rafmacalaba/datause-encoder-data huggingface dataset:
to 16:00 NYT.<sup>3</sup> Trades are submitted via authorized brokers registered at the exchange and buy and sell orders meet via the Automated Trading System. Short-sells were allowed during our entire period of study, however, investors were required to close any short position by the end of each trading day.<sup>4</sup> There are no individual or aggregate ownership limits on foreign investors, and they are allowed to either reinvest or transfer earnings such as dividends, coupons or capital gains with little restrictions. Since the Central Bank has discretionary control on net currency flows, including those to equity instruments, foreign investors are required to report their initial investments and any subsequent transactions that involve any currency exchange.

# **2.2 Data**

The database, which includes all transactions in the period, was provided by the CSE. The data disclose the date and time of each transaction, a stock identifier, order type (buy or sell), transaction price, number of shares, and broker ID. The key novel aspect of the data, and most importantly for our analysis, is that every transaction record has a unique investor ID number that allows us to track all the transactions for each investor throughout the entire sample period. In addition, the transaction records also disclose whether an investor is foreign or domestic. Domestic investors are further classified into individuals or institutions. Institutions can be broadly categorized into corporations, pension funds, mutual funds, banks, insurance companies, and brokerage firms trading on their own accounts. It is important to point out that our database does not allow us to distinguish whether a foreign investor is an individual or an institutional investor. Based on this, for the rest of the paper we sometimes refer to foreign investors interchangeably as funds.<sup>5</sup> There were over 13 million transactions during the 10-year period. Table 1-Panel A presents the share of trading volume attributable to different investor types and the number of investors by type over two sub-samples: 2006-2010 and 2011-2016. Domestic institutions dominate the Colombian

> 3For non-liquid stocks, an opening auction starts 15 minutes earlier, but trading at this time represents less than 0.01 percent of the daily traded value.

> 4Other rules include a suspension mechanism on each stock based on volatility relative to a “base price” published on a daily basis. When a stock price changes more than 10% relative to its reference price, the exchange suspends trading in that stock temporarily for 30 minutes. In addition, when the representative stock index (COLCAP) decreases more than 10% during a trading session, all transactions are suspended until the next trading day.

> 5Informal conversations with registered brokers suggest that most (and in several cases all) of their foreign clients are institutions.

6
incomes out to 2030 for each country.<sup>20</sup> Finally, to complete our baseline specification, we assume there is zero growth in _σ_ so that _σi,_ 2030 = _σi,T_ (i.e., inequality is equal to the latest reported value for each country).<sup>21</sup> Next, we consider four counterfactual scenarios relative to our baseline scenario: (1) inequality declines by 1% per-annum over 2020-2030 while mean incomes follow baseline trajectory; (2) inequality increases by 1% per-annum over 2020-2030 while mean incomes follow baseline trajectory; (3) mean incomes increase by 1% per-annum over 2020-2030 relative to their baseline trajectory while inequality remains constant at baseline value; and (4) mean incomes decrease by 1% per-annum over 2020-2030 relative to their baseline trajectory while inequality remains constant at baseline value. World headcount poverty rates by year are presented in Figure VIII under our baseline scenario and our counterfactual scenarios. Moreover, regional poverty rates for 2030 under our baseline and alternative scenarios are presented in Table III.

TABLE III: Poverty Projections for 2030 Under Various Scenarios

||B|(1)|(2)|(3)<br>(4)|
|---|---|---|---|---|
|Region|Baseline|1% decr. _σ_|1% incr. _σ_|1% incr. ̄_y_<br>1% decr. ̄_y_|
|EAP|0.73|0.37|1.30|0.53<br>0.99|
|ECA|0.15|0.06|0.35|0.09<br>0.23|
|LAC|2.74|1.30|5.13|2.11<br>3.52|
|MNA|4.40|3.60|5.63|3.57<br>5.41|
|OHI|0.00|0.00|0.00|0.00<br>0.00|
|SAS|3.66|1.90|6.26|2.56<br>5.13|
|SSA|32.32|28.69|36.33|28.54<br>36.37|
|World|5.69|4.52|7.29|4.78<br>6.78|

_Note:_ Headcount poverty is multiplied by 100, i.e., we project 32.32% of the population in SSA to be under the poverty line in 2030 under our baseline specification. World Headcount poverty is the sum of weighted regional poverty rates.

20. For example, our latest observation of mean income for India is 2011; thus, we use the 2011 observation for _y_ ̄, along with per-capita GDP growth rates for India from 2012-2030 to calculate mean income for 2012-2030. Note, we only include countries where the latest observation is from 2010 onward. For example, because our latest observation for mean income and inequality for Jamaica is 2004, Jamaica would not be included in this projection exercise.

21. E.g., because the latest observation for India is 2011, we assume _σ_ India _,_ 2030 = _σ_ India _,_ 2011 24
# **APPENDIX 2**

# **IMPACT OF NON-TAX PARAMETERS ON THE ESTIMATE OF EFFECTIVE TAX RATES**

# **Expected Inflation Rate**

The expected inflation rate affects the effective tax rate on capital through its impact on the nominal interest rate. For a given real interest rate, the higher the inflation rate, the higher the nominal interest rate will be. _When there is no regulation for adjusting the inflation impact,_ the nominal interest rate interacts with taxes mainly through the following three channels. Firstly, interest cost is deductible for income tax purposes at the nominal rate. As a result, the higher the nominal interest rate in relation to a fixed real interest rate, the lower the real after-tax financing cost, and hence the lower the METR. This effect is particularly favorable for leveraged land financing. Secondly, The accumulated present value of a given annual tax depreciation allowance decreases as the nominal interest rate rises. Since higher inflation lowers the present value of tax depreciation allowance, it increases METR on depreciable assets. And finally, if the first-in-first-out method is used for the inventory accounting, it may results in inflated taxable income and, hence, a higher METR on inventory when prices rise. Since inflation thus affects METR on different assets in different directions, its net impact on capital will depend upon the capital structure related to a given industry. (See the end section of this appendix for further explanation of the capital structure by industry.)

# **Expected Real Interest Rate**

The impact of the real interest rate on the effective tax rate is in part similar to the impact of inflation. For example, as the real rate rises, so will the nominal rate, thus increasing the effective tax rate on depreciable assets. For a given debt-asset ratio, however, unless inflation is high, there is unlikely to be much of a distortion in effective tax rate arising from the deductibility of interest. We use the U.S. real interest rate for our study assuming a full mobility of investment fund within NAFTA and the American's dominant role in the North America financing market. As shown in Table A2, the real interest rate in the U.S. is 6.1 percent corresponding to the nominal interest rate of 8.4 percent and the inflation rate of 2.3 percent.

# **Debt-Asset Ratio**

The ratio of debt to assets is sometimes referred to as the financing structure. As already noted, the impact of this ratio on the effective tax rate is related to the expected inflation rate and (real) interest rate. For a given inflation rate and real interest rate, the higher the debt-asset ratio, the more the potential benefit from the tax deductibility for debt financing cost, or interest expenses. A higher debt-asset ratio may thus reduce effective tax rate through lowering the real after-tax cost of financing. For simplicity, we apply a debt to assets ratio of 40 percent across sector and across border in our study.

22
**Table 12 The Z-score with Lagged Fee Income and Non-deposit Funding Shares**

The dependant variable is Z score defined as

# _ROA_ + _CAR SROA_

where ROA is return on assets, CAR represents capital assets ratio and SROA stands for standard deviation of return on assets. _Assets_ is the natural logarithm of total assets in US $. e _quity_ is the capital to asset ratio, which is defined as equity as a percentage of total assets. We used one period lag of equity in the regressions. _Assets growth_ is the inflation-adjusted growth rate of bank assets. _Overhead_ is defined as overheads as a percentage of total assets. _Fee income_ is the share of non-interest income in total operating income. _Non-deposit funding_ is the share of non-deposit short-term funding in total deposits & short-term funding. _Fee income sq_ and _non-deposit funding sq_ are the square of fee income and non-deposit funding, respectively. _Inflation_ is the inflation rate based on consumer prices. _GDP growth_ is the inflation adjusted growth rate of GDP per capita of the country. _GDP per capita_ is in thousands of 2000 constant US$. We take mean value of all the variables for period 2002-2007, except fee income, non-deposit funding and their squares, which take mean value for period 1995-2001. We obtain bank level data from Bankscope and macroeconomic data from World Development Indicators (WDI) of World Bank. We estimate all regressions with clustering at country level. Standard errors are in parentheses. *, ** and *** denote significance <u>at 10%, 5% and 1%, respectively.</u>

||(1)|(2)|(3)|(4)|
|---|---|---|---|---|
|Assets|-1.343**|-2.134***|-1.410*|-1.925**|
||(0.564)|(0.611)|(0.753)|(0.746)|
|Equity|23.322**|3.851|23.162**|27.375***|
||(9.621)|(12.757)|(10.190)|(9.653)|
|Assets growth|23.120**|15.683|22.294**|24.729***|
||(9.235)|(10.163)|(9.003)|(8.927)|
|Overhead|-57.868***|-65.862*|-68.477***|-61.727***|
||(10.156)|(33.888)|(18.715)|(21.078)|
|Fee income|-31.464***||-33.336***|19.507|
||(3.360)||(4.302)|(14.262)|
|Non-deposit funding||-11.158**|7.236|36.337|
|||(5.573)|(8.220)|(21.856)|
|Fee income sq||||-56.347***<br>(14.472)|
|Non-deposit funding sq||||-32.323<br>(26.227)|
|Inflation|-63.906|-55.436|-59.687|-58.445|
||(56.038)|(56.494)|(55.748)|(53.300)|
|GDP growth|-130.403|-141.872|-124.794|-105.808|
||(82.474)|(85.392)|(80.344)|(76.240)|
|GDP per capita|0.001***|0.001***|0.001***|0.001***|
||(0.000)|(0.000)|(0.000)|(0.000)|
|N|704|709|693|693|
|R-sq|0.185|0.137|0.185|0.197|
|Country fixed effects|N|N|N|N|
|Time fixed effects|N|N|N|N|
|Clusteringlevel|Country|Country|Country|Country|

50
<u>Table 4 (cont.)</u>

|||Unaid<br>Wo|men|Leisure<br>Single/|Widow|Unaid<br>M|en|Leisure|
|---|---|---|---|---|---|---|---|---|
||Market<br>work|<br>p<br>domestic<br>work|Personal<br>care|<br>,<br>social and<br>study|Market<br>work|<br>p<br>domestic<br>work|Personal<br>care|<br>,<br>social and<br>study|
|Albania|1.48|3.43|11.92|7.14|2.64|0.42|12.38|8.53|
|Argentina|3.78|3.84|n.a.|0.03|5.54|1.72|n.a.|0.03|
|Bangladesh|1.68|3.32|14.03|4.94|4.52|1.10|12.82|5.56|
|Colombia|2.90|2.65|10.46|8.15|3.89|1.03|10.26|8.77|
|Ecuador|2.57|3.15|9.86|6.37|3.61|1.07|9.68|6.55|
|Ghana|2.39|4.14|12.46|5.94|2.29|2.88|11.89|7.91|
|Guatemala|3.29|4.55|11.17|3.44|5.31|0.89|11.29|4.50|
|Iraq|0.57|3.76|n.a.|7.56|3.39|1.15|n.a.|8.56|
|Mexico|3.50|3.11|n.a.|15.63|4.56|1.45|n.a.|15.52|
|Moldova|1.80|3.22|11.41|6.33|2.11|2.42|11.47|6.92|
|Mongolia|2.42|5.10|11.79|4.85|2.35|4.76|11.72|5.66|
|Pakistan|0.87|4.43|13.84|4.83|3.62|1.96|12.53|5.86|
|West Bank and Gaza|1.15|0.28|11.84|8.39|3.79|0.09|11.25|8.48|
|Peru|3.10|3.69|9.93|6.83|4.54|1.84|9.77|7.37|
|South Africa|3.45|4.05|12.56|4.25|4.16|2.22|12.68|5.30|
|Timor|1.20|1.72|n.a.|n.a.|1.38|1.19|n.a.|n.a.|
|Turkey|1.13|3.04|11.37|6.18|2.89|0.32|10.96|6.22|
|Uruguay|2.91|3.34|9.89|3.83|4.08|1.34|9.80|3.29|
|USA|3.05|2.15|11.13|6.73|3.79|1.38|10.67|7.59|

|||<br>Wo|men|<br>Divorced/S|eparated|<br>M|en||
|---|---|---|---|---|---|---|---|---|
||Market<br>work|<br>Unpaid<br>domestic<br>work|Personal<br>care|<br>Leisure,<br>social and<br>study|Market<br>work|<br>Unpaid<br>domestic<br>work|Personal<br>care|<br>Leisure,<br>social and<br>study|
|Albania|3.23|4.85|11.42|4.50|1.51|0.41|13.48|8.60|
|Argentina|2.51|2.61|n.a.|0.03|3.69|1.04|n.a.|0.02|
|Bangladesh|4.37|4.02|13.16|2.44|5.13|1.27|14.69|2.91|
|Colombia|4.34|3.90|9.91|5.83|6.39|1.61|9.89|6.21|
|Ecuador|4.09|5.48|9.71|3.56|6.63|2.09|9.42|3.79|
|Ghana|3.49|4.70|13.29|3.79|2.58|3.34|13.36|5.83|
|Guatemala|5.03|4.61|10.44|2.58|5.31|1.64|11.86|4.54|
|Iraq|0.99|4.38|n.a.|5.54|5.47|1.37|n.a.|6.38|
|Mexico|5.15|5.44|n.a.|11.91|7.14|1.95|n.a.|12.23|
|Moldova|3.78|4.24|10.87|3.75|4.07|3.09|10.94|4.80|
|Mongolia|5.03|4.91|11.61|2.70|3.86|4.82|11.96|3.88|
|Pakistan|0.52|6.63|14.37|2.35|3.94|2.61|14.26|3.17|
|West Bank and Gaza|n.a.|n.a.|n.a.|n.a.|n.a.|n.a.|n.a.|n.a.|
|Peru|5.01|5.30|9.56|3.60|7.49|2.88|9.29|4.42|
|South Africa|2.20|3.96|12.46|5.63|3.04|2.15|12.38|6.80|
|Timor|2.73|1.94|n.a.|n.a.|3.06|1.32|n.a.|n.a.|
|Turkey|1.94|4.08|11.22|4.75|3.53|0.78|11.24|4.99|
|Uruguay|6.20|6.79|9.11|1.72|7.33|3.22|8.91|1.90|
|USA|4.32|3.01|10.57|5.14|4.52|2.48|10.35|6.16|

Source: Authors’ elaboration based on survey data. Notes: Women and men aged 15 to 64. In Argentina, the starting age is 18. West Bank and Gaza survey only distinguishes between married and not married people.

32
# Modal 82 **Teacher** Turnovar in segondary Sbhool.

The varlable for thL **analysis was** also taken from the statistical return and consLsts of **the** years ea-h teacher has been at the particular ascondary school in which s/he was teaching in 1989. These figures were then averaged for the wholo teaching force in each school. The values of the coefficients of this variable (loosely) imply an increase of about one grado point if the **teachers** at the school have **been** teaching **there** for three or four years, for Mathematics and English, **respectlvely.** The effect of this variable on the components of variance and the total variance for each subject is about the **acme as** for Model 7, a reduction in the school level variance of 30% for Mathematics and 54% for English, and reductions of 12% and 18% _on_ the t-tal varianc¢r, respectively.

THE EFFECT O DIFFERENT COMBINATIONS O THE SCHOOL LEVEL VARIABLES: THE SEARCH FOR THE 'BEST' MODEL Different combinations of the above three variables **were tested** to see which produced a model with **the** groatest explanatory power. The 'final' Model 11 for each subject is reported in Table 3.5. Besides comparing **the** different candidate 'final' models in this section, comparisons need to be made with Model 1 for each subject, for Model 1 illustrates the initial total observed variance and how this is broken down between the three different levels. How much of .his observed variance is explained by the 'final' models can then be analyzed.

34
Policy Research Working Paper 6162

# **Abstract**

Do exports resume when import-restricting _temporary_ trade barriers such as antidumping are finally removed? To establish the importance of this question for emerging economies, this paper uses newly available data from the World Bank’s _Temporary Trade Barriers Database_ to update a number of inter-temporal indicators of import protection along three dimensions: additional time coverage through 2011, additional policy-imposing country coverage, and a more comprehensive depiction of impacted trading partner coverage. It then turns to the emerging economy exporters affected by temporary trade barriers and highlights the economic significance of frequently bilateral import restrictions imposed by other emerging economies, i.e., South-South protectionism. Finally, it then investigates empirically whether countrylevel exports resume when the previously imposed—but temporary—import protection is _finally_ removed. China’s exporters respond quickly and aggressively to the market access opening embodied in the removal of such import restrictions. This differs markedly from the slow and tepid export response of other emerging economies, especially when the import protection had been imposed by another emerging economy trading partner. This evidence suggests a previously unidentified long-run cost associated with such South-South protectionism that merits further research and inquiry.

This paper is a product of the Trade and Integration Team, Development Research Group. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The author may be contacted at cbown@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
|Hungary|14.6|2.6|3.7|4.7|
|---|---|---|---|---|
|Lithuania|15.9|11.1|0.9|4|
|**Latvia**|**10.5**|**5.5**|**1.3**|**3.8**|
|Poland|22.5|12.7|2.7|7.1|
|Romania|25.4|21.9|0.9|2.6|
|Slovenia|11.2|3.8|2.6|4.7|
|Slovakia|7.8|0.4|2.7|4.7|
|CEC-10|19.5|11.3|2.4|5.8|

Source: Eurostat, Employment and labor market in Central Europe 2001/2

**Second jobs play an important role.** The number of part-time jobs in the economy greatly exceeds that of part-time workers. Apart from those whose main job is a part-time one, 7 percent of those employed in 2002 have at least one second job. Second jobs are typically part-time. The prevalence of second jobs is most likely underestimated in the 2002 LFS, because respondents whose information was given by another household member report a second job 1.5 times less frequently than those interviewed directly.

According to an enterprise survey on wages and hours worked, 13 percent of public sector employees had second jobs in 2002. In such sectors as financial intermediation, business, education and other community and personal services, the proportion was much higher. Two thirds of second jobs are in the service sector. The shares of individuals engaged in managerial, professional and technical occupations in secondary jobs are higher that those in primary jobs. Appendix Figure 4 sums up some recent trends in employment patterns in Latvia.

# **E. UNEMPLOYMENT RATES**

This section will look at the structure of unemployment in Latvia. In Figure 6, it was shown that the level of unemployment in Latvia is still high by international standards. As elsewhere, youth unemployment is higher than unemployment among older workers (see Appendix Figure 3). However, as shown in Figure 11, the ratio of youth to adult unemployment rates, as well as the female – male unemployment ratio is among the lowest in Europe. This suggests that, relative to other European countries, youth and women in Latvia have relatively advantageous employment prospects.

**Figure 11.  Ratio of Unemployment Rates by Age and Gender. CEC-10 and EU-15, 2002**

<!-- Start of picture text --> 3.50<br>3.00<br>2.50<br>2.00 young-adult<br>1.50 women-men<br>1.00<br>0.50<br>0.00<br>RO SI PL CZ HU SK BG LV EE LT EU-<br>15<br><!-- End of picture text --> _Source_ : Canstat 2003/3, OECD, and author’s calculation.

15
<!-- Start of picture text --> Author(s) Year = Intervention<br>A.Barrera-Osorio Teachers and Raju 2010 Group bonuses |,<br>Muralidharan and Sundararaman 2010 Feedback to teachers ><br>Muralidharan and Sundararaman2013b Contract teachers Cl<br>Banerjee et al 2007 Balsakhi (year 1) a<br>Muralidharan and Sundararaman2011 Group bonuses ><br>Duflo, Hanna and Ryan 2012 Attendance verification<br>Muralidharan and Sundararaman 2011 Individual bonuses ~~<br><!-- End of picture text -->

<!-- Start of picture text --> Banerjee et al. 2007 ~ Balsakhi (year 2) ~~<br>Lakshminarayana et al. 2013 Remedial teaching etc. ~-_<br>Subtotal ><br>B. Schools<br>Linden 2008 Computer-assisted learning 1<br>Rao 2014 = Mixing students —<br>Das et al, 2013 School grants al<br>Sarr et al. 2010 Grants to schools ————<br>Banerjee et al. 2007 + Computer-assisted learning ——<br>Linden 2008 Computer-assisted learning 2 ‘<br>He, Linden and MacLeod 2008 = English curriculum 1 —<br><!-- End of picture text --> <!-- Start of picture text --> He, Linden and MacLeod 2008 English curriculum 3 —_—_s—<br>He, Linden and MacLeod 2008 English curriculum 4 —_—_—<br>Burde and Linden 2013 New schools —_<br>Barrera-Osorio and Raju 2010 Sanctions to schools ——_+_<br>Barrera-Osorio et al. 2013 New private schools _+—<br>Subtotal c+)<br>C. Households.<br>Muralidharan and Sundararaman2013a School-choice program ~~<br>Banerji, Berry and Shotland 2013 Classes for mothers +<br>Banerji, Berry and Shotland 2013 = Training for mothers od<br>Banerji, Berry and Shotland 2013 Classes and training for mothers C3<br><!-- End of picture text --> <!-- Start of picture text --> DB. Community<br>Andrabi, Das and Khwaja 2013 Report cards ><br>Subtotal co)<br>22 “A 0 1 2<br>Intervention reduces composite test scoresntervention increases composite test scores<br><!-- End of picture text -->