rafmacalaba/datause-encoder-data huggingface dataset:
6 <!-- Start of picture text --> Value-Added Tax Re'venue, 1 988<br>Countries with Single VAT Rate<br>,0 - .~ - is ..<br>o., asU<br>0<br>Q 7-<br>>2 3 --<br>>4- 2<br>(1 _~<br>0 2 £ 6 8 10 12 14 16 1i 20 22<br>Value-Added Tax Rate<br>U Actual VAT Revenue - Predicted VAT Rev.<br>Figure 1<br><!-- End of picture text --> that Bulgaria's VAT has the potential to mobilize approximately 8.1 percent of GDP in VAT revenues once the VAT system is in fully operational. Whiether this revenue _potential_ will turn into _actual_ revenue, however, will depend on other VAT characteristics. In particular, two caveats should be kept in mind when making similar predictions. First, although the estimated relationship is fairly robust, prediction assumes that other important characteristics of Bulgaria's VAT (i.e., size of the base or the number of exemptions) are equal or very close to the average characteristics of the countries in the sample. To the extent a country's proposed VAT features, for instance, larger number of exemptions (and, hence, smaller base), the predicted relationship on the basis of the above model will overestimate the VAT revenue potential; or, more precisely, the _actual or expected_ revenues will be lower than the estimated potential. The second caveat is analogous but has to do with tax administration capacity: the lower this capacity than for an average country in the samrrple, the lower will be the potential revenues than predicted by the model. Nevertheless, the model provides a very rough picture of the order of magnitude when anticipating revenue potential of a new VAT. In the particular case of Bulgaria, it is probably safe to assume that the above potential will not be reached in the near future, since it takes time to make the VAT fully operational. Furthermore, it takes time to make Bulgaria's tax base and administrative capacity more in line with the average country in the sample. Assuming a realistic tax "gap" (ratio oI potential and actual
# Tables

1. Taxonomy of Instruments for Reducing Externalities from Vehicles 2. Changes in Emissions (g/mile) for selected scenarios using MOBILE4 emissions information

3. Average Tailpipe Emissions. Diesel vs. Gasoline 4. Characteristics of Eight Transportation Models 5. Gasoline Elasticities of Demand (Bohi and Zimmerman) 6. Decomposition of Gasoline Demand Elasticities (Bohi and Zimmerman)

# Figures

1. Speed-Volume Curve for 60 mph Freeway

2. Speed Correction Factors. Light-Duty Gasoline Vehicles. Hydrocarbons. Low Altitude (Shih, 1990) 3. Household Vehicle Demand (Train, 1990) 4. TRIPS Structure and Information Flows (Cameron, 1991)
# **Health and subjective well-being**

Emergency environments tend to be chaotic and are often characterized by the collapse of public service delivery, including water, sanitation, and health care. In such environments, health or human welfare may deteriorate rapidly. Humanitarian assistance programs can be a powerful tool for mitigating the negative consequences of this type of crisis. In our review, five studies examined the impacts on health outcomes ranging from children’s anthropometric measures (e.g., height-for-age, weight-for-height, and mid-upper arm circumference), instances of diseases (e.g., malaria and diarrhea), and mental health, to behavior changes and nutrition knowledge, as well as access to water and health care.

In our review, three out of five studies find significant positive effects on most health outcomes, while the remaining two studies do not find any significant effects. For instance, Ecker et al. (2019) find that providing unconditional cash transfers (UCTs) to conflict-affected households during the civil war in Yemen significantly improved children’s anthropometric outcomes (measured as weight-for-height z-score and mid-upper arm circumference z-score). Similarly, Chaaban et al. (2020) find that an unconditional cash transfer program significantly improved a broad range of health outcomes among Syrian refugees in Lebanon.<sup>17</sup> Lastly, Kurdi et al. (2019) find that a conditional cash transfer (CCT) program during the civil conflict in Yemen significantly decreased child malnutrition.

On the other hand, evidence on the effectiveness of vouchers and food transfers is limited. Quattrochi et al. (2020) find a statistically insignificant, positive effect of non-food item vouchers on weight-for-height z-scores, and they did not find any impact on mid-upper arm circumference. Similarly, Tranchant et al. (2019) find an statistically insignificant, negative effect of food aid on the height of children aged 2–5. These findings suggest either that vouchers and food transfer

> 17 The authors find a significant increase in the access to safe drinking water by 15–30 percentage points on a base of 67 percent for the control group. Moreover, cash beneficiaries reported that their access to primary healthcare improved and their need for hospitalization decreased. Cash support presumably improved their ability to cover healthcare costs and other indirect costs such as transportation costs. Importantly, the authors find that 55 percent of those who received cash over a longer period (more than 12 months) reported better mental health (measured using the Mental Health Inventory–MHI-5), relative to 18.5 percent in the control group. However, similar effects were not detected for those who received cash over a shorter period (less than 12 months).

23
# **4 Results**

## **4.1 Initial results**

Our results are presented in Table 1. cial crisis on the relative volume of outward FDI in financially vulnerable sectors was stronger in more financially developed countries. The coefficient on the triple interaction term is negative and statistically significant.<sup>27</sup> In column (2), a similar result is obtained when we use an institution-based The estimates are also eco- measure of financial development instead of an outcome-based measure. nomically significant. They indicate, holding other factors constant, that the ratio of relative outward FDI in financially vulnerable sectors during the global financial crisis to relative outward FDI in financially vulnerable sectors during non-crisis years was about 20-30% lower in a highly financially developed country like the United States than in a less financially developed country such as Italy.<sup>28</sup> Table 1: _Volume of outward FDI, by sector_

||(1)|(2)|(3)|(4)|(5)|
|---|---|---|---|---|---|
|ED×CREDIT/GDP×GFC0810|-1.596<sup>∗∗∗</sup>||-1.949<sup>∗∗∗</sup>||-1.323<sup>∗∗∗</sup>|
||(0.336)||(0.499)||(0.347)|
|ED×FIN_INST×GFC0810||-0.162<sup>∗∗</sup><br>(0.078)||||
|Control function term|||0.479<br>(0.652)|||
|TANG×CREDIT/GDP×GFC0810||||5.446<sup>∗∗∗</sup>|3.669<sup>∗</sup>|
|||||(1.949)|(1.880)|
|Observations|5504|5336|5504|5504|5504|

∗∗∗p<0.01 ∗∗p<0.05 ∗p<0.10. Cluster-robust standard errors in parentheses. Time-invariant country-sector fixed effects, time-varying country fixed effects, and time-varying sector fixed effects are included in all regressions. ED: sector-specific measure of dependence on external finance. GFC0810: global financial crisis 2008-2010. TANG: sector-specific measure of asset tangibility.

In column (3), we adopt an IV approach to address any potential endogeneity bias. In line with the

> 27Similar results are found when we use the log of the private credit to GDP ratio or the log of the stock market capitalisation to GDP ratio. They also hold when we control for the sector-specific impact of the few currency crises which occurred during the period 2008-2010.

> 28Relative outward FDI in financially vulnerable sectors is defined as the ratio of outward FDI in the Transportation Equipment sector (a typical high ED sector; 75<sup>th</sup> percentile of ED) to outward FDI in the Beverages sector (a typical low ED sector; 25<sup>th</sup> percentile of ED). The private credit to GDP ratio for the United States is 1.70 and the private credit to GDP ratio for Italy is 0.74. Regarding the quality of financial institutions, on a scale of 1 to 15, the United States scores 15 and Italy scores 9 .

15
documented in a number of studies using different methodological approaches.<sup>2</sup> A variety of technological factors, most notably the revolution in information and communication technology (ITC), lie beneath this transformation. But institutions, and in particular trade institutions, are also recognized as a determinant and a consequence of the evolving international trade structure. Orefice and Rocha (2014) show that signing deeper agreements increases trade in parts and components between PTA members and that, on the other hand, higher levels of trade in parts and components increase the likelihood of signing deeper agreements. In this paper, we dig further into the relationship between deep trade agreements and the process of internationalization of production. The specific question that we address is how deep agreements relate to the way goods are traded internationally (i.e. inside or outside the boundaries of the firm). When firms choose their global sourcing strategy, a key decision is the extent of control they want to exert over their foreign production processes. Certain firms in certain sectors choose to own foreign assets through vertical Foreign Direct Investment (FDI) as a means to enhance such control.<sup>3</sup> Others offshore production, but instead rely on independent foreign suppliers, a sourcing strategy commonly known as foreign outsourcing. Importantly, these control decisions are associated to different modes of international trade: FDI gives rise to within-firm trade, while foreign outsourcing results in arm's length trade.

As is well understood from the trade and industrial organization literature, the incomplete nature of international contracts affects firms' vertical integration decisions (i.e. FDI versus foreign outsourcing).<sup>4</sup> In the so called “property rights” approach adopted in Antràs (2003) and in much of the international trade literature, ownership is a means to reduce the hold-up problem created by contractual incompleteness. Underlying this notion, there is the idea that contractual frictions are pervasive in international transactions because of differences in legal systems, poor institutional quality in certain countries involved in one end

> 2 Different measures are provided by Feenstra and Hanson (1996), Hummels, Ishii, and Yi (2001), Johnson and Noguera (2012). Koopman et al. (2014) provide a unifying framework to measure the international fragmentation of production.

> 3 The theoretical literature has long distinguished market seeking (i.e. horizontal) FDI and efficiency seeking (i.e. vertical) FDI (Markusen 1984, Helpman 1984). For brevity, unless otherwise specified, whenever we refer to FDI in the rest of the paper, we imply vertical FDI. As is well known, in practice this distinction is not the only relevant one and we will come back to this point in the next section.

> 4 There are a number of excellent surveys that discuss different angles of this literature, including Helpman (2006), Antràs (2012), and Antràs and Yeaple (2013).

3
Where εi,t + ηi are the idiosyncratic disturbances (capturing the unobserved region-specific effect and the observation-specific errors of the regions, respectively) and i,t represent the regions and time periods. FDI lag of one period is used as an independent variable to assess its dynamic effect and to test for the agglomerate effect. To account for the endogeneity between variables of the model, the above equation is estimated using as instruments the lagged values of the right-hand side variables in levels.

In the second part of the analysis, using a spatial lag model which incorporates a spatially autoregressive dependent variable, we attempt to unpack the regional interdependencies that we suspect may account for certain spillover effects across regions. Caughlin and Segev (1999) point out several reasons that could provide a theoretical support for this spatial dependence, such as the spillover of beneficial effects of FDI, labor and cost competitiveness and some geographical attributes such as proximity to port, or mountains and other infrastructure platforms. The application of the spatial econometrics, Garretsen and Peeters (2009) argue, can be a very useful tool to improve our understanding of FDI patterns.

In line with similar research, the spatial lag is derived by multiplying the dependent variable by a distance matrix<sup>2</sup> W that identifies the spatial relationship among regions. This matrix design allows nearby outcome (FDI) to have a spillover effect on the outcome of the region weighted by the distance that separates both regions. We construct the distance matrix based on the two approaches found in the literature (Villaverde and Maza, 2012, Blonigen, Davies and Naughton 2007, Coughlin and Segev 1999 and Villaverde and Maza 2012). The first approach is based on the inverse of the distance between all regions using their respective longitude and latitude coordinates. The second approach uses the contiguity matrix which in its simple form takes a binary value to reflect whether two regions share the same borders. It is equal to 1 in the presence of common borders and 0 otherwise. In this paper we only report the results from the second approach. Our spatial model can be constructed as follows: FDI = α0 + α1 Host Variables + ρ . W . FDI + ε

> 2 The matrix W is constructed from shapefiles (maps) we have obtained over the web listing the latitudes and longitude coordinates of all 24 governorates in Tunisia.
22

# **Table 5. Bank Regulation and Political Institutions in 1999, Survey I**

|`--------------------`<br>`--------------------`|`------------`<br>`Private`<br>`Monitorin`<br>`Index`<br>`------------`|`------------`<br>`Official`<br>`g Sup.`<br>`Power`<br>`Index`<br>`------------`|`-----------`<br>`Entry`<br>`Req.`<br>`Index`<br>`-----------`|`-----------------`<br> <br>`Activity`<br>`Restrictions`<br>`Index`<br>`-----------------`|`--------------------`<br>`Government`<br>`Owned`<br>`Banks`<br>`--------------------`|
|---|---|---|---|---|---|
|`Voice &`|`0.194`|`-1.092**`|`-0.0659`|`-1.824***`|<br>`-7.928`|
|`Accountability`<br>`--------------------`|`(0.271)`<br>`------------`|`(0.472)`<br>`------------`|`(0.174)`<br>`-----------`|`(0.333)`<br>`-----------------`|`(6.590)`<br>`--------------------`|
|<br>`Observations`<br>`--------------------`|<br>`65`<br>`------------`|<br>`66`<br>`------------`|<br>`67`<br>`-----------`|<br>`66`<br>`-----------------`|<br>`39`<br>`--------------------`|

``` Notes: This table presents five regressions, where each column reports the findings from an instrumental variables regression.
``` ``` The dependent variables, measured in 1999, are:Private Monitoring Index measures the degree to which regulatory policies encourage and facilitate private sector monitoring of banks; Official Supervisory Power Index measures the extent to which official supervisory agencies have the authority to take specific actions to prevent and correct problems; Entry Restrictions Index measures the number of specific legal requirements for obtaining a banking license; and Restrictions on Activities Index measures regulatory restriction on the degree to which banks can engage in securities, insurance, and real estate activities plus restrictions on banks owning and controlling nonfinancial firms. Government Owned Banks equals the percentage of the banking system’s assets held by banks that are 50% or more government owned.
``` ``` The explanatory variable of focus is Voice & Accountability, which measures the degree to which average citizens have voice in the political process and the degree of accountability on the part of politicians (Kaufmann, Kraay, and Zoido-Lobaton, 1999).
``` ``` The unreported, though included in the second-stage instrumental variables, arethe legal origin of each country (that is, we include three dummy variables for Common, French civil, and German civil law countries respectively).
``` ``` The excluded instrumental variables are the latitude of the capital, the percentage of years since 1776 that the country has been independent, the fraction of the population that is Catholic, the fraction of the population that is Muslim, and the fraction of the population that is not Catholic, Muslim, or Protestant.
Heteroskedasticity-consistent standard errors are in parentheses under the estimated coefficients. To designate statistically significant benchmarks, *** indicates significance at the 0.01 level, ** at the 0.05 level, and * at the 0.10 level.
```
specified by equation 8 is exact (the sum of the contributions is equal to total inequality) and treats factors symmetrically.<sup>13</sup> The Shapley decomposition can handle satisfactorily decompositions by income factors and population subgroups given that the underlying model, _f_ ( _·_ ), had been appropriately specified. In the context of income factor decompositions, equation 8 can be further simplified. Taking the square of the coefficient of variation ( _CV_<sup>2</sup> ) as our preferred inequality index and defining the underlying model, _f_ ( _·_ ), simply as the sum of inequality shares attributed to the different income sources (similar to the model defined by equation 1), then Shorrocks (1999) shows that the Shapley contribution of income factor _k_ is equal to: And the _proportion_ of total inequality explained by factor _k_ is equal to: Equation 10 is exactly the unique decomposition rule proposed in Shorrocks (1982). This is quite a powerful result because it allows us to interpret the unique decomposition results (equation 2) as the expected value of the factor’s contribution over all possible scenarios, given that _CV_<sup>2</sup> is chosen as the income inequality index and equation 1 is the underlying income distribution model.

# **4 Decomposition Results**

## **4.1 Non-Parametric Approach**

Given the advantages and disadvantages of the different decomposition methods, in this paper we will undertake conventional non-parametric decompositions and complement them with the semi-parametric approach just described.<sup>14</sup>

> 13A point that it is not clear from the Shapley decomposition rule is the way in which interactions ought to be treated. The basic idea is to compute the average marginal impact of factor _k_ over all possible eliminating sequences. However, the elimination of, say, _Xm_ will capture part of the contribution made by, say, _Xm_<sup>_′_</sup> if model 6 includes interaction term _XmXm_<sup>_′_</sup> .

> 14The method developed by Bourguignon, Fournier and Gurgand (2001) goes beyond the scope of this paper. For two recent applications of this kind using Mexican data see: De Hoyos (2005b) and

15
questions on income received in the previous calendar year. We use the official poverty lines for the US, which gives a poverty rate of 12.3% in 2018 (Semega et al., 2019).

For Mexico, we use the National Survey of Occupations and Employment (ENOE). This is a trimonthly survey applied to a representative household sample in Mexico. The survey aims at providing statistical information on the population’s occupational and substantive sociodemographic characteristics at the national level. We do the currency conversion at Purchasing Power Parity (PPP).<sup>9</sup> However, we also allow for extra costs of living in the U.S.not fully reflected in the PPPs, such as higher housing costs.

Using these data, we can now explore the impact of creating a market for WPs through a series of simulations. Recognizing the parameter uncertainty (especially on migration costs) we consider a seemingly wide range of parameter values in our model, though with an “inner range” of what appear to be the most plausible values based on what we know. The aim is not exact calibration but rather to get an idea of the scale and economic feasibility of this policy. In particular, we ask whether the observed distributions of earnings in the two countries, and likely frictions to migrating, are such that Mexican workers would be willing to purchase temporary WPs at prices that would attain something like recent basic income proposals for the U.S. U.S. worker rents out her work permit for a year if offered a price exceeding the product of the replacement rate ( ) and her current yearly wage ( UU ); the total number of U.S. ii pp citizens willing to rent out their work permit is then given by: rr ww = ∑ U UU (4) mm ss mm=1 mm NN 1[rr. ww < pp] In setting the replacement rate, we draw on the surveys that have been done to determine what share of pre-retirement income will make a retiree no worse off on a pension. Binswanger and Schunk (2012) report their estimates from such surveys for the U.S. The mean replacement rate is 80%, but they find that it is higher for lower-income workers (as one would expect). Based on their results, we set A Mexican migrant purchases a work permit if his expected earnings in the U.S. ( MU ) rr= 0.9. is higher than the price of the permit and additional fees and costs associated with moving to the jj ww� U.S. The net earnings reflect the standard taxes on earnings levied by the U.S. government. An

> 9 We use the Mexico PPP rate for 2018 of 9.38 (World Bank 2021).

7
Table 21 Real Per Capita Aggregate Family Income and Welfare<sup>In USSR Republics In 1980 Ukrainian Prices</sup>

|Reublics|-__|PerCa|haReal lnc|ome|-|Welfare<br>1390|R|anking. b|Per Capita**R**|**eal**Ineom|e<br>-|**Wefare**<br>RLnking|
|---|---|---|---|---|---|---|---|---|---|---|---|---|
|p|1960|198S|1907|1968|1990||1980|1965|1937|1186|1990|1990|
|Russia|148.7|154|157|159.6|172.2|126.2|13|13|12|12|12|12|
|Ukraine|123|135.6|138.3|139.3|_159.6_|121|9|_9_|9|9|9|11|
|Bularue|132.5|144.1|149.8|148|176.4|132.6|11|11|11|10|13|13|
|Uzbekistan|90.6|90.4|87.9|90|101.1|73.9|3|2|2|2|3|3|
|Kazakhstan|125.7|128.5|132.5|124.9|14S.4|104.1|10|8|a|a|8|_|
|Georgia|118.9|139.8|149.8|156.7|161.2|114.9|8|10|10|11|10|9|
|Azefballan|86.5|96.1|100.8|103|so|691.5|2|3|3|3|2|2|
|Lthuania|148.7|152.1|159.9|165.4|161.2|118.4|12|12|13|13|11|10|
|Maiddve|109.5|11568|121|121.9|137.5|101.9|7|7|6|6|7|7|
|Latvia|166.3|170.9|178.8|178.5|207|152.8|14|14|14|14|14|14|
|Kyighvzstan|94.6|9819|10098|104.5|116.9|85.2|4|4|4|5|5|5|
|Tjkisktan|78.4|77.7|77.8|78.4|**69.5**|51.7|||1<br>1|1||1|
|Annenia|109.1|11538|123,9|124.8|134.3|98.8|6<br>I||7|7|6|6|
|Turkmmnistan|98.7|100.3|103 7|103|102.7|74.5|5|5|5|4|4|4|
|Estonia|177.1|160.8|188.7|193|230.7|169.9|15|1S|15|15|15|15|
|USSR|135.2|1413|144.1|145.1|158|113.4|_<br>,|||_ __|||