rafmacalaba/datause-encoder-data huggingface dataset:
economy in developing countries renders such models inadequate in those contexts. The use of marginal lands or the utilization of crop residues for biofuel production will deny access to fuelwood and fodder, which can hurt the poor. In such cases the production of biomass suited to local needs may be socially more optimal than the production of biofuels for cars in cities. Demand-side policies that discourage the consumption of fossil fuels or carbon emissions as a way of improving energy security or protecting the environment seem should also be given greater attention by researchers and policymakers despite the political economic barriers to such policies. From a formal modeling perspective, the dynamic relationship of food price in relation to energy price as they become increasingly correlated is an area of future research. Most of the models that exist today are simulation based but, as the time series of biofuels grows, econometric verification of the impacts should also be accorded priority. There should also be an effort to develop tools that would allow assessments of the impacts of biofuels at a country-specific level.

We believe this report is a broad and comprehensive survey given that the emphasis is on liquid biofuels. We are aware that the use of biomass for household heating or electricity production is very important especially among the poor in developing countries, while demand for liquid biofuels is largely from the richer sections of society. But in terms of the potential for impact on the broader economy and the environment, we think ethanol and biodiesel warrant greater attention given the scale of the commitments in terms of investments and policies that are being made in these countries today. Even at low levels of production of biofuels, rising food prices are already being evidenced in many countries. In terms of future impact on poverty and the environment, modern biofuels may dwarf the impacts of traditional biomass. Because of international trade possibilities, actions of rich nations assume equal or greater significance for the poorer nations, which are bound to feel both positive and negative effects from the rising demand for agriculture.

The most important conclusion of our survey is that not all biofuels are created equal. Biofuels exhibit considerable spatial and temporal heterogeneity in production. The 77
countries since the mid-1980s.<sup>11</sup> Between 1987-89 and 2000-01, net financing to developing countries rose from a negative 0.3% to 2% of the recipient economies’ GDP. This increase reflected a parallel rise in net FDI flows by a similar magnitude. Over the same period, net FDI flows went up from 0.5% to 2.5% of GDP. Net portfolio equity flows also rose, although by a very modest amount, from virtually zero in the late 1980s to some 0.1 percent of GDP in 20002001. In turn, net debt flows to developing countries rose in the early 1990s but then collapsed following the East Asia and Russia crises, becoming sharply negative by 2000-01. As a result of these trends, in the latter years net FDI exceeded total net financing flows to LDCs (see World Bank 2001).

Table 1 also shows the figures for Latin American countries, which were the primary destination of the FDI boom of the 1990s. For these countries, total net flows rose from -2.2% to 3.1% of GDP between the late 1980s and the early 2000s. Over half of this increase took the form of higher net FDI. In fact, increasing FDI between the first half of the 1990s and the early 2000s more than made up for the collapse in all other flows over the same period.

Unlike with developing countries, net FDI flows to industrial economies showed little change over the period under consideration. Closer inspection reveals that both inflows and outflows rose markedly, leaving the net difference virtually unchanged.

Table 2 offers a detailed breakdown of FDI flows over the same time period. In industrial economies, almost all of the increase in inflows took the form of higher cross-border M&A, of which a very small portion was due to privatization of public enterprises. As a result, in industrial countries M&A transactions were about 7 times larger than greenfield FDI in 2000-01.

> 11 Table 1 reveals that in our sample net flows to both industrial and developing countries do not add up to zero.  This is primarily due to the fact that our sample of countries is incomplete (especially concerning a few developing economies that are international financial centers where large FDI outflows originate).

7
**<u>Table 4: Village fixed effects regressions for area with winter and summer crops</u>**

||<br>**Winter c**|<br>**rop area**|<br>**Summer **|**crop area**|
|---|---|---|---|---|
|Conflict indicator (_βt_)|-104.226***<br>(16.547)|-51.621***<br>(16.521)|-577.364***<br>(14.393)|-461.845***<br>(13.420)|
|Conflict indicator|-376.087***|-174.551***|-874.098***|-577.472***|
|Lagged (_βt-1_)|(21.236)|(21.380)|(16.855)|(15.787)|
|Area damaged (DA)||-0.244***||-0.800***|
|growing period (_δt_)||(0.024)||(0.011)|
|DA growing period||-0.587***||-0.993***|
|lagged (_δt-1_)||(0.029)||(0.013)|
|DA planting and||-0.608***|||
|dormancy||(0.020)|||
|Winter crop area|||-0.646***<br>(0.004)|-0.719***<br>(0.004)|
|Year 2020 (_λ20_)|-68.272***|-62.789***|<br>-61.460***|<br>-79.488***|
||(15.090)|(14.777)|(13.087)|(11.900)|
|Year 2021 (_λ21_)|<br>-73.541***|<br>-77.702***|<br>-167.837***|<br>-154.500***|
||(22.358)|(21.892)|(11.800)|(10.729)|
|Year 2022 (_λ22_)|<br>-119.288***<br>(13.635)|<br>-121.134***<br>(13.350)|<br>-247.974***<br>(15.779)|<br>-206.403***<br>(14.354)|
|Year 2023 (_λ23_)|<br>-151.201***|<br>-157.758***|<br>-152.950***|<br>-127.412***|
||(14.444)|(14.143)|(13.595)|(12.365)|
|Mean of dep. var.|830.096|830.096|1,677.799|1,677.799|
|STD of dep. var.|1,137.696|1,137.696|1,543.028|1,543.028|
|No. of obs. (VC*year)|50,010|50,010|52,780|52,780|
|R<sup>2</sup>|0.886|0.891|0.958|0.965|
|**Linear combination tests**|||||
|_βt _-_βt-1_|271.861***|122.930***|296.733***|115.627***|
||(31.835)|(31.643)|(26.267)|(24.442)|
|_δt - δt-1_||<br>0.343***<br>(0.033)||<br>0.194***<br>(0.016)|
|_λ22 _– (_λ20_+_λ21_)/2|-48.382***|<br>-50.888***|-133.326***|<br>-89.409***|
||(10.729)|(10.514)|(8.464)|(7.710)|
|_λ23_– (_λ20_+_λ21_)/2|-80.295***|-87.512***|-38.301***|-10.418|
||(9.520)|(9.323)|(7.892)|(7.181)|

_Source:_ Original table for this paper.

_Note:_ The results are from 2019–23 panel regressions with VCs as the unit of observation. The dependent variable is the land area allocated to winter or summer crops. Geographic coverage excludes Crimea and regions occupied in 2014. Summer crops include maize, soybean, sunflower, and summer cereals, while winter crops are winter cereals (wheat, barley, and rye) and rapeseed. The planting period for summer crops is March 16 to May 15, and growing period is from May 16. GDD (> 5°C) and its square, rainfall during the planting and growing season and its square, as well as zero rain days during the planting and growing period and a constant are included, with coefficients reported in appendix tables A2 and A3. Conflict indicators and damaged area are zero for all years before 2022. Standard errors are in parentheses, and * p < 0.10, ** p < 0.05, and *** p < 0.010. DA = area damaged due to conflict; GDD = growing degree days; VC = village council.

21
entially impacts perceptions of empowerment. In particular, we expect urban based displacement to be especially conducive to empowerment, while we expect camp-based displacement is especially harmful to IDP empowerment.

# **4 Research Design**

To evaluate our hypotheses, we analyze original cross-section surveys we conducted that sample IDPs and similarly at-risk but not displaced populations in Iraq (2019) and the Philippines (2010). To explore temporal change within a single case, we additionally analyze a repeated cross-sectional survey in northern Uganda (in 2007 (n=1404) and 2010 (n=2498)) that also captures IDPs and similarly at risk populations. We restrict the analysis of the Uganda repeated cross-sectional waves just to Acholi, the main site of displacement within northern Uganda and the region for which we have consistent and comparable coverage across the two survey waves. Figure 2 plots the spatial distribution of our samples in the Philippines (Figure 2a), Iraq (Figure 2b), and Uganda (the 2010 wave, Figure 2c). Combined, these surveys provide 11,170 unique responses that capture the IDP experience across three different continents, regimes, and conflict zones. An additional case contributing to our reflection, Colombia, is presented in appendix because of significant methodological differences.

Each survey was collected independently as part of our broader research agenda to understand the consequences of forced displacement. As a result, the survey samples and instruments are not identical across cases.<sup>12</sup> Each survey was designed to capture and respect the particularities of the local context. We bring these surveys together precisely because they enable us to examine the experience of IDPs across contexts while prioritizing the internal validity of each case. The three surveys presented in this paper provide the best balance of consistency in questions, similarity of populations sampled, and analytically fruitful contextual differences between cases in our broader research agenda and data gathering efforts on the topic.<sup>13</sup> Although specific design decisions vary by country, consistency across the surveys enables us to explore the ramifications of displacement across different macro (i.e. countries, regime types, conflict status) contexts, as described in Section 2. Because the surveys were all carried out by our research team, we have a common pool of overlapping questions that enable comparison of patterns

> 12We provide additional information on the original sampling procedures for each project in the Section A.1 of the Appendix.

> 13We originally proposed analyzing Colombia in conjunction with the three contexts presented in the main text. We discuss the decision to drop Colombia from the main text and present the results from our analysis of the case in the Appendix, Section D.1.

12
During the verification exercise, the verifiers were unable to locate 98 women (and 27 households). This leaves us with a sample of 2,637 women and 991 households. We were able to verify all 727 confidants.

Attrition of women was unrelated to treatment assignment (see Appendix Table A3), although we note that T2 households are more likely to attrit. Since the overall household attrition rate is very low (2.6 percent), this is not a source of concern. In addition, 158 women claimed to have cast a vote but did not have the requisite ink mark. To be conservative, we treat these women as not having voted, although the results do not change if we consider them as voters.

Verification was followed by a post-election survey of the same 2,637 women in March 2008. We ensured that the team of enumerators that visited a given household at follow-up was different from the one that had delivered the awareness campaign. The survey collected information on household demographics, recall of the door-to-door visits, access to and use of various media, and knowledge of the balloting process and political candidates, among other issues. Finally, we collected the official electoral results by gender, candidate and political party for each of the 21 polling stations which served our sample villages.<sup>20</sup> Average turnout among women in our sample is 59 percent, while female turnout in the 21 polling stations is 47.3 percent. At the constituency level, total turnout was 39.2 percent in Khairpur and 48.6 percent in Sukkur. The corresponding turnout rate (including both males and females) in the sample polling stations was 41.7 percent and 43 percent, respectively. While there is significant variation in contestation at the polling station level, the share of PPPP at the constituency level was a large 70 percent in Khairpur and 73 percent in Sukkur.

Table 2 reports the difference in means of household and woman characteristics across different samples. See Appendix Table A1 for the definition of the variables used in the paper. Column 1 in Panel A compares treated households to all control households, irrespective of whether they live in treated or control clusters. Columns 2 and 3 compare T1 and T2 households, respectively, to all control households. Column 4 compares all households in treated clusters (both treated and control households) to those in control clusters, Column 5 compares treated households to

> 20 Results by gender are tabulated at each polling station. This is possible because in Pakistan polling areas and polling booths are separated by gender.

12
of the National Sample Survey (NSS), which cover over 100 thousand households. In addition to standard household modules on consumption, health, education and employment, it includes specialized surveys that rotate each year. The NSS has a significantly larger set of questions and therefore provides more detailed data in comparison to the Census, but for a much smaller sample of the population.

The census asks two different questions pertaining to the migration status of the respondents – one based on birthplace and one on place of last residence. The last residence question is less common in censuses, but it is more relevant for economic analysis of internal mobility (Carletto, Larrison, and Özden 2014). We define an individual as a migrant “if the place in which he is enumerated during the census is other than his place of immediate last residence” (Census, 2001). The Census includes additional questions based on the last residence criteria. These questions include reason for migration (marriage, education, employment etc.), the urban/rural status of the location of last residence and the duration of stay in the current residence since migration. Such information sheds additional light on the patterns and determinants of internal mobility.

While the census questionnaire asks these questions to each respondent, the resulting individual level data are not made publicly available. Instead, the data are aggregated up to the geographic units – depending on the purpose – and are disseminated through tables. For example, we can find the number of people living in a given district whose previous residence was in a different state or another district within the same state. In some cases, the publicly available tables include additional variables on gender, education or reason for migration. However, these datasets do not present bilateral migrant stocks at the district level, and therefore, they do not lend themselves to empirical analysis, especially to gravity type estimation. We therefore requested detailed bilateral (district-to-district) migration data from the Census Bureau, which provided us with a series of tables under a special administration agreement. These tables contained the following for all pairs of districts in India: (i) migration stocks by gender and educational attainment levels, (ii) migration stocks by gender and age 7
32 captures the historical political environment of the countries previous to the devaluation. It measures a number of variables, such as political riots, successful coups and the like, from 1948 (the first year in the data set) to the year prior to the devaluation. The second and third sets of indicators deal with the political environment in the period immediately following the devaluation. They measure the same political indicators one year after the devaluation and for the three year period following the devaluation.

Ten variables in each of these sets of indicators for measuring the degree of political instability were actually used:

- (1) politically motivated attacks;

- (2) politically related deaths;

- (3) successful coups attempts;

- (4) politically motivated strikes;

- (5) political demonstrations and riots;

- (6) politically motivated assassinations;

- (7) frequency of government transfers, either regular or unscheduled (via coups);

- (8) an overall measure of violence defined as the frequency of protests, strikes, deaths, assassination attempts and attacks;

- (9) frequency of unsuccessful government transfers, including unsuccessful coups;

- (10) frequency of political repression computed as the number of related executions plus political sanctions imposed by the government on its opponents.

In order to correct by country size, those variables were defined in per capita terms. Table 10 contains some summary statistics for our last four indicators -- frequency of government change, violence index, unsuccessful
||**Loca**|**tion**|**Gen**|**der**|**Educa**|**tion***||**Occupation**||
|---|---|---|---|---|---|---|---|---|---|
|**Country**|**Urban**|**Rural**|**Female**|**Male**|**Low**|**High **|**Agriculture**|**Industry**|**Services**|
|Lao PDR|0.75|0.57|0.69|0.62|0.61|0.74|0.57|0.69|0.76|
|Madagascar|0.51|0.31|0.36|0.34|0.25|0.38|0.24|0.42|0.50|
|Malawi|0.66|0.49|0.50|0.53|0.51|0.69|0.53|0.61|0.69|
|Mali<sup>g</sup>|0.67|0.38|0.44|0.44|0.43|0.72|-|-|-|
|Mexico|0.93|0.78|0.91|0.90|0.88|0.96|0.75|0.91|0.93|
|Morocco|0.94|0.85|0.92|0.91|0.90|0.95|0.84|0.90|0.93|
|Mozambique|0.62|0.30|0.43|0.40|0.39|0.57|0.31|0.51|0.59|
|Myanmar|0.73|0.51|0.61|0.56|0.56|0.60|0.49|0.57|0.65|
|Nepal<sup>g</sup>|0.79|0.59|0.63|0.64|0.63|0.88|0.50|0.60|0.76|
|Nicaragua|0.83|0.51|0.76|0.67|0.67|0.86|0.49|0.75|0.82|
|Niger|0.57|0.21|0.33|0.26|0.26|0.73|0.21|0.42|0.52|
|Nigeria<sup>o</sup>|0.65|0.48|0.58|0.54|0.51|0.60|0.61|0.63|0.65|
|_Kano_<sup>_g,l,e,o_</sup>|0.51|0.47|0.54|0.47|0.36|0.58|-|0.34|0.55|
|_Lagos_<sup>_e,o_</sup>|0.70|0.60|0.73|0.70|0.60|0.79|-|0.67|0.74|
|_Kadua_<sup>_g,o_</sup>|0.60|0.43|0.52|0.51|0.40|0.55|0.70|0.57|0.66|
|Pakistan|0.79|0.62|0.69|0.68|0.66|0.84|0.60|0.66|0.73|
|Peru|0.92|0.74|0.89|0.87|0.86|0.94|0.77|0.91|0.92|
|Philippines|0.78|0.67|0.75|0.71|0.70|0.88|0.61|0.72|0.77|
|Romania|0.96|0.89|0.92|0.93|0.91|0.98|-|-|-|
|Russian Federation<sup>l,g</sup>|0.93|0.94|0.93|0.93|0.93|0.93|-|-|-|
|Rwanda|0.66|0.52|0.53|0.56|0.54|0.76|0.52|0.56|0.62|
|Senegal|0.77|0.51|0.73|0.61|0.63|0.81|-|-|-|
|Sierra Leone<sup>o</sup>|0.51|0.35|0.42|0.41|0.40|0.52|0.35|0.46|0.48|
|South Africa|0.88|0.79|0.84|0.86|0.83|0.92|-|-|-|
|Sri Lanka<sup>g</sup>|0.86|0.79|0.80|0.80|0.80|0.91|0.75|0.80|0.84|
|Sudan<sup>o</sup>|0.70|0.50|0.55|0.57|0.63|0.51|0.45|0.65|0.66|
|Tanzania<sup>g</sup>|0.71|0.40|0.52|0.50|0.49|0.81|0.41|0.61|0.70|
|Togo<sup>g</sup>|0.70|0.36|0.53|0.52|0.50|0.80|0.38|0.59|0.64|
|Tunisia|0.97|0.92|0.95|0.95|0.96|0.95|0.93|0.95|0.96|
|Turkey<sup>o</sup>|-|-|0.91|0.92|0.92|0.94|0.88|0.93|0.93|
|Uganda|0.67|0.44|0.52|0.50|0.48|0.71|0.41|0.55|0.63|
|Ukraine|0.95|0.82|-|-|-|-|-|-|-|
|Uzbekistan|0.97|0.93|0.95|0.94|0.93|0.96|-|-|-|
|Vietnam|0.94|0.85|0.89|0.87|0.88|0.86|0.81|0.91|0.93|
|Zambia|0.71|0.37|0.50|0.52|0.48|0.59|0.36|0.67|0.71|

1 All differences are significant (at least) at 95 percent level except for countries marked as follows: l Location dimension; e Education dimension, g Gender dimension; o Occupation dimension (industry / services)

**Note** : _Adequate Housing Index_ disaggregated along several dimensions (Source: authors’ calculation based on countries’ available household consumption and expenditure survey). *Low education refers to household heads with at most secondary education completed, while high education refers to household heads with more than secondary education. – refers to respective disaggregation unavailable in the countries’ latest HCES survey.
The lower bound of $1.25 is binding for the same 15 poorest countries used in setting our absolute line. The point at which the poverty line rises is at _C_ =$1.95 per day.

This schedule of relative poverty lines has a high correlation with the fitted values in Figure 2 (r=0.994) as well as with the data on national poverty lines (r=0.836). Indeed, the precision in predicting the national poverty lines is actually slightly greater using equation (5) than the nonparametric regression in Figure 2.<sup>29</sup> Nor are either the fitted values from the latter regression or a cubic polynomial in _C_ significant when added to a regression of _Z_ on _Z R_ .30

# **5. Comparisons with past poverty lines**

The method of measuring global poverty used by Chen and Ravallion (2001, 2004, 2007) does not assume comparability across ICP rounds. The salient features of their method are that the international poverty line is converted to local currency units in the ICP base year (using the same consumption PPP used for the national poverty lines) and is then converted to the prices prevailing in the relevant survey year using the best available CPI for that country. The PPP conversion is only done once, and all estimates are revised back in time. Thus, at no point will the new PPP’s from the 2005 ICP round be compared with those from previous ICP rounds. Nonetheless, it is of interest to compare our new poverty liens for 2005 with past lines, both absolute and relative.

**_Absolute poverty lines:_** Comparisons with past international lines are complicated by methodological changes in PPPs and changes in the data and methods used for setting an international line. However, the practice of looking for an average line amongst the poorest countries has been present throughout the various revisions. Based on the 1985 PPPs from Penn World Tables, RDV had used two poverty lines, one of which was the predicted poverty line in the poorest country which gave a line of $23 per month at 1985 PPP, while the higher line was $31 per month, which was the line found in six countries (Indonesia, Bangladesh, Nepal, Kenya, Tanzania and Morocco) and was considered more representative of the lines found in low income countries. Using the 1993 PPPs, Chen and Ravallion (2001) used instead the median line

> 29 The standard deviation of the error is $36.13 for the relative poverty lines versus $36.55 for the fitted values from Figure 1. Note that a (sufficiently) less smoothed nonparametric regression would do better than our piece-wise linear model.

> 30 The joint F test of the null that the three parameters in the cubic function of _C_ are all zero in the regression of _Z_ on _Z_<sup>_R_</sup> gave F(3,69)=0.14 (prob.=0.93) while the t-test on the coefficient on the fitted values when added to the same regression was t=0.44.

15
# **TABLE A23:  Political Risk Services (PRS)**

## **A23: Political Risk Services (PRS)**

http://www.prsgroup.com The PRS group is an affiliate of Investment Business with Knowledge (IBK), a United States-based corporation providing up-to-date country information for international business. PRS was founded in 1980 and is headquartered in Syracuse, New York. Since 1982, PRS has produced the International Country Risk Guide (ICRG) which provides assessments of a political, economic and financial risks in a large number of developed and developing countries. These assessments are based on the analysis of a worldwide network of experts, and is subject to a peer review process at subject and regional levels to ensure the coherence and comparability across countries. The ICRG assesses three major categories of risk: political (with 12 components), financial (5 components) and economic (6 components). We use components of the Political Risk Index, which report subjective assessments of the factors influencing the business environment in a particular country.

In the table below we list the variables included in each of the governance indicators. In this paper we use data from December 1996, 1998, 2000, and 2002-2005.

73