rafmacalaba/datause-encoder-data huggingface dataset:
terms of trade synchronization do not seem to be signi...cant in explaining the variance of the relative changes in FX reserves. The most important ...nding here is that the e¤ect of in‡ation synchronization is signi...cantly positive while interest rate synchronization is insigni...cant in the regression.

# 4.2.4 The Covariance Terms

Table (5) reports the estimation results from regressions of the covariance terms on the vector of employed explanatory variables. Looking across all three regressions one can observe that the e¤ect of output synchronization on the covariance terms is insigni...cant in the case of the covariance of exchange rates and interest rate di¤erentials, cov (�4st; �4 (it � i<sup>�</sup> t<sup>)), and the covariance of interest rate di¤erentials and</sup> relative movements in FX reserves, cov (�4 (it � i<sup>�</sup> t<sup>) ; �(4rst�4rs�</sup> t<sup>)),andsigni...-</sup> cantly negative in the case of the covariance of exchange rates and relative movements in FX reserves, cov (�4st; � (4rst �4rs<sup>�</sup> t<sup>)).</sup> The impact of in‡ation synchronization on the covariance terms is signi...cant in all three cases, however, signing of these e¤ects varies. For the covariance of exchange rates and interest rate di¤erentials, and the covariance of interest rate di¤erentials and relative movements in FX reserves the coe¢ cients on the in‡ation synchronization are signi...cantly negative, where the coe¢ cient corresponding to the former regression is of substantially higher magnitude than other coe¢ cients on the in‡ation synchronization measure in the remaining regressions for the EMPI components. On the other hand, in the regression for the covariance of exchange rates and relative movements in FX reserves the e¤ect of the in‡ation synchronization is signi...cantly positive.

The measure of money velocity synchronization appears to be consistently insigni...cant in explaining the covariance terms, i.e. the covariance of exchange rates and interest rate di¤erentials, the covariance of exchange rates and relative movements in FX reserves, and the covariance of interest rate di¤erentials and relative movements in FX reserves.

On the other hand, synchronization of interest rates is found to have a positive 22
**The actions aimed at reducing the pressure to the market by raising part of the funding needs by other means included three mechanisms** : (i) Channeling the excess liquidity available within the public sector; (ii) Use of non market funding sources such as multilaterals, and; (iii) Expanding the investor base by using new debt instruments and distribution channels that had not been explored before.

**Most countries reduced or delayed the borrowing from the private sector by recurring to liquid resources within the public sector.** The use of cash reserves allowed the authorities in Peru to stay out of the market and effectively borrow less than originally planned. In Uruguay the authorities also avoided borrowing from market sources; however in their case multilaterals loans were contracted to close the financing gap and reconstitute the liquidity cushion. While Peru does not have an official target for cash reserves, Uruguay adopted in 2005 a prefunding policy that established that cash reserves, at any point in time, should cover the financing needs of the next 12 months<sup>24</sup> . Central Banks in Egypt, Indonesia, Mexico and Hungary were permitted to buy government bonds and served as a buffer to the fall in bond prices. In some countries with less developed local markets, the central bank extended credit lines to the government.

**EM debt managers also stepped up the borrowing from multilaterals.** Hungary received substantial resources from the IMF to deal with the stabilization of the financial sector; Romania filled up a substantial part of the borrowing program with resources from IMF, EC and WB; Peru used contingent credit lines contracted with the WB; and Indonesia also used contingent funding from multi and bilateral entities to backstop its borrowing needs.  The substantial increase in the demand from multilaterals loans put significant pressure on the capacity of these entities to expand their lending programs. For instance in 2009 the World Bank lent almost three times the volume that was anticipated before the crisis<sup>25</sup> .

**Some EMs started/expanded retail debt programs or tried new debt instruments in an effort to diversify funding sources and tap segments of investors not explored before.** While this route was marginal compared to the two previous mechanisms, it is worth noting as a complementary alternative that was worth exploring in some countries. Indonesia aggressively expanded the retail program, introduced Sharia-compliant sukuk market instruments and launched a Samurai bond. Hungary also introduced a new 3-year CPI linker for the retail market. Turkey tried new revenue indexed bonds and CPI linkers, all for the wholesale market.

# **B. Revisions of  funding programs to respond to shifts in the demand for government paper**

> 24 See Uruguay Debt Report, July 2009. Other countries like Colombia borrowed $1.8b in 2008 and $3.8b in 2009 to build up the liquidity position.

> 25 This is one of the reasons why the World Bank sought an increase in capital from its shareholders.

21
25

# Information

In the literature on voting behavior in the United States, the extent to which voters are informed is assessed directly through surveys of their political knowledge (see, e.g., Bartels 1996). Such information is, unsurprisingly, unavailable for cross-country research. Three proxies for voter information that are available, however, ranging from voter education to government control of media, should capture the same underlying phenomenon tracked in US political research.23 Following Brunetti and Weder (1998) and others, the regressions first employ two variables collected by Charles Humana (1985, 1992), one evaluating the independence from government control of radio and television networks, and the other the independence of newspapers. Since they exhibit significant differences (they are correlated at 0.69), both are used. The 1985 value of each variable is applied to crises of the 1980s and the 1992 value to crises of the 1990s. The highest (most independent) score goes to countries in which there is “unqualified respect” for the independence of the respective media. Occasional breaches of independence earn a country the next highest score, followed by countries in which there are frequent intrusions on independence and finally countries that exhibit a constant pattern of violations of independence.24 23 Measures of levels of voter information range from interviewer assessments of respondents’ level of information, to respondent answers to factual questions. These turn out to be highly correlated. One would expect that voters exposed to slanted or limited media would be less informed according to these criteria, and that less educated voters would exhibit less political knowledge than more educated voters. 24 Notes in many of the country entries provide greater insight into the criteria used. Government ownership and control of all radio and television networks (as in Ghana in the early 1980s) earns countries the lowest score; countries score the next to lowest score if they exhibit some limited private ownership or, as with newspapers in Chile in the 1980s, evidence of seizures of private newspapers and mandatory prior approval of articles. Argentina in the early 1980s, under the military government, was considered to have fully independent newspapers, but because of evidence of self-censorship and financial pressures by government on electronic media, was only given the second-highest score for radio and television independence.
Evaluating the Asset Based Minimum Tax on Corporations: Ar. Option Pricing Approach by Antonio Estache and Sweder van Wijnbergen

# Table of Contents

|1.|Introduction|2|
|---|---|---|
|2.|The Design of a Minimum Tax|4|
|3.|A Framework to Assess the Value of Minimum Taxes|6|
||3.1 Introduction<br>3.2 Option Pricing and the Minimum Asset Tax|6<br>8|
|4.|Monte Carlo Evaluation of the Minimum Asset Tax|11|
||4.1 A Simulation Model<br>4.2 Simulating the Incentive Effects of Various Tax<br>Designs<br>|12<br>13|
||4.3 The Lessons for Brazil|22|
|5.|Conclusions|26|
|Refer|ences|27|

The authors are indebted to S. Ahmed for providing the data used in this paper. They thank Stijn Claessens, Ricardo Martin, and Jack Mintz for helpful comments and discussions.
17 turnover ratio.<sup>8</sup> These instrutments, except REV, are predetermined. We **use** these instrumental varnables to extract the predetermined component of GOVY. PI, BMP, and STOCK. Throughout the analysis, we use White's (1980) heteroskedasticity-consistent standard errors.

**Data:** As discuissed earlier, we obtained the stock market data from the IFC's EMDB and the IMF's IFS. Data on real per capita GDP growth, LSEC, GOVY, PI, and information on export and iniports are fronm the World Bank's National Accounts data base. We obtained REV from Barro (1991) and computed LRGDP from Summers and Heston (1988). BMP data are from Picks Currency Yearbook, World Currency Yearbook, and from International Currency Analysis.

> 8 We use the term "initial" to refer to variables measured at the start of the estimation period. Since we use pooled cross-country time-series data over the period 1976-1985 and 1986-1993, "initial" refers to measures in 1976 and 1986 respectively.
(Kaufmann et al. 2010). The indicator ranges from -2.5 (i.e., the lowest level of voice and accountability) to 2.5 (the highest level of voice and accountability). Data on population density and the median age of the population come from the DELVE COVID-19 database (Bhoopchand et al., 2020). We also control whether a country belonged to the axis of powers during WW2,<sup>5</sup> and if the country was a member of the communist bloc.<sup>6</sup> The descriptive statistics for our main variables and a summary of data sources are shown in Table 1.

Figure 1 provides a scatterplot of the cumulative deaths from COVID-19 per million people on the total losses in WW2 as a proportion of the pre-war population. Consistent with our hypothesis, we see that countries with the highest death rates during WW2 tended to have lower COVID-19 mortality rates. Next, we see if this is robust to adding controls relevant to the probability of experiencing a big shock, and whether the finding is robust to various changes in the specifications for our test.

# **5. Controls and tests for robustness**

Adding controls for other variables likely to influence COVID-19 mortality, we still find that it is negatively correlated with total losses during WW2.<sup>7</sup> Our most parsimonious specification is in Table 2, which includes five controls. The specification in column (1) uses total WW2 deaths per capita. At mean points, the elasticity of COVID-19 deaths to WW2 deaths is about -0.4. We also see that countries with higher GDP per capita have lower death rates from COVID-19. On the other hand, countries with older and larger populations, and countries with a higher share of educated people have significantly higher deaths per million from COVID-19.

Our variable for WW2 deaths includes both civilian and military losses. Some countries in our sample that suffered military losses (e.g., the United Kingdom or Italy) had limited military activities on their territories. Thus, the population of these countries was, to a degree,

> 5 The “Axis powers” formally took the name after the Tripartite Pact was signed by Germany, Italy, and Japan on 27 September 1940, in Berlin. The pact was subsequently joined by Hungary, Romania, and Bulgaria (Hill 2003). 6 The Council for Mutual Economic Assistance (COMECON) was an economic organization from 1949 to 1991 under the leadership of the Soviet Union that comprised, among other countries, Albania, Bulgaria, Czechoslovakia, Hungary, Poland, Romania and the Soviet Union (Kaser 1967).

> 7 While a nonlinear relationship is suggested by Figure 1, we chose a more parsimonious linear regression. We did two tests on functional form. First, we included the squared value of WW2 mortality, but its coefficient was not significantly different from zero. Second, we tested a specification with the inverse hyperbolic sine transformation of deaths per million as a dependent variable, with the same transformation applied to  WW2 deaths. This gave qualitatively similar results.

10
data at the time of the last release. For example, the African Development Bank CPIA data for 2005 was not available at the time that we released our indicators for 2005, so we used the most recently available data at the time, for 2004, as an input to the 2005 indicators. Now that the 2005 (but not the 2006) data are available from this source, we have gone back and updated our 2005 indicators to reflect the 2005 CPIA from this source (and again we use the same data for 2006 since it is the most recently available at the time of writing). In other cases we have revised the data to take into account differences between the year of publication of our underlying sources and the year to which the data refers. For example, the 2007 Freedom in the World indicators from Freedom House refer to the year 2006. In Appendix A, we have detailed the precise relationship between the year of our indicators and the year of each underlying data source from which our individual indicators are drawn. For details on the changes in individual indicators, readers may compare Appendix A of this paper with Appendix A of Kaufmann, Kraay, and Mastruzzi (2006) describing the 1996-2005 dataset.

Together these revisions in virtually all cases result in only minor changes in our earlier estimates of governance. Averaging across our six indicators and seven periods over which we have made revisions, the correlation between our previous and revised indicators is 0.98, and half of all correlations were greater than 0.99. Of the 8275 aggregate indicator/country/year data points in the dataset between 1996 and 2005, there were only 13 cases (i.e. less that 1 significant change for every 600 data points) where our backwards revisions resulted in changes in scores that were significant at the 90 percent level. Most of these were concentrated in 1998 and 2000 where the two most substantial changes in our underlying data set occurred (the deletion of the two World Bank surveys noted above, and the addition of new data from WMO).

# **2.3  Aggregation Methodology**

We combine the many individual data sources into six aggregate governance indicators, corresponding to the six dimensions of governance described above. The premise underlying this statistical approach should not be too controversial – each of the individual data sources we have provides an imperfect signal of some deep underlying notion of governance that is difficult to observe directly. This means that as users of the individual sources, we face a signal-extraction problem – how do we isolate an 10
who self-select into our sample are not all existing entrepreneurs and they fall well below median levels of consumption in rural Uganda, in contrast to most studies that focus on urban entrepreneurs. Our results highlight that the population where skills-based constraints bind may not be the population typically targeted by entrepreneurship programs.

Third, our paper makes two methodological contributions that provide a comprehensive understanding of the value of entrepreneurship skills in the lives of ultra-poor, rural women. First, we use high-frequency SMS data to better understand how the program we study affects income volatility and women’s ability to cope with large economic shocks. Understanding volatility is first-order to alleviating poverty in rural areas, but traditional surveys are ill-suited to measure such dynamics. Second, we demonstrate the effectiveness of a relatively fast, low-cost method for collecting social network data using randomly ordered photobooks of study participants. Our photobooks allow low-literacy respondents to fully participate, avoid concerns about different names being used for the same person, and reduce respondent fatigue by allowing respondents to quickly identify network links. This is helpful in any setting where measuring social networks is important but is not the central focus of an intervention. We additionally demonstrate that in settings where network-based spillovers may occur, researchers can use baseline network data to quantify them.

Positive spillovers through business networks, but not friendship networks, speak to peer effects.<sup>8</sup> Our results stand in contrast to Field et al. (2016), who Bjorvatn, and Tungodden (2015), Bruhn and Zia (2013), deMel, McKenzie, and Woodruff (2014), Brooks, Donovan, and Johnson (2018), Campos et al. (2017), Arráiz, Bhanot, and Calero (2019), Alibhai et al. (2019), Anderson et al. (2020)). By contrast, five out of seven studies on existing entrepreneurs in rural markets find positive effects from training (Calderon, Cunha, and Giorgi (2020), Bakhtiar, Bastian, and Goldstein (2022), Buvinic et al. (2020), Giné and Mansuri (2021)), though Avdeenko, Frölich, and Helmsmüller (2021) find null results and Giné and Mansuri (2021) document null results for female entrepreneurs.

> 8The literature on social networks finds that both the size and composition of an individual’s network can have large effects on outcomes ranging from employment to technology adoption (e.g., Munshi (2003); Bandiera and Rasul (2006); Magruder (2010); Beaman and Magruder (2012); Beaman, Keleher, and Magruder (2018); Munshi and Rosenzweig (2016)), but women often benefit less from these social networks. For instance, Magruder (2010) finds that inter-generational network effects only increase employment rates for sons, and Bea-

7
# **Appendix Table I. 1: Effect of Reforms on Organized Sector Log (Employment)**

||(1)|(2)|(3)|
|---|---|---|---|
||De-licensing|Trade Liberalization|FDI Liberalization|
||_Other-_|_sector Employment Sp_|_illovers_|
|Horizontal Employment Spillovers||||
|from Unorganized Sector|0.1473***|0.1770***|0.1305***|
||(0.0226)|(0.0182)|(0.0166)|
|Backward Employment Spillovers||||
|from Unorganized Sector|0.4228***|0.6319***|0.5070***|
||(0.1142)|(0.1424)|(0.1135)|
|Forward Employment Spillovers from||||
|Unorganized Sector|0.1177|-0.2009|0.1677|
||(0.1730)|(0.2148)|(0.1777)|
|Reform|-0.9419|-0.0064|-0.7522|
||(0.9728)|(0.0127)|(2.4141)|
|Reform*Horizontal Spillovers from||||
|Unorganized sector|-0.0466**|0.0011***|-0.0555**|
||(0.0199)|(0.0002)|(0.0238)|
|Reform*Forward Spillovers from||||
|Unorganized sector|0.1551**|-0.0020**|0.0848|
||(0.0766)|(0.0009)|(0.1362)|
|Reform*Backward Spillovers from||||
|Unorganized sector|0.1539***|0.0015**|0.0721|
||(00444)|(00007)|(00805)|
||.<br>_Other-_|.<br>_sector Productivity Sp_|.<br>_illovers_|
|Average TFP of Horizontal-linked||||
|Organized Plants|0.2255***|0.2283***|0.2262***|
||(0.0427)|(0.0429)|(0.0431)|
|Average TFP of Backward-linked||||
|Organized Plants|0.3817***|0.4068***|0.4553***|
||(0.1064)|(0.1052)|(0.1115)|
|Average TFP of Forward-linked||||
|Organized Plants|0.3387***|0.2723**|0.2859***|
||(0.1090)|(0.1145)|(0.1095)|
||_Own-s_|_ector Employment Spi_|_llovers_|
|Forward Employment Spillovers from||||
|Organized Sector|0.9891***|1.2869***|1.0483***|
||(0.2537)|(0.2546)|(0.2437)|
|Backward Employment Spillovers||||
|from Organized Sector|1.3650***|1.2281***|1.3991***|
||(0.1657)|(0.1544)|(0.1523)|
|Reform*Forward Spillovers from||||
|Organized sector|-0.1285*|0.0019*|-0.2108|
||(0.0768)|(0.0011)|(0.2238)|
|Reform*Backward Spillovers from||||
|Organized sector|-0.0622|-0.0016*|0.1107|
||(0.0763)|(0.0010)|(0.1334)|
|Constant|-30.1597***|-30.9048***|-32.6377***|
||(4.3677)|(4.2954)|(4.2795)|
|Observations|5888|5888|5888|
|R-squared|0.2038|0.2083|0.1992|

_Note: All specifications contain panel (state-industry) fixed effects as well as year fixed effects. All specifications also include state-year fixed effects. Standard errors are clustered by the state-industry level. Significance at the 20% level is indicated by ++, at the 15% level by +, at the 10% level by *, at the 5% level by ** and at the 1% level by ***._ 54
Table 2. Share of men and women that own specific assets, by country

||Eth|iopia|Tanz|ania|Mal|awi|Cam|bodia|
|---|---|---|---|---|---|---|---|---|
||Men|Women|Men|Women|Men|Women|Men|Women|
|(A) Mobile phone|0.50***|0.27***|0.78***|0.58***|0.56***|0.36***|0.91***|0.77***|
|Exclusive|0.48***|0.25***|0.76***|0.56***|0.55***|0.35***|0.47***|0.40***|
|Joint|0.03***|0.01***|0.02|0.02|0.01|0.01|0.44***|0.37***|
|(B) Financial account|0.31***|0.18***|0.15***|0.09***|0.24|0.25|0.14**|0.12**|
|Exclusive|0.27***|0.16***|0.15***|0.09***|0.22|0.23|0.09|0.08|
|Joint|0.06***|0.03***|0.00|0.00|0.03|0.02|0.05***|0.04***|
|(C) Livestock|0.51***|0.45***|||||0.39**|0.42**|
|Exclusive|0.22*|0.21*|||||0.16***|0.21***|
|Joint|0.35***|0.28***|||||0.23|0.23|
|(D) Non-residential land|||||||||
|Reported ownership|0.27***|0.23***|0.31|0.29|0.35***|0.49***|0.56|0.57|
|Exclusive|0.05|0.04|0.15|0.13|0.18***|0.32***|0.17|0.18|
|Joint|0.22***|0.19***|0.16|0.16|0.20|0.20|0.39|0.38|
|Economic ownership|0.26***|0.21***|0.25*|0.20*|0.22***|0.32***|0.43**|0.45**|
|Exclusive|0.06|0.04|0.08*|0.05*|0.07***|0.15***|0.05**|0.06**|
|Joint|0.20***|0.17***|0.17|0.15|0.16**|0.20**|0.38|0.38|
|(E) Residential land|||||||||
|Reported ownership|0.41**|0.39**|0.47|0.48|0.56***|0.64***|0.75**|0.78**|
|Exclusive|0.06***|0.08***|0.24|0.22|0.42***|0.51***|0.19**|0.22**|
|Joint|0.36***|0.31***|0.24|0.26|0.16|0.16|0.55|0.55|
|Economic ownership|0.40**|0.37**|0.34|0.31|0.42***|0.48***|0.56***|0.62***|
|Exclusive|0.07**|0.09**|0.11**|0.07**|0.10***|0.17***|0.02***|0.05***|
|Joint|0.33***|0.28***|0.23|0.24|0.33|0.32|0.53|0.54|
|Durables(Cambodia)<sup>(3)</sup>|||||||||
|(F1) Computer|||||||0.05***|0.02***|
|(F2) Bicycle|||||||0.30***|0.36***|
|(F3) Motorcycle|||||||0.78***|0.60***|
|(F4) Car|||||||0.09***|0.07***|
|(F5) Tuk tuk|||||||0.04**|0.03**|
|(F6) Boat|||||||0.04**|0.03**|
|<br>(F7) Tractor|||||||0.15***|0.11***|
|Any asset (exclusive or joint)<sup>(2)</sup>|0.83***|0.77***|0.70|0.69|0.80|0.81|0.87***|0.91***|
|Any asset excluding mobile phones|0.74***|0.69***|0.54|0.55|0.64***|0.72***|0.86*|0.88*|
|<br>Number of respondents|<br>7,235|8,153|1,407|1,576|2,243|2,669|1,845|2,093|
|Notes:|||||||||

(1) Estimates weighted with the household sampling weights. Significant gender differences are indicated by asterisks; ***p<0.01 **p<0.05 *p<0.10 (2) Because of some missing observations for specific assets, the share of men and women with any asset can be lower than the shares of individual assets (Hasanbasri et al., 2021b, 2021c).

(3) In Cambodia, joint versus exclusive ownership of durables was not asked explicitly.

12