rafmacalaba/datause-encoder-data huggingface dataset:
place), the net cost of the program is expected to amount to PLN 22.2 million per year (1.3 percent of GDP).

To compare the <u>Figure 9. Spending Efficiency</u> efficiency of the new 0.020 Redistributive efficiency 0.025 program relative to 0.018 Poverty-reducing efficiency 0.016 0.020 other existing 0.014 programs, we 0.012 0.015 calculate the change 0.010 in poverty and 0.008 0.010 0.006 extreme poverty per 0.004 0.005 zloty spent for each 0.002 program. We find that 0.000 0.000 the change in poverty and inequality per zloty spent is lower for the Family 500+ program compared to <u>Source: World Bank estimates based on HBS 2014.</u> social assistance, family benefits, social pension and housing benefits, as these are more targeted, but it is more cost efficient than nursing allowance and nursing benefit (Figure 9). However, it must be kept in mind that last two benefits are not means-tested or targeted to the poor as their aim is to support persons suffering from disability.

# **Increasing the tax-free allowance for Personal Income Taxes**

In addition to the Family 500+, the new government campaigned on a promise to increase the taxfree allowance for PIT from PLN 3,000 to PLN 8,000. However, the plans have not yet been detailed, and the size of the increase might end up being lower, potentially to be implemented in several steps. The simulations below present two scenarios, one in which the tax-free allowance increases to PLN 5,000, and the other where it increases to PLN 8,000. We present the impact on poverty and inequality, along with the estimated fiscal cost of these measures. Given the introduction of the Family 500+, we also show the impacts of these alternative reforms in combination with the Family 500+ program.

Increasing the value of the tax-free allowance leads to more progressive personal income tax and health insurance contributions (since health contributions partly depend on the amount paid in PIT), resulting in an overall more progressive and redistributive direct tax system (Figure 10). The increase in disposable income as a result of the tax-free allowance is assumed to slightly increase the burden of indirect taxes. However, the net effect is a net gain for all households.

28
**TABLE 6 CORRELATION MATRIX**

||SCORE|COMPETE|I Ln PCI|I LOCI|LOC2|FORFIN|[MNC|
|---|---|---|---|---|---|---|---|
|SCORE|1.00|||||||
|COMPETE|0.53|1.00||||||
|Ln PCI|0.40|0.21|1.00|||||
|LOC1|-0.59|-0.13|0.17|1.00||||
|LOC2|-0.01|-0.29|-0.16|-0.32|1.00|||
|FORFIN|-0.62|-0.55|-0.05|0.37|-0.28|1.00||
|MNC|0.24|0.29|0.02|-0.32|0.06|-0.28|1.00|
|SOE|-0.17|-0.50|-0.25|-0.02|-0.10|0.54|-0.36|
|Ln EMP|0.31|0.06|-0.05|-0.29|-0.34|0.24|0.03|
|EMP|0.33|0.11|0.01|-0.30|-0.28|0.05|-0.04|
|CLEAN|-0.33|-0.00|0.11|0.52|0.21|-0.12|_-0.05_|
|FORMREG|0.25|-0.20|0.39|0.00|0.27|-0.12|-0.13|
|PRESSURE|0.45|0.12|0.17|-0.31|-0. l5|-0.00|-0.40|
||SOE||_Ln EMP|[_EMP|[_CLEAN||_FORMREG|PRESSURE|]||
|SOE|1.00|||||||
|Ln EMP|0.38|1.00||||||
|EMP|0.16|0.88|1.00|||||
|CLEAN|-0.48|-0.39|-0.33|1.00||||
|FORMREG|-0.11|0.01|0.09|0.00|1.00|||
|PRESSURE|0.47|0.40|0.42|-0.53|0.00|1.00||

Notes:

1. Ln PCI and Ln EMP are the Natural Logs of PCI and EMP.

2. All variables are defined in Box 1.

22
divisions. In particular, the Former Republic of Yugoslavia was divided into Bosnia, Croatia and Serbia in 1996 and Serbia was further divided into Serbia and Montenegro in 2006. Furthermore, some countries recognize Kosovo as an independent state rather than a part of Serbia since 2009. For technical and consistency purposes we treat Serbia, Montenegro and Kosovo as a single destination since disregarding these changes in names would bias some calculations, especially those related to the geographical diversification and market dynamics.<sup>15</sup> For example, destination entry and exit rates in a given year would be overestimated as the appearance of a new destination in the dataset would not represent the true expansion of exporters into a different territory but would instead be due to the change in territory classification. Likewise, destination measures that are calculated using only one year of data (e.g., number of destinations, export volume per destination, etc.) would not be comparable to each other.<sup>16</sup> In sum, after taking into account the operations mentioned above, the number of destinations in our dataset is 246.<sup>17</sup>

**d) Product:** The product classification system we use is the Harmonized System at 6-digit level (HS6). Although most countries record their export transactions at a higher level of disaggregation following their domestic or regional nomenclature (8-12 digits), we aggregate their information to HS 6-digit, since this is the most detailed level comparable internationally. That is, a specific HS code at 6 digits represents the same product in all country datasets in a given year.

The cleaning operations applied to the product variable have two components:

> 15 In the Database at the country-year-destination level the combined destination of Serbia, Montenegro, and Kosovo is coded as the 3-digit country code SRB.

> 16 While some other territories have undergone changes in names, those changes either occurred before the beginning of our sample or do not involve the merger or separation of states (e.g., Zaire changed its name to Democratic Republic of Congo in 2006) and therefore they do not introduce any biases in our calculations.

> 17 The list of destinations is available for download at http://econ.worldbank.org/exporter-dynamics-database.

9
et al., 2021b) and international financial assets (Koijen and Yogo, 2020; Gourinchas et al., 2022; Greenwood et al., 2023).<sup>2</sup> Similar to our study, Choi et al. (2022) analyze the effects of a downward-sloping demand on the optimal issuance of safe government bonds. In contrast, we focus on the interplay between a downward-sloping demand curve, default risk, and the provision of risky bonds. We show that the demand elasticity interacts with default risk and influences a government’s supply of risky bonds. Failing to account for this endogenous link can lead to biases when estimating demand elasticities.

Third, our paper also connects to a body of work examining how changes in the investor base of government debt impact bond yields (Warnock and Warnock, 2009; Dell’Erba et al., 2013; Peiris, 2013; Arslanalp and Poghosyan, 2016; Ahmed and Rebucci, 2022). A closely related paper (Fang et al., 2022) develops a demand system to quantify how changes in the composition of investors (domestic versus foreign, banks versus non-banks) affect government bond yields in international markets. In this paper, we exploit exogenous changes in the composition of the investor base (passive versus active funds) to provide evidence of downwardsloping demand curves for risky sovereign bonds.

Fourth, our paper relates to a large literature on quantitative sovereign debt models. Our framework extends standard models (Aguiar and Gopinath, 2006; Arellano, 2008; Chatterjee and Eyigungor, 2012) by incorporating two different investor types (active and passive) and introducing a downward-sloping demand. This richer structure allows us to discipline the model using our reduced-form estimates.<sup>3</sup> We then use the model to isolate the role of default risk behind those estimates and to back out the structural demand elasticity.

In our analysis, we are agnostic about the mechanisms behind the downward-sloping demand. Previous work by Borri and Verdelhan (2010), Lizarazo (2013), Pouzo and Presno (2016), and Arellano et al. (2017) analyze sovereign debt models with risk-averse investors. In their models, investors’ downward-sloping demand is a by-product of their risk aversion. In other words, investors are inelastic only because they must be compensated for each additional unit of risky debt they hold. However, several different mechanisms can generate a downward-sloping demand. For example, it can be explained by regulatory limitations, such as a Value-at-Risk (VaR) constraint (as in Miranda-Agrippino and Rey, 2020), by investors’ buy-and-hold strategies (which can be rationalized by a taste for simplicity or

> 2A related literature focuses on U.S. and international corporate bond markets (Dathan and Davydenko, 2020; Bretscher et al., 2022; Calomiris et al., 2022; Kubitza, 2023).

> 3In this regard, our paper connects with recent work by Costain et al. (2022), who introduce endogenous default risk into a Vayanos-Vila preferred habitat model to analyze the term structure of interest rates in the European Monetary Union.

5
reduction in its energy intensity. By 2030, China’s CO2 intensity could be 60 percent below its 2004 level. This is followed by Other Europe & Central Asia with a 56 percent drop. India, which exhibited just a 3 percent drop over the last 25 years, is expecting to drop its future intensity by 54 percent over the next 25 years through the reduction in energy intensity<sup>14</sup> . Other countries expecting CO2 intensity drops by more than 40 percent are Russia (50 percent), Africa (47 percent), South Korea (44 percent) and OECD Europe slightly above 40 percent.

**Figure 3: CO2 Emission Intensity**

(Metric Tons of CO2 per Thousand Dollar of GDP Measured in 2000 Purchasing Power Parity) <!-- Start of picture text --> 2.00 United States<br>Canada<br>1.80<br>Mexico<br>1.60<br>OECD EU<br>1.40 Japan<br>South Korea<br>1.20<br>ANZ<br>1.00 Russia<br>OECA<br>0.80<br>China<br>0.60<br>India<br>0.40 Other Asia<br>Middle East<br>0.20<br>Africa<br>0.00 Brazil<br>OLA<br>Metric Tons CO2 Per Thousand 2000 Dollar PPP<br>1980 1985 1990 1995 2000 2004 2010 2015 2020 2025 2030<br><!-- End of picture text --> Source: EIA (2007a) for historical intensities and IEA (2007b) for projected intensities.

> 14 EIA (2007b) assumptions on the reduction of energy intensities in China and India for the next 25 years might look too optimistic.

-16-
**Table 2: Specialization in resource, labor and capital intensive industries in Guangdong**

||Resource intensive<br>industries|Labor intensive<br>industries|Capital intensive<br>industries|
|---|---|---|---|
|2006||||
|Pearl River Delta|0.91|0.96|1.02|
|East|0.89<br>|1.96|0.71|
|West|2.60|1.03|0.81|
|Mountain area|2.10|1.20|0.81|
|2007||||
|Pearl River Delta|0.91|0.95|1.03|
|East|0.92|1.89|0.70|
|West|2.70|1.05|0.80|
|Mountain area|2.37|1.15|0.80|
|2008||||
|Pearl River Delta|0.88|0.95|1.03|
|East|0.85|2.03|0.66|
|West|3.17|0.94|0.77|
|Mountain area|2.77|1.20|0.72|
|2009||||
|Pearl River Delta|0.86|0.94|1.03|
|East|1.11|2.05|0.62|
|West|3.47|0.97|0.80|
|Mountain area|3.34|1.35|0.68|
|2010||||
|Pearl River Delta|0.91|0.91|1.03|
|East|1.10|2.73|0.54|
|West|6.21|1.23|0.70|
|Mountain area|5.22|1.27|0.73|
|2011||||
|Pearl River Delta|0.86|0.91|1.04|
|East|1.13|2.12|0.57|
|West|3.09|1.09|0.78|
|Mountain area|4.07|1.21|0.64|
|2012||||
|Pearl River Delta|0.92|0.91|1.03|
|East|1.13|2.62|0.50|
|West|4.92|1.21|0.76|
|Mountain area|4.36|1.06|0.83|
|2013||||
|Pearl River Delta|0.83|0.88|1.07|
|East|1.58|1.77|0.61|
|West|1.63|1.08|0.91|
|Mountainarea|4.00|1.01|0.74|

Data Source: Guangdong Bureau of Statistics, 2014.

Further, to examine the extent to which the capital-, labor-, and resource-intensive industries concentrate in the four regions, we follow Wen (2004) and Amiti and Wen (2001) to calculate the Gini coefficient as follows: where _sij_ stands for the ratio of the value-added of the sector _i_ to the industrial value-added of province _j_ ; _sik_ the ratio of the value-added of sector _i_ to the industrial value-added of province _k; n_ the number of regions ( _n_ =4); _si_ the average portion of the sector _i_ in comparison of the entire three sectors. If the sector _i_ is equally distributed among all regions, _Gi_ is equal to zero; if the sector _i_ is 10
# **<u>Table 6a.</u>** <u>Multivariate Analysis of Fruit Trees</u>

|||_Adoption Ana_|_lysis (Probit)_|_D_|_eterminants of sh_|_are of farmland with trees_|
|---|---|---|---|---|---|---|
||_Dep. V_|_ariable: Fruit t_|_rees on farm (yes=1)_|_Dep. Vari_|_able: Share of far_|_m area with presence of fruit trees_|
||(I)|(II)|ShapleyValue|(III)|(IV)|ShapleyValue|
|**_Household Controls_**|||**0.008 (2.99%)**|||**0.010 (3.32%)**|
|Household Size|0.002|0.003||0.014**|0.012**||
||[0.003]|[0.003]||[0.006]|[0.006]||
|Number of Children (<14 years old)|-0.001|-0.002||0.002|0.002||
||[0.004]|[0.003]||[0.009]|[0.009]||
|Head's Age (years)|0.002***|0.002***||0.003***|0.003***||
||[0.000]|[0.000]||[0.001]|[0.001]||
|Head Female (yes=1)|-0.018**|-0.019***||-0.043|-0.044||
||[0.008]|[0.007]||[0.026]|[0.027]||
|Head education (years)|<br>0.005*|<br>0.005*||<br>0.006*|<br>0.007**||
||[0.003]|[0.003]||[0.003]|[0.003]||
|**_Assets and land_**|||**0.005 (1.93%)**|||**0.096 (59.30%)**|
|Land Owned (area - ha)|0.005***|0.005***||0.088**|0.086**||
||[0.001]|[0.001]||[0.032]|[0.032]||
|Tropical Livestock Units (TLU)|<br>-0.007***|<br>-0.006***||<br>-0.001|<br>-0.001||
||[0.002]|[0.001]||[0.000]|[0.000]||
|**_Geo- and climate variables_**|||**0.053 (18.46%)**|||**0.006 (4.35%)**|
|Tree Cover % around 20km (mean) (2010)|0.003***|0.003***||0.003**|0.003**||
||[0.001]|[0.001]||[0.001]|[0.001]||
|Log Population Density around 20km (people/ km<sup>2</sup>) (2010)|-0.000<br>|0.000<br>||0.000<br>|0.001<br>||
||[0.001]|[0.001]||[0.001]|[0.001]||
|Fertile Soil % around 20 km (mean) (2010)|<br>0.000|<br>-0.000||<br>-0.002|<br>-0.003||
||[0.005]|[0.005]||[0.004]|[0.004]||
|Log. Annual Mean Temperature ( C )|0.014***|0.018***||0.020**|0.021***||
||[0.005]|[0.003]||[0.008]|[0.006]||
|Log. Annual Precipitation (mm)|0.000|0.000||-0.000|-0.000||
||[0.000]|[0.000]||[0.000]|[0.000]||
|**_Country Fixed Effects (Baseline Country: Ethiopia)_**|||**0.13 (48.00%)**|||**0.059 (36.81%)**|
|Malawi|-0.041|-0.038||-0.058|-0.050||
||[0.026]|[0.023]||[0.049]|[0.052]||
|Nigeria|-0.211***|-0.245***||-0.117*|-0.111||
||[0.031]|[0.017]||[0.067]|[0.068]||
|Tanzania|<br>0.159***|<br>0.143***||<br>0.564***|<br>0.510***||
||[0.051]|[0.050]||[0.076]|[0.080]||
|Uganda|<br>-0.133***|<br>-0.128***||<br>-0.056|<br>-0.058||
||[0.021]|[0.008]||[0.082]|[0.107]||
|(pseudo) R-Square|0.260|0.299||0.162|0.177||
|Observations|11243|11243||11243|11243||
|District/Zone Fixed Effect|No|Yes||No|Yes||

_Note:_ This table presents the multivariate results for fruit trees on farms. Robust standard errors in brackets, clustered at strata level. Sampling weights used for all regressions. * p<0.10 ** p<0.05 *** p<0.01.

29
In addition, this study underscores that remarried people form a – maybe surprisingly – large proportion of the general population and an even larger proportion of those who are currently married in the population. In most countries, there are many more remarried individuals than formerly married individuals. With a few exceptions, remarried individuals constitute over 15 percent and sometimes as high as 40 percent of all those currently married. It is important to recognize that there are large numbers of this high-risk group who are now in marriages where they might infect their spouse as well as potential extramarital partners with HIV.

# **Section 6: Conclusion**

The results from this analysis provide important information about the number and HIV prevalence of remarried individuals in Africa. These analyses go beyond the previous literature because the data are from a large number of nationally representative surveys, enabling a far more complete picture of remarried individuals than has been described before.

The results first establish that remarried individuals generally have a significantly high risk of HIV, sometimes similar to or just lower than the HIV risk of formerly married individuals (widows, divorced, and separated individuals) that had been shown in previous literature. An important cause for concern comes from the second finding of this study, which is that remarried individuals form a large portion of the population, much higher than those formerly married. Those remarried constitute between 10 and 40 percent of the currently married population. The large number of remarried individuals combined with their high HIV prevalence is an important source of vulnerability and further infection that needs to be acknowledged. Based on the results in this study, it is important that prevention efforts be directed at this distinctly high-risk group of remarried individuals. Voluntary testing and counseling, possibly joint testing, paired with 14
# **7. Conclusions**

Changes in aggregate human development outcomes can be additively decomposed into four components: (i) a pure “growth” effect associated with differences in mean income; (ii) a redistribution effect attributed to differences in the distribution of income; (iii) “non-income” factors and (iv) a structural component reflecting any differences in the model parameters — the “human development returns” to income or non-income characteristics. The analytical complication over other decompositions (in the Blinder-Oaxaca tradition) is that to convincingly estimate the decomposition we have to use a flexible, non-parametric, representation of the economic gradient in human development outcomes across households. Non-parametric regression methods allow us to implement the decomposition proposed here, and we have programmed it in STATA.

In applying this decomposition tool to data for Morocco and Vietnam, we find that growth and distributional change have played only a modest role in the changes in school enrollments over time observed in both countries. There are a couple of notable exceptions to this generalization; in particular, growth emerges as the most important factor in the changes in higher level school enrollments in urban Vietnam and also for ethnic minority primary school enrollments. However, taken as a whole, our results for these two countries do not suggest that aggregate economic growth or changes in the distribution of income have played an important role. Nor did improvements in relevant non-income factors (such as parental education) account for much of the aggregate gains over time. Rather, the bulk of the changes observed over time are accountable to changes in the structure of the model linking these variables to schooling

> 14 Since the model is inherently nonlinear the standard identification conditions for linear models do not strictly apply, although many researchers would probably prefer not to have to rely on the nonlinearity for identification.

22
**Table 2a: Consumption per capita growth volatility and private credit as a share of GDP**

|**Dep. Var:**|OLS|OLS|FE|GMM|OLS|2SLS|GMM|
|---|---|---|---|---|---|---|---|
|**std_cons. pc**|(1)|(2)|(3)|(4)|(5)|(6)|(7)|
|privatecredit|-0.017***|-0.0507***|0.0008|-0.0520***|-0.056***|-0.153**|-0.060***|
||(0.000)|(0.000)|(0.947)|(0.000)|(0.000)|(0.022)|(0.001)|
|privatecredit<sup>2</sup>||0.002***|0.0000|0.0002***|0.0002***|0.0008**|0.0002**|
|||(0.000)|(0.838)|(0.000)|(0.000)|(0.045)|(0.020)|
|financ.open.|-0.001***|-0.0001***|-0.0001|-0.0001***|-0.0007|-0.002|-0.0001**|
||(0.004)|(0.000)|(0.475)|(0.003)|(0.130)|(0.135)|(0.012)|
|tradeopen.|0.0098***|0.0108***|-0.0001|0.0113***|0.0122***|0.0175***|0.0113***|
||(0.000)|(0.000)|(0.987)|(0.002)|(0.000)|(0.001)|(0.003)|
|initialgdppc|-0.476***|-0.3445***|-0.7918*|-0.3261**|-0.222**|0.1961|-0.1502|
||(0.000)|(0.000)|(0.067)|(0.033)|(0.049)|(0.539)|(0.576)|
|financialcrisis|||||1.7668***||1.2206*|
||||||(0.000)||(0.057)|
|ECA|||||-0.835***|-0.8344***|-0.7240|
||||||(0.000)|(0.003)|(0.386)|
|Constant|4.021***|4.607***|4.3413***|4.6308***|6.1907***|5.0621***|5.8678***|
||(0.000)|(0.000)|(0.000)|(0.000)|(0.000)|(0.000)|(0.001)|
|# of Obs.|781|781|781|781|740|737|746|
|R-squared|0.148|0.166|0.01|0|0.189|0.080||
|Prob > F|0|0|0.282||0|0|0|
|# of countries|||133|133|||124|

*: in 2SLS, legal origin is used as instrument. Robust p-values are presented in parentheses and significance at 1% (***), 5% (**), and 10%(*) levels are indicated.

12
**Table 4: Cross-country regressions explaining variation in returns to schooling and returns to experience estimated at selected quantiles**

||||**Qu **<br>|**antile**<br>|||||
|---|---|---|---|---|---|---|---|---|
||**0.**<br>|**10**<br>|**0.5**<br>|**0**<br>|**0.9**<br>|**0**<br>|**90**<br>|**-10**<br>|
||**_Ed:_**���|**_Exp:_**���|**_Ed:_**���|**_Exp:_**���|**_Ed:_**���|**_Exp:_**���|**_Ed:_**���|**_Exp:_**���|
||0.034|0.124***|0.078**|0.077**|0.103***|0.072**|0.068*|-0.053**|
|Economic Freedom Index|(0.82)|(3.10)|(2.02)|(2.62)|(2.72)|(2.05)|(1.88)|(2.29)|
||-0.025|-0.059*|0.007|-0.033|0.016|-0.045|0.041|0.014|
|Globalization Index|(0.56)|(1.86)|(0.20)|(1.30)|(0.47)|(1.61)|(1.01)|(0.65)|
||0.151|0.087|0.247|0.018|0.179|0.351*|0.028|0.265*|
|Empowerment rights index|(0.60)|(0.36)|(1.18)|(0.12)|(0.91)|(1.88)|(0.12)|(1.68)|
|Democracy (Freedom|0.030|-0.327|-0.140|-0.277|0.018|-0.342*|-0.012|-0.014|
|House/Imputed Polity)|(0.12)|(1.34)|(0.67)|(1.61)|(0.09)|(1.98)|(0.05)|(0.08)|
||0.032|0.002|0.122**|0.016|0.109**|0.011|0.077|0.009|
|Women in Legislature|(0.69)|(0.08)|(2.26)|(0.54)|(2.09)|(0.42)|(1.60)|(0.35)|
||6.212***|2.928|1.054|3.162**|-1.284|2.421|||
|Constant|(2.78)|(1.52)|(0.48)|(2.27)|(0.62)|(1.52)|||
|R2|0.030|0.123|0.145|0.106|0.234|0.136|||
|Number of observations|122|122|122|122|122|122|||
|F(5, 85)<sup>a</sup>|||||||2.88**|1.71|

Dependent variable in the first six columns is the estimated return to schooling or return to experience from the associated quantile earnings function. t-statistics corrected for clustering are in parentheses. Associated elasticities are in brackets. aJoint test of changes in the five coefficients between the 90th and 10th quantiles, correcting for clustering t-statistics corrected for clustering are in parentheses. ** p<0.05, * p<0.1 24
# Globalization and Factor Income Taxation<sup>∗</sup>

Pierre Bachas,<sup>†</sup> Matthew Fisher-Post,<sup>‡</sup> Anders Jensen,<sup>§</sup> Gabriel Zucman<sup>¶</sup> ∗ The database built in this paper is available online at http://globaltaxation.world. We acknowledge financial support from the Weatherhead Center for International Affairs at Harvard University and the World Bank Development Economics’ Research Support Budget. Zucman acknowledges support from the Stone Foundation. We are indebted to Elie Gerschel, Rafael Proença, Roxanne Rahnama and Anton Reinicke for excellent research assistance. We thank Damien Capelle, Denis Cogneau, Gordon Hanson, Sergey Nigai, Steven Pennings, Thomas Piketty, Tristan Reed, Ariell Reshef, Bob Rijkers, Dani Rodrik, Emmanuel Saez and Roman Zarate for insightful comments. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not represent the views of the World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent.

†World Bank Research, pbachas@worldbank.org ‡Paris School of Economics, mfp@psemail.eu §Harvard Kennedy School and NBER, anders_jensen@hks.harvard.edu ¶UC Berkeley and NBER, zucman@berkeley.edu
# **Regionalism and Trade Facilitation: A Primer**<sup>***, †**</sup> **Jean-Christophe Maur**<sup>**‡**</sup>

# **Keywords: trade facilitation; customs; regional trade agreements; public goods**

> * This work is part of a World Bank research on trade facilitation and regional integration experiences conducted with John Wilson and Patrick Messerlin and supported through a trust fund of the U.K. Department of International Development (DFID). The views expressed in this paper should solely be attributed to the author and not be construed as representing the views of DFID or the World Bank **.** The findings, interpretations, and conclusions expressed in this paper are entirely those of the author. They do not necessarily represent the view of the World Bank, its Executive Directors, or the countries they represent.

> † The author would like to express his warm thanks to Peter Holmes, Gerard McLinden, Patrick Messerlin, Ben Shepherd, Matthew Stern, Chris Stevens, and John Wilson for invaluable comments and inputs. All errors remain the author’s sole.

> ‡ Economic Adviser, DFID and Research Fellow, Groupe d’Economie Mondiale. jc-maur@dfid.gov.uk.