rafmacalaba/datause-encoder-data huggingface dataset:
**Table 2. Margins between major producing areas and the U.S. Gulf Ports.**

|Crop<br>|Corn prices<br>|$/bu.<br>||Wh<br>|eat prices $/met<br>|ric ton<br>|
|---|---|---|---|---|---|---|
|year|Central Illinois|Gulf Port|Margin|Kansas<br>City<br>HRW|Gulf Port<br>HRW|Margin|
|2002|2.34|2.70|0.36|155|160|5.00|
|2003|2.52|2.94|0.42|148|156|8.00|
|2004|1.93|2.48|0.55|147|151|4.00|
|2005|2.00|2.69|0.69|164|168|4.00|
|2006|3.33|3.94|0.61|198|204|6.00|
|2007|4.43|5.16|0.72|335|341|6.00|
|Increase|2002-07 (percent)||15.53|||0.65|

Source: USDA Feed Grains and Wheat Yearbook Tables, July 2008.

**Increased biofuel production has increased the demand for food commodities** . The use of maize for ethanol grew especially rapidly from 2004 to 2007 and used 70 percent of the increase in global maize production (Figure 3). In contrast, feed use of maize, which accounts for 65 percent of global maize use, grew by only 1.5 percent per year from 2004 to 2007 while ethanol use grew by 36 percent per year. The share of global feed use of total use declined in response to maize price rises from 69 to 64 percent from 2004 to 2007, and from 70 to 67 percent when the feed by-products from biofuel production are included in feed use.<sup>11</sup>

The United States is the largest producer of ethanol from maize and is expected to use about 81 million tons for ethanol in the 2007/08 crop year. Canada, China and the European Union used roughly an additional 5 million tons of maize for ethanol in 2007 (USDA 2008a), bringing the total use of maize for ethanol to 86 million tons, which was about 11 percent of global maize production. The large use of maize for ethanol in the U.S. has important global implications, because the U.S. accounts for about one-third of global maize production and two-thirds of global exports and used 25 percent of its production for ethanol in 2007/08.

**About 7 percent of global vegetable oil supplies were used for biodiesel production in 2007 and about one-third of the increase in consumption from 2004 to 2007 was due to biodiesel.**<sup>12</sup> The largest biodiesel producers were the European Union, the United States, Argentina, Australia, and Brazil, with a combined use of vegetable oils for biodiesel of about 8.6 million tons in 2007 compared with global vegetable oils production of 132 million tons according to the USDA (2008f). From 2004 to 2007, global consumption of vegetable oils for all uses increased by 20.8 million tons, with food use accounting for 80 percent of total use and 60 percent of the increase. Industrial uses of vegetable oils (which include biodiesel) grew by 15 percent per annum from 2004

> 11 Biofuels production from maize uses only the starch in the maize kernel and 30 percent of the maize kernel remains as by-product called distillers dried grains with soluabales  (DDGS) which is a high-protein livestock feed.

> 12 Data on biodiesel are incomplete and do not allow a precise estimate.

7
The third scenario is the enhanced biofuel targets ( _ET_ ) scenario. This scenario generally considers a doubling of the announced targets, keeping the timing of the implementation of the targets unchanged. In India, however, we retain the AT target level because it is already very high in 2020 (see Table 3).

# **Simulation Results**

# **5.1 Selected CGE Results**

The effects of expansion of biofuels on world prices of feedstock are presented in Figure 2. Under the AT scenario where biofuels targets announced by various countries are assumed to be fully implemented by 2020, the price of sugar, the main biofuel feedstock globally, exhibits the highest increase, more than 7 percent from the _BaU_ scenario. If the targets were doubled (i.e., ET scenario) the increase in sugar price from the BaU scenario would be around 10 percent in 2020. In contrast, the increase in prices of other commodities would be less than 2 percent under AT scenario and less than 4 percent under ET scenario.

**Figure 2: World prices of feedstock in 2020 (% change from the BaU Scenario)**

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16
35 developing countries combined, while the African coverage ratio for clothing is about 18 points below the 63 percent developing country average. This pattern is reversed, however, for several food and feed product groups where African countries encounter a higher incidence of NTBs than all developing countries. If coffee were excluded from the tabulations the African food trade coverage ratios would be considerably lower than those for developed and developing countries as a group. Coffee exports are subject to quantitative controls (voluntary export restraints) imposed under the International Coffee Agreement. Special taxes are also applied to coffee imports in several European markets.

Analysis of NTB protection in individual OECD markets indicates that EU trade barriers are directed against African and other developing countries' exports with an above average frequency. While Table 8 shows that only 2 to 3 percent of sub-Saharan African exports to EFTA, Japan and the United States encounter nontariff measures the coverage ratio for the European Union is about _ten times higher_ (21.2 percent). In contrast, less than 9 percent of EU imports from the OECD face nontariff measures. Six African countries have more than one-quarter of their total exports to the European Union covered by NTBs, with the coverage ratios for Reunion and the Seychelles ranging from 88 to 96 percent. Sugar's importance in Reunion's and several other African countries' exports (sugar faces variable levies in Europe) accounts for the high pre-Uruguay Round ratios while the results for the Seychelles are largely due to EU reference prices for imports of tuna and skipjack.

In the United States the pattern of nontariff protection is the reverse of that for the European Union. Here, less than 2 percent of all African exports face nontariff restrictions, while the NTB coverage ratio for US imports from developed countries is 15 percent. Almost 21 percent of Burkina Faso's exports to the US encounter NTBs - - mostly due to global quotas on imports of feathers totalling $77,000 -- while Mozambique's ratio is 32 percent due to US tariff quotas and variable levies on sugar.

Aside from the Republic of South Africa, nontariff barriers in Japan do not appear to pose much of a problem for Africa. Malawi is an exception as almost all of its exports (which consist solely of tobacco) are required to pass through a regulatory state import agency. Between 16 to 20 percent of Equatorial Guinea and the Comoros exports face Japanese NTBs -- the former's exports (parts of tropical plants) are also required to pass through a state agency while the Comoros exports of whalebone is subject to a global quota. For most other African
Another impediment to higher quality of poverty research is the lack of microlevel data. Only two nationally representative micro-level datasets are currently publicly accessible; most of data from the regional surveys are proprietary. The results of the official surveys conducted by Goskomstat are available only in the aggregate form, with no access to the unit-record data. Clearly, this is not enough for a country the size of Russia with its highly diverse economic, geographic, and ethnic characteristics. Without open access to data, is it impossible to replicate, scrutinize and refine new findings and to filter out poor quality studies. Such lack of data also prevents Russian universities from introducing applied, data-intensive microeconomic courses into their curriculum.

Judging by the number of references to international sources, Russian poverty scientists are barely, if at all, integrated into the world research community<sup>11</sup> . As a result, Russian poverty scientists are inadequately aware of the latest developments in their field. New techniques and methods of economic analysis are taking a long time winning their acceptance and becoming instrumental in their scientific research. The language barrier is probably one of the main obstacles preventing new economic knowledge from informing Russian poverty research.

Finally, none of the journals from our sample employs the double-blind peer review process used by many international journals in selecting and screening articles for publication. It might be difficult for an editor of a scientific journal to evaluate the quality of highly-specialized research on a new subject. It can be impossible for an editor to find mistakes in a complicated piece of scientific work. Subjecting manuscripts to the scrutiny of the anonymous and independent experts in the field would ensure that the published articles meet the standards of the discipline and the general standards of scientific research<sup>12</sup> .

Despite these serious problems, we firmly believe that the quality of scientific research on poverty in Russia could be improved. Developments in the fields of macro-

> 11 We analyzed the dataset of publications in the top-200 international economic journals from 1985 to 2006 constructed by Das, et al. (2007). Publications that have at least one of the authors affiliated with Russian institutions represent 0.45 percent of the total number of published articles.

> 12 The ongoing project of the editors of the Russian physics journals aims at the development of the independent system of quality control for scientific research in physics. The leading Russian physicists and mathematicians emphasize that the lack of the independent assessment of the quality of scientific research is one of the most critical issue for the progress and even the existence of the Russian science.

> <u>(www.scientific.ru).</u>

19
observations that lie at the 95<sup>th</sup> percentile of the labor productivity distribution. Therefore, the higher the value of the labor productivity efficiency, the closer the country’s labor productivity to the global labor productivity frontier implying that the country’s labor productivity is globally competitive. Finally, data on trade are sourced from the CEPII-BACI dataset. The dataset contains bilateral import and export values and quantities across many countries at the 6-digit harmonized system classification (HS). Using appropriate concordance table, we map the trade data into Broad Economic Classification (BEC) Revision 4 to identify imported goods that are classified as intermediate and capital goods. For each African country, we then aggregate the imported good type (i.e., capital and intermediate good) across the partner countries into two categories based on their origin. Consistent with our research objective, we consider two origins: the Global North and the Global South. Our definitions of the Global South and Global North strictly follow Demir and Razmi (2022). Table A1 reports the correlation matrix among the variables of interest in our analysis.

# _3.2 Model Specification_

To examine how structural transformation is affected by the patterns of trade in Africa, we estimate the following empirical model: Where, the subscripts , , and denote country, region, and year, respectively. The level of economic transformation i is independently captured by structural change ( i ) and ii rr tt productivity convergence ( Fi ), then i i , Fi } . is initial industrial structure, measured as SS ZZ a country’s value-added share in 2000. is the corresponding coefficient to be estimated. ii is SS ∈{ZZ ψψ an indicator measuring the pattern of trade, while is the corresponding coefficient. The trade ii,ii φφ TT patterns we are interested in are capital and intermediate goods African countries source from θθ either the global North or South. Because of the high correlation between the trade variables (see Table A2), in the estimation we introduce the variables independently. is a vector of time- ιι varying country characteristics and the respective vector of coefficients. As discussed in ii,ii WW 1 × ιι the previous section, the vector ιι includes time-varying country characteristics such as human θθ 1 × ιι ιι capital, inflation rate, natural resource rent, and Institutional quality. ii,ii is regional dummies to WW rr δδ 10
WPS 1203 This paper-a product of the Country Operations Division, Europe and Central Asia, Country Department **I-** is **part** of a largereffort in the department to emphasize public finance **reform** issues inpolicy dialogue and economic and sector work. Copies of the pcper are available free from the World Bank, 1818 H **Street** NW, Washington, DC 20433. Please contact Faith Smith, room H5-245, extension 36072 (October 1993, 14 pages).

Value-added tax (VAT) has become a major tax instmment in over **SO** countries and an important element in tax policy advice to developing countries. But few studies have empirically tested some basic hypotheses about the performance and key feature of VAT as a revenueraisiuig instrument, Bogeti6 and Hassan examine the main determinants of VAT revenue in a simple crosscountry framework using data from 34 countries to answer certain key questions: What empirical rlationship emerges from existing data on VAT revenue and VAT rates for countries with a single VAT rate? How much, on averdge, can a 1 percent increase in the VAT rate be expected to raise VAT revenue as measured by VAT-toGDP ratio? What key determinants of VAT revenue emerge from a cross-country analysis of the full sample of countries? Is there a statistically significant difference in VAT revenue performance between countries with a single VAT rate and countries with multiple VAT rates?

The results of their regressions generally confirn the conventional views on the key variables influencing VAT revenue performance: the rate, the base, and **rate** dispersion. **The** rate and the base coefficients **are** signiflcant and with the expected positive sign in **all** of the estimated versions of the model. An esdmated model is used with appropriate ctveats to predict VAT revenue potential in countries (such as Bulgaria) that are thinking of introducing a single rate VAT.

- They also find that other things being constant - VAT generates higher revenue in countries with a single VAT rate than in countries with multiple VAT rates. The difference in the esdmated models for the two country groups is statistically significant, indicating a structural change. However this change in the pattem of VAT revenues cannot be explained exclusively in terms of differences in rate structure. A satisfactory explanation must include other factors, such as the base and tax administration capacity.

The key policy implications are simple: to provide superior revenues, VAT should be levied in a single rate on as broad a base as possible. And tax administration and enforcement must be tough to ensure compliance.

The Policy Research Working Paper Series disseminates **the** frndings of work under way in the Bank. Anobjective of the series is to get these findings out quickly, even if presentations are less than fuDy polished. The findings, interpretations, and conclusions in these papers do not necessarily represent official Bank policy.

Produced by the Policy Reseach Dssermiation Center
_13 Why Infrastructure Financing Facilities Often Fall Short of Their Objectives_

|Country<br>(Status)|Sector|Ownership Structure/<br>Legal Structure|Management<br>Structure|Instrument|Funding Base|
|---|---|---|---|---|---|
|Tbailand<br>(operational since 1994)<br>_Source:_W<br>Table 4:|*<br>Multi-Sectoral<br>orld Bank reports.<br> <br>OVERVIEW|*<br>Public and private<br>OF OTHER GOVERNM|*<br>Private<br>ENT SPONSORED F|*<br>Loan<br>guarantee<br>bond<br>insurance<br>INANCING FACILITI|*<br>US $ 150 million<br>from govemment;<br>USAID, domestic<br>and foreign<br>financial<br>institutions<br>ES|
|Country<br>(Status)|Sector|Ownership Structure/<br>Lekal Structure|Management<br>Structure|Instrument|Funding Base|
|Argentina<br>(canceled)|*<br>Financial Sector|*<br>100% public.<br>*<br>Set up as a shell<br>company|*<br>Private|*<br>Refinancing<br>commitments|*<br>Proceeds of WB<br>US$500 rillion<br>loan|
|Bosnia<br>(operational since<br>December 1996)|*<br>Pre-export|*<br>100% public<br>*<br>Set up as a separate<br>cotporate entity|*<br>Public|*<br>Pre-export<br>partial risk<br>guarantee|*<br>Proceeds of WB<br>US$10 million<br>loan; plus US$5<br>million from<br>Switzerland|
|Moldova<br>(canceled)|*<br>Pre-Export|*<br>100% public<br>*<br>Set up as a shell<br>cornpany|*<br>Public|*<br>Pre-export<br>political risk<br>guarantee|*<br>WB contingent<br>line of credit of<br>US$30million|

_Source:_ World Bank reports.
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To examine whether actual levels of insecurity changed as a function of refugee presence, we use ACLED data, which geocodes violent events. For each parish-year, we construct a binary variable, _Any Violent Event_ , which equals 1 if any of the following events occurred: violence against civilians, riot, attack, mob violence, or violent event.

# **4.4 Control Variables**

We (flexibly) control for the following covariates in our parish-year analyses. From the 2002 census, we include measures of each parish’s population, average age, proportion male, literacy rate, unemployment rate, agriculture share, share of the parish population that is coethnic with the president, and average household wealth (based on a composite index of household items). We also include a binary indicator from ACLED for any violent events from 2002, as well as each parish’s distance to the nearest oil well, distance to the nearest border, distance to a major road, and distance to Kampala, Uganda’s capital. To prevent post-treatment bias, we only use the 2002 measures of these variables. We interact these time-invariant variables with year to allow their effects to change over time.

For the individual-level analyses—the _Health Utilization_ outcome based on the DHS and Afrobarometer survey items—we control for respondents’ gender, age, urban or rural residency, household wealth, and education attainment, and for location-based controls (distance to the nearest border, to the nearest major road, and to the capital). We further interact these demographic and location-based controls by year.

# **4.5 Empirical Strategy**

Our main analysis uses a time-varying DiD research design. We run the following OLS model interacting refugee presence (time varying) with year, interacting time-invariant controls with year, and including parish, year, and region fixed effects, with standard errors clustered at the parish level: _yit_ = _ηi_ + _ηt_ + _ηr_ + _β_ 1exposure _it_ + _β_ 2exposure _it ×_ **1** _{yearit_ = 2006 _}_ + _β_ 3exposure _it ×_ **1** _{yearit_ = 2011 _}_ + _β_ 4exposure _it ×_ **1** _{yearit_ = 2016 _}_ + _β_ 5exposure _it ×_ **1** _{yearit_ = 2020 _}_ + _λ_ **1xi** _×_ **1** _{yearit_ = 2006 _}_ + _λ_ **2xi** _×_ **1** _{yearit_ = 2011 _}_ + _λ_ **3xi** _×_ **1** _{yearit_ = 2016 _}_ + _λ_ **4xi** _×_ **1** _{yearit_ = 2020 _}_ + _εit_ When we only have 1 year of observations for an outcome (certain Afrobarometer questions were only asked in a single round), we run a cross-sectional analysis with region fixed effects.

18
Policy Research Working Paper 8244

# **Abstract**

Internal mobility is a critical component of economic growth and development, as it enables the reallocation of labor to more productive opportunities across sectors and regions. Using detailed district-to-district migration data from the 2001 Census of India, the paper highlights the role of state borders as significant impediments to internal mobility. The analysis finds that average migration between neighboring districts in the same state is at least 50 percent larger than neighboring districts on different sides of a state border, even after accounting for linguistic differences. Although the impact of state borders differs by education, age, and reason for migration, it is always large and significant. The paper suggests that inter-state mobility is inhibited by state-level entitlement schemes, ranging from access to subsidized goods through the public distribution system to the bias for states’ own residents in access to tertiary education and public sector employment.

This paper is a product of the Trade and International Integration Team, Development Research Group. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The authors may be contacted at at zovanga@googlemail.com, yuanyuan.maggie.liu@gmail.com, amattoo@worldbank. org, cozden@worldbank.org, and ssharma1@worldbank.org.

_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 views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team