rafmacalaba/datause-encoder-data huggingface dataset:
goals. Governance in this context and as we use it here includes the effects of the goals of different ownership types, as well as the ability of the owners to minimize agency costs with management.

There is a wealth of research on bank governance and performance. Many studies assess the static effects of different types of bank ownership – the long-run performance effects associated with constant domestic, foreign, or state ownership – but typically do not consider more than one static difference at a time. Others address the dynamic effects of changes in bank ownership – the performance effects associated with domestic M&As, foreign acquisitions, or privatization – but typically do not consider more than one of these types of dynamic changes together. In some cases, these studies also examine the selection effects of which banks undergo these dynamic governance changes, but typically do not examine more than one of these types of selection effects together. The static, selection, and dynamic effects of any of the types of governance are typically not all examined in the same model.

We argue that it is important to account for the static, selection, and dynamic effects of all the major different types of governance that are important for a nation in the same model of bank performance. To the extent that any of the relevant effects are excluded, the model may be misspecified, and might give biased and misleading results. Consider the example of a nation with significant numbers of all three types of dynamic events – domestic M&As, foreign acquisitions, and privatization – in which all three types of events significantly improve bank performance. If only privatization is included in the model, the performance effect of privatization might mistakenly be measured as unfavorable simply because it raises performance less than the other two types of dynamic events. That is, the privatized banks might be measured as having negative effects on performance simply because they were not involved in domestic M&As or foreign acquisitions.

We test the effects of governance on bank performance using data from Argentina in the 1990s. We include the static, selection, and dynamic effects of all the major different types of governance that are relevant in Argentina –domestic ownership, foreign ownership, and state ownership – in the same model. The dynamic events analyzed include domestic M&As, foreign acquisitions, privatizations, and state restructurings, where the latter refer to events in which state-owned banks were restructured without privatization.

We also run some “nonrobustness” checks with various exclusions from our performance model. These allow us to examine the consequences of not specifying all of the important different types of governance or changes in governance in the same empirical model. Thus, the nonrobustness checks allow 2
# **Urbanization and the Geography of Development**

## **J. Vernon Henderson, Brown University and the National Bureau of Economic Research**

**Keywords** :  cities, urbanization, geography of development, industrialization, growth

**JEL codes** :  R11, R58

This paper is part of a collection of papers prepared for the World Bank's Sixth Urban Research and Knowledge Symposium, October 2012. Financial support for this paper was provided by the Urban Development and Resilience Department and the Research Support Budget of the World Bank.
# **<u>Table 2. Ex-Ante and Ex-Post Match of NHIS Members and Nonmembers</u>**

# **a. Ex-ante results**

|Multivariate L1:||0.326||||||
|---|---|---|---|---|---|---|---|
|Univariate Imbalanc|e|||||||
||L1|Mean|Min.|25%|50%|75%|Max.|
|Urban|0.0472|0.0472|0|0|0|0|0|
|Infant <3 months)|0.0040|–0.0040|0|0|0|0|0|
|Child (<18 years)|0.0679|0.0679|0|0|0|0|0|
|Pregnant woman|0.0293|0.0293|0|0|0|0|0|
|Elderly (≥70 years)|0.0203|0.0203|0|0|0|0|0|
|SSNIT pensioner|0.0029|0.0029|0|0|0|0|0|
|SSNIT contributor|0.0187|0.0187|0|0|0|0|0|
|Region|0.1266|0.1266|0|1|1|1|0|
|Ln(hh cons. pae)|0.0616|0.0599|0|0.0428|0.0839|0.0940|–1.081|

# **b. Ex-post results**

|Multiva<br>Univariate Imbalanc|riate L1:<br>e<br>|0.286<br>||||||
|---|---|---|---|---|---|---|---|
||L1|Mean|Min.|25%|50%|75%|Max.|
|Urban|0.0451|0.0451|0|0|0|0|0|
|Infant <3 months)|0.0042|–0.0042|0|0|0|0|0|
|Child (<18 years)|0.0871|0.0871|0|0|0|0|0|
|Pregnant woman|0.0300|0.0300|0|0|0|0|0|
|Elderly (≥70 years)|0.0176|0.0176|0|0|0|0|0|
|SSNIT pensioner|0.0028|0.0028|0|0|0|0|0|
|SSNIT contributor|0.0163|0.0163|0|0|0|0|0|
|Region|0.0247|0.0247|0|0|0|0|0|
|ln(hhcons. pae)|0.0447|0.0055|0|0.0200|0.0035|0.0139|0|

_Note_ : SSNIT = Social Security and National Insurance Trust. ln(hh con. pae) = log of household consumption per adult equivalent. Min. = minimum. Max. = maximum. Pregnant woman = a woman who is currently pregnant, has been pregnant in the past year, or currently has an infant under 3 months old.

To achieve this match, some reweighting of the sample is required. In all calculations that follow, we weight each observation by its sampling weight times the weights generated by the matching procedure.

To estimate a distribution of (possible) healthcare expenditures for each individual, we first divide the sample into NHIS members and nonmembers. For each group, we regress the log of OOP healthcare expenditures on the log of household consumption per adult equivalent, draw 100 random observations of the residuals, and add those 100 draws to the predicted value from 12
# **7. Extensions**

So far, the analysis has focused on the relationship between inequality in access to opportunities and intra-generational mobility, based on one’s assessment of expected socio-economic mobility in the near future. An interesting question to ask is how this relationship may apply, or not, to the inter-generational mobility context. Another angle that can be explored, in addition to the link between perceived IO and mobility expectations, is whether IO is also linked with current preferences for redistribution. For instance, individuals may support more redistribution today if they feel that they may not be able to move up the socio-economic ladder as much because of unequal access to opportunities, as they may be more amenable to the government’s efforts to aid those at the bottom. The inter-generational context is explored in section 7.1, whereas preferences to redistribution are discussed in Section 7.2.

# _7.1. Unequal access to opportunities and inter-generational mobility_

Inequality of opportunity is particularly pernicious to the extent to which it generates immobility (or persistence) not only within a single generation, but across generations, such that children born to disadvantaged parents at the bottom of the income distribution are stuck at the bottom of the income distribution in their generation through no fault of their own, but on account of circumstances that they have no control over.

To investigate the inter-generational mobility context with the LiTS data I rely on responses to the question “To what extent do you agree with the following statement: children who are born now will have a better life than my generation” with responses ranging from“strongly disagree” to “strongly agree” on a five-step Likert scale. About one third of adults in the LiTS sample either agree or strongly agree with the assertion that children will have a better life than the current generation. Interestingly, perceptions of intra-generational mobility and inter-generational mobility are strongly related in the data (Figure 5). Those who expect themselves to be upwardly mobile in the near future are also more likely to be more positive with respect to the mobility prospects of children, with a correlation coefficient of 0.78.

22
preceding round, starting with round 15 for July 1959-June 1960 (and using the predicted food share for the poor from round 14 for July 1958-June 1959).<sup>25</sup> The reweighted indices for successive rounds are then combined to form chain price indices which give our preferred measures of inflation in rural and urban areas corresponding to the evolving food and non-food budget shares of the poor.

The population numbers are from the censuses and assume a constant growth rate between censuses. They are also centered at the mid-points of the NSSO’s survey periods. The trend increase in the urban population share was 0.24 percentage points per year in the period 1951 to 2005-6 (with a robust standard error of 0.04). In the 40 years after 1950, the urban sector’s population share rose from 17% to 26%, and by 2005 it rose to 29%.

We use private final consumption expenditure and net domestic product from the NAS. To mesh the NAS data with the poverty data from the NSSO, we have linearly interpolated the annual national accounts data to the mid-point of the survey period for different rounds. We follow Ravallion and Datt (1996) in only using both NAS and NSS data in the same regressions for the period 1958 onwards, given the poor mapping between NSS rounds and NAS annual data prior to 1958 in view of the shorter survey periods of the early rounds.

While our use of household surveys, such as the NSS, in measuring poverty is standard practice, it has been questioned by some observers. Bhalla (2002), in particular, has argued that the NSS underestimates consumption levels, leading to an overestimation of the level of poverty in India and underestimation of the pace of poverty reduction. The main reason given is the large and rising gap between the measure of aggregate household consumption implied by the NSS and the estimate of private consumption that can be derived from India’s national accounts (NAS). The gap is unusually large for India. The NSS data suggest a consumption aggregate that is only about half of the household consumption component of the NAS.

Figure 3 plots consumption per person from the NSS (urban and rural) and the private consumption component of the national accounts, also per person, and mapped into NSS rounds.

> 25 Predicted food shares are derived from grouped data on budget shares, using a regression for the previous round of food budget shares as a cubic function of the cumulative proportion of the population ranked by per capita monthly total expenditure.  Food shares for the poor for the current round were then predicted at the estimated headcount index for the previous round.  In the case of MRP survey rounds, the regression for the most recent round with a uniform recall period was used.

13
|**Authors**|**Year**|**Subjects**|**Criterion measure**|**Correlation**|
|---|---|---|---|---|
|Deno,<br>Mirkin,<br>and|1982|<br>45|Cloze|.86 (3rd) .87|
|Chiang, 1982; see<br>also Fuchs, Tindal, and||1st to 6th grade with 18 LD||(6th)|
|Deno,1984.|||||
|Marston & Deno, 1982|1982|26<br>3rd grade general ed. 64 4th to<br>6th grade using lowest readers<br>& Title 1|Questions<br>below<br>grade<br>level<br>Stanford Achievement Test (SAT)<br>Science<br>Research<br>Associates<br>(SRA)<br> <br>|<br> <br> <br>.40 .88 .80|
||||Ginn 720 Reading Series Mazes|.83 (3rd)<br>.76 (4th)<br>.76 (5th)|
|||||<br>.59 (6th)|
|Buehner, 1983|1983|92<br>4thgrade generaled.|Comprehensive Tests of Basic<br>Skills (CTBS)|<br>0.63|
|Fuchs, Tindal,<br>Fuchs et al.,1983|1983|21<br>4thgrade generaled.|Holt Basic Reading Series|0.79|
|Fuchs, Tindal,<br>Shinn, Fuchs, and<br>Deno, 1983; see also<br>Tindal, Fuchs et al.,|1983|47<br>5th grade general ed.|Ginn Basal Reader Mastery Test|0.72|
|1985|||||
|Lomax. 1983|1983|101<br>1st to 6th grade LD|DiagnosticReading Scales (DRS)<br>Comprehensive Tests of Basic<br>Skills(CTBS)|0.76<br> <br>0.75|
|Tindal, Fuchs et al.,<br>1983; see also<br>Tindal, Fuchs et al.,<br>1985|1983|25<br>4th grade general ed.|Scott-Foresman<br>Basal<br>Mastery<br>Test|<br>0.7|
|Tindal, Shinn et al.,<br>1983; see also<br>Tindal, Fuchs et al.,<br>1985|1983|47<br>6th grade general ed.|Houghton-Mifflin Basic Reading<br>Test|<br>0.66|
|Fuchs, Fuchs, and|1988|35|SAT|0.91|
|Maxwell, 1988||4th to 8th grade with 27 LD, 7|Questions<br>|0.84|
|||ED, & 1 MR|Cloze|0.75|
||||Retell|0.74|
|Collins, 1989|1989|58<br>2nd grade general ed.|California<br>Achievement<br>Test<br>(CAT)<br>&<br>Harcourt-Brace-<br>Jovanovich(HBJ)|<br><br>.75|
||||Basal Reader end of level test for<br>comprehension|<br>.60|
||||Word Meaning|.57 (3rd) .56<br>|
|||||(6th)|
|Markell, 1991|1991|42|Mazes below grade level Mazes at|<br>.86|
|||3rd grade generaled.|<br>gradelevel|<br>.89|
|Bain & Garlock, 1992|1992|479<br>1st to 3rd grade (1st was Title<br>1)|<br>Comprehensive Tests of Basic<br>Skills (CTBS)|<br>.62 (1st)<br>.79 (2nd)<br>.72 (3rd)|
|Shinn, Good, Knutson,<br>Tilly, and Collins, 1992|1992|238<br>3rd & 5th grades with 5%<br>special ed.|<br>Cloze SDRT Retell|.77 (3rd)<br>.63 (5th)<br>.59 (3rd)<br>.58 (5th)|

64
Figure 8: Quantile Regressions with District and Year Fixed Effects

- (A) Coefficients from QR of Wage on IMR

(D) Local Mean Effect of IMR on Wages <!-- Start of picture text --> Local polynomial smooth<br>0 .2 .4 .6 .8 1<br>Percentile 0 .2 .4 .6 .8 1<br>Quantile<br>Coefficient 95% CI<br>kernel = epanechnikov, degree = 0, bandwidth = .02<br>(B) Coefficients from QR of Wage on Sanitation (E) Local Mean Effect of Sanitation on Wages<br>Local polynomial smooth<br>0 .2 .4 .6 .8 1<br>Percentile 0 .2 .4 .6 .8 1<br>Quantile<br>Coefficient 95% CI<br>kernel = epanechnikov, degree = 0, bandwidth = .02<br>(C) Coefficients from QR of Consumption on IMR (F) Local Mean Effect of IMR on Consumption<br>Local polynomial smooth<br>0 .2 .4 .6 .8 1<br>Percentile 0 .2 .4 .6 .8 1<br>Quantile<br>Coefficient 95% CI<br>kernel = epanechnikov, degree = 0, bandwidth = .02<br>.004<br>-.0005<br>.002<br>-.001<br>0<br>-.0015<br>-.002<br>Smoothed Coefficient<br>-.002<br>-.004<br>-.0025<br>.01<br>.005<br>.005<br>.004<br>0 .003<br>.002<br>-.005 Smoothed Coefficient<br>.001<br>-.01<br>0<br>.002<br>-.0006<br>.001<br>0 -.0007<br>-.001 -.0008<br>Smoothed Coefficient<br>-.002 -.0009<br>-.003<br>-.001<br><!-- End of picture text -->

- (C) Coefficients from QR of Consumption on IMR

37
|**Table A27: Political and Economic Ri**<br>**Data Provider**<br>Political and Economic Ris|**sk Con**<br>k Cons|**sultan**<br>ultancy|**cy Co**<br>|**rruptio**|**n in A**|**sia Sur**|**vey (P**|**RC)**||
|---|---|---|---|---|---|---|---|---|---|
|**Description**<br>Commercial business infor|mation|firm he|adquart|ered in|Hong K|ong, Ch|ina|||
|**Website**<br>www.asiarisk.com<br> <br>||||||||||
|**Data Source**<br>Corruption in Asia||||||||||
|**Type**<br>Survey||||||||||
|**Respondents**<br>Expatriate business people||||||||||
|<br>**Frequency**<br>Annual since 1998<br>||||||||||
|**Coverage**<br>Asian countries||||||||||
|**Public Access**<br>Commercially available||||||||||
|**Description**<br>This survey asks responde<br>effectiveness of efforts to r|nts to ra<br>educe c|te sever<br>orruptio|ity of c<br>n, on a|orruptio<br>10-poin|n, attitu<br>t scale.|des tow|ards co|rruptio|n, and|
|**Voice and Accountability**|2007|2006|2005|2004|2003|2002|2000|1998|1996|
|NA|..|..|..|..|..|..|..|..|..|
|**Political Stability and Absence of Violence**||||||||||
|NA|..|..|..|..|..|..|..|..|..|
|**Government Effectiveness**||||||||||
|NA|..|..|..|..|..|..|..|..|..|
|**Regulatory Quality**||||||||||
|NA|..|..|..|..|..|..|..|..|..|
|**Rule of Law**||||||||||
|NA|..|..|..|..|..|..|..|..|..|
|**Control of Corruption**||||||||||
|To what extent does corruption exist in a way that<br>detracts from the business environment for<br>foreign companies?|X|X|X|X|X|X|X|X|X|
|**Country Coverage**|**13**|**13**|**13**|**12**|**12**|**12**|**12**|**12**|**12**|
|**Year of publication**|**2007**|**2006**|**2005**|**2004**|**2003**|**2002**|**2000**|**1998**|**1997**|

65
**Figure 13: Growth recovered across sectors during Q2, 2017-18 to Q4, 2017-18**

13 A: Growth slowdown has likely bottomed out...

<!-- Start of picture text --> GVA at Basic Price<br>10<br>9 7.6<br>8 6.6<br>7<br>6<br>5 7.2 6.1<br>4 5.6<br>3<br>2<br>1<br>0<br>13 C: Industrial growth has revived...<br>Industry<br>14<br>12<br>8.8<br>10<br>8<br>7.1<br>6<br>7.0<br>4<br>2<br>0<br>Q1 2014 Q2 2014 Q3 2014 Q4 2014 Q1 2015 Q2 2015 Q3 2015 Q4 2015 Q1 2016 Q2 2016 Q3 2016 Q4 2016 Q1 2017 Q2 2017 Q3 2017 Q4 2017 Q1 2018 Q2 2018 Q3 2018 Q4 2018<br>Q1 2014 Q2 2014 Q3 2014 Q4 2014 Q1 2015 Q2 2015 Q3 2015 Q4 2015 Q1 2016 Q2 2016 Q3 2016 Q4 2016 Q1 2017 Q2 2017 Q3 2017 Q4 2017 Q1 2018 Q2 2018 Q3 2018 Q4 2018<br><!-- End of picture text --> 13 E: Manufacturing has picked up

# 13 B: ...as agricultural growth has picked up

<!-- Start of picture text --> Agriculture<br>10<br>8<br>6 4.5<br>2.6 2.6<br>4<br>2 3.1<br>0<br>-2<br>-4<br>Q1 2014 Q2 2014 Q3 2014 Q4 2014 Q1 2015 Q2 2015 Q3 2015 Q4 2015 Q1 2016 Q2 2016 Q3 2016 Q4 2016 Q1 2017 Q2 2017 Q3 2017 Q4 2017 Q1 2018 Q2 2018 Q3 2018 Q4 2018<br><!-- End of picture text -->

# 13 D: ...and services continue to do well

<!-- Start of picture text --> Services<br>14<br>12<br>10<br>7.7<br>8<br>9.0<br>7.7<br>6<br>6.8<br>4<br>2<br>0<br>Q1 2014 Q2 2014 Q3 2014 Q4 2014 Q1 2015 Q2 2015 Q3 2015 Q4 2015 Q1 2016 Q2 2016 Q3 2016 Q4 2016 Q1 2017 Q2 2017 Q3 2017 Q4 2017 Q1 2018 Q2 2018 Q3 2018 Q4 2018<br><!-- End of picture text --> 13 F: Construction sector activity has revived 27
# **1. Introduction**

COVID-19 has exposed the risks associated with the interconnected nature of global trade. The reliance on foreign input producers can lead to a disruption of production when source countries experience a negative shock. A view shared by many observers is that firms will respond to this shock reconsidering the balance between efficiency and resilience in production, leading to long term changes in the structure of global value chains (GVCs) in the form of reshoring, nearshoring and diversification (e.g. Javorcik, 2020; Kilic and Marin, 2020; Lund et al., 2020; UNCTAD, 2020). In contrast, others have argued that the same technological and institutional factors that have underpinned the international fragmentation of production in the past decades would make a retrenchment of GVCs post-COVID-19 unlikely, unless there is a radical change in the policy environment (Antràs, 2021; Baldwin, 2020).<sup>2</sup> Ultimately, the long-term impact of natural disasters on global value chains and their organization is an empirical question.

To understand how firms behave when faced with new risks, we examine the 2011 earthquake in Japan. The earthquake off the Pacific coast of Tōhoku was the most powerful ever recorded in Japan. It triggered a tsunami that swept the Japanese mainland, causing devastation of physical infrastructure and approximately 16,000 deaths. The earthquake also severely disrupted supply chains. For example, a shortage of over 100 parts left Toyota’s North American operations operating at 30 percent capacity for several weeks (Canis, 2011). Boehm et al. (2019) show that Japanese multinationals in the United States lost access to intermediate inputs and experienced severe reductions in production as a result. In the short run, the effects were highly disruptive because there were few substitutes for Japanese suppliers. For Japanese firms operating in the US, the elasticity of substitution across material inputs was estimated to be only 0.2 in the short run.

Most of the existing literature on natural disasters focuses on how GVCs transmit shocks, domestically (Carvalho et al., 2016) or internationally (Boehm et al., 2019).<sup>3</sup> In this paper, we study instead the longer2 For an analysis of the long-term evolution of global value chains, see Constantinescu et al. (2020) and World Bank (2020).

3 An exception is Zhu et al. (2016) which uses Japanese firm-level data from 2010-2013, to show that the earthquake increased manufacturing offshoring from Japan among firms in the prefectures most affected by the disaster.

2
# **Table** of Contents

|Abstract<br>.<br>.<br>.<br>.<br>.<br>.<br>.<br>.<br>.<br>.<br>.<br>.<br>.<br>.<br>.<br>.<br>.<br>.<br>. *. .. .|. . . . .|i|
|---|---|---|
|**I.**Introduction.<br>.<br>.<br>.<br>.<br>.<br>.<br>.<br>.<br>.<br>.<br>.<br>.. . . . . . . .|. . . .<br>.|1|
|**II.**<br>**Trends**In<br>ducation<br>.<br>.<br>.<br>.<br>.<br>.<br>.<br>.<br>.<br>.<br>.<br>.<br>.<br>.<br>.<br>.|.<br>.<br>.<br>.<br>.<br>.|2|
|Patterns<br>and Trends. .......<br>.<br>.<br>.<br>.......|<br>.<br>. ..|2|
|Educational Policy Context .... . . . . . . . .<br>..|.......<br>.|6|
|III.**Framework,**Data,**and**Variables<br>.<br>....<br>.<br>.<br>.<br>.|.<br>.<br>.|12|
|The Schooling Decision. ....<br>.<br>....<br>.. .. .<br>.|.<br>.|12|
|Empirical Model<br>.<br>..<br>. . . . . . .<br>.|......|16|
|Variables<br>.<br>.<br>.<br>.<br>.<br>.<br>.<br>.........<br>...<br>.|....<br>.<br>.<br>.|18|
|IV. Data DescriptionandSample Profiles.......<br>.<br>Sample Distribution by Gender, Cohort, and Residence<br>EducationalProfiles by Gender and Residence..<br>Changesin Educational Attainment Over Time<br>.<br>.<br>.<br>.|.<br>.<br>.<br>.<br>.<br> .<br>.<br>.<br>.<br>.<br>.<br>.<br>.<br>.<br>.<br>.<br>.<br>.<br>.<br>.|24<br>24<br>26<br>27|
|V.<br>Results .... . . . . . . .<br>. . .<br>.<br>.<br>.. . . . . .|. . . .<br>.|30|
|FullSample Regressions.<br>.<br>.<br>.<br>.<br>.<br>.<br>.<br>.<br>.<br>. . . .|. . . .<br>.|30|
|Regressionsby Cohort .... . . . . . . .<br>........|<br>.<br>.|37|
|VI.SummaryandConclusions.<br>.<br>.<br>.<br>.<br>.<br>.<br>. . . . . . .|. .<br>..<br>.|44|
|References........||47|
|Appendices. . . . . . . . . . . . . . . . . . . . . . . <br>.50|. . .<br>.||
|AppendixA|||
|Educational Attainment of Peruvians Aged 15 and Over,<br>1940-1981|Table|A.1|
|Percent of Peruvians Aged 6-14 and 15-19 Enrolled<br>in School, 1940-1981|Table|A.2|
|Trends in Peruvian Education: Selected Indicators,<br>1950-1980|Table|A.3|
|Average Annual Enrollment Growth Rates, 1950-1980|Table|A.4|
|Appendix B|||
|Means and Standard Deviations of Regression Variables<br>for All Cohorts, For Hales and Females|Table|B.1|
|Means and Standard Deviations of Variables in Male<br>Regressions,<br>By Birth Cohort|Table|B,2|
# **Appendix C. Raw cashews cost break - down**

Table C1 presents a theoretical cost break down of raw cashews presented by ANCA for year 2014. This structure shows identified exporters’ costs (such as transport, loading, scale, financial and other costs), export taxes (FUNPI, ACI and other export taxes) and traders’ costs (transport costs), and includes theoretical margins for both exporters and traders, but it excludes other cost components (such as labor costs, capital costs, and so on).

**Table C1. Raw cashews cost break-down (estimated 2014)**

|**Description**|**XOF/ kg**|**USD/ ton**|
|---|---|---|
|Producerprices|250.00|531.91|
|Intermediariespurchaseprice|250.00|531.91|
|Intermediaries margin|10.00|21.28|
|Transport within the countryto Bissau|10.00|21.28|
|Intermediate Bissauprices|270.00|574.47|
|Acquisitionprice byexporter|270.00|574.47|
|ExtraordinaryTax/Customs(6% of FOB/MT - Bissau)|23.97|51.00|
|CPR/DGCI(2% FOB value Bissau)|7.99|17.00|
|ACI/DGCI(3% FOB value Bissau)|11.99|25.50|
|FUNPI|50.00|106.38|
|APGB|4.65|9.89|
|Transport Warehouse – Port|3.16|6.72|
|Loadingand Unloading|2.50|5.32|
|Scale, pre-shipping (APGB)|0.26|0.55|
|BankingCost|19.00|40.43|
|Bags(packaging)|8.75|18.62|
|Certificate of Origin|0.00|0.00|
|Phytosanitarycertificate|0.05|0.11|
|Fees and displacement|4.00|8.51|
|Customs broker fees|0.41|0.88|
|SGS|1.50|3.19|
|Warehouse leasing|1.50|3.19|
|**Exporter costs(1)**|**409.73**|**871.76**|
|Administrative costs|0.02|0.05|
|Losses|1.30|2.76|
|Unexpected|0.50|1.06|
|Exporter margin|30.00|63.83|
|**Other exporter costs and margins(2)**|**31.82**|**67.70**|
|**EXPORT PRICE - FOB PORT BISSAU(1)+(2)**|**441.55**|**939.46***|

Source: ANCA (2014). Export reference price is set at 850 USD/ton. Exchange rate is 470 XOF / ton.

* Recently updated export prices for 2014 season round 1050-1100 USD/ton.

43