rafmacalaba/datause-encoder-data huggingface dataset:
taskforce involving several state and federal law-enforcement agencies.<sup>5</sup> Monthly figures have been released publicly since December 2006. Among these, drug vs. non-drug related homicide rates are disclosed for each municipality in the country. In the analysis that follows, we collapse each of the crime variables available (total homicide rate, drug and non-drug related homicides) on a yearly basis for each municipality. We also employ data on aggregate figures of public expenditures at the municipal level, obtained from the State and Municipal System of Databases (SIMBAD) compiled by the National Institute of Statistics, Geography, and Informatics (INEGI).

# **4. Regional convergence analysis**

The traditional convergence framework follows the contributions of Barro and Sala-i-Martin (1991). This paper looks at β-convergence, which refers to a negative association between the rate of growth and the initial level of an attribute—in this case, per capita income.

In order to analyze conditional β-convergence, we make use of the following regression specification: where yi,t refers to the log of per capita income in municipality i at period t, Ci,t is the crime rate at the municipal level, Xi,t is a vector of conditioning variables (public expenditures and education in the municipality), vi,t is a stochastic error term, and τ is the length of the period under analysis. Moreover, in order to account for possible geographical correlation between crime rates in Mexico, we cluster the standard errors at the state level using the wild cluster bootstrap of Cameron, Gelbach, and Miller (2008).<sup>6</sup> The analysis starts by looking at the overall levels of crime and violence in 2007, as measured by the – homicide rates per 100,000 inhabitants, and their effect on growth over the 2005 2010 period. As illustrated in Table 1, the homicide rates of 2007 are not found to have any effect on the economic growth rate of municipalities between 2005 and 2010 (see Column 1). While the coefficient for _Total Homicides Rate 2007_ shows the (expected) negative sign, the effect is not statistically distinguishable from zero. If we use five-year lags of the total homicide rate as instruments for the levels of crime

> 5 The Center for Investigation and National Security (CISEN); the National Center for Information, Analysis and Planning to Fight Crime (CENAPI) within the Office of the Federal Attorney General (PGR); the Public Security Secretariat (SSP); the Secretary of National Defense (SEDENA); the Secretary of the Navy (SEMAR); and the Secretary of the Interior (Secretaría de Gobernación) are the institutions that participate in this collaborative effort, as described by Molzahn et al. (2012).

> 6 This method offers a solution in cases where the number of clusters (N=32 in our case) is small, which would impose limitations to methods such as cluster-robust standard errors that rely on an infinite number of clusters in order to be asymptotically consistent.

5
Thus we have seen that the simple expectation, although correlated with the subjective mean and median, can differ substantially from them. The key question is then which does a better job of predicting future outcomes. The subsequent wave of the Sri Lankan Microenterprise Surveys collected data on December 2005 profits, allowing us to see which method gets closer to the realized profits. A first measure of fit is obtained by comparing the mean and median absolute error between the two methods. The mean (median) absolute error is 3798 (2000) when the simple what to expect question is used, versus 3047 (1852) when the subjective mean taken from eliciting the full distribution is used. Thus the mean absolute error is 25 percent higher using the simple question.

Table 1 regresses actual December profits on the different elicited expectations. Columns 1 to 3 use the simple what do you expect question, and columns 4 to 6 the subjective mean. Both are positively associated with true profits, but the coefficient is substantially larger for the subjective mean: for each 100 rupees more of higher expected profits according to the simple expectation, true profits are 26 rupees higher, compared to 47 rupees higher with the subjective mean. We can reject that either coefficient is one, consistent with attenuation bias due to the expected mean equal to the true mean and some measurement error – or with those who give very high expectations being too optimistic and those who give very low expectations being too pessimistic.

Columns 3 and 6 of Table 1 add actual September 2005 profits, collected at the same time as the expectations questions were asked. They show that the expectations continue to have predictive power about future profitability, even conditional on current profitability. Part of the reason for the relatively poor performance of the simple expectation appears to be the presence of outliers. The standard deviation of the simple expectation is 1.8 times that of the subjective mean. Eliciting subjective expectations, by forcing microenterprise owners to consider the likelihood of each different range of profits occurring, seems less prone to outliers than a one-off casual answer to the simple expectation. Even after we trim the top and bottom 1 percent of the simple expectation and the subjective mean, the mean squared error from using the simple expectation is still 1.63 times that using the subjective mean. Hence there is considerable accuracy gain from eliciting the subjective mean rather than simply asking “what do you expect”.

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Table 7. Change in household consumption from the base case (%).

||Agricu|lture|Co|al|Crude|oil|Mini|ng|Food,|paper|Petrol|eum|
|---|---|---|---|---|---|---|---|---|---|---|---|---|
||S1|S2|S1|S2|S1|S2|S1|S2|S1|S2|S1|S2|
|Total|-0.05|-0.07|-0.80|-0.80|-0.47|-0.49|-0.55|-0.54|-0.12|-0.14|-0.54|-0.54|
|BJ|0.18|0.13|-0.09|-0.12|0.00|0.00|0.00|0.00|0.17|0.12|-0.44|-0.47|
|TJ|0.15|0.15|-0.29|-0.26|0.03|0.05|0.34|0.32|0.11|0.11|-0.48|-0.46|
|HB|-2.35|-2.40|-3.40|-3.42|-2.67|-2.72|-1.84|-1.95|-2.50|-2.57|-3.18|-3.21|
|SX|1.81|1.89|0.27|0.39|0.00|0.00|0.00|0.00|1.74|1.82|0.08|0.23|
|IM|0.39|0.42|0.83|0.87|0.69|0.73|0.00|0.00|0.57|0.60|-0.21|-0.15|
|LN|-1.00|-1.07|-1.41|-1.45|-1.35|-1.41|0.00|0.00|-1.07|-1.15|-1.53|-1.58|
|JL|-0.40|-0.52|-0.62|-0.69|-0.50|-0.62|0.00|0.00|-0.52|-0.65|-1.17|-1.26|
|HLJ|-1.31|-1.38|0.14|0.13|-1.50|-1.56|0.00|0.00|-1.32|-1.40|-2.32|-2.35|
|SH|0.26|0.28|-0.14|-0.10|0.00|0.00|0.00|0.00|0.14|0.16|-0.61|-0.56|
|JS|0.40|0.42|0.03|0.07|0.00|0.00|0.00|0.00|0.50|0.52|-0.02|0.02|
|ZJ|0.19|0.09|-0.14|-0.23|0.00|0.00|0.00|0.00|0.18|0.05|-0.29|-0.39|
|AH|-0.29|-0.25|-0.76|-0.70|0.00|0.00|0.00|0.00|-0.37|-0.34|-1.11|-1.04|
|FJ|0.36|0.38|0.63|0.67|0.33|0.36|0.00|0.00|0.48|0.50|-0.10|-0.06|
|JX|-0.10|-0.15|-0.31|-0.35|-0.26|-0.33|0.00|0.00|-0.13|-0.21|-0.95|-0.99|
|SD|0.00|-0.02|-0.55|-0.55|0.00|0.00|0.20|0.13|0.07|0.04|-0.38|-0.39|
|HN|-0.18|-0.16|-1.45|-1.40|-0.57|-0.55|-0.56|-0.55|-0.21|-0.20|-1.25|-1.19|
|HB|-0.77|-0.74|-1.25|-1.20|0.00|0.00|-0.68|-0.68|-0.77|-0.75|-1.68|-1.62|
|HN|-0.50|-0.46|-1.69|-1.59|-1.04|-0.98|0.00|0.00|-0.74|-0.70|-1.52|-1.44|
|GD|0.86|0.86|0.47|0.48|0.78|0.79|0.90|0.88|0.85|0.84|0.52|0.53|
|GX|-0.05|-0.04|-0.36|-0.33|0.00|0.00|0.00|0.00|-0.06|-0.06|-0.60|-0.57|
|HAN|-0.21|-0.10|-0.40|-0.27|0.00|0.00|0.00|0.00|-0.17|-0.05|-0.64|-0.51|
|CQ|-0.24|-0.21|0.00|0.00|-0.71|-0.66|0.00|0.00|-0.35|-0.32|-1.15|-1.09|
|SC|-0.42|-0.39|-1.51|-1.41|-0.87|-0.81|0.00|0.00|-0.54|-0.50|-1.66|-1.57|
|GZ|-0.84|-1.40|-0.62|-1.10|-1.16|-1.90|0.00|0.00|-0.94|-1.64|-1.85|-2.47|
|YN|0.20|0.24|-0.53|-0.46|0.00|0.00|0.00|0.00|0.15|0.20|-1.12|-1.03|
|SAX|-0.03|-0.04|0.08|0.08|0.00|0.00|-0.12|-0.15|-0.10|-0.11|-0.69|-0.67|
|GS|0.78|0.79|1.07|1.10|0.00|0.00|1.06|1.05|0.88|0.88|0.50|0.52|
|QH|0.04|0.04|-0.32|-0.27|-0.59|-0.55|0.00|0.00|0.06|0.06|-0.06|-0.01|
|NX|0.40|0.24|0.21|0.13|0.23|0.05|0.00|0.00|0.50|0.31|-0.57|-0.71|
|XJ|-1.06|-1.08|0.32|0.32|-2.16|-2.15|0.00|0.00|-1.44|-1.48|-2.78|-2.75|

Note:

1. S1 and S2 denote ETS and ETS-cum-RES cases, respectively.

2. The value 0 in the table indicates there is no household consumption of commodities in the base case.

29
# **App. Table 4:  Table 7's analysis with additional outcome variables**

||DV is indicator<br>variable for wage<br>rate increase on<br>current oDesk<br>contract|DV is indicator<br>variable for<br>worker being<br>hired again on<br>oDesk<br>All hourly|DV is indicator<br>variable for<br>worker being<br>rehired by<br>company<br>contracts|DV is log wage<br>of worker's<br>NEXT oDesk<br>contract|DV is indicator<br>variable for wage<br>rate increase on<br>current oDesk<br>contract|DV is indicator<br>variable for<br>worker being<br>hired again on<br>oDesk<br>Worker's first h|DV is indicator<br>variable for<br>worker being<br>rehired by<br>company<br>ourlycontract|DV is log wage<br>of worker's<br>NEXT oDesk<br>contract|
|---|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
|||Es|The sa<br>timates include fi|mple is contracts fo<br>xed effects for year|rmed with workers in<br>x job type x country|India<br>of company conta|ct||
|Probability that contract|-0.003|-0.002|0.004|-0.035*|-0.002|-0.015|-0.000|0.028|
|is ethnicity-based|(0.003)|(0.010)|(0.005)|(0.018)|(0.008)|(0.027)|(0.011)|(0.038)|
|Observations|45,656|45,656|45,656|36,339|9,311|9,311|9,311|5,811|
|Mean of DV|0.022|0.796|0.039|2.156|0.035|0.624|0.062|2.051|
|Relative effect|-0.136|-0.003|0.103|-0.016|-0.057|-0.024|0.000|0.014|

Notes: See Table 7.
# The Potential Impact of COVID-19 on GDP and Trade: A Preliminary Assessment

Maryla Maliszewska, Aaditya Mattoo and Dominique van der Mensbrugghe<sup>**1**</sup> _JEL Codes: F17, I15, C68_ _Keywords: COVID-19, Infectious Disease, International Trade, Economic Growth, CGE Modeling_

> 1 A joint product of the Chief Economist Office of East Asia and Pacific and the Trade and Regional Integration Global Unit. Maria Pereira provided excellent research assistance. We are grateful to Caroline Freund, Ergys Islamaj, Antonio Nucifora and Ekaterine Vashakmadze for helpful suggestions and discussions. 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.
# **C.   Revenue**

56. **Total projected revenues from international maritime fuels in 2020 are $26.2 billion under the $25 per tonne CO2 pricing scenarios, $15.7 billion for the $15 per tonne scenario, and $42 billion under the $40 per tonne scenario** (Figure 3a). Revenues are calculated using the same procedure as for aviation, assuming baseline 2020 emissions (without carbon pricing) of 1,050 million tonnes.<sup>42</sup> Again, with compensation schemes, developing countries would raise significant amounts of domestic revenue (Figure 3b). For example, with a $25 per tonne CO2 price developing countries raise estimated revenues of $10.0 billion and even with the $15 per tonne price, they raise an estimated $6.1 billion. **–** Estimated revenues for climate finance and other uses total $9.2 $15.0 billion in the more realistic pricing scenarios ($15 and $25 per tonne of CO2), net of developing country compensation (Figure 3c).

**Figure 3. Revenue from Taxes on International Maritime**

**(a) Total Global Revenue**

<!-- Start of picture text --> 40<br>35<br>30<br>25<br>20<br>15<br>10<br>5<br>0<br>(b) Compensation for Developing Countries<br>40<br>35<br>30<br>25<br>20<br>15<br>10<br>5<br>0<br>(c) Revenue for Climate Finance and Other Uses<br>40<br>35<br>30<br>25<br>20<br>15<br>10<br>5<br>0<br>$25 CO2 price $15 CO2 price compensation)<br>(developing country developing country<br>compensation) compensation) compensation)<br>$25 CO2 price (no developing country (developing country $40 CO2 price (no<br>$billion<br>$billion<br>$billion<br><!-- End of picture text --> Source: See text.

> 42 IMO (2009) projects baseline emissions of about 1,100 million tonnes in 2020, not accounting for expected reductions due to mandatory efficiency and operational improvements discussed in Box 1 (the exact figure is not well established).

34
Policy Research Working Paper 8002

# **Abstract**

This paper studies the trading behavior and performance of foreign investors with different management styles. The analysis uses a comprehensive Colombian data with complete transaction records and unique investor identification, and finds that the aggregate under-performance of foreign investors is attributable to foreign passive funds, that is, those that replicate a benchmark index. These funds pay higher prices to increase the speed of their trades to accommodate daily flows proportionally to their index before market closing. Passive funds face higher transaction costs on days when they trade multiple stocks in the same direction, buy (sell) the same stock multiple times, and make large trades near the daily closing time. Meanwhile, foreign active funds trade at more favorable prices and display higher risk-adjusted returns than any other investor group, including domestic funds with similar active management. The findings highlight the potential costs of index investing in developing countries or in securities with low trading activity (small stocks).

This paper is a product of the Finance and Private Sector Development 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 authors may be contacted at apedrazamorales@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
4 0.6 percent of total German exports. This is a results of its traditionally restrictive stance with respect to foreign investors. The two examples illustrate that host countries' policies can disguise the underlying economic factors explaining FDI. On the other hand there are countries where German FDI stock is unusually high and probably almost completely covered by public guarantees of the German federal government (for example Egypt). Thus the availability of supportive measures in the source countries potentially increases FDI in a developing country by driving a wedge between the generally perceived country risk and the risk of the foreign direct investors of the particular source country.

Both issues will be addressed in this paper. The major investment regulations in the host countries and their effects on German FDI inflows will be discussed in the next section followed by an analysis of a major source country's policy measure, namely public guarantees (section III). The results are summarized in a concluding section.

II. The Effect of Host Countries' Policy Measures on FDI Inflows from Germany Whatever the economic determinants of FDI in the developing countries are, their empirical investigation will be partially disguised by the rules and regulations governing the inflow of direct capital into the developing countries. The purpose of this section is twofold: first to give an overview of the most important aspects of investment legislation and policy in the host countries and second to develop and test indicators for the degree of openness of the host countries. The first issue has been covered thoroughly in various studies; examples are Becsky, Lee and Ordu (1989), Rosenn (1989) and IMF (1985). Thus only those factors of particular interest for the further investigation will be
Table 2: ITT Effects on Business Outcomes: Extensive Margin

||Business|Creation|Main|Business|Other<br>Businesses|All Businesses||
|---|---|---|---|---|---|---|---|
||(1)<br>Own a Business|(2)<br>No. Businesses|(3)<br>_>_0 Sales|(4)<br>_>_0 Profts|(5)<br>_>_0 Profts|(6)<br>_>_0 Profts|(7)<br>Index|
|||_Panel _|_A: Midline (_|_6 months)_||||
|ITT|0.164<sup>_∗∗∗_</sup>|0.256<sup>_∗∗∗_</sup>|0.140<sup>_∗∗∗_</sup>|0.183<sup>_∗∗∗_</sup>|0.055|0.171<sup>_∗∗∗_</sup>|0.300<sup>_∗∗∗_</sup>|
||(0.038)|(0.066)|(0.041)|(0.041)|(0.034)|(0.040)|(0.082)|
||[0.010]|[0.010]|[0.010]|[0.010]|[0.119]|[0.010]|[0.010]|
|Observations|546|546|547|547|547|547|547|
|Control Mean|0.566|0.833|0.420|0.486|0.179|0.521|-0.051|
|Adj. R<sup>2</sup>|0.206|0.293|0.177|0.190|0.139|0.186|0.183|
|||_Panel B: _|_Endline (18_|_–24 months)_||||
|ITT|0.080<sup>_∗∗_</sup>|0.221<sup>_∗∗∗_</sup>|0.140<sup>_∗∗∗_</sup>|0.095<sup>_∗∗_</sup>|0.083<sup>_∗∗_</sup>|0.103<sup>_∗∗_</sup>|0.258<sup>_∗∗∗_</sup>|
||(0.039)|(0.066)|(0.043)|(0.042)|(0.034)|(0.041)|(0.088)|
||[0.059]|[0.010]|[0.010]|[0.040]|[0.040]|[0.030]|[0.020]|
|Observations|545|544|545|545|545|545|545|
|Control Mean|0.667|0.903|0.457|0.589|0.155|0.593|0.022|
|Adj. R<sup>2</sup>|0.093|0.163|0.097|0.066|0.041|0.079|0.063|

_Note:_ We winsorize all sales and profit measures at the 99th percentile. Coefficients are ANCOVA estimates that control for the outcome at baseline, the respondent’s location, marital status, household size, number of children, age, network size and level of education. ITT shows traditional intent to treat effects. We record revenues and profits for women without a business as zero to preserve balance from randomization. Column (3) presents a dummy variable equal to 1 if the sales for the 3 days prior the survey are greater than 0, columns (4)–(6) present a dummy variable equal to 1 if the self-reported profits for the last month are greater than 0, for either the main business, other businesses or all businesses. Index is an Anderson index of all outcomes using the swindex command in Stata. We report White robust standard errors in parentheses. Romano-Wolf multiple hypothesis test q-values are presented in brackets. Q-values include the same controls as above and are computed within pre-specified families of outcomes.
|<br>Table 10: Heterogeneous impacts of_Mais Educação_by spending|Test score<br>per student in s|Test score<br>chool|
|---|---|---|
|Dependent variable<br>Dropout rates<br>|mathematics<br>|Portuguese<br>|
|(1)|(2)|(3)|
|Panel A: Impacts on fifth-grade variables|||
|Dummy_Mais Educação_* year 2011<br>1.932|–3.686**|–2.178|
|(1.364)|(1.798)|(1.612)|
|Dummy_Mais Educação_* year 2009<br>1.579|–4.197**|–2.554*|
|(1.144)|(1.628)|(1.487)|
|Dummy_Mais Educação * ME_spending per student<br>–0.0100*|0.00468|0.00137|
|(0.00586)|(0.0176)|(0.0148)|
|_R_ <sup>2</sup><br>0.667|0.786|0.789|
|Observations<br>2,988|2,988|2,988|
|Dummy year 2009<br>Yes|Yes|Yes|
|Dummy year 2011<br>Yes|Yes|Yes|
|School fixed effects<br>Yes|Yes|Yes|
|Panel B: Impacts on ninth-grade variables|||
|Dummy_Mais Educação_* year 2011<br>–0.822|–3.677*|–0.614|
|(1.146)|(1.896)|(1.857)|
|Dummy_Mais Educação_* year 2009<br>–0.549|–5.193***|–2.312|
|(0.994)|(1.741)|(1.729)|
|Dummy_Mais Educação * ME_spending per student<br>0.00820|–0.00222|–0.000773|
|(0.00522)|(0.0125)|(0.0129)|
|_R_ <sup>2</sup><br>0.736|0.755|0.741|
|Observations<br>2,595|2,595|2,595|
|Dummy year 2009<br>Yes|Yes|Yes|
|Dummy year 2011<br>Yes|Yes|Yes|
|School fixed effects<br>Yes|Yes|Yes|

_Sources:_ Authors’ calculations using MEC/FNDE administrative data, _Prova Brasil,_ School Census, and INEP.

_Note:_ Treatment group are schools that joined _Mais Educação_ in 2008 and remained in the program until 2011. Spending per student was deflated by IPCA, and is in 2007 prices. This table reports coefficients from the ordinary least squares estimation of equation (6) in the paper. The dependent variable is in column (1) is dropout rates, in column (2) mathematics grades in _Prova Brasil,_ and in column (3) Portuguese grades in _Prova Brasil._ All variables are at school level. ***significance at the 1% level; **significance at the 5% level; *significance at the 10% level. Robust standard errors, clustered at school level, are reported in parentheses. All regressions include school fixed effects and time dummies. All regressions are weighted by propensity score matching weights. Panel A reports these estimates for the schools from the fifth-grade sample. Panel B reports these estimates for schools from the ninth-grade sample.
**Table 8: Entry and Exit Rates**

||**Entry**<br>**2005**|**Entry**<br>**2004**|**Entry**<br>**2006**|**Total Closure**<br>**(Permanent Exit**<br>**& Seasonal**<br>**Closure)**<br>**2006**|**Permanent Exit**<br>**2006**|**Seasonal Closure**<br>**2006**|
|---|---|---|---|---|---|---|
|All|15%|13%|17%|25%|8%|17%|
|**_Sector_**|||||||
|Manufacturing|12%|14%|16%|20%|7%|13%|
|Trade|19%|15%|20%|30%|9%|21%|
|Services|19%|12%|23%|17%|8%|9%|
|**_Region_**|||||||
|Tigray|14%|15%|15%|31%|15%|16%|
|Amhara|14%|11%|16%|22%|9%|13%|
|Oromia|19%|13%|21%|27%|8%|18%|
|SNNP|15%|16%|17%|23%|6%|17%|

_Source:_ Authors’ calculations based on RICS-AgSS.

**Table 9: Determinants of Entry into the Nonfarm Enterprise Sector**

||**Marginal effects**|
|---|---|
|Household size|<br>0.005***<br>(0.001)|
|**_Household head_**||
|Age|-0.006***<br>(0.001)|
|Age squared|0.022**<br>(0.010)|
|Female|0.089***<br>(0.006)|
|Years of schooling|0.023***<br>(0.003)|
|Years of schooling squared|-2.118***<br>(0.282)|
|**_Rural Town_**||
|Rural town|0.081***|
||(0.021)|
|**_Distances_**||
|Distance to all weather road|-0.004<br>(0.003)|
|Distance to the market (road)|-0.017***<br>(0.004)|
|**_Region_**||
|Tigray|-0.006|
||(0.008)|
|Amhara|-0.042***<br>(0.007)|
|SNNP|0.047***|
||(0.010)|
|Number of observations|12,528|
|Pseudo R2|0.130|

_Source:_ Authors’ calculations based on RICS-AgSS. _Note:_ An intercept is included in the regression. Standard errors are clustered by enumeration area. Significance at the 10%, 5% and 1% level is indicated by *, ** and ***, respectively.

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