rafmacalaba/datause-encoder-data huggingface dataset:
_Rural-urban gradient in Latin America and the Caribbean_ _page_ 7 people are more than an hour distant from cities. These estimates contrast sharply with the official estimate of an urban proportion of 75.8% for Latin America and the Caribbean<sup>6</sup> .

It is interesting to compare these results with early tabulations of GRUMP v1 (Balk, pers. comm.; see also Balk et al 2004). Those tabulations find about 36% of LAC population living in settlements of less than 5000 people (including rural areas). Another 6% live in settlements of 5000-20000 population (with a mean population density of 332 – below some thresholds for urbanness). About 48% live in settlements of greater than 100,000 population.

Figure 5 graphs the ‘remote’ (>1 hour) columns of table 2, with countries arranged in ascending order according to the density-150 threshold. It shows that density-100, density150, and density-200 thresholds yield roughly the same ranking of countries, and roughly similar estimates of rurality. However, the density-20 threshold is very poorly correlated with the others. For instance, Costa Rica, Cuba, and Peru have roughly similar rural proportions according to the density-150 threshold but Peru has proportionately far more people living at densities below 20.

Figure 6 compares the density-150/>1 hour rurality criterion to census measures of rurality. There are some striking disparities. Census measures ascribe much lower rural populations to Argentina, Brazil, and Uruguay, and much lower populations to El Salvador and Guatemala, compared to the density/distance based measure.

Finally, tables 7 and 8 cross tabulate population and land area by population density, suitability for rainfed crops, and forest cover. About one fifth of LAC population lives at densities below 150 on non-forested areas, suitable for rainfed crops. They occupy 29% of LAC’s land area. Almost the same number of people live at densities under 150 and occupy land that is rated marginal or unsuitable for rainfed crops; these lands constitute 45% of LAC, including 16% that is both poor for crops and under forest cover. And about 6% of the total population lives at densities <150, occupying land that is suitable for crops but currently largely forested. These lands, constituting about 25% of LAC, may present development-environment trade-offs.

# **Conclusions**

The quantitative results presented here must be taken cautiously. They are very much affected by the size and heterogeneity of the reporting unit. They are not necessarily comparable between countries (though they may be more consistent than existing estimates). Because these population densities are computed, for the most part, over municipios, lowdensity areas are likely to contain market towns or even small cities. Thus the mean population densities tallied here do not closely correspond to the ‘small-radius’ ideal discussed above. The GRUMP v 1, when available, should provide an improved estimate of density distributions.

Our intention was to illustrate some general propositions:

> 6 World Bank, _The Little Green Data Book 2003_ .
Table 5: Present-Day Population Controls

||World B<br>All|ank aid 1<br>All|995–2014<br>PrC|
|---|---|---|---|
|Mission|0.10<sup>_∗∗∗_</sup>|0.07<sup>_∗∗∗_</sup>|0.18<sup>_∗∗∗_</sup>|
||(0.03)|(0.03)|(0.05)|
|Population in 1995 (4th-order polynomial)|Yes|Yes|Yes|
|Populated place dummy|No|Yes|No|
|Ethnic dummies|Yes|Yes|Yes|
|Mean dependent variable|0.25|0.25|0.62|
|_R_-squared|0.40|0.43|0.64|
|Observations|6,876|6,876|698|

_Source_ : Authors’ analysis based on mission locations from Beach (1903); aid projects from AidData, World Bank geocoded research release, version 1.3; and control variables from multiple sources, detailed in appendix A.

_Note_ : “PrC” denotes a sample of cells that contain the capital of a province. Estimation is by ordinary least squares. Conley (1999) standard errors are given in parentheses, with the cutoff at 2 degrees (220 km). The dependent variable is a dummy for at least one project commitment in the sample period. Control variables include: log distances to coast, explorer route, colonial railway, and Arab trade route; a third-order polynomial in the 18th-century population; presence of a city as of 1800; country dummies; precolonial ethnic dummies; average altitude; Terrain Ruggedness Index; percentage area within 10 km from water source; Caloric Suitability Index; Malaria Ecology Index; and a tropics dummy (which is also interacted with mean altitude). Cells that are more than 90 percent covered by barren land or more than 90 percent covered by forest are excluded from the sample. _∗ p < ._ 1 _∗∗ p < ._ 05 _∗∗∗ p < ._ 01 21
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30
# **Externalities from Investment in Trade Facilitation**

Trade facilitation improvements benefit not only the countries that implement them, but also their trading partners. The more intense are trade relations between countries, the greater the benefit partner countries will enjoy should one (or more) of the countries improve its trade facilitation regime, all others being equal. Given the importance of intra-regional trade between the EU15 and the new and candidate EU members, the expected gains to the former resulting from the trade facilitation improvements in the latter will likely be significant.

Table 3 shows that if all four dimensions of trade facilitation for each of the new member (EU8) and candidate member countries (Bulgaria, Romania, and Turkey) improve up to half-way the EU15 level, almost $10 billion trade gains will be expected for the EU15, the EU8, and the three candidate members as a whole in 2001.<sup>11</sup> Among the total gains for trading partner countries, $2.7 billion (or 28%) comes from their export gains, and $6.9 billion (or 72%) come from their import gains (Table 3, Table 4). In other words, if the trade facilitation capacity of new and candidate EU members improve measurably, their trade volumes with other European countries will also increase; in particular, partner countries will increase their volumes of imports from the improving countries by a larger margin than partner exports increase to those countries.<sup>12</sup> These findings echo our argument that trade facilitation improvements in new and candidate EU member countries will strongly increase their intra-regional trade with the EU15. The trade gains for the EU15 represent 74% of the total trade gains for all European

> 11 Here, we use the data of bilateral import volume of Bulgaria from EU15, other EU8 countries, Romania and Turkey in 2000 since the data in 2001 is not available. As argued above, the IT infrastructure development level of Estonia is better than the EU15 average, we neglect the IT infrastructure improvement in Estonia. That is to say, we suppose that no improvements of IT infrastructure occur in Estonia. When we simulate the trade gain of a EU8 or a candidate member country resulting from the EU8 or candidate member countries, we neglect the trade gain of the country in question. In other words, we simulate the trade gain on the other EU8 countries (or other candidate member countries) resulting from the improvement of trade facilitation in the new or candidate member country in question.

> 12 The reason for the higher import gains for partner countries stems from a higher elasticity of export to trade facilitation improvement than the corresponding elasticity for imports, as found by WMO (2004).  This implies that if a country improves its trade facilitation, its export volume increase will be higher than its import volume increase.

16
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26
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24
Table 9: Judgement text regressions, ethnicity, defendant win

||(1)<br>Num. citations|(2)<br>Times cited|(3)<br>Num laws cited|(4)<br>Words in judg.|
|---|---|---|---|---|
|Judge-defendant same ethnicity=1|-0.147|-0.137<sup>_⇤⇤_</sup>|-0.229|-97.65<sup>_⇤_</sup>|
||(0.137)|(0.0583)|(0.177)|(57.53)|
|DV mean|2.054|0.220|2.253|1505.6|
|Court-year FE|Yes|Yes|Yes|Yes|
|Observations|9433|9433|9433|9433|

The regressions test whether in-group bias is associated with significantly different aspects of judges’ written judgements. If in-group bias is associated with different characteristics for judgement texts, then we should see significant coefficients for the defendant-win sample but not the defendant-lose sample, the coefficients in the defendant-win sample should be larger than in the full sample. This table presents the defendant-win sample results. Standard errors, in parentheses, are clustered at the judge level. All columns are based on a linear regression model. For specification details, see equation 6. Num. citations refers to the number of citations in the judgement. Times cited refers to the number of times the case has been cited. Num. laws cited refers to the number of laws and and acts cited in the judgement. Words in judg. refers to the number of words in the written judgement.

Table 10: Judgement text regressions, ethnicity, defendant lose

||(1)<br>Num. citations|(2)<br>Times cited|(3)<br>Num laws cited|(4)<br>Words in judg.|
|---|---|---|---|---|
|Judge-defendant same ethnicity=1|-0.0923|-0.0321|-0.130|-46.70|
||(0.147)|(0.0361)|(0.181)|(61.11)|
|DV mean|1.979|0.211|2.157|1458.6|
|Court-year FE|Yes|Yes|Yes|Yes|
|Observations|11395|11395|11395|11395|

The regressions test whether in-group bias is associated with significantly different aspects of judges’ written judgements. If in-group bias is associated with different characteristics for judgement texts, then we should see significant coefficients for the defendant-win sample but not the defendant-lose sample, the coefficients in the defendant-win sample should be larger than in the full sample. This table presents the defendant-lose sample results. Standard errors, in parentheses, are clustered at the judge level. All columns are based on a linear regression model. For specification details, see equation 6. Num. citations refers to the number of citations in the judgement. Times cited refers to the number of times the case has been cited. Num. laws cited refers to the number of laws and and acts cited in the judgement. Words in judg. refers to the number of words in the written judgement.

23
are small and declining over time. The table shows the statistics for the panel households as well as for the full sample, indicating that the panel households are reasonably representative of the full sample.<sup>13</sup> The midpoint farm size, that is the farm size at which half of the total cultivated land is operated by farms larger than the midpoint (and the remaining half by farms below the midpoint), also shows a declining trend, but it is significantly higher than the median, confirming the skewed distribution of farm sizes. The average farm size above the midpoint is over four times the average farm size below the midpoint for each year.

_Table 2: Farm Size Indicators in Bangladesh_

|**Farm Size Indicators(Estimation Sample)**|**2000**|**2004**|**2008**|
|---|---|---|---|
|Mean Farm Size(Ha)|0.77|0.71|0.66|
|Median Farm Size(Ha)|0.52|0.50|0.43|
|Midpoint Farm Size(Ha)*|1.20|1.05|0.95|
|Average farm size below/above the midpoint size|0.47/2.07|0.45/1.74|0.41/1.67|
|**Farm Size Indicators(All Farm Sample)**||||
|Mean Farm Size(Ha)|0.69|0.61|0.56|
|Median Farm Size(Ha)|0.44|0.41|0.37|
|Midpoint Farm Size(Ha)*|1.14|0.94|0.85|
|Average farm size below/above the midpoint size|0.42/2.00|0.37/1.64|0.34/1.50|

_Source_ : Authors’ calculation using the 62-village panel survey.

_Note:_ *: Midpoint farm size is the farm size at which half of all land is operated by larger farms and the other half is operated by smaller farms.

Before discussing the findings, one remaining methodological concern is the potential endogeneity of inputs in production. To address this issue, it is important to recognize that agriculture production is best characterized as a sequential decision-making process (agricultural production decisions are inherently sequenced with time playing an important role on when and how decisions are made) (Antle, 1983). The first decision a household makes, at the start of a production season, is how much land to cultivate and its allocation across crops. Once land is allocated, decisions on other (variable) inputs in the production process are subsequently made. As such, given that the interest of this paper is on characterizing the physical production process, and not the behavioral aspects or the dynamics of farm portfolio allocation decisions, the production function is interpreted as a conditional production function – conditional on the acquisition and allocation of

> 13 The panel includes non-split households while ‘full sample’ includes split, non-split, and newly added households.

15
-51-

# B. PUBLIC INVESTMENT

Government reacted to the coffee and cocoa boom of the mid-seventies by increasing its capital expenditures from 8% of GDP in 1975 to 22% in 1977. Private investment remained relatively sLable during that period at around 1315% of GDP. The increase of public investment was correlated with ail apparent decline in the return on capital; comparing ICORs between 1970-75 and 1976-81, a very rough measure of the productivity of investment, indicates a 60% decline in investment efficiency. The agriculture sector received the highest share of public investment, 31% in 1981. A large part of this investment tooK place in extension services which includes the recurrent cost of extension agencies (mainly wages). The rate of return on those investments are difficult to measure: the data does not indicate an increase in agricultural output and value added as a result of these expenditures. On the other hand, one can remark that the financing of extension services for coffee and cocoa (SATMACI) and cotton (CIDT), did not encourage diversification out of traditional agricultural exports, as recommended by numerous Bank missions.

The second most important sector of public investment was that of transport and roads, (23% of total public investment in 1981). As a result of the public investment boom, Cote d'Ivoire today has the most developed highway and road system in Africa. However, their rate of return turn out to be of dubious value. In 1981, 16% of the investment budget was allocated co education. This included the construction of the higher institutes ("grandes ecoles") of Yamoussoukro where the cost per student is higher than in Ivy League colleges in the United States. When the public sector entered into
Armenia, Czech Republic, Estonia, Georgia, Hungary, Lithuania, Slovak Republic, and Ukraine have social protection systems that, in general, appear to be more robust and able to respond to shocks. These countries are characterized by relatively higher spending on social protection and higher quality (targeting, coverage and generosity) social programs. For the low readiness countries, the situation is reversed. Social protection in these countries appears particularly weak in terms of spending and the quality of last resort social assistance and child benefit programs. It is important to note that there is significantly less variation across countries in terms of unemployment benefit duration and amount.

# **5. IMPROVING ADAPTABILITY: POLICY IMPLICATIONS**

Putting together results from the previous section, a typology emerges of four types of countries depending on whether they have high/low vulnerability to an energy price shock and high/low capacity to adapt to such a change. After looking at the vulnerability and adaptability components of our analysis separately, we now combine them – using PCA analysis- to see the relative position of countries in terms of how vulnerable and adaptable they are to an energy price change.

Some countries may be highly vulnerable but have a high capacity to adapt to the increase in the price of carbon, for example; others may be in the opposite situation, with very little capacity to adapt. There is significant variation within ECA. As an illustration, we use three levels of vulnerability and three levels of adaptability according to the PCA grouping as shown in Table 6. Figure 9 provides more detail indicating each country’s actual PCA score in a continuum instead of broad categories. The higher the PCA score, the higher the vulnerability and the adaptability.

**Table 6 - Principal Component Analysis Results**

||**High Adaptability**|**Medium**<br>**Adaptability**|**Low Adaptability**|
|---|---|---|---|
|**High**<br>**Vulnerability**|Azerbaijan, Georgia,<br>Kazakhstan, Ukraine||FYR Macedonia,<br>Russian Federation|
|**Medium**<br>**Vulnerability**|Armenia|Estonia, Moldova|Romania, Serbia,<br>Turkey|
|**Low**<br>**Vulnerability**|Slovak Republic|Bulgaria, Hungary,<br>Latvia, Lithuania|Croatia, Poland|

Source: Authors’ calculation

**Figure 9 – Policy Action is Most Urgent in High Vulnerability/Low Adaptability Countries**

Principal component analysis – relative position of ECA countries in terms of vulnerability and adaptability 17