rafmacalaba/datause-encoder-data huggingface dataset:
region within the last 15 years, including NAFTA and MERCOSUR. In fact, the World Bank (2005) estimates that 88 percent of trade in Latin America is now potentially eligible for preferential treatment. In addition, while applied tariffs in Latin America are normally high relative to those in advanced countries, in about 98 percent of cases (in our sample) they are set well below their bound levels at the WTO. This implies that those countries have ample room to either raise or lower their applied tariffs subsequent to the formation of a regional agreement, making it easier to identify the influence of RTAs.

We use data on preferential and most-favored nation (MFN) applied tariffs in ten Latin American countries and 100 ISIC 4-digit industries over the period 1990-2001. The data are unique because they include preferential tariffs across partners, industries, and years. We examine whether sectors with relatively large preferences have been liberalized or protected to the same extent as other sectors, and whether these effects differ by type of RTA, free trade areas (FTAs) or customs unions (CUs). Typically, CUs are distinguished from FTAs because they presume a coordination of the external trade policies of their members.

Our results imply that regionalism is a building bloc to free trade in Latin America. There is no clear evidence that trade preferences lead to higher tariffs or smaller tariff cuts in our sample. There is strong evidence that preferences induce a faster decline in external tariffs in free trade areas. The results are both statistically and economically significant. For example, if a country that follows a strict policy of non-discrimination offers free access to another country in a sector where it applies a 15% MFN tariff (the average in our sample), it would tend to subsequently reduce that tariff by over three percentage points. On the other hand, extending preferences in customs unions does not seem to lead to any significant change in multilateral tariffs; at best, it could induce a reduction in the MFN tariff that is considerably smaller than the 2
Nonetheless, it is possible to study the magnitude of sample selection by analyzing data from the 2000 census, which asked about respondents’ current place of residence, the province in which they were born, and their current residential registration status (agricultural or non-agricultural _hukou_ ). We can then examine, at the province level, educational attainment outcomes for individuals registered as urban residents and currently living in urban areas, for urban-registered individuals born in the province and continuing to reside in the province, and for urban-registered individuals born in the province and living anywhere in the country. In Figure 5, we present evidence on educational attainment for these three different groups for each CULS province.

The results for Shanghai are particularly instructive because Shanghai is both a city and a province, so that the census and CULS cover the same urban population. Shanghai is also a city that experienced very large educational disruptions. Figure 5 shows that in Shanghai the disruption to education was substantial and virtually indistinguishable for individuals born in Shanghai and still living in Shanghai (the solid black line) and for individuals born in Shanghai and living anywhere in China (the dotted gray line). For the other provinces where CULS cities are located, we also find that the patterns of educational attainment across cohorts are nearly identical for current urban residents born in the province and still residing in the province (solid black line) and for urban residents born in the province but residing anywhere in China (grey dotted lines). Overall, the evidence presented in Figure 5 suggests that selectivity bias associated with migration is very unlikely to be important.<sup>15</sup>

> 15 Figure 5 also shows that education disruptions due to the Cultural Revolution are not as apparent for provinces other than Shanghai. This could be driven by two factors: reporting of correspondence degrees as actual degrees and the fact that disruptions to education were not as great in smaller cities as in large cities. As the power base of the _Gang of Four_ , who were viewed as responsible for many of the excesses of the Cultural Revolution, Shanghai was far more strident in promoting revolutionary policies during the Revolution.

21
NIGER’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE

**Table 5. Road conditions along major transit ECOWAS corridors that run through Niger**

||Perce<br>|ntage in con<br>|dition<br>|Percentage<br>d|Per|centage in traffi<br>|c band<br>|
|---|---|---|---|---|---|---|---|
|**Corridors**|Good|Fair|Poor|pave|<300|300–1,000|>1,000|
|**Gateways to the sea**||||||||
|**_Lomé-Niamey_**|**_50.2_**|**_30.1_**|**_19.8_**|**_100.0_**|**0.0**|**_82.6_**|**_17.4_**|
|Togo|51.7|0.0|48.3|100.0|0.0|37.2|62.8|
|Burkina Faso|35.4|38.9|25.8|100.0|0.0|77.3|22.7|
|Niger|99.0|1.0|0.0|100.0|0.0|100.0|0.0|
|**_Cotonou-Niamey_**|**_49.5_**|**_7.9_**|**_42.6_**|**_98.5_**|**_1.5_**|**_26.4_**|**_70.0_**|
|Benin|38.1|2.2|59.7|97.8|2.2|15.8|81.5|
|Niger|77.7|22.3|0.0|100.0|0.0|52.8|41.4|
|**Intraregional corridors**||||||||
|**_Nouakchott-Ndjamena_**|**_63.4_**|**_21.1_**|**_10.2_**|**_97.3_**|**_9.9_**|**_46.2_**|**_43.4_**|
|Mauritania|50.6|23.8|25.6|100.0|0.0|21.5|78.5|
|Senegal|10.9|26.5|62.6|28.6|0.0|100.0|0.0|
|Mali|62.9|31.3|0.0|94.2|5.8|75.8|16.4|
|Burkina Faso|70.7|29.3|0.0|100.0|6.2|37.6|56.3|
|Niger|66.4|4.4|29.1|99.1|3.8|46.8|48.5|
|Nigeria|—|—|—|100.0|0.0|0.0|100.0|
|Cameroon|84.8|15.2|0.0|100.0|31.0|56.5|12.5|

_Source:_ AICD 2010. Note: ECOWAS = Economic Community of West African States — = Not available.

# **Challenges**

Although only one-third of the Nigerien road network is in poor condition, most of the remainder is in fair rather than good condition. If no routine and periodic maintenance is performed, a large share of the main network could fall into poor condition within a short period of time. Even though 58 percent of the unpaved network – mostly rural - is in good or fair condition, above the average condition in LICs (table 6), the rural network is in fragile condition.

A major factor contributing to the poor or fair condition of the network is truck overloading. To compensate for the small number of trips per month (typically only one), truckers try to maximize their load. Some trucks exceed 70 tonnes, while the maximum allowed for six axles is only 51 tonnes (Enhanced Integrated Framework 2008); this contributes toward the deterioration of the road network and increases rehabilitation costs.

Coverage of the road network is an important challenge vis-à-vis the large size and relatively low population density of Niger. Total road density over arable land is around one-tenth the density of LICs (13 km compared to 132 km/1,000 km<sup>2</sup> , table 6). There are only 1.2 km of roads per 1,000 people—onethird the average for Sub-Saharan Africa. The rural network density is even lower, at 0.6 km per 1,000 people, one of the lowest on the continent.

13
# **Oil Price Risks and Pump Price Adjustments**

Masami Kojima<sup>1</sup> JEL codes: N70, Q40 Key words: vulnerability, gasoline, diesel, kerosene, liquefied petroleum gas, oil imports, energy intensity, oil intensity, oil pricing policy, decomposition analysis, pass-through Sector Board: Energy and Mining

> 1 World Bank. The author is grateful to Veasna Bun, Eric Dacosta, Salam Almaroof, Sylvie Nenonene, and Amir Althibah, all of the World Bank, and Alzira Ferreira of the United Nations World Food Programme for obtaining petroleum product price information for July 2012 in Cambodia, Guinea Bissau, Iraq, Togo, Yemen, and Tajikistan, respectively. The paper benefitted from helpful comments provided by Punam Chuhan-Pole, Ariel Yepez-Garcia, and Enrique Blanco Armas, all of the World Bank; Mumtaz Hussain of the International Monetary Fund; Armin Wagner of Deutsche Gesellschaft für Internationale Zusammenarbeit (GIZ); and Tara Laan, Kerryn Lang, and Peter Wooders, all of the International Institute for Sustainable Development (IISD). All remaining errors are the sole responsibility of the author. The findings, interpretations, and conclusions are the author’s own and should not be attributed to the World Bank, its Executive Board of Directors, or any of its member countries.
41

|**Country**|**Price**|∆**P**<br>**Output**|**GDP**|**Price**|**Oil**|∆**C**<br>**Energy**|**GDP**|**Exch.**|
|---|---|---|---|---|---|---|---|---|
|Venezuela, RB|-61.6|0.7|46.6|15.7|2.7|-2.3|-23.3|12.3|
|Vietnam|-20.7|-0.2|15.8|12.5|1.8|-1.3|-6.0|1.3|
|Yemen, Rep.|-89.0|13.7|68.7|27.5|0.8|2.1|-22.4|4.5|
|Zambia|0.0|0.0|0.0|6.1|-0.2|-0.9|-6.1|1.7|

_Sources:_ EIA 2012, IEA 2012, WDI 2012, and author’s calculations.

_Note_ : Reporting to the first decimal point is for formatting purposes and does not imply that numbers exceeding 10 percent have three or four significant figures.

Table A1.5: Distribution of the change in oil share of energy by income and region for 1999– 2008 (percentage of countries in each category except for the median)

|**Category**|**Below -5%**|**-5 to 0%**|**0 to 5%**|**Above 5%**|**Median**<sup>**a**</sup>|
|---|---|---|---|---|---|
|**Income**||||||
|Low|3|32|58|6|0.1|
|Lower middle|0|40|58|2|0.2|
|Upper middle|4|51|43|2|-0.1|
|High|0|67|31|2|-0.1|
|**Region (excluding high-income**|**countries)**|||||
|East Asia & Pacific|0|50|50|0|0.0|
|Europe & Central Asia|5|40|45|10|0.1|
|Latin America & Caribbean|0|37|63|0|0.1|
|Middle East & North Africa|8|58|33|0|-0.7|
|South Asia|0|71|29|0|-0.6|
|Sub-Saharan Africa|2|35|58|5|0.1|

_Sources:_ Table A1.4 and author’s calculations. a. The median is in percentage of GDP.

Table A1.6: Distribution of the change in energy intensity by income and region for 1999–2008 (percentage of countries in each category except for the median)

|**Category**|**< -15%**|**-15 to -5%**|**-5 to 0%**|**0 to 5%**|**>5%**|**Median**<sup>**a**</sup>|
|---|---|---|---|---|---|---|
|**Income**|||||||
|Low|0|6|52|39|3|-0.4|
|Lower middle|2|4|56|33|4|-0.5|
|Upper middle|8|6|61|22|2|-0.9|
|High|0|0|78|22|0|-0.4|
|**Region (excluding high-incom**|**e countries)**||||||
|East Asia & Pacific|0|0|63|31|6|-0.3|
|Europe & Central Asia|15|25|60|0|15|-3.6|
|Latin America & Caribbean|0|4|67|30|0|-0.5|
|Middle East & North Africa|8|0|42|42|8|-0.1|
|South Asia|0|0|57|43|0|-0.5|
|Sub-Saharan Africa|2|2|51|40|5|-0.3|

_Sources:_ Table A1.4 and author’s calculations. a. The median is in percentage of GDP.
Natural Disasters and Economic Dynamics:

# Evidence from the Kerala Floods<sup>_∗_</sup>

Robert C. M. Beyer<sup>_†_</sup> Abhinav Narayanan<sup>_‡_</sup> Gogol Mitra Thakur<sup>_§_</sup>

**JEL Codes:** Q54, R22, D12, O44, E21, E23

**Keywords:** natural disasters, aggregate activity, household behavior, spatial analysis

> _∗_ The views and opinions expressed in this paper are those of the authors and do not necessarily represent the views of the World Bank, the Asian Infrastructure Investment Bank, or any other institution with which the authors may be affiliated. This paper has benefited from comments and suggestions from Martin Rama, Norman Loayza, and Rangeet Gosh, as well as from participants of the 2019 Annual Conference on Economic Growth and Development organized by the Indian Statistical Institute, the 2021 South Asian Economic Development Conference organized by the South Asian University, the 2021 Economic Theory and Policy Conference organized by the National Institute of Public Finance and Policy, and of seminars at the Centre for Development Studies, (Thiruvananthapuram), the Indian Institute of Management (Kolkata), the Indian Institute of Management (Lucknow), the New Castle University Business School, and the Fourth Meeting of the Finance and Macroeconomics Group, India. Previous versions of this paper have been circulated under the titles _Natural Calamities and Household Finance: Evidence from Kerala Floods_ and _Natural Disaster and the Economy: Evidence from the Kerala floods of 2018_ .

> _†_ World Bank. Email: rcmbeyer@worldbank.org.

> _‡_ Asian Infrastructure Investment Bank. Email: abhinav.narayanan@gmail.com.

> _§_ Centre for Development Studies, Thiruvananthapuram, India. Email: gogol@cds.ac.in.
One of the innovations of our data base is that we restrict the income concept to be the same over time for a given country. This avoids any spurious changes arising from a change in the welfare concept being used.<sup>34</sup> For each country, income or consumption was chosen so as to maximise the number of benchmark years covered (subject to the two conditions in the previous paragraph).<sup>35</sup> As Table 1 shows, in the overall sample the number of consumption and income surveys is almost equal. In earlier years, the majority of surveys collected information on income, whereas in recent years the reverse is true. This can be explained by the improved survey coverage of poor countries where consumption surveys are more common (with the exception of Latin America).<sup>36</sup>

# **2.3** **<u>Welfare concept</u>**

We are interested in analysing the global distribution of (annual) per capita income (in 2005 $PPP). Per capita incomes ignore any economies of scale in household consumption and withinhousehold inequality. Per capita incomes have the advantage that they are simple to compute and have natural counterparts in the national accounts (which do not compute equivalised incomes). The effect of using a different equivalence scale on world inequality is not clear a priori.<sup>37</sup> In our database, each country-year distribution of per capita income is represented by the average incomes of the ten deciles.<sup>38</sup> In the analysis, each decile is weighted by its population (i.e. 10% of 34 Income and consumption inequality might move in different directions over the same period of time (e.g. Krueger and Perri, 2006).

35 For Bulgaria, Botswana and Croatia the number of benchmark years with consumption and income information were the same. In all cases we chose the type of survey in order to maximise the number of surveys drawn from PovcalNet. For Nicaragua, we had both types of surveys in all years from PovcalNet. We chose income surveys since this is the prevalent type of survey in Latin America.

36 In China, India and Middle East/North Africa, our database only uses consumption surveys (with the exception of All-China where income surveys are used). In Sub-Saharan Africa 98% of surveys are consumption surveys. In other Asia, 91% are consumption surveys. On the other hand, in the mature economies and Latin America, 97% and 96% respectively are income-surveys.

37 In their study of the LIS data, Atkinson et al (1995) find that the inequality of per capita household income is greater than the inequality of household income adjusted by a square root equivalence scale. The precise effect on cross-country comparisons of inequality depends on the joint distribution of family size and income.

38 For Switzerland in 2008, the bottom decile had average income of zero. We have recoded this to missing, because otherwise the bottom 10% in Switzerland would be the poorest people in the world. Hence we have one missing observation in 2008.

19
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19
Dix-Carneiro and Kovak Trade Reform and Regional Dynamics Figure 22: Individual Average Earnings - Nontradable Sector Worker Sample - 1990-2009 <!-- Start of picture text --> 0.9<br>0.7<br>0.5<br>0.3<br>0.1<br>1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009<br>-0.1<br>-0.3<br>-0.5<br><!-- End of picture text --> Each point reflects an individual regression coefficient where the dependent variable is the average earnings from 1990 to the year listed on the x-axis as a multiple of the worker’s average pre-liberalization (1986-89) earnings, calculated using RAIS. For all years, the independent variable is the regional tariff change reflecting tariff changes from 19901995 (described in the text), with various controls (described in the text). Positive estimates imply larger declines in average formal earnings for workers initially employed in regions facing larger tariff declines. Vertical bar indicates that liberalization was complete in 1995. Dashed lines show 95 percent confidence intervals. Standard errors adjusted for 112 mesoregion clusters.

61
power in a¤ecting ...rm’s product scope and productivity. This is in sharp contrast to the previous ...nding where foreign sibling presence is consistently important in explaining ...rm performance. This suggests that the previous ...nding may not have been a ‡uke, and the measurement errors based on supplier information of 2003 may not have been empirically important, and the list of top three suppliers may just be enough to relate the ...rms.

# 6.3 Alternative Measure of FDI In‡uence

In this section, we explore using another variable to capture the in‡uence of FDI siblings on domestic ...rm performance, which is the fraction of a ...rm’s top 3 local suppliers that also sell to FDI ...rms (FDIsuppliersh it).<sup>23</sup> If a domestic ...rm has more local suppliers that also sell to FDI ...rms, it is likely that this domestic ...rm will bene...t more from the presence of these FDI siblings. Similar to FSPit, this variable is endogenous, which we will instrument with the EBA variables the same way as we have instrumented for FSPit, (see Section 5.1.3). Table 7 presents the empirical results for the full sample of domestic ...rms, including the highly signi...cant ...rst-stage F-statistics at the bottom of the table. Results based on the restricted sample of domestic ...rms who do not export to the EU are very similar and are available upon request. All regressions control for ...rm ...xed e¤ects, ...rm-speci...c time trend, industry-location-year ...xed e¤ects, age, the share of intermediate inputs in total sales and the share of imported intermediate input in total materials, similar to Table 5, with standard errors clustered by industry-year. The results are qualitatively very similar to the previous results using FSPit, which is not surprising, since FDIsuppliersh it and FSPit are highly correlated in the sample, both within ...rms as well as across ...rms.<sup>24</sup> Overall, a within ...rm increase in FDIsuppliersh it is positively associated with better ...rm’s performance, in terms of product scope and productivity. In particular, according to the IV estimate in Column (8), a one standard deviation increase FDIsuppliersh it causes ...rm’s productivity to improve by nearly 30 percent, which is signi...cant statistically as well as economically. These results are consistent with the previous ...ndings based on FSPit; which support the hypothesis of shared supplier spillovers of FDI ...rms.

> 23 I thank the editor, Nathan Nunn, for this suggestion.

> 24 Results available upon request.

23