rafmacalaba/datause-encoder-data huggingface dataset:
and Tarr (2009), using simple averages. Since these are MFN tariff rates, they exceed the collected tariff rates due to tariff preferences to regional partners and due to other preference items or tariff exemptions. Thus, they exaggerate the protection received by Kenyan industry and agriculture. Consequently we obtained the value of overall customs duties and other taxes applied only on imports from the Kenyan _Economic Survey_ for 2006. We then took the ratio of the total taxes on imports to the total value of imports to obtain the average value of import taxes in the Kenyan economy. In 2005, this was 8.4 percent. That is, on average, Kenyan importers paid 8.4 percent of the value of imports on import taxes that did not apply to domestic production. We then scaled all the MFN tariffs in the model of Balistreri, Rutherford and Tarr (2009) so that the weighted average import tax is 8.4 percent. For the present model, we mapped the 55 sectors of Balistreri, Rutherford and Tarr (2009) to the 18 sectors of our model and took a trade weighted average of the tariff rates of the 55 sectors of Balistreri, Rutherford and Tarr (2009) to obtain the tariff rates for the 18 sectors of the present model. The results are presented in table 4a.

**4.5.2 Tanzania.** We were fortunate to receive unusually detailed collected tariff data from the Tanzania Revenue Authority. That is, we received data on collected import duties (tariffs) and import values at the eight digit tariff line level. The collected tariff rates for the sectors in our model are obtained by first aggregating the eight digit tariff line level tariff collections and import values to the 52 sectors of our the model of Tanzania by Jensen, Rutherford and Tarr (2010). The ratio of tariff collections to import values for each sector of the model of Jensen and Tarr (2010) is then calculated to give estimates of the collected tariff rates. Applying these tariff rates across all sectors implies that tariff revenue in the revised database is about 1.3% of GDP, which is consistent with collected revenues in Tanzania.<sup>20</sup> The data for import tariffs are replaced with collected tariff rate data for the year 2006. For the present model, we mapped the 52 sectors of Jensen, Rutherford and Tarr (2010) to the 18 sectors of our model and took a trade weighted average of the tariff rates of the 52 sectors of Jensen, Rutherford and Tarr to obtain the tariff rates for the 18 sectors of the present model.

Given that Tanzania participates in preferential trade areas with the East Africa Customs Union and the South African Development Community, it was necessary to make further adjustments. That is, since, in principle, tariff rates should be zero within these preferential trade

> 20 For the year 2006, aggregate data from Tanzania show that tariff collections are 1.47 percent of GDP.

15
# **Should Income Inequality Be Reduced and Who Should Benefit? Redistributive Preferences in Europe and Central Asia**

Alexandru Cojocaru World Bank Mame Fatou Diagne<sup>1</sup> World Bank _JEL codes: D63, I32, P20_ _Keywords: Transition Economies, preferences for redistribution, inequality, mobility_

> 1 The World Bank. 1818 H Street, NW, Washington D.C., mfdiagne@worldbank.org and acojocaru@worldbank.org The findings, interpretations and conclusions in this paper are entirely those of the authors and not those of the World Bank, its Executive Directors, or the countries they represent. We are grateful to Joao Pedro Wagner de Azevedo, Andrew Clark, Carolina Sanchez, Sarosh Sattar, Paolo Verme, Nobuo Yoshida and participants of World Bank seminars for helpful comments and remarks. All remaining errors are ours alone.
_Social Capital, Household Welfare and Poverty in Indonesia_ 26 household's endowment of social capital. Human capital is measured conventionally by the years of education of the adult members of the household.'<sup>6</sup> The LLI study data set contains information on land, cattle and farm equipment owned by the household. Direct inclusion of these variables as regressors in equation (1) is problematic due to possible endogeneity. Indeed, as Table 3 indicated, 26% of households sold assets to pay for consumption expenditures. Unfortunately, the data do not contain the stock of assets at the beginning of the consumption reference period. For that reason, we chose not to include the asset variables as regressors. Instead, we created a dummy variable to indicate whether the head of households was a farner. This must be seen as an occupational variable as well as a proxy for ownership of agricultural assets.'<sup>7</sup> In addition, the regressions include demographic variables, such as household size and gender of the head of household. Age of the head of household and its squared term were included to capture the life cycle of household welfare. Lastly, two dummy variables were included to indicate province (Jambi was used as omitted category). These variables capture the general economic and social conditions of the provinces along dimensions other than those which we were able to include in the model.<sup>1 8</sup> The first column in Table 4 replicates, as far as the data permit, the model that was used by Narayan and Pritchett (1997) for Tanzania. It consists of one aggregate social capital index, which is a multiplicative index between the density of associations, their internal heterogeneity and the index of active participation in decision making. The

> **16** The LLI questionnaire recorded only the level of educational achievement of each adult in the household and the number of years of education was imputed from that information. 17 In order to assess the impact of this decision, we re-estimated all equations reported in this paper with three asset variables capturing ownership of land, cattle and farm equipment. The substantive findings on the role of social capital were never affected.
database of global administrative areas—ADM1, corresponding to states or governorates, and ADM2, corresponding to districts, municipalities, or communes—using their center points (“centroids”). The baseline specification to be estimated by ordinary least squares (OLS) is where _i_ and _k_ are indices for cell and country, respectively; EverAid _ik_ is a binary variable that equals 1 if cell _i_ had at least one active aid project in the period of study and equals 0 otherwise; Mission _ik_ is an indicator that equals 1 if at least one historical mission was located in cell _i_ and equals 0 otherwise.

The baseline estimation exploits only within-country variation. The inclusion of country dummies _δk_ controls for all time-invariant country-level characteristics, many of which are important determinants of foreign aid.<sup>8</sup> To test for robustness, additional specifications include dummies for subnational administrative divisions (ADM1 or ADM2) instead of country-level dummies.

The vector **X** _ik_ consists of control variables at the cell level, including a number of historical and geographical factors described in Section 2.2. The error term _εik_ is allowed to be spatially correlated within a radius of 220 kilometers (approximately four times the side length of a cell) from the centroid of each cell. This means that clusters are unique to each cell and that a typical landlocked cluster covers about 45 contiguous cells.<sup>9</sup> Clustered standard errors are calculated using the estimator developed by Conley (1999).<sup>10</sup> This type of standard error is appropriate because missions are highly clustered in the data (see fig. 1).

The coefficient _β_ has a causal interpretation if and only if **X** _ik_ and _δk_ contain all relevant determinants of mission locations that are also correlated with present-day aid allocation. Failure to include important controls will bias the size of the coefficient. The most obvious threat to a causal interpretation of _β_ is nonrandom selection of mission stations. Missions are

> 8For example, to be eligible for International Development Association (IDA) funds from the World Bank, a country must be below a threshold level of GNP per capita (Galiani, Knack, Xu, & Zou, 2017).

> 9Coastal clusters are obviously smaller.

> 10The standard errors are calculated using the Stata program `x_ols` written by JeanPierre Dube, available at http://economics.uwo.ca/faculty/conley/.

6
|Crops with purchased seeds (number of crops)|-.390***|.233|-.255**|.088|
|---|---|---|---|---|
||(.132)|(.185)|(.126)|(.153)|
|Fertilizer or pesticides (=1)|-.019|.035|.013|-.008|
||(.021)|(.024)|(.041)|(.043)|
|Number of crops grown by the household|.117|-.353|.211|-.419**|
||(.230)|(.239)|(.204)|(.209)|
|Marketable crop farming (=1)|.122**|-.050|.195***|-.016|
||(.048)|(.046)|(.051)|(.054)|
|Marketable crop selling (=1)|.112***|-.013|.191***|.025|
||(.042)|(.042)|(.053)|(.050)|
|Tree farming (=1)|.044|-.060|.053*|-.016|
||(.043)|(.046)|(.032)|(.037)|
|**Panel C: Acces**|**s to extension**<br>|**services**<br>|||
||Round 1 x<br>RCBP|Round 1 x RCBP x<br>High initialassets|Round 2 x<br>RCBP|Round 2 x RCBP x<br>High initialassets|
|Contact with extension officer for advice or information (=1, head)|.025|-.021|-.0003|.003|
||(.046)|(.048)|(.015)|(.015)|
|Contact with extension officer was regular (=1, head)|.103**|-.055|.001|-.031|
||(.049)|(.059)|(.023)|(.030)|
|Rated DA advice very useful (=1, head)|.184***|-.062|-.030|-.102|
||(.064)|(.072)|(.055)|(.065)|
|Received advice from peer (=1, head)|.053|-.111*|-.004|.123*|
||(.051)|(.061)|(.050)|(.069)|
|Received advice from contact farmers (=1, head)|.130**|-.037|.015|.009|
||(.055)|(.056)|(.057)|(.069)|
|Received advice from extension officers (=1, head)|-.00004|.006|-.030|.028|
||(.044)|(.047)|(.034)|(.035)|
|Received advice from an NGO (=1, head)|-.029|.008|.039*|-.002|
||(.019)|(.029)|(.020)|(.030)|

Notes: Standard errors in parentheses, clustered at the kebele level. * indicates significance at 10% level, ** at 5% level, *** at 1% level. Each row shows the coefficients of the regression of the outcomes in the left-hand side column on interactions of treatment and time (Round 1 x RCBP, Round 2 x RCBP), and the interaction of the time indicator, treatment variable and high initial level of assets (Round 1 x RCBP x High initial assets, Round 2 x RCBP x High initial assets). All regressions control for region, time period, and the following contemporaneous household variables: presence of a female spouse of the head, household size, number of men in the household, number of elderly in the household, number of adults, number of adolescents, number of children, dependency ratio, number of literate household members, number of students in household, number of rooms in the dwelling, roof material is iron sheets. Outcomes shown in Panel C and "Tried new agricultural activities in the past year" are individual respondent level variables, with only household heads included in the sample.

23
|**Table S1.4 Robustness Check using Non-Transportation Source**<br>**Pollutant**|
|---|
|**Metro Extension**<br>**SO2 at ITO**|
|(Percentage change in level<br>of pollutant)|
|**Yellow Line First Extension (Jul 03, 2005)**<br>89.7**|
|**Std. Error**<br>(33.9)|
|**Observations**<br>1469|
|**Blue Line Introduction (Dec 31, 2005)**<br>200.8**|
|**Std. Error**<br>(84.1)|
|**Observations**<br>1622|
|Source: Authors' analysis based on data described in the text.|
|Results pertain to a nine week window.|
|Std. errors are clustered at one week. * indicates significantly different from|
|zero at 10 percent level, ** at 5 percent level, and *** at 1 percent level.|

|**Table**<br>**2006**|**S1.5 Mo**|**nth Wise Share (in percen**|**t) of Mis**|**sing O**|**bservati**|**ons, 2**|**004-**|
|---|---|---|---|---|---|---|---|
|**Year**|**Month**|**Extension Made**|**NO2**|**CO**|**PM2.5**|**NO2**|**CO**|
||||**ITO**|||Siri F|ort|
|2004|Jan||20|18|100|54|54|
|2004|Feb||**8**|**4**|100|**0**|**1**|
|**2004**|**Mar**|**Red Line Second Ext.**|**1**|**1**|100|24|24|
|2004|Apr||30|20|100|42|31|

63
consumption will translate into higher carbon emissions than would take place if the grid was cleaner.

In Figure 1, we take the World Development Indicators database for the year 2018 and graph the log of carbon dioxide emissions per-capita with respect to the log of GDP per-capita. For the 181 nations, the income elasticity estimate is 1.26. The figure’s dots are weighted by national population and India and China stand out for their contribution to emissions. In this cross-sectional figure, higher income nations have a less positively sloped elasticity than middle-income nations. In Figure 2 for 174 nations, we graph the percentage change in percapita carbon dioxide emissions from the year 2000 to 2018 with respect to the log of national per-capita GDP in 2000. The slope is -.34 in this case. The poorer nations that are urbanizing and industrializing have a larger growth in carbon emissions than richer nations. Together, these two figures highlight the challenge that lower-middle-income nations pose for creating a low carbon emissions coalition. As these nations grow richer, their carbon emissions increase.

One possible offsetting force is the recognition of the co-benefits of reducing reliance on coal fired power. <mark>Barrows, Garg and Jha (2019) quantify the air pollution externality associated with coal burning in India. Cesur, Tekin and Ulker (2017, 2018) use data from Turkey to document the local air pollution gains as the nation increases its reliance on natural gas for generating power and closed coal fired power plants. In developing nations, the value of a statistical life increases as economic development takes place (Costa and Kahn 2004). This means that the Pigouvian damage to India from air pollution created by its own coal fired power plants increases as the nation grows richer and more people live in a vicinity of the power plants. At least up until this point, this local benefit of substituting away from coal has not been a sufficient incentive.</mark>

# **Electric Vehicles Adoption in California**

In the developing world, people are purchasing durable products that increases their carbon footprint. In the United States, a promising trend is that more and more people are purchasing electric vehicles with wealthier people purchasing vehicles such as the Tesla. If the 10
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# 1 Introduction

This paper develops a framework to investigate the importance of foreign exporters and domestic importers’ lobbying efforts in determining the tariff preferences granted by an importing country. Jobst (2002) argues that coalitions among domestic and foreign interests are capable of decreasing political opposition to trade liberalization since the pooling of resources from interested parties reduces the costs of lobbying to each member. However, there is no formal analysis that investigates whether joint lobbying by domestic importers and foreign exporters can enhance the market access of a group of exporting countries. To the best of my knowledge, this paper is the fi rst to provide an analysis of this subject.

There is substantial evidence that lobbying by domestic importers and foreign exporters plays an important role in the determination of tariff preferences. In particular, the trade relations between the U.S. and Latin America have consistently been shaped by pressure groups. In 2001, the National Retailers’ Federation (NRF)–a pressure group representing the interests of U.S. importers–lobbied vigorously against the application of tougher rules of origin on apparel products originating in countries which were members of the Caribbean Basin Initiative (CBI).<sup>1</sup> Jobst (2002) reports that the National Supermarket Association, in conjunction with the Colombian Association of Flower Exporters, lobbied successfully to prevent trade regulations that would have placed restrictions on exports of Colombian fl owers to the U.S. in the late 1980s.<sup>2</sup> Similarly, the Association of American Chambers of Commerce in Latin America, in partnership with the U.S.-Chile Free Trade Coalition, led the effort to secure Congressional Approval, effective in January 2004, of the U.S.-Chile Free Trade Area.<sup>3</sup>

> 1The NRF claimed that U.S. retailers are the major buyers of apparel produced in the the Caribbean Basin region, and if retailers do not have the proper incentives to provide apparel under the CBI, then all other U.S. industries down the supply chain will not be able to sell their products under the CBI. See the NRF website (www.nrf.com) for more information.

> 2See Jobst (2002), pp. 10

> 3The AACCLA is composed of 23 non-pro fi t, independent, business organizations based in 21 Latin American countries. Its primary objective is to promote trade and investment between Latin America and the U.S. See the AACCLA website (www.aaccla.org) for more information.

2
We find an important impact of the quality of the firm’s human capital on TFP. Firms which employ a higher fraction of skilled workers are significantly more productive. In unreported regressions, we also find that firms with a higher fraction of college-educated workers have higher TFP. Firms with more experienced and more educated managers are more productive than other firms. These results point out to the importance of human capital formation for the performance of manufacturing firms.<sup>37</sup> The findings on the role of manager quality for firm TFP are interesting per se, but they also strengthen our findings on the other determinants of TFP. Our econometric specifications suffer the following potential endogeneity problem that better managers lead their firms to achieve higher TFP but simultaneously influence some of the determinants of TFP such as, e.g., exports, skills of the workforce, or access to finance. Since our regressions control for managerial education and experience, we argue that the effects of other determinants on TFP are not driven by unobserved managerial ability.

The results in Table 4 show the important benefits for firm TFP of integration into world markets. Firms with foreign ownership are about 10% more productive than other firms. This finding is obtained in regressions that control for industry, location, and year fixed effects, and hence is not driven by macroeconomic fluctuations (i.e., business cycles in the FDI-sending countries could make some years more prone to foreign investment), nor by a composition effect (i.e., certain industries are more prone to receive FDI), and it is also not driven by a location effect (i.e., FDI firms are more likely to locate in certain regions such as EPZs).<sup>38</sup> Our findings suggest that foreign-owned firms in Bangladesh have an advantage in terms of both tangible assets (e.g., better technology) as well as intangible assets (e.g., better access to distribution and marketing channels and networks) relative to domestically-owned firms.

Our findings also highlight very important benefits of exporting for firm TFP. In columns (1) and (4) of Table 4, the regressions include a dummy variable identifying firms engaged in export markets whose estimated effect on TFP is positive and

> 37 Our findings on the importance of manager education and experience are qualitatively similar to those in Burki and Terrell (1998) and in Lall and Rodrigo (2001) for firms in Pakistan and in India, respectively, despite methodological differences relative to our study in the estimation of productivity. Both studies use data envelopment analysis to obtain efficiency measures for firms.

> 38 Our findings mirror those obtained by Kee (2006) using a subsample of our dataset for the ready-made garments industry.

21
|MX Modernization of the Water & Sanitation Sector Technical Assis Martin P. Gambrill|
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|MX Modernization of the Water & Sanitation Sector Technical Assis Martin P. Gambrill|
|MX Modernization of the Water & Sanitation Sector Technical Assis Martin P. Gambrill|
|MX Modernization of the Water & Sanitation Sector Technical Assis Martin P. Gambrill|
|MX Modernization of the Water & Sanitation Sector Technical Assis Martin P. Gambrill|
|MX Modernization of the Water & Sanitation Sector Technical Assis Martin P. Gambrill|
|MX Modernization of the Water & Sanitation Sector Technical Assis Martin P. Gambrill|
|Agricultural Acquis Cohesion Project<br>Vera Dugandzic|
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|Agricultural Acquis Cohesion Project<br>Vera Dugandzic|
|Agricultural Acquis Cohesion Project<br>Vera Dugandzic|
|Agricultural Acquis Cohesion Project<br>Vera Dugandzic|
|Agricultural Acquis Cohesion Project<br>Vera Dugandzic|
|Agricultural Acquis Cohesion Project<br>Vera Dugandzic|
|Agricultural Acquis Cohesion Project<br>Vera Dugandzic|
|Agricultural Acquis Cohesion Project<br>Vera Dugandzic|
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|Agricultural Acquis Cohesion Project<br>Vera Dugandzic|
|Agricultural Acquis Cohesion Project<br>Vera Dugandzic|
|Second Trade and Transport Facilitation Project<br>Liljana Sekerinska|
|Second Trade and Transport Facilitation Project<br>Liljana Sekerinska<br> <br>|
|Second Trade and Transport Facilitation Project<br>Liljana Sekerinska|
|Second Trade and Transport Facilitation Project<br>Liljana Sekerinska|
|Second Trade and Transport Facilitation Project<br>Liljana Sekerinska|
|Second Trade and Transport Facilitation Project<br>Liljana Sekerinska|
|Second Trade and Transport Facilitation Project<br>Liljana Sekerinska|
|Second Trade and Transport Facilitation Project<br>Liljana Sekerinska|
|Second Trade and Transport Facilitation Project<br>Liljana Sekerinska|
|School EducationQualityAssurance<br>Michel J. Welmond|
|School EducationQualityAssurance<br>Michel J. Welmond|
|School EducationQualityAssurance<br>Michel J. Welmond|
|School EducationQualityAssurance<br>Michel J. Welmond|
|School EducationQualityAssurance<br>Michel J. Welmond|
|School EducationQualityAssurance<br>Michel J. Welmond|
|School EducationQualityAssurance<br>Michel J. Welmond|
|School EducationQualityAssurance<br>Michel J. Welmond|
|School EducationQualityAssurance<br>Michel J. Welmond|
|School EducationQualityAssurance<br>Michel J. Welmond|
|School EducationQualityAssurance<br>Michel J. Welmond|
|School EducationQualityAssurance<br>Michel J. Welmond|
|School EducationQualityAssurance<br>Michel J. Welmond|
|School EducationQualityAssurance<br>Michel J. Welmond|
|Public Sector Reform CapacityBuildingLoan<br>Robert P. Beschel|
|Public Sector Reform CapacityBuildingLoan<br>Robert P. Beschel|
|Public Sector Reform CapacityBuildingLoan<br>Robert P. Beschel|
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