rafmacalaba/datause-encoder-data huggingface dataset:
> should be noted that our estimates of overall investment needs are probably lower bound estimates since they do not include rehabilitation needs. On the other hand, they may be appropriate estimates of what will actually be spent, except that the resources that in an ideal world would go to maintenance will more likely continue to be used for rehabilitation.

**Table 7. Expected annual investment needs 2005-2010**

||Ne|w|Mainte|nance|To|tal|
|---|---|---|---|---|---|---|
||US$Mn|%GDP|US$Mn|%GDP|US$Mn|%GDP|
|_Byincomegroup_|||||||
|LowIncome|49,988|3.18%|58,619|3.73%|108,607|6.92%|
|MiddleIncome|183,151|2.64%|173,035|2.50%|356,187|5.14%|
|Highincome|135,956|0.42%|247,970|0.76%|383,926|1.18%|
|_Developing countriesbyre_|_gion_||||||
|EastAsia&Pacific|99,906|3.67%|78,986|2.90%|178,892|6.57%|
|SouthAsia|28,069|3.06%|35,033|3.82%|63,101|6.87%|
|Europe&CentralAsia|39,069|2.76%|58,849|4.16%|97,918|6.92%|
|MiddleEast&N.Africa|14,884|2.37%|13,264|2.11%|28,148|4.48%|
|Sub-SaharanAfrica|13,268|2.84%|12,644|2.71%|25,912|5.55%|
|LatinAmerica&Caribb.|37,944|1.62%|32,878|1.40%|70,822|3.02%|
|_Alldevelopingcountries_|233,139|2.74%|231,654|2.73%|464,793|5.47%|
|_World_|369,095|0.90%|479,624|1.17%|848,719|2.07%|

GDP deflator used is an average of the 2005-10 projections.

New investment needs are estimated to be approximately US$370 Billion per annum for the period 2005-10, amounting to nearly 1% of worldwide GDP. Another $480 billion (1.2% of global GDP) are needed for maintenance. The total resources needed are therefore approximately 2.1% of GDP, excluding any expenditure on rehabilitation or upgrading. Results for each country are presented in Annex n.<sup>7</sup> The burden for developing countries is much heavier, however, both because of their greater need for new investments and because of their much smaller resource base. Estimated needed new investment decreases with income - from a high of 3.2% of GDP for low income countries to a low of 0.4% of GDP, with a middle point of 2.6% for middle income countries. Maintenance follows a similar pattern, so that total resources needed are 6.9% in low income countries and 5.1% in middle income countries, for a developing country average of 5.5% of GDP.

Our investment estimates are similar to the results obtained elsewhere. The 1994 World Development Report estimated that developing countries spent on average 4% of GDP on

> 7Note however that we are much more confident about regional or income group averages than we are about individual country results. This type of approach is indeed much better suited to producing aggregate results, which usually are fairly accurate, than it is to producing individual country predictions. Thus, whereas we are reasonably confident on the overall estimates, _we do not recommend relying on individual country level estimates except in a very indicative manner._
If a municipality’s poverty estimate was below the median, it is designated as poor; if the poverty estimate was above the median, it is designated as rich. The results in columns (2) and (3) of Table 1 suggest that incumbents were more likely to win close elections primarily in poor municipalities. Specifically, when the sample is restricted to the poorer half of municipalities, a constant of 0.027 is obtained and there is a discontinuous increase of 0.025 in the pdf at the zero threshold, indicating that incumbents were about twice as likely to win tightly contested elections than to lose them. In the richer half of municipalities, the discontinuous decrease of 0.0061 is small relative to the constant of 0.044 and is not statistically significant at conventional levels. Below, the paper shows that the pattern of violence observed after the election is also concentrated in poor municipalities.

# **3.2 Narrow Incumbent Victories and Civil**

The data on conflict violence come from incident reports collected by the Armed Forces of the Philippines (AFP); they include information on insurgents, government forces and civilian casualties. These incident-level data have been used to analyze the impact of aid and the impact of economic conditions on conflict intensity (Crost et al., 2014; Berman et al., 2011b) – and are similar to SIGACTS the U.S. incident-level military data, which have been used to study the insurgency activities in Afghanistan and Iraq (Berman et al., 2011a; Iyengar et al., 2011; Beath et al., 2011). The data also contain information on the date and the location of the incident and identify the insurgent group involved. During the period under study (November of 2006 through November 2007), 2,745 conflict incidents leading to 1,045 casualties were reported by units belonging to the Armed Forces of the Philippines deployed throughout the country. The focus of this paper is on the number of casualties and the number of violent incidents that occurred in the 153 municipalities in which the incumbent’s `philippines-small-area-poverty-estimates-2012-2009-2006` 15
Country-level controls include the year the survey was conducted ( _Year_ ); GDP per capita (log values), PPP adjusted and at constant 2005 international dollars ( _Income_ ) taken from World Development Indicators (WDI), World Bank; weighted average of tariff rates (percent) across all products ( _Tariff rate_ ) taken from WDI; a measure of trade openness, the Heritage Foundation’s _Trade Freedom index_ ; and a dummy variable indicating if the country is landlocked or not (compiled from various sources). For tariff rates and the trade freedom index, average values 2-3 years prior to the survey are taken to expand coverage; for income, values are for the year prior to the survey.

# **3. Estimation**

Regression results are provided in Table 1 (for _Import dummy_ ) and Table 2 (for _Imports_ ). Table 1 shows a strong negative association between _Import dummy_ and _Population_ . Adding various controls to the specification only serves to strengthen this negative association. The estimated marginal effect of a unit increase in _Population_ on the likelihood of a firm using imported inputs ranges between a decrease of 5.8 (column 3) to 7.7 (column 7) percentage points, significant at less than the 1 percent level irrespective of the set of controls used. Using the most conservative estimate (column 3), a one standard deviation increase in _Population_ is associated with an 8.1 percentage points decrease, and a move from the smallest to the largest country in our sample is associated with a 36 percentage points decrease in the probability of a firm using imported inputs. These are economically large changes given that on average 60 percent of firms in our sample use imported inputs. Larger firms show a higher probability of using imported inputs. However, higher severity of the obstacles ( _Obstacles_ ) is also associated with a higher likelihood 5
Table 1: Regional Distribution of Employment by Sector (ENVISAGE simulations)

||Larg|e Emitters||Swi|ng Emitters||Carbon De|pendent Coun|tries|
|---|---|---|---|---|---|---|---|---|---|
|Sector|Unskilled<br>Labor<br>Ski|lled Labor<br>Rati<br>Uns<br>Skill|o of<br>killed to<br>ed Labor<br>Un<br>La|skilled<br>bor<br>Ski|lled Labor<br>Rat<br>Uns<br>Skil|io of<br>killed to<br>led Labor<br>Un<br>La|skilled<br>bor<br>Ski|lled Labor<br>Rat<br>Un<br>Ski|io of<br>skilled to<br>lled Labor|
|Agriculture|4.1%|0.7%|5.02|12.1%|0.8%|14.99|15.3%|1.1%|18.72|
|Coal|0.2%|0.1%|3.36|0.2%|0.1%|1.48|0.3%|0.2%|2.11|
|Oil|0.2%|0.1%|1.31|0.4%|0.4%|1.13|2.2%|3.2%|0.92|
|Natural gas|0.1%|0.1%|1.04|0.2%|0.1%|1.16|0.9%|1.1%|1.13|
|Other mining|0.5%|0.2%|2.98|1.1%|0.7%|1.38|1.3%|0.8%|2.09|
|Nondurable manufacturing|10.6%|5.4%|1.72|9.1%|6.9%|1.25|11.1%|6.4%|2.27|
|Refined oil|0.1%|0.1%|1.77|0.1%|0.1%|1.11|0.5%|0.2%|3.03|
|Ethanol|0.0%|0.0%|2.69|0.1%|0.0%|4.02|0.0%|0.0%|4.27|
|Bio‐diesel|0.0%|0.0%|1.02|0.0%|0.0%|1.02|0.0%|0.0%|1.06|
|Durable manufacturing|16.1%|8.1%|1.73|7.1%|5.8%|1.16|9.9%|5.0%|2.57|
|Nuclear power|0.1%|0.1%|0.62|0.1%|0.2%|0.88|0.0%|0.0%|1.09|
|Coal‐base power|0.1%|0.0%|1.76|0.1%|0.2%|0.60|0.1%|0.1%|1.56|
|Gas‐base power|0.0%|0.0%|1.13|0.0%|0.0%|0.65|0.0%|0.1%|0.93|
|Wind power|0.0%|0.0%|0.95|0.0%|0.0%|0.35|0.0%|0.0%|0.76|
|Hydro power|0.1%|0.0%|1.85|0.1%|0.1%|0.91|0.1%|0.1%|0.99|
|Oil‐base power|0.0%|0.0%|0.63|0.0%|0.0%|0.73|0.0%|0.0%|0.75|
|Other power|0.0%|0.0%|1.29|0.0%|0.0%|1.04|0.0%|0.0%|1.57|
|Solar power|0.0%|0.0%|0.68|0.0%|0.0%|0.51|0.0%|0.0%|0.93|
|Coal based CCS|0.0%|0.0%|1.65|0.0%|0.0%|0.62|0.0%|0.0%|1.57|
|Gas based CCS|0.0%|0.0%|1.03|0.0%|0.0%|0.68|0.0%|0.0%|0.92|
|Advanced nuclear|0.0%|0.0%|0.58|0.0%|0.0%|0.89|0.0%|0.0%|1.14|
|Electricity distribution and transmission|0.2%|0.4%|0.52|0.3%|0.8%|0.32|0.2%|0.5%|0.56|
|Construction|13.6%|3.2%|3.76|14.5%|4.7%|2.90|12.7%|4.5%|3.67|
|Trade|15.1%|9.2%|1.43|17.2%|11.1%|1.46|16.4%|6.7%|3.18|
|Transportation|4.2%|2.8%|1.28|6.4%|3.4%|1.78|5.2%|4.3%|1.57|
|Other services|34.7%|69.3%|0.44|30.8%|64.5%|0.45|23.7%|65.8%|0.47|

26
- 2.43 -

# **XI.** STANDARD **ARGUMENTS** FOR<sup>**OUTPUT TAXES**</sup>

The remaining arguments for cocoa taxation Involve balancing the need for Government revenue against<sup>the cost of raising that revenue In terms of deadweight</sup> losses and adverse impacts on the distributlon<sup>of Income. The main arguments for taxing</sup> cocoa production are elther<sup>that cocoa farmers are richer than non-cocoa farmers, and</sup> should therefore be subjected to a higher rate<sup>of tax than can be achieved by the</sup> system of Indirect taxes, or<sup>that the elasticity of cocoa supply Is sufficiently</sup> low that the inefficlency losses make<sup>It an attractive</sup> source ot revenue.

Appendix 2, entitled 'OpthTal trade<sup>taxes on agriculture In developing countries',</sup> gives a skiplified model of the<sup>economy (even If the mathematics appears somewhat</sup> complex!) which aliows one to<sup>characterize the optinal rate of taxation of cocoa, on the</sup> assumption that other agricultural<sup>products are not taxed, (and In particular that cereals</sup> are kmported duty free) and<sup>that non-food consumption goods are subject to Indirect</sup> tax. The technical analysis sets out the<sup>mathematical formulas and In the final section</sup> quantifies them for Ghana<sup>using the results of the Ghana Living Standards Survey and the</sup> Agricultural Economic Survey (AES). The<sup>discussion here will be confined to a non-</sup> technical summary of the main<sup>findings of that Appendix paper.</sup> The essence of the argument<sup>Is that cocoa taxes have to be compared with</sup> alternative methods of raising<sup>government revenue, and the assumption Is that these will</sup> have to be Indirect taxes<sup>on non-food manufactured (orknported)goods.</sup> The argument here Is simple -- Income<sup>taxes have a relatively lknited coverage and may already be set</sup> at unsatisfactory levels, while Indirect taxes<sup>fall on all consumers and thus have a</sup> reasonably broad base. It Is hard to tax domestically<sup>produced food</sup> and nonmanufactured goods, which<sup>leaves the main cholce as specified.</sup> It Is possible to knposo tarlffs on cereal Imports and that<sup>Issue Is addressed beiow.</sup> The exact levels at which<sup>various tax rate will need to be set will depend on the</sup> amount of revenue required<sup>by the Government, and this Is not yet known. I therefore</sup>
Ghate, C., R. Pandey, and I. Patnaik. 2013. “Has India Emerged? Business Cycle Facts from a Transitioning Economy.” _Structural Change and Economic Dynamics_ 24: 157–172.

Greenwald, B. C., and J. E. Stiglitz. 1993. “Financial Market Imperfections and Business Cycles.” _The Quarterly Journal of Economics_ 108 (1): 77–114.

Hayashi, F. 1982. “The Permanent Income Hypothesis: Estimation and Testing by Instrumental Variables.” _The Journal of Political Economy_ 90 (5): 895–916.

Honohan, P. 2006. “Household Financial Assets in the Process of Development.” Policy Research Working Paper 3965, World Bank, Policy Research Department, Washington, DC.

International Labour Organization, June 2012. Statistical Update on Employment in the Informal Economy.

URL <u>http://laborsta.ilo.org/informal_economy_E.html</u> (Last accessed May 15, 2015) Iyigun, M. F., and A. L. Owen. 2004. “Income Inequality, Financial Development, and Macroeconomic Fluctuations.” _The Economic Journal_ 114 (495): 352–376. Kim, S. H., M. A. Kose, and M. G. Plummer. 2003. “Dynamics of Business Cycles in Asia: Differences and Similarities.” _Review of Development Economics_ 7 (3): 462–77.

King, R. G., and S. T. Rebelo. 1999. “Resuscitating Real Business Cycles.” In J. B. Taylor and M. Woodford, eds., _Handbook of Macroeconomics_ . Vol. 1B. Amsterdam: Elsevier.

Kose, M. A., and K.-M. Yi 2006. “Can the Standard International Business Cycle Model Explain the Relation Between Trade and Comovement?” _Journal of International Economics_ 68 (2): 267–95.

Kydland, F. E., and E. C. Prescott. 1990. “Business Cycles: Real Facts and a Monetary Myth.” In K. D. Hoover, ed., _Real Business Cycles: A Reader_ . London: 32
Table 4. Militarization and Montalvo and Reynal-Queirol’s measures of fractionalization, 1988– 2002

||(1)|(2)|(3)|(4)|(5)|(6)|
|---|---|---|---|---|---|---|
||Military<br>Expend.|Military<br>Expend.|Military<br>Pers.|Military<br>Pers.|Arms Imports|Arms Imports|
|Ethfrac (Montalvo|-1.580**|-1.688**|-0.771|-0.777|-2.985*|-2.979*|
|& R-Q.)|||||||
||(2.02)|(2.03)|(1.22)|(1.21)|(1.68)|(1.68)|
|Relfrac (Montalvo|1.173|1.002|0.786|0.769|0.670|0.684|
|& R-Q.)|||||||
||(1.40)|(1.11)|(1.37)|(1.35)|(0.37)|(0.38)|
|GNI p.c. (ln)|0.062|-0.026|0.218*|0.213*|-0.406|-0.407|
||(0.25)|(0.08)|(1.76)|(1.69)|(1.09)|(1.10)|
|Economic growth|0.848|0.855|-0.967|-0.968|-4.458|-4.354|
||(1.21)|(1.22)|(0.86)|(0.87)|(1.14)|(1.12)|
|Polity|-0.036**|-0.032*|-0.005|-0.005|-0.160**|-0.160**|
||(1.98)|(1.80)|(0.76)|(0.74)|(2.09)|(2.12)|
|Government<br>|0.135***|0.128***|0.016**|0.016**|0.208***|0.205***|
|expenditures|||||||
||(5.01)|(4.84)|(2.01)|(2.00)|(2.60)|(2.58)|
|Population (ln)|-0.121|-0.204|-0.223**|-0.231**|0.370|0.361|
||(0.84)|(1.21)|(2.49)|(2.49)|(1.31)|(1.28)|
|Contiguous|0.104**|0.090*|0.579**|0.574**|0.115|0.113|
|militarization|||||||
||(1.99)|(1.80)|(2.13)|(2.12)|(1.37)|(1.35)|
|Peace years (civil|-0.023***|-0.023***|-0.006***|-0.006***|-0.039**|-0.039***|
|war)|||||||
||(4.07)|(3.95)|(2.94)|(2.97)|(2.56)|(2.60)|
|Peace years (intern.<br>conflict)|0.010|0.011|-0.007|-0.007|-0.036**|-0.036**|
||(1.19)|(1.36)|(1.23)|(1.21)|(2.33)|(2.36)|
|Civil war|0.598***|0.603***|0.068|0.068|2.185**|2.191**|
||(2.92)|(2.96)|(0.93)|(0.94)|(2.16)|(2.18)|
|International|<br>0.254|<br>0.233|<br>0.300*|<br>0.298*|<br>1.886**|<br>1.903**|
|conflict|||||||
||(0.87)|(0.81)|(1.76)|(1.76)|(2.16)|(2.21)|
|Observations|1307|1307|1161|1161|1164|1164|
|Countries|102|102|109|109|109|109|

Notes: Absolute t- and z-statistics in brackets. Constant and year-specific time-dummies included, but coefficients not reported. *, **, *** significant at .1, .05 and .01 level, respectively.

24
Table 4. Population shares and location effects by rural and urban and by four tiers of locations

|**Population by tier (perce**|**nt)**<br>|||||||
|---|---|---|---|---|---|---|---|
||Small<br>rural|Large<br>rural|Small<br>urban|Large<br>urban|Rural|Urban|Total|
|Clusters|6.9|21.8|24.4|65.1|10.1|36.7|17.7|
|Top locations|2.6|10.4|12.4|47.3|4.3|23.0|9.6|
|Catchment areas|4.3|11.4|12.0|17.8|5.8|13.7|8.1|
|Average locations|72.0|72.0|68.8|34.9|72.0|58.5|68.1|
|Bottom locations|21.2|6.2|6.9|0.0|17.9|4.8|14.2|
|Total|100.0|100.0|100.0|100.0|100.0|100.0|100.0|
|**Population by type ofpla**|**ce (percent)**<br>|||||||
||Small|Large|Small|Large||||
||rural|rural|urban|urban|Rural|Urban|Total|
|Clusters|21.6|19.1|27.4|31.9|40.7|59.3|100.0|
|Top locations|15.0|16.8|25.6|42.6|31.8|68.2|100.0|
|Catchment areas|29.5|21.9|29.5|19.1|51.4|48.6|100.0|
|Average locations|59.0|16.4|20.1|4.4|75.5|24.5|100.0|
|Bottom locations|83.5|6.8|9.7|0.0|90.4|9.7|100.0|
|Total|55.9|15.6|19.9|8.7|71.4|28.6|100.0|
|**Average location effect (**|**weighted by po**<br>|**pulation)**<br>||||||
||Small|Large|Small|Large||||
||rural|rural|urban|urban|Rural|Urban|Total|
|Clusters|0.31|0.35|0.35|0.42|0.33|0.39|0.37|
|Top locations|0.39|0.42|0.44|0.47|0.40|0.46|0.44|
|Catchment areas|0.26|0.28|0.27|0.29|0.27|0.28|0.27|
|Average locations|-0.03|0.01|0.04|0.13|-0.02|0.06|0.00|
|Bottom locations|-0.30|-0.27|-0.27||-0.30|-0.27|-0.30|

_Note:_ Location effects are measured relative to the median place in India, and expressed in log.

The relationship between MLD and location effects is convex (Figure 8). At the low end, in places where labor productivity is low, the relationship is actually flat: increases in the location effect are not associated with increases with the MLD. As expected, a positive relationship emerges when moving to locations with higher labor productivity. But there are also important nuances at the top end, as shown by the straight line in the upper right side of the figure. This line represents the estimated relationship between location effects and MLD for all large cities in India (both clusters encompassing large urban areas and other large urban areas). The line is slightly downward-sloping, but the slope is not statistically significant. This means that the most productive urban centers in India are not more unequal than other major cities. This result is partly driven by the relatively poor performance of large urban areas in the Ganga basin. Thus, Agra registers much lower productivity and much higher inequality than the cluster of Delhi, Faridabad and Gurgaon. And Kanpur and Varanasi show much lower productivity than the cluster of Mumbai, Surat and Thane for a similar level of inequality.

27
- First, investor psychology is strongly influenced by the scale of abnormal _volatility_ in the marketplace, particularly when that volatility spans several asset classes, signaling an overall climate of uncertainty and risk aversion. In recent months, global equity markets have shown record volatility.

- Second, investor confidence is related to the _performance_ of their investments, as measured by wealth creation or destruction. The scale of the contraction in financial wealth that has occurred during the current crisis has been unprecedented since the Great Depression.

- Third, investors and traders typically look at a broad array of _macroeconomic data_ releases that provide insights into economic fundamentals and shape perceptions of the future state of the economy. Relevant series include monthly payrolls, industrial production, sales and trade data, personal income, and housing starts. These data typically lag behind the financial data, but during the course of 2008 and into 2009 the one-sided stream of negative economic news has had a dramatic impact on confidence.

- Fourth, market participants and traders pay close attention to the stance of _government policy makers_ and continually assess the credibility of their responses. Governments can influence investors’ confidence in many ways: through macroeconomic policy (for example, by easing monetary policy or providing fiscal stimulus), through regulatory policy, and through other legislative actions that can strengthen transparency and enhance corporate financial disclosure and integrity (for example, actions taken by the U.S. government in the aftermath of the Enron scandal). While the measures announced or taken to date have no doubt helped somewhat to stem the deterioration in confidence, they have not been able to offset the negative effects of grim financial and macroeconomic developments.

- We use publicly available data to proxy the first three dimensions of investor confidence, while

- providing a more selective discussion of the various policy responses, in particular the expansion of central bank liquidity, before turning to what remains to be done. The above framework for measuring investor confidence enables us to gauge the extent to which confidence has deteriorated and to link changes in confidence to various economic events. What is striking is the extent to which the various indicators move together, though each has a somewhat different story to tell and provides different insights. We report on individual indicators as well as calculating common factors that express the extent of co-movement of different indicators.

6
Table 3: Honduran Import Tariffs and Import Values, 2004 <u>(Percent</u> and Millions of Lempiras)

||CACM|and DR||EU||US||RoW|
|---|---|---|---|---|---|---|---|---|
|Sector|_τ_|_M_|_τ_|_M_|_τ_|_M_|_τ_|_M_|
|Bananas|15.0|3.6|0.0|0.0|15.0|0.7|15.0|0.0|
|Cofee|15.0|1.1|14.3|0.0|15.0|4.0|15.0|9.6|
|Sugar|14.8|5.5|4.2|4.9|5.5|50.2|4.4|5.9|
|Mining|0.0|0.4|5|0.1|2.2|2.2|2.0|0.3|
|Livestock|8.4|240.1|5.0|0.0|0.2|35.0|1.1|22.6|
|Wood|6.8|91.1|9.9|313.9|9.6|48.1|10.0|52.1|
|Non-traditional crops|10.1|1673.3|8.2|113.0|10.5|950.6|11.2|741.1|
|Domestic crops|14.3|1226.2|4.6|167.4|6.2|2588.5|7.3|428.9|
|Oil|2.3|57.6|4.2|196.3|4.7|5691.9|4.8|4673.8|
|Food, beverages, tobacco|13.8|818.3|11.1|147.7|11.7|1073.2|11.2|668.9|
|Textiles|11.8|400.3|13.1|93.0|10.8|886.6|12.4|1156.1|
|Paper|10.7|1011.5|1.8|129.8|3.5|1507.6|5.9|463.5|
|Chemicals|4.7|1308.3|2.8|506.9|2.3|2316.4|3.7|1418.2|
|Metals, minerals, mach.|8.8|2510.3|1.8|2527.8|2.2|7626.7|3.7|6395.9|
|Other manufacturing|8.6|2667.4|4.9|1262.7|7.4|4307.1|7.7|7108.2|
|Merchandise trade|9.6|12014.8|3.8|5463.6|5.0|27088.6|6.1|23145.0|
|Agriculture|11.5|3240.8|8.1|599.2|7.3|3677.0|9.6|1260.2|
|Manufacturing|8.9|8774.0|3.3|4864.3|4.6|23411.6|5.9|21884.8|

_Note:_ For each trading partner, the first column ( _τ_ ) contains the ad-valorem tariff rates collected by the Honduran authorities and the second column ( _M_ ) contains total imports for each commodity. All tariffs are import-weighted. “RoW” stands for rest of the world,i.e., all trading partners other than CACM countries and the Dominican Republic, the EU, and the US. The manufacturing aggregate includes oil and mining.

_Source:_ Author’s calculations using data from UN COMTRADE and UN TRAINS.

the structure of this foreign protection is radically different across the country’s export markets. Table 4 shows the distribution of tariffs and export values by commodity and destination. These data illustrate three main points. First, Honduras has virtually tariff-free access to the EU and the US, which account for 58 percent of its total merchandise exports.<sup>13</sup> Second, Honduran exports are heavily biased toward agricultural products, which comprise 59 percent of all goods exports. Third, its trade with other DR-CAFTA members is intensive in manufactured goods, but is also subject to relatively high protection.

Together, Table 3 and Table 4 show that the direct benefits of liberalizing trade with the United States as part of the DR-CAFTA accession are likely to be fairly limited. Pursuing additional liberalization with other DR-CAFTA partners is likely to have larger effect on welfare in Honduras, but only if the country is able to successfully negotiate improved access for its manufactured exports. At the same time, further integration with the rest of DR-CAFTA countries will likely involve lowering Honduras’ own sizable tariff barriers against these partners, and making up for lost tariff revenue could put additional strain on the public budget.

> 13The only exceptions to this are the EU banana tariff and the US textile tariff, but exports of these goods are only 7.6 and 6.3 percent of total exports to each destination.

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