as weights).<sup>25</sup> The resulting difference in the exchange rate series creates a small discrepancy (less than 0.5 percent in all cases) between our figures and COMTRADE’s figures for the countries that provided their export values in local currency.

Export values in our dataset are Free on Board (FOB) figures, except for El Salvador and Senegal, whose export values represent Cost, Insurance and Freight (CIF) figures rather than just the pure value (FOB) of the good. This difference should be taken into account for cross-country comparisons of measures related to the size of exporters, exports per product or destination but is not expected to affect other measures related to concentration, diversification and firm, product and market dynamics.

**g) Period:** The information in the cross-country raw dataset has a yearly periodicity.

> 23 Possible reasons for this lack of data are confidentiality reasons or the fact that goods exported through pipeline  are not recorded at customs but instead are recorded by other government/private institutions in the countries.

> 24 The specific series used for this conversion is PA.NUS.FCRF taken from the World Development Indicators (whose original source is the IMF’s International Financial Statistics). The series is based on an annual average of daily exchange rates determined by national authorities or in the legally sanctioned exchange market.

> 25 http://unstats.un.org/unsd/tradekb/Knowledgebase/Calculation-of-dollar-value-in-trade-statistics-Current-value-or-constantdollar-value.

12
# **Table A1** External Validity: Comparison of our sample to random sample of females in same communities

||(1)|(2)|(3)|(4)|(5)|
|---|---|---|---|---|---|
||**Baseline Mean**|**Buehren et al. (2017)**<br>**Baseline Mean**|**Buehren et al. (2017)**<br>**Baseline Control Mean**|**(1)-(2)**<br>**p-value**|**(1)-(3)**<br>**p-value**|
|Age|16.355|16.676|16.621|.023|.097|
|Married|0.044|0.022|0.024|.024|.052|
|Own house|0.605|0.606|0.626|.951|.560|
|Has child|0.089|0.068|0.073|.115|.283|
|Ever had sex|0.241|0.228|0.242|.557|.948|
|Use condom|0.182|0.188|0.189|.739|.765|
|Highest grade|8.273|8.342|8.239|.648|.835|
|Dropout|0.236|0.212|0.217|.243|.438|
|Never enrolled|0.012|0.015|0.019|.518|.234|
|Observations|1,621|4,954|1,708|||

_Notes._ Column 1 shows baseline means among females in Dodoma and Iringa in the current study sample, collected in 2016. Columns 2 and 3 show baseline means (before ELA clubs were established) among females in the Buehren et al. (2017) study, collected in 2009. The Buehren et al. (2017) sample comprises a random sample of adolescent females from the same communities as the current study in Dodoma and Iringa. Buehren et al. (2017) treatment communities are where ELA clubs were eventually established. Column 2 shows means using the entire baseline sample from Buehren et al. (2017). Column 3 shows means using baseline data restricted to the Buehren et al. (2017) control communities. Column 4 presents the p-values from a test of equality between the means in columns 1 and 2. Column 5 presents the p-values from a test of equality between the means in columns 1 and 3. The latter comparison is made to alleviate concern that communities where clubs are established are different from surrounding communities. Age is age in years; Married is an indicator that the female is married; Own house is an indicator that the female’s household owns the house she lives in; Has child is an indicator that the female has a child; Ever had sex is an indicator that the female has ever had sex; Use condom is an indicator that the female uses a condom during sexual intercourse; Highest grade is the highest grade completed by the female; Dropout is an indicator that the female dropped out of school; and Never enrolled is an indicator that the female was never enrolled in school. _Source._ Column 1: Female baseline data, restricted to females in Dodoma and Iringa. Columns 2–3: Buehren et al. (2017) female baseline data. In columns 1–3, the sample is restricted to females aged 13-22 in order for their to be a common age range across samples.

60
-19-

in samples compiled by the Ystanbul Chamber of Industry (ICI):24

||19|81<br>1|982|1|983|
|---|---|---|---|---|---|
||CMB|ICI<br>CMB|ICI|CMB|<br>ICI|
|Averageassets|-|-|-|||
|(Million Ti)|3264|222<br>5079|347|6848|487|

Besides possible selectivity bias, the reader should also be cautioned that additional biases exist since the data is not corrected for inflation. The section will proceed as follows. First profitability will be defined. It will then be decomposed into standard finarpial ratios that capture real and financial factors that affect profitabilitj as well as the firm's financial response to movemerts in these factors. After summarizing the movements in the ratios over time, a simple analysis of variance model will be used to statistically compare average values of the ratios across time and groups of firms.25 Profitabilit! and its com2onents: I define profitability (PR) as the ratio of pre-tax income (Y) to the book value of equity (E): PR - Y/E.<sup>6</sup> Then, PR can be decomposed in the following way: (1) PR - [(EBIT.AU) - (FC*GR)] / (1-GR) where EBIT - EBIT margin, earnings before interest and taxes divided by net sales income, AU - asset utilization ratio; net sales income over total assets FC - financial costs ratio; interest expenses over total debt 24 The ICI samples comprise more than 1200 firms.

25 The approach adopted here is similar to Petrei and Tybout (1985).

26 The variables are defined In Appendix 1.
Table 5: Tax revenue multiplier over both sub-periods

|Quarters|1|4|8|12|20|Peak|
|---|---|---|---|---|---|---|
||||**1970:Q**|**1-1990:Q**|**4**||
|Impact multiplier|-0.38*|-0.23*|-0.14|-0.07|0.01|-0.38*(1)|
|Govt. spending|-0.19 *|-0.08*|-0.02|0.00|0.01||
|Net tax revenue|1.00*|0.53*|0.16*|0.03|0.03||
|Cumulative multiplier|-0.38*|-0.40*|-0.45*|-0.51*|-0.36|-0.51*(13)|
||||**1991:Q**|**1-2010:Q**|**4**||
|Impact multiplier|-0.21*|-0.02|-0.10|-0.12|-0.03|-0.21*(1)|
|Govt. spending|-0.20*|-0.18*|-0.10|-0.02|0.04||
|Net tax revenue|1.00*|0.36*|0.18*|0.06|-0.04||
|Cumulative multiplier|-0.21*|-0.10|-0.17|-0.28*|-0.43*|-0.44*(22)|

Notes: This table reports spending multiplier and responses of government spending and net tax revenues to a government spending shock over the two sub-periods. The multipliers is the median, and * denotes that 0 does not belong to the confidence region. The last column reports the maximum multiplier (in absolute value) and numbers in brackets denote the quarters in which the maximum is observed.

economy (characterized by the unemployment rate). They identify a government spending shock as a news shock using expected change in military spending. The comparison with their linear VAR specification shows that over the first sub-period, the multiplier in this study is below their multiplier, but over the second-period, it is slightly above. It should be noted that in both studies, the multiplier is increasing with the number of periods following the initial increase in spending. Also, in the linear case, spending multiplier in both studies is almost below one, characterizing the theoretical prediction of the size of the spending multiplier in open economies.

Table 6: Comparison with Owyang et al. (2013)

||2-year integral|4-year integral|Peak|
|---|---|---|---|
|_My results_||||
|Sub-sample 1|0.12|0.38|0.44|
|Sub-sample 2|0.82|1.01|0.85|
|_Owyang et al. (2013)_||||
|Linear VAR|0.67|0.79|0.57|
|High unemploment|1.60|1.16|0.65|
|Low unemployment|0.44|0.46|0.49|

Notes: This table compares the multiplier associated with spending increases to Owyang et al. (2013)’s multipliers. The integral measure is computed as the cumulative multiplier. The peak measure is the ratio of the IRF of GDP and government purchases at their respective peaks scaled by the inverse of the average ratio of G/Y.

16
**<u>Appendix table 10: Sales price regressions for agricultural land before January 2024</u>**

||<br>**All tran**<br>**Commercial**|<br>**sactions**<br>**Personal**|<br>**With registr**<br>**Commercial**|**y information**<br>**Commercial**|
|---|---|---|---|---|
|Area (ha)|0.1171***|-0.2220***|0.2393***|0.2227***|
||(0.0027)|(0.0056)|(0.0064)|(0.0066)|
|Area^2|-0.0274***|0.0510***|-0.0924***|-0.0896***|
||(0.0018)|(0.0023)|(0.0051)|(0.0051)|
|Buer is leal entit|06105***|03740***|||
|y g y|.<br>(0.1328)|.<br>(0.0297)|||
|# transactions in village|-0.0172***|-0.0505***|-0.0165***|-0.0199***|
||(0.0019)<br>*|(0.0034)<br>***|(0.0054)|(0.0054)|
|Parcel part of a field|-0.0230<br>(0.0121)|-0.1622<br>(0.0106)|||
|Field area (ha)|<br>-0.0038|<br>-0.0084***||0.0340***|
||(0.0032)|(0.0026)||(0.0106)|
|Field area^2|<br>0.0049***|<br>0.0090***||<br>0.0003|
||(0.0005)|(0.0006)||(0.0015)|
|Unmapped area share|-0.2659***|-0.0326||-0.0352|
||(0.0231)|(0.0263)||(0.0803)<br>|
|<1% of area unregistered||||-0.0092|
|||||(0.0119)|
|1-10% of area unregistered||||-0.0099|
|||||(0.0106)|
|>10% of area unregistered||||-0.0596***<br>|
|||||(0.0143)|
|Oner area <1%||||<br>-00207|
|w||||.<br>(0.0172)|
|Owner area 1-10%||||<br>-0.0234**|
|||||(00094)|
|Owner area >10%||||.<br>-0.0102|
|||||(0.0188)|
|Largest user's cult. area share||||0.2043***|
|||||(0.0246)|
|>5 registered users||||-0.0022<br>|
|||||(0.0148)|
|Dist. to tertiary road (km)|-0.0145***|-0.0300***|-0.0061|<br>-0.0089**|
||(0.0016)|(0.0030)|(0.0042)|(0.0042)|
|Dist. to secondary road (km)|<br>0.0042**|<br>-0.0191***|<br>-0.0023|<br>-0.0049|
||(0.0017)|(0.0033)|(0.0048)|(0.0048)|
|Dist. to primary road (km)|-0.0100***|-0.0211***|0.0056|0.0042|
||(0.0019)|(0.0035)|(0.0052)|(0.0052)|
|Dist. to motorway (km)|<br>-0.0106***|<br>-0.0115***|<br>0.0173**|<br>0.0131*|
||(0.0024)|(0.0043)|(0.0077)|(0.0076)|
|Dist. to railway (km)|<br>-0.0137***|<br>-0.0024|<br>-0.0050|<br>-0.0026|
||(0.0021)|(0.0039)|(0.0057)|(0.0057)|
|Dist. to port (km)|0.0700***|0.0593|-0.0251|-0.1176|
||(0.0264)|(0.0610)|(0.1844)|(0.1831)|
|Dist. to elevator (km)|-0.0133***|-0.0266***|-0.0249***|-0.0293***|
||<br>(0.0035)|<br>(0.0066)|<br>(0.0094)|<br>(0.0094)|
|Dist to nearest city (km)|<br>-00107*|<br>-01244***|<br>-00113|<br>-00141|
|.|.<br>(0.0060)|.<br>(0.0100)|.<br>(0.0190)|.<br>(0.0189)|
|Share built-up area|<br>0.9075***|<br>0.4571***|<br>-0.2899|<br>-0.3083|
||(00828)|(00536)|(03838)|(03809)|
|Share cultivated area|.<br>0.5582***|.<br>0.3911***|.<br>0.2234|.<br>0.1692|
||(0.0360)|(0.0452)|(0.1775)|(0.1761)|
|Share forested area|0.4186***|0.2448***|0.0982|0.0821|
||(0.0405)|(0.0484)|(0.2009)|(0.1993)|
|Share grassland|0.3607***|0.2662***|-0.0825|-0.0630|
||<br>(0.0362)|<br>(0.0450)|<br>(0.1795)|<br>(0.1780)|
|Soil bulk density (g/cm^3)|<br>-2.5201***|<br>-2.4086**|<br>-3.2283*|<br>-2.8201|
||(0.6333)<br>|(1.1551)<br>|(1.8849)<br>|(1.8699)<br>|
|Density^2|2.2925*|2.8329|4.5623|4.1983|

43
enrolled students.<sup>15</sup> Finally, to measure the effects of group composition on academic performance, schools provided administrative records of math and science grades at the end of the academic year. Appendix 1 includes a description of all collected data and main outcomes. As shown in Appendix Table A5, the average matching rate of administrative data of enrolled children was 94% at baseline and 97% at follow-up. All matching rates were balanced between treatments and C groups except for the fraction of math grades at baseline between the HM and C groups, significant at 10%; and in absenteeism between both tracking groups, also significant at 10%. To account for this difference, I include the imputed grade for missing observations at baseline and a missing value indicator in all specifications for the academic outcomes. Additionally, the average matching rate of administrative data of nonenrolled students was 85% at baseline and 98% at follow-up.

The share of initially enrolled students that filled out the follow-up survey after the intervention was 92%, on average; for the HM and HT groups, it was 91% and 94%, respectively. There were no statistical differences between treatment and C groups in overall attrition rates. Therefore, results are not driven by the absence of follow-up survey data for any group. For the neurophysiological measures, after filtering electroencephalogram (EEG) recording data, the average attrition share was 49%. In Dinarte and Egana (2019), we present several checks to verify that this attrition rate was not correlated with the intervention. We argue that attrition was caused mainly by the quality of the data recordings. For example, long, dense, or dirty hair and/or “frozen” computers were the most common troubles that the Matlab toolbox encountered in reading the EEG recordings. As a summary, the time line of the study is shown in Figure 2.

> 15More details on the data-collection process for this third group of outcomes are discussed in Dinarte and Egana (2019).

16
# **Rent-Seeking Activities, Misallocation, and Innovation in Argentina**

Gabriel Zaourak<sup>1</sup> World Bank

**JEL code:** E62, O11, O41.

**Keywords:** growth, misallocation, rent-seeking, innovation.

> 1 Chad Syverson, Ana Cusolito, Peter Siegenthaler and Xavi Cirera provided great comments to improve the paper. I thank Cecile Niang and Daniel Gomez for the opportunity to write this paper. The views expressed herein are my own and should not be attributed to the World Bank, its executive directors, or the countries they represent. All errors or omissions are my own.

Email: gzaourak@worldbank.org