rafmacalaba/datause-encoder-data huggingface dataset:
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acceptable data for analysis were obtained from 214 schools and 8736 students.3

# Student Variables

27. Dependent Variables. This study examines school effects on two student achievement and four student attitude outcome variables. The achievement variables are the science and mathematics core test scores, unadjusted for guessing, from the IEA Second International Science Study. The attitude scales were constructed from factor analyses of the student attitude survey from the - same study. For this study, attitude items were recoded (1 agree, 0 - - uncertain, and -1 disagree) and factor scores were constructed from these recoded items using data from the total student sample. The minimum number of cases for the attitude factors was 10,222 students; for this sample all factor scores have a mean of 0 and a standard deviation of 1. Positive attitude toward science includes eight items of the type "Science is an enjoyable school subject". Negative attitude toward science is comprised of four items of the type "Scientific discoveries do more harm than good." Positive attitude toward school is comprised of four items of the type "I enjoy everything about school", and negative attitude toward school is comprised of four items of the type "School is not very enjoyable." 28. Student-level predictors. In this paper, we analyze three student-level variables: gender, age and socio-economic background (SES). SES is a factor score with a mean of 0 and a standard deviation of 1 and is based on father's

> **3The** original school data file contained 269 records for which **data** from the teacher questionnaire, the school questionnaire **and** mean values from student records could be matched. Thirty-four schools were deleted due to missing **data** on five or more veriables; 17 schools were deleted for lack of within school variation; **end** four schools were deleted for other miscellaneous reasons.
Second, regarding technical risk, in addition to the risk of going beyond the plant‟s own expertise, another important question concerns the intrinsically higher sophistication of product innovations, especially when they are of a more radical nature. Thus, introducing a product that is new to the plant and to the country (i.e., that no other plant has introduced before) is likely to be more risky than introducing a product that is new only to the plant. Columns (7)-(9) of Table 7 show the results from estimating our main specifications considering separately product innovations that are new to Chile and those that are new only to the plant. Both types of product innovations increase survival odds of Chilean plants and the t-tests show that the effects are not significantly different. A different way to capture technical risk is to consider the degree of sophistication of the new products introduced by Chilean plants, under the assumption that the higher is that degree the more risky is the innovation potentially. We use a trade-based proxy to measure the sophistication of each product following Hausmann et al. (2007) and its application by Jarreau and Poncet (2012). The proxy, described in detail in Appendix 3, gives a larger score indicating higher sophistication to products that are mostly exported by countries with higher GDP per capita, thus inferring from observed trade patterns the products that require greater levels of development to be exported. We estimate our main specifications splitting product innovations into those that are less versus more sophisticated. Columns (10)-(12) of Table 7 show that while the innovation-survival relationship is significant only for plants introducing less sophisticated product innovations, the t-tests indicate that the difference of coefficients is not significant. Again, a shortcoming of this approach is that the product sophistication measures do not cover all Chilean innovators due to difficulties in establishing concordances between the 7-digit ISIC level and the HS classification of the trade data.

We examine the third dimension of market risk by considering in Table 8 alternative measures of innovation that attempt to capture the various challenges associated with getting new products to be successfully sold on the market. First, as is widely discussed in the innovation literature, the value of an innovation depends on the actions of competitors; its value will be much lower if competitors introduce novel products that are very close to the plants‟ innovations. Columns (1)-(3) show the results from estimating our main specifications distinguishing across product innovations in a 7-digit category with more than versus less than 10 competitors who introduce innovations concurrently. The estimates indicate that product innovation improves Chilean plant survival odds significantly _only_ in industries with a lower degree of competition.

19
Figure A15: Specification Charts for Skew of Daily Temperature <!-- Start of picture text --> Ethiopia Malawi<br>p < 0.05 n.s. p < 0.05 p < 0.05 n.s. p < 0.05<br>1.5 2<br>1<br>1<br>.5<br>0<br>* Weather Product * 0-.5 * Weather Product * -1<br>MERRA-2 MERRA-2 -2<br>ERA5 ERA5<br>CPC CPC<br>* Dep. Var. * * Dep. Var. *<br>Value Value<br>Quantity Quantity<br>* Model * * Model *<br>Weather + FE + Inputs Weather + FE + Inputs<br>Weather + FE Weather + FE<br>Weather Weather<br>0 2 4 6 8 10 12 14 16 18 0 2 4 6 8 10 12 14 16 18<br>Niger Nigeria<br>p < 0.05 n.s. p < 0.05 p < 0.05 n.s. p < 0.05<br>6 2<br>42 1<br>0 0<br>* Weather Product * -2 * Weather Product *<br>MERRA-2 -4 MERRA-2 -1<br>ERA5 ERA5<br>CPC CPC<br>* Dep. Var. * * Dep. Var. *<br>Value Value<br>Quantity Quantity<br>* Model * * Model *<br>Weather + FE + Inputs Weather + FE + Inputs<br>Weather + FE Weather + FE<br>Weather Weather<br>0 2 4 6 8 10 12 14 16 18 0 2 4 6 8 10 12 14 16 18<br>Tanzania Uganda<br>p < 0.05 n.s. p < 0.05 p < 0.05 n.s. p < 0.05<br>.5 1<br>0 .5<br>-.5 0-.5<br>* Weather Product * -1 * Weather Product * -1<br>MERRA-2 MERRA-2 -1.5<br>ERA5 ERA5<br>CPC CPC<br>* Dep. Var. * * Dep. Var. *<br>Value Value<br>Quantity Quantity<br>* Model * * Model *<br>Weather + FE + Inputs Weather + FE + Inputs<br>Weather + FE Weather + FE<br>Weather Weather<br>0 2 4 6 8 10 12 14 16 18 0 2 4 6 8 10 12 14 16 18<br><!-- End of picture text --> _Note_ : The figure presents specification curves, where each panel represents a different country, with three different models presented within each panel. Each panel includes 18 regressions, where each column represents a single regression. Significant and non-significant coefficients are designated at the top of the figure. For each Earth observation product, we also designate the significance and sign of the coefficient with color: red represents coefficients which are negative and significant; white represents insignificant coefficients, regardless of sign; and blue represents coefficients which are positive and significant.

63
In this section we spell-out our estimation strategy. This involves specifying a sample selection model. Employing a sample selection model allows us to take account of the censoring process that leads to zero or missing bilateral trade flows. More precisely, in our estimating framework the outcome variable (the dependent variable in the second stage equation) is only observed if the defined selection criterion is met. In our case, the amount of the trade can only be observed if trade occurs. We therefore employ a sample selection estimation, combining the analysis of the probability of trade flows with the analysis of trade volumes. (Similarly, Felbermayr and Kohler (2004) employ a Tobit estimator to examine bilateral zeros.)

# **2.1 Data**

We work with a panel of bilateral trade, trade policy, geographic characteristics, and income data spanning from 1988 to 2002. Our trade and tariff data were obtained from the UN/World Bank WITS system (World Integrated Trade Solution). The data in WITS come, primarily, from the UNCTAD TRAINS and COMTRADE systems and the World Trade Organization’s integrated tariff database (IDB). The countries included in the sample are listed in the annex.<sup>2</sup> There are several country combinations for which trade is not reported. Following the recent literature, we assume that these missing observations from the database represent zero trade. (See Coe et al 2002, Felbermayr and Kohler 2004, Santos and Tenreyro 2005.) We use import data as it is likely to be more reliable than export data since imports consti-

> 2While trade data are available for a wide range of country pairs, the available tariff data are more limited. For this reason, we utilize a standard WITS procedure of matching the nearest adjacent year to represent otherwise missing tariff data. Interpolation is then used for wider gaps. A further complication is when tariff data are never reported for a country pair. In order to obtain an approximate tariff value applicable between these country pairs we then utilize the average applied tariff for the reporting countries for a given year.

7
Paper with Section Robustness 0.24 0.43 0.00 <u>checks</u> 1 37 Source: Own Estimation based on R&D Evaluations Review Figure 1 shows the heterogeneity in the papers included in the meta-analysis. The papers are separated by Region and Type of data used, plotted against the precision of estimates and then by R&D program characteristics (years of implementation and investment). In both cases, there is wide variability, particularly in papers that evaluate R&D projects in European Union (EU) and used panel data (Figures 1 and 2). The meta-comparability of papers depicts comparability across publications given their number of citations (Figure 3). The size of the circles indicates the number of citations and the closer and more overlapped the circles are, the higher their comparability. In general terms, given the small size of the sample there is sufficient comparability from a graphical perspective.

Figure 1. Variability in Papers’ Standard Errors and Correlations (Between Estimations) by Region and Type of Data Used <!-- Start of picture text --> ECA LAC EU OTHER ECA LAC EU ECA EU OTHER<br>Panel Cross Section Pooled Cross Section/TS<br>Standard Error pcorr<br>.4<br>.3<br>.2<br>.1<br>Standard error/Adjusted precision<br>0<br><!-- End of picture text --> Source: Own estimations Figure 2. Variability in Papers’ Cumulative Program Investment and Years of Implementation by Region and Type of Data Used 19
25 their monthly electricity bill to avoid the outages experienced in the month one year before the survey.

However, when we divide the WTP amounts by the number of kWh lost due to unscheduled outages and load shedding, our preferred estimates of the resulting VoLL (5 – 15 NR/kWh, or ¢4.7 – ¢14/kWh) are low when compared with the marginal cost of adding capacity or delivering electricity when the demand is high. Imports from India have a marginal cost of approximately 30 cents per kWh during the times of highest demand. The highest marginal cost for the limited oil-based generation in Nepal is 20-30 cents/kWh. The cost of spinning reserve shortfall is likewise estimated at about 20 cents/kWh.<sup>18</sup> Each of these alternate estimates of the marginal cost of bringing additional electricity to the grid exceeds the VoLL.

It is even more striking that our estimates of the VOLL, based on data collected in 2017, is no larger than the figures obtained by Karki et al. (2008) in a survey in 2008. Furthermore, the respondents that we call "attentive" (because they appear to recollect the number of days with outage from the prior year correctly) are the ones that announce most often that they are not willing to pay at all to be rid of outages.

Economic theory and common sense suggest that the value of avoiding the loss of one kWh should be higher when the outage is unannounced, and we find evidence of this when we use the full sample of responses. But when attention is restrictive to the "attentive" respondents, it appears that the VoLL from unscheduled and scheduled outages is the same. Given the many daily hours without electricity before October 2016, it is likely that the notions of "intensive" and "extensive" margin (corresponding to one more kWh lost in scheduled and unscheduled outages, respectively) get blurred for such "attentive" respondents.

> 18 Dr. Debabrata Chattopadhyay, head of the World Bank’s Energy Planning Team, personal communication, 2 January 2020.
conduct an online experiment. Our study extends beyond this previous paper in three ways. Firstly, our study is arguably far more generalizable to other LICs and LMICs, as rates of COVID-19 vaccine acceptance in Zambia are similar to the average for Sub-Saharan Africa (PNG has among the lowest COVID-19 vaccine take-up and highest levels of hesitancy in the world), and this experiment was conducted when vaccines were widely available (the PNG study took place before vaccines were widely available). Secondly, there are several design features of our experiment that allow for more credible heterogeneous treatment effect analysis, such as having a larger sample size, a reduced number of treatment groups, and preregistering the hypotheses. Finally, the treatments we provided were more targeted towards specific research questions, whereas the previous experiment was part of a “first generation” of surveys examining what type of information, if any, could boost the acceptance of COVID19 vaccines. A limitation of this type of research (including our study) is that respondents were asked to self-report their willingness to be vaccinated. While intention to vaccinate is an important issue to consider, it does not necessarily guarantee vaccine uptake as other barriers may block an individual from turning their intent into action.

The rest of the paper is structured as follows. Section 2 provides background about the related literature and the hypotheses that emerge from them, as well as details about the setting of the study. Section 3 outlines the methodology behind the in-person survey and shows the main results. Section 4 describes the methodology behind the online experiment and presents the key findings. Section 5 provides details about the methodology behind the SMS message experiment and presents the results. Section 6 discusses the findings across the two data sources and outlines the implications of this research.

5
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addition, the cost of quasi-fiscal activities undertaken by the central bank (including the central bank's implicit bearing of foreign exchange risk for domestic residents who borrow externally and the rescue of failed commercial banks and the corresponding issue of liabilities by the central bank to a fragile financial system) is estimated at close to 3% of GDP in 1997. These estimates are preliminary, but they reveal the presence of a considerable quasi-fiscal deficit and indicate that substantial resources will be needed to re-capitalize and improve the operations of non-central government institutions.

**Table 1: Zambia: Conventional Fiscal and Quasi-Fiscal Deficits**

**(in percent of GDP)**

||**1995**|**1996**|**1997**|
|---|---|---|---|
|Central Government Deficit Excluding Grants|-4.6|-2.5|-2.3|
|Non-financial Public Sector Deficit|-9.3|-11.6|-10.2|
|Central Bank||||
|Foreign Exchange Losses||||
|Special Holdings of Govt Securities (prior to 1996)|-9.1|-4.6|-2.9|
|Payments to Failed Banksa|NA|-1.5|-0.5|
|Central Bank Deficit|-9.1|-6.2|-3.4|
|Quasi-Fiscal Deficit|-18.4|-17.8|-13.6|

a. Bank failures occurring in late 1995 are included in payments made in 1996. Failures occurring in 1997 are included in payments made in 1997.

_Source:_ Zambia: Fiscal Management Report. World Bank Report 18552-ZA _53._ Analysis of Zambia's fiscal stance indicates that to achieve solvency and sustainability, significant additional fiscal adjustment is required at all government levels. In order to evaluate the required fiscal efforts, we first calculated the primary deficit that would make the public sector solvent given the stock of public debt at the end of the previous period, the real interest rate, and the real GDP growth rate (the real interest rate was assumed to be constant throughout the period). This primary deficit required for solvency is then compared to the actual (or expected) primary deficit to find the adjustment needed for public sector solvency<sup>1 8</sup> . For fiscal sustainability, we first calculated the primary deficit needed to achieve debt sustainability (that is, to maintain the ratios of internal and external debt to GDP at the same levels). A measure of the required adjustment is calculated by comparing this sustainable primary deficit with the actual (or expected) primary deficit. All variables are expressed as percentages of GDP.

**18** This is equivalent to the so-called myopic one-period gap. See Olivier Blanchard (1990).
Sánchez-Martín, Escribano and de Arce, 2014.

“How regional integration and transnational energy networks have boosted FDI in Turkey”

# **7. Bibliography**

Akdemir, I.O., 2011. “Global energy circulation, Turkey's geographical location and petropolitics”, _Procedia - Social and Behavioral Sciences_ , Volume 19, 2011, Pages 71-80, ISSN 1877-0428, http://dx.doi.org/10.1016/j.sbspro.2011.05.109 Arpac, Ö., Bird, G. and Mandilaras, A., 2008. "Stop Interrupting: An Empirical Analysis of the Implementation of IMF Programs," _World Development_ , Elsevier, vol. 36(9), pages 1493-1513, September.Baran, Z., 2008. “Security Aspects of the South Stream Project”. Center for Eurasian Policy, Hudson Institute.

Basar, M. and Tosunoglu, S., 2006. "EU Integration Process: Will Turkey Overcome the FDI Obstacles?," Managing Global Transitions, University of Primorska, Faculty of Management Koper, vol. 4(2), pages 115-128.

Berkoz, L. and Turk, S., 2009. “Locational Preferences of FDI Firms in Turkey: A Detailed Examination of Regional Determinants”, European Planning Studies, 17: 8, 1243 — 1256.

Berkoz, L., 2001. “The interregional location of foreign investors in Turkey”, European Planning Studies, 9: 8, pp. 979–994.

Bhadrakumar **,** M., 2008. “Russia and Turkey tango in the Black Sea”. Asia Times Online, 12<sup>th</sup> September.

Bilgili, F., Tülüce, N. S. H. and Doğan, İ, 2012. "The determinants of FDI in Turkey: A Markov RegimeSwitching approach," Economic Modelling, Elsevier, vol. 29(4), pages 1161-1169.

Blomstrom, M. and Kokko, A., 1997. "Regional Integration and Foreign Direct Investment," NBER Working Papers 6019, National Bureau of Economic Research, Inc.

Bosco, M. G., 2011. "FDI in Turkey: An Out-Of-Sample Analysis Of Unexploited Potential," _Review of Middle East Economics and Finance_ : Vol. 7: No. 3, Article 4. DOI: 10.1515/1475-3693.1404 Botarav, K. and Yeldan, E., 2001. _Turkey, 1980-2000: Financial Liberalization, Macroeconomic (In)Stability, and Patterns of Distribution_ . Bilkent University. Available athttp://www.bilkent.edu.tr/~yeldane/B&YCEPA2002.PDF Brzezinski, Z., 1997. _The Grand Chessboard: American Primacy And Its Geostrategic Imperatives_ . Basic Books. New York.

Çetin, R. and S. Taban, 2009. “The Impact of Foreign Direct Investment From Major Source Countries on the Turkish Trade with the European Union”, The South East European Journal of Economics and Business, Vol.4, No.2, 45-55, 2009.

Celasun, O., Denizer, C. and Dong He, 1999. "Capital flows, macroeconomic management, and the financial system - Turkey, 1989-97," Policy Research Working Paper Series 2141, The World Bank.

29
# **Appendix D. Estimates of the privatization effects using TFP_CS**

# **Table D: Effects of privatization: Alternative TFP measures**

This table re-estimate the economic effects of privatization in Table 6 using the index function TFP using cost shares from ASIF data, i.e., _TFP_CS_ (see Appendix C for details). Specifications are the same as those in Table 6. Columns (1) and (2) reports the simple OLS regression results with and without firm fixed effects, respectively. Column (3) reports the economic effects of privatization using FE-FT model (Brown, Earle and Telegdy 2006; Brown, Earle, and Gehlbach 2009). We regress the detrended performance measures the detrended dummy variable of _Privatized_ . In column (4), we use a dummy variable, _Dum(Age≥58)_ , as the instrumental variable of privatization, which equals 1 when the age of the official is equal to or above 58 and 0 otherwise. We control for quadratic polynomials and city-level macro variables ( _GDP Per capita_ and _GDP Growth_ ). The control variables are the same as those in Table 6. We also control for firm-level fixed effects (except for column (1)), and the industry-year fixed effects, and the interaction of year dummies with initial sales, initial ROA, and initial TFP. For the lack of enough data to calculate TFP, the observations in (1) -(4) columns ends in 2007. Standard errors clustered at the firm level are reported in parentheses. *, **, and *** represent statistical significance at the 10%, 5%, and 1% level, respectively.

|Dependent variable:<br>_TFPCS_|OLS<br>(without firm FE)|Firm FE|FE-FT model|IV Regression<br>(2<sup>nd</sup>stage)|
|---|---|---|---|---|
|___|(1)|(2)|(3)|(4)|
|_Privatized_|0.153***|0.132***|0.037***|1.616**|
||(0.014)|(0.011)|(0.012)|(0.666)|
|Other controls|Yes|Yes|Yes|Yes|
|N|163,758|155,379|112,887|155,379|
|R-square|0.995|0.998|0.998|0.997|

``` 44 ```
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**Table 2: Hungary: Taxes on Household Income, 1989 - 1994**

(in billions of Forint)

||1989|1990|1991|1992|1993|1994<br>(Budget)*|
|---|---|---|---|---|---|---|
|Household Incomes (cash and in kind)|1,122.0|_1,645.5_|2,140.5|2,569.9|2,979.8|3,435.9|
|of which:|||||||
|- interest|30.0|40.0|60.0|100.0|100.0|110.0|
|- dividends|18.0|30.0|40.0|50.0|50.0|50.0|
|Taxes on Personal Income|98.6|140.5|182.8|225.3|284.6|305.1|
|of which:|||||||
|- on interest|6.0|8.0|12.0|20.0|20.0|11.0|
|- on dividends|3.6|6.0|8.0|10.0|10.0|10.0|

* The Budget Law assumes a tax rate of 10% / 20% on interest / dividends over the entire year. Actually, rates were reduced to 0%! 10% on November 1, 1994.

Sources: Ministry of Finance and World Bank (1995), Appendix Table 5.5.

As shown in table 2, interest and dividends accounted for 4.7% of total household income, on average, between 1989 and 1994. At the same time, taxes on interest and dividends raised 10.2% of total personal income taxes, thus contributing more than proportionally to tax revenues. More importantly, a comparison with total dividend and interest payments unveils that tax revenues equal 20% of the tax base -- in other words, APEH, the tax office, was able to completely exploit the tax base of capital income.<sup>14</sup> This is a very significant result given that rates of tax evasion are very high. According to recent estimates, evasion in Hungary varies between 7% of the tax amount due (corporate income tax) and 25% - 30% (value-added tax, personal income tax).

In 1995, interest received by individuals and interest on government bonds was tax exempt. Accordingly, the Government has foregone potential revenues from taxing this source. These revenue losses can be estimated on the basis of financial savings of households (see table 3). At the end of 1995, financial savings of Hungarian households totalled HUF 2,182 billion. Assuming average interest rates of 20% on forint deposits and bank securities and 3.5 % on foreign exchange deposits, potential tax revenues would add up to HUF 20.7 billion, given a tax rate of 10%. Note that government bonds and bills remain tax exempt. The additional revenue foregone would thus

> 14 This experience is common to other countries operating withholding schemes (see below). Ministry of Finance figures do not match National Accounts data. According to the latter, interest income in 1991 - 1994 amounted to HUF

> 48.9; 70.7; 100.6 and 130.2 billion, respectively. During the same period, corporate dividends totalled HUF 12.6; _15.5;_ 20.2 and 32.4 billion, respectively. The difference can only be partially explained by the fact that data in table 2 refers to _taxable_ payments.