rafmacalaba/datause-encoder-data huggingface dataset:
Country Risk Guide (ICRG) – a commercial database geared at providing information to firms that plan to invest abroad – as a proxy for political risk. This index is a measure of country’s political instability constructed on the basis of experts’ subjective assessments of a country’s socio-economic conditions, investment profile, internal and external conflict, corruption, the influence of the military in politics, religious tensions, law and order, ethnic tensions, democratic accountability, and bureaucratic quality in a country. The political instability score ranges from zero to 10, with higher scores indicating more instability. According to the ICRG methodology, a score above 5 indicates high degree of political instability, while a score below 2 would indicate that the country is characterized by a very low degree of political instability.

While we will use the terms political risk, political uncertainty, and political instability interchangeably, this paper is not concerned with risk or uncertainty in the Knightian sense. The ICRG index is based on prevailing political conditions and since our regressions include countryfixed effects our interpretation of the political risk measure is that it reflects changing political circumstances rather than second-moment shocks/changes in their variance. This interpretation is consistent with evidence presented in Appendix A1 which demonstrates that the ICRG indicator correlates well with objective proxies for political violence, notably violent conflicts and protests, which are unfortunately available only for a few countries in the region.

Figure 1 plots the evolution of political instability and aggregate greenfield FDI over time, separately for countries in Northern Africa, GCC countries, and other Middle Eastern economies. The left panels of Figure 1 show that both the average level and evolution of political instability vary dramatically across countries. Political instability is consistently high in Iraq over the sample period, whereas the GCC countries are among the most stable economies. The onset of the Arab Spring is clearly associated with a surge in political instability, which appears especially pronounced in Northern Africa, and specifically in Libya, Egypt, and Tunisia, as well as Syria in the Middle East. By contrast, in most GCC economies, the political instability index did 10
tariffs against U.S. imports, and, in the second, the U.S. unilaterally responds, i.e., it preferentially liberalizes imports from Nicaragua.<sup>23</sup> As anticipated, the opening up of the Nicaraguan market accounts for almost three-quarters of the CAFTA variation in imports, exports, and domestic output recorded by the reciprocal case. As shown in Table 10, in the case of unilateral U.S. liberalization, effects on imports and local sales are more or less muted, and the most visible effects consist of some additional specialization in exports of food processing products.

_<u>Table 10: Decomposing sectoral effects of CAFTA</u>_

||**Import**|**Nica**<br>**s and**<br>**Sales**|**ragua U**<br>**Local**|**nilat.**<br>**Exp**<br>**pr**|**Lib**<br>**orts an**<br>**oductio**|**d**<br>**n**|**Impor**|<br>**ts and**<br>**Sales**|**US Unil**<br>**Local**|**at. Lib**<br>**Exp**<br>**pr**|**orts an**<br>**oductio**|**d**<br>**n**|
|---|---|---|---|---|---|---|---|---|---|---|---|---|
||Δ**M**|Δ**S**|Δ**Pd**|ΔΕ**x**|Δ**Xp**|Δ**Px**|Δ**M**|Δ**S**|Δ**Pd**|ΔΕ**x**|Δ**Xp**|Δ**Px**|
|**Agriculture**|19|-0.1|-1.9|5|1.0|-1.7|3|0.2|1.0|0|0.1|1.0|
|**Food Processing**|1|0.5|-1.1|3|1.3|-0.9|1|-0.1|0.7|3|0.6|0.9|
|**Mining and Energy**|0|-0.2|-0.2|0|-0.2|-0.2|0|0.0|0.5|-2|-0.2|0.5|
|**Other Manufacturing**|1|-0.7|-0.4|1|-0.3|-0.3|0|-0.2|0.3|0|-0.2|0.3|
|**Services**|-1|-0.1|-0.1|0|-0.1|-0.1|2|0.1|0.5|-2|0.0|0.5|
|**Total**|1|-0.1|-0.6|3|0.3|-0.5|1|0.0|0.6|1|0.1|0.6|

Notes: Δ **M** represents the percent variation in total import volumes with respect to the initial levels, Δ **S** the percent variation in the volumes of domestic sales of domestic output, Δ **Pd** the percent variation in domestic prices for local sales, Δ **Ex** the percent variation in the volumes of exports, Δ **Xp** the percent change of domestic output, and Δ **Px** the percent change of output prices.

As highlighted in Table 10, the two unilateral liberalizations are consistent in their sectoral output effects. Both induce additional growth of agricultural and food processing sectors and, in this sense, help Nicaragua exploit its comparative advantage. Although the U.S. has already granted preferential access to Nicaraguan exports in the past, the remaining current U.S. tariffs seem to inhibit potential growth in some key sectors in Nicaragua, and obtaining full access to the U.S. markets may then bring some advantages.

> 23 This decomposition is not exact given that the sequence in which these reforms are carried out matters for the final results. However, in this particular case, given that the magnitude of the shocks, especially the reduction of the U.S. tariffs against Nicaraguan products, is not too large, the order in which the two simulations are carried out is almost indifferent.

31
Welfare Costs of U.S. Quotas in Textiles, Steel, and Autos by Jaime de Melo and David Tarr

# TABLE OF CONTENTS

|1.|Intoduction ..........<br>1|
|---|---|
|2.|ModelOutline<br>.........................................................<br>3|
|3 .|Elasticity and Prernia Esdmates .............................<br>7|
|4.|Welfare CostEstmates ...............................<br>10|
|_5'._|Eployment ...............................<br>15|
|6.|Conclusions ...............................<br>17|
|Fo|otnotes<br>...............................<br>19|
|Re|ferences<br>...............................<br>22|
|Ap|pendix A: Model Equations ............<br>..<br>.................<br>26|
|Ap|pendix B: Data Sources and Data Preparation .............................<br>34|
|Ta|bles|
||Table i ............................<br>6<br>Table 2 ...........................<br>9<br>Table 3 ...........................<br>1 1<br>Table4 ............................<br>13<br>Table 5 ............................<br>16<br>Table Al ............................<br>27<br>TableA2 ............................<br>31|

The paper draws on a larger study by Tarr (1988) while he was at the US Federal Trade Commission. We thank participants of the General Equilibrium Trade Policy Modelling Workshop at the University of Western Ontario for comments. The views are those of the authors, not those of the USFTC, nor those of the World Bank. We thank Julie Stanton for extremely valuable research assistance, Maria Ameal for logistic support, and Soren Nielsen for computer support.
<!-- Start of picture text --> Sri Lanka<br>© ett. gee efg@ee® a 2<br>od °<br>zgse28=S €Befrig5<br>ee . ee i<br>oof yg ° e<br>2000 2005 Year 2010 2015 2000 2005 Year 2010 ais<br>@ LFS-RPweek @ HIES-RP-usual<br>BE HIES- RP-week<br><!-- End of picture text -->
(2019) and Fernandes et.al. (2020) argue that countries with the right endowments are in a better position to participate in GVCs. However, while the authors find robust associations, the channels through which this transfer occurs—presumably through input-output linkages—are not welldeveloped. This happens in part because the econometric analyses are performed at the countrylevel. Unlike our analysis, they miss some of the inter-sector connections that define international production-sharing.

# 3 From GVC and FDI trends to conceptual framework

## 3.1 GVC and FDI trends

Most countries benefit from GVC participation, although fewer did so in 2019 before the pandemic compared to 2011. The level of GVC participation for almost 3 decades shows that GVCs have stopped growing at the global level since 2011, though they continued to expand in some regions. This contrasts sharply with the growth of FDI, GDP and trade (Figure 1). FDI has mostly kept pace with the growth of GDP. Trade growth has slowed but has kept pace with GDP, unlike GVC participation growth.

**Figure 1: FDI, trade and GVC trends globally**

<!-- Start of picture text --> 140 35%<br>Index,<br>Trade growth<br>2010 = 100<br>120 slows, but keeps<br>pace with GDP<br>100<br>30%<br>80<br>60<br>25%<br>40<br>Underlying FDI<br>trend and GVCs<br>20<br>stagnate<br>0 20%<br>GVCs FDI underlying trend Trade GDP<br>GVC Share of trade (%)<br>1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019<br><!-- End of picture text --> Note: Trade is global exports of goods and services. GVC share of trade is proxied by foreign value added in exports, based on the UNCTAD-Eora GVC database. The underlying FDI trend is an UNCTAD indicator capturing the long-term dynamics of FDI by netting out fluctuations driven by one-off transactions and volatile financial flows.
<mark>WPS3555</mark>

# **HOW DOES THE COMPOSITION OF PUBLIC SPENDING MATTER?**

Stefano Paternostro, Anand Rajaram and Erwin R. Tiongson PRMPR and PRMPS The World Bank, 1818 H Street, NW, Washington, DC 20433 World Bank Policy Research Working Paper 3555, March 2005 _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 view of the World Bank, its Executive Directors, or the countries they represent. Policy Research Working Papers are available online at http://econ.worldbank.org._
1) <!-- Start of picture text --> 0.5<br>0.4<br>0.3<br>0.2<br>0.1<br>0<br>-0.1<br>-0.2<br>-0.3<br>-0.4<br>1980 1985 1990 1995 2000 2005 2010 2015<br><!-- End of picture text -->
BURKINA FASO’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE How expensive would utility bills become if cost-reflective tariffs were applied? With a power costrecovery tariff of $35 cents per kWh and a monthly subsistence consumption of 25 kWh—which is capable of meeting the most basic needs—the associated utility bill would come to $10 per month. Based on the distribution of household budgets in Burkina Faso, monthly utility bills at these levels would be affordable by about 50 percent of the population (figure 31). Subsistence consumption of 4 m<sup>3</sup> per month for water would cost $4.60 and would be affordable for most of the population.

**Figure 31. About half of households are able to afford subsistence consumption when the monthly bill is $10**

<!-- Start of picture text --> 100<br>80<br>60<br>40<br>20<br>0<br>2 4 6 8 10 12 14 16<br>Monthly utility bill (US$)<br>Burkina Faso<br>LIC<br>Average cost of power is $10/month per 25 kwh or $20/month per 50 kwh subsistence consumption<br>Average cost of water is $4.6/month per 4 m3 or $11.5/month per 10 m3 subsistence consumption<br>Power cost recovery tariff is $0.40/kwh<br>Water cost recovery tariff is $1.15/m3<br>Source: Banerjee and others 2009<br>Note: LIC = low-income country; kWh = kilowatt-hour.<br>budget<br>be less than 5 percent of the monthly household<br>Share of urban households whose utility bill would<br><!-- End of picture text --> Undercollection of bills cost Burkina Faso $16 million a year in power and a further $1 million a year in water. Burkina Faso’s power utility, SONABEL, is relatively less efficient than other utilities in neighboring countries and than its low-income peers (figure 32). It managed to collect 91 percent of its billings as of 2008. Distribution losses are high, at 16-17 percent—compared to the best practice benchmark of 10 percent—likely an indication of an overstretched transmission network. As a result, SONABEL’s operational inefficiencies absorb 0.49 percent of GDP (figure 32a). In the case of the water sector, and in contrast to many other African countries, distributional losses are not a concern since they are at a level of nonrevenue water, at 18 percent, which is below the international benchmark of 20 percent of water production. ONEA’s implicit collection ratio, at 98 percent of billings, is relatively high, and therefore the associated burden of 0.013 percent of GDP is lower than that of other LICs, which absorb 0.17 percent of their GDP (figure 32b).

42
# **Appendix Table A3a: Impact on Individual Record Keeping Practices (ITT)**

||(1)|(2)|(3)|
|---|---|---|---|
||Recorded Every<br>Purchase and Sale|Itemized Business<br>Revenues and<br>Expenses|Estimated Cash on<br>Hand|
|Assigned Handbook|0.015|0.032|0.019|
||(0.013)|(0.029)|(0.030)|
|Assigned Handbook & Movie|0.029**|0.077***|0.054*|
||(0.014)|(0.029)|(0.031)|
|Assigned Handbook & Assistance|0.026*|0.081***|0.070**|
||(0.014)|(0.030)|(0.031)|
|Assigned All Three|0.016|0.082***|0.051*|
||(0.014)|(0.030)|(0.030)|
|R-squared|0.112|0.142|0.127|
|N|2204|2205|2204|
|Dependent Variable Mean in Control Group|0.035|0.230|0.323|
|F-test (p-value): Book = Book & Movie|0.335|0.139|0.248|
|F-test (p-value): Book = Book & Assistance|0.428|0.117|0.105|
|F-test (p-value): Book = All Three|0.927|0.105|0.278|
|F-test (p-value): Book & Movie = Book & Assistance|0.858|0.894|0.623|
|F-test(p-value): Book & Move = All Three|0.402|0.866|0.917|

**Notes:** This table presents impact analysis (Intent-to-Treat) for individual record keeping practices across midline and endline survey rounds. Regressions use an ANCOVA specification, where a midline survey round dummy, baseline value of the outcome variable as well as stratification controls are included on RHSs and standard errors are clustered at retailer level. Statistically significant p-values are highlighted by: * (10% significance level), ** (5% significance level), and *** (1% significance level).  Details on business practices can be found in Appendix Table A10.
We calculate tariff revenues at the tariff line level first assuming all imports pay the ad valorem tariff rate and then also calculate tariff revenues at the tariff line level assuming all imports pay the specific tariff. We then take the maximum of the two values as our MFN tariff. Our MFN tariff divided by the value of imports for the tariff line is the tariff rate for the tariff line. (The formulas are below.) Data are available on the quantity of imports in physical units and the value of imports. These data are also available by country or region of origin. In addition, we have the specific tariff and ad valorem tariff rates. Since we are interested in the MFN tariff, we focus on the non-CIS data, since CIS imports enter tariff free for the most part.<sup>14</sup> The methodology is as follows.

For each tariff line with a combined system, we first calculate unit values at the tariff line level. The value of imports is reported in US dollars, so the unit values are in US dollars.

# **1. Unit value = (value of imports from non-CIS countries)/(quantity of imports from non-CIS countries)**

Then we calculate tariff rate per unit assuming that the ad valorem tariff rate applies.

# **2. Tariff per unit if ad valorem = (ad valorem rate) * (unit value)**

We also calculate the tariff rate per unit assuming that the specific tariff applies. Since specific tariffs are defined in euros per unit, we need to convert euros to US dollars to be comparable with the unit value data. ER is the exchange rate in US$ per euro.<sup>15</sup>

> 14 In addition to the CIS agreement, Russia has both customs union agreements in place with several CIS countries, namely, Kazakhstan, Belarus, Kyrgyzstan, Uzbekistan and Tajikistan (with selected application of the common external tariff applying in all countries) as well as bilateral free trade agreements with others. Despite these agreements there are exceptions to the principle of tariff free access to the Russian market by CIS exporters, but these exceptions are reportedly not common.

> 15 We applied the average for the year exchange rate from the IMF Financial Statistics. Specifically, the dollars per euro exchanges rates we took were the following: 0.8 in 2001; 0.95 in 2002; 1.2 in 2003; 1.24 in 2004; and 1.33 in 2005.  Since specific tariffs are defined in euros and the trade data are denominated in US dollars, the appreciation of the euro against the US dollar has the tendency to increase the ad valorem equivalence of the specific tariff. This is because if the initial contracts are defined in US dollars (such as many imports from the US), then the appreciation of the euro against the US dollar increases the specific tariff in dollars and the ad valorem equivalence of the specific tariff.  But if the initial contracts are defined in euros, such as many contracts from the euro zone, the change in the exchange rate will not affect the ad valorem equivalence of the specific tariff. This is because the trade data are converted to US dollars at the same exchange rate as the conversion of the specific tariff. That is, the ad valorem equivalence of the

11
two variables, since this correlation is dominated by the strong positive effects of governance on per capita income.

What, then, should we make of this finding of negative feedback? We underscore two implications. The first is rather obvious: negative feedback implies that improvements in institutional quality or governance are unlikely to occur merely as a consequence of economic development. It is important not to exaggerate the conventional wisdom that as countries become richer, higher incomes lead to demands for better institutional quality. The second follows from the first: in the absence of positive feedback, we should not expect to see virtuous circles from higher incomes to better institutions, which in turn support higher incomes in the very long run. This is, perhaps, not too surprising. It is not hard to think of a variety of reasons why entrenched elites in a country benefit from the status quo of misgovernance and can successfully resist demands for change even as incomes rise over very long periods of time. In the last section of the paper, we use recent evidence gathered through detailed governance diagnostic surveys at the country level to cast light on a mechanism through which influential elites can resist demands for improvement in govemance-namely, the phenomenon of state capture. We then discuss the implications of this state capture hypothesis for strategies to improve governance.

## **MEASURING GOVERNANCE**

This section summarizes our earlier work, in which we organize a large set of indicators of perceptions of govemance into six clusters corresponding to six basic dimensions of governance.' We then describe the data sources and explain our methodology for combining the many indicators within each cluster into six aggregate governance indicators.

# **Governance Clusters**

We construct six aggregate governance indicators, motivated by a broad definition of governance as the traditions and institutions by which authority is exercised in a country. This includes the process by which govemments are selected, monitored, and replaced; the capacity of the goverrnent to effectively formulate and implement sound policies; and the respect of citizens and the state for the institutions that govern economic and social interactions among them. This classification of indicators into clusters corresponding to this definition of governance is not intended to be definitive. Rather, it reflects our own

> 1. Kaufmann, Kraay, and Zoido-Lobat6n (1999a, 1999b, 2002).

5
14 group inequality, _IB_ , is defined by assigning the mean income of group _j_ , μ _(_ **_y_** _j)_ to each member of the group and calculating: Cowell and Jenkins (1995) then show that the within- and between-group components of inequality, defined as above, can be related to overall inequality in the simplest possible way: IB + IW = I. They then suggest an intuitive summary measure, _RB_ , of the amount of inequality explained _I B_ <u>(Π)</u> by a particular characteristic or set of characteristics (i.e. by a partition Π ): _RB_ = . _I_ The _RB_ statistic, which can be interpreted as the share of total inequality which can be accounted for or 'explained' by the attributes defining partition Π , is presented in Table 3 for the two Theil indices described in Section 2, for partitions by each of the characteristics discussed earlier.<sup>23</sup>

**Table 3. The Percentage of Total Income Inequality Accounted for by Between-Group Differences.**

||198|1|199|3|200|4|
|---|---|---|---|---|---|---|
||Rb||Rb||R|b|
||E(0)|E(1)|E(0)|E(1)|E(0)|E(1)|
|Age|1%|1%|1%|1%|3%|2%|
|Education|38%|42%|34%|36%|35%|38%|
|Gender|0%|0%|0%|0%|0%|0%|
|Race|n.a.|n.a.|13%|11%|12%|11%|
|Family type|6%|7%|6%|7%|10%|11%|
|Region|13%|11%|9%|7%|10%|8%|
|Urban/rural|17%|13%|9%|6%|7%|5%|

Notes: Racial characteristics are not available for 1981. Source: Authors' calculations from PNAD 1981, 1993 and 2004.

Taken together, the decomposition results are suggestive. Gender of the household head has no explanatory power at all. As we know that participation rates and wages differed significantly by gender in Brazil throughout this period, the nil share of gender in these decompositions must

> 23 The Theil-L index, or mean logarithmic deviation, is the E(0) measure; whereas the Theil-T index is the E(1)