rafmacalaba/datause-encoder-data huggingface dataset:
# **1 Introduction**

Trade costs, which include international transportation costs, transaction costs, and distribution costs in countries of origin, destination, and transit, are an important barrier to trade. Estimates from Anderson and van Wincoop (2004) and Hummels (1998) indicate that these costs can in fact be much larger than tariffs and other trade policy barriers. Even in places where formal trade barriers are almost fully eliminated, trade costs still remain as strong barriers to exports and imports.

Trade costs prevent the full realization of the gains from trade. In developing countries, these costs can also wither the poverty alleviation role of export opportunities.<sup>1</sup> In addition, some of the costs associated with exports, and thus the impacts of trade on incomes and poverty, depend to a large extent on complementary domestic factors like improved infrastructure, adequate competition policies and, especially in Africa, enhanced access to credit, better education and health, and low marketing or intermediation costs.

In this paper, we explore the costs of trade for domestic producers when exporting requires intermediaries, a widespread phenomenon in many developing countries. The need for intermediaries in export activities can generate wide marketing costs in agriculture.<sup>2</sup> The focus of our investigation is intermediation costs in export crops, like coffee, tea and cotton, in rural Uganda. Specifically, we claim here that, in rural Uganda, the lack of local outlets for export crops (coffee, tea, cotton) raises their marketing costs, prevents the adoption of high-return crops in favor of subsistence crops (maize, matooke), and impedes farmers to take full advantage of high export prices and enhanced market access opportunities abroad.<sup>3</sup> We begin by developing a model of agricultural production with marketing costs. The

> 1See the reviews in Winters, McCulloch and Mckay (2004) and Goldberg and Pavcnic (2004, 2006).

> 2These marketing costs are non-traditional trade costs like those in Limao and Venables (2001), who analyze trade costs beyond transportation costs. They show that the total cost of transportation depends on the level of infrastructure of trading partners.

> 3There is an increasing interest in these complementarities between trade and domestic factors. Three recent edited volumes on trade and poverty, Harrison (2007), Hertel and Winters (2006), and Hoekman and Olarreaga (2007), testifies to this. A few more specific examples include Porto (2005), who shows that informal barriers to trade have significant effects on poverty in Moldova; Welch, McMillan, and Rodrik (2003), who argue that the negative impacts of the liberalization of the cashew sector in Mozambique was mainly due to the structure of the internal markets; and Nicita (2006), who studies policies that facilitate the transmission of international prices to the household in Mexico.

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**Table 3: Social security contributions rates in the 10 new member states in 2010, percent**

|**Social Contri**|**butions**|**SK**|**HU**|**CZ**|**RO**|**PL**|**LT**|**SI**|**LV**|**EE**|**BG**|
|---|---|---|---|---|---|---|---|---|---|---|---|
||Employer|14.0|24.0|21.5|20.8|9.8|23.3|8.9|-|-|8.9|
||Employee|4.0|9.5|6.5|10.5|9.8|3.0|15.5|-|-|7.1|
|**Old-age pensions**|**Total**|**18.0**|**33.5**|**28.0**|**31.3**|**19.5**|**26.3**|**24.4**|**-**|**-**|**16.0**|
||Employer|1.0|-|1.2|0.5|-|1.1|0.1|-|-|0.4|
|**Unemployment**|Employee|1.0|-|0.0|0.5|-|-|0.1|-|-|0.6|
|**insurance**|**Total**|**2.0**|**-**|**1.2**|**1.0**|**-**|**1.1**|**0.2**|**-**|**-**|**1.0**|
||Employer|10.0|2.0|9.0|5.2|0.0|3.0|7.1|-|-|0.0|
||Employee|4.0|6.0|4.5|5.5|9.0|6.0|6.4|-|-|8.0|
|**Health insurance**|**Total**|**14.0**|**8.0**|**13.5**|**10.7**|**9.0**|**9.0**|**13.5**|**-**|**-**|**8.0**|
||Employer|10.2|4.0|2.6|1.4|4.9|3.7|0.1|-|-|1.8|
||Employee|4.4|3.0|0.0|-|4.0|-|0.1|-|-|2.1|
|**Other**|**Total**|**14.6**|**7.0**|**2.6**|**-**|**8.9**|**3.7**|**0.2**|**-**|**-**|**3.9**|
||Employer|35.2|30.0|34.3|27.9|14.7|31.1|16.1|24.1|33.0|11.1|
||Employee|13.4|18.5|11.0|16.5|22.7|9.0|22.1|9.0|0.0|17.8|
|**Total**|**Total**|**48.6**|**48.5**|**45.3**|**44.4**|**41.2**|**40.1**|**38.2**|**33.1**|**33.0**|**30.5**|

**_<mark>Source:</mark>_** _<mark>European Commission (2011) ''Taxes in Europe - Tax reforms'' database, http://ec.europa.eu/taxation_customs/tedb/taxSearch.html</mark>_

The pension reforms reduced pension expenditures by the state pension system and, together with strong economic growth in the 2000s, which boosted contributions from wage earners, improved the financial performance of the state pension system (table 4). The pension system even managed to achieve a small surplus of revenues over expenditures in 2006 and 2007. However, several changes were implemented in 2007 which increased pension benefits, such as restoring the indexation of net benefits to gross wages, and the introduction of a mandatory gross replacement ratio of 45 percent.<sup>10</sup> There were further changes in 2009 and 2010, including the guarantee of a minimum pension for low income pensioners and increases in contribution rates. Another “pension reform” was introduced for agriculture pensioners, in 2010, which implies significant additional costs for the state. As a result, pension expenditures have risen steeply, from 5.4 percent of GDP in 2006 to 8.3 percent of GDP in 2010. The increase in expenditures

> 10 The replacement ratio is the ratio of net average pensions to gross average earnings. The average replacement ratio in Romania was 0.56 in 2009, compared to the average for the EU of 0.51.

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Cassar, A., Healy, A. and von Kessler, C. (2017) ‘Trust, Risk, and Time Preferences After a Natural Disaster: Experimental Evidence from Thailand’, _World Development_ , 94, pp. 90–105. doi:10.1016/j.worlddev.2016.12.042.

Challinor, A.J. _et al._ (2014) ‘A meta-analysis of crop yield under climate change and adaptation’, _Nature Climate Change_ , 4(4), pp. 287–291. doi:10.1038/nclimate2153.

Chaudhary, N. (2021) _Weather- and disease-related shocks in agriculture using data from the Rural Livelihoods Information System (RuLIS)_ . RuLIS Brief. Rome: Food and Agriculture Organization. Available at: https://www.fao.org/3/cb5593en/cb5593en.pdf (Accessed: 17 December 2021).

Cole, S. _et al._ (2013) ‘Barriers to Household Risk Management: Evidence from India’, _American Economic Journal: Applied Economics_ , 5(1), pp. 104–35. doi:10.1257/app.5.1.104.

Conforti, P., Markova, M. and Tochkov, D. (2020) _FAO’s methodology for damage and loss assessment in agriculture_ . FAO Statistics Working Paper 19–17. Rome: Food and Agriculture Organization. doi:10.4060/ca6990en.

Delavande, A., Giné, X. and McKenzie, D. (2011) ‘Eliciting probabilistic expectations with visual aids in developing countries: how sensitive are answers to variations in elicitation design?’, _Journal of Applied Econometrics_ , 26(3), pp. 479–497. doi:10.1002/jae.1233.

Drechsler, M. and Soer, W. (2016) _Early Warning, Early Action: The Use of Predictive Tools in Drought Response through Ethiopia’s Productive Safety Net Programme_ . Policy Research Working Papers. Washington, DC: World Bank. doi:10.1596/1813-9450-7716.

European Union, UN Development Group and World Bank (2013) _Post Disaster Needs Assessment: Agriculture, Livestock, Fisheries & Forestry_ . PDNA Guidelines Volume B. Available at: https://bit.ly/3tyFBEJ.

FAO (2018) _The impact of disasters and crises on agriculture and food security 2017_ . Rome: Food and Agriculture Organization of the United Nations.

FAO (2020) _Introduction to FAO’s Damage and Loss Assessment Methodology_ , _FAO elearning Academy_ . Available at: https://elearning.fao.org/course/view.php?id=608 (Accessed: 12 April 2021).

FAO _et al._ (2020) _The State of Food Security and Nutrition in the World 2020_ . Rome: FAO, IFAD, UNICEF, WFP and WHO. doi:10.4060/ca9692en.

FAO (2021) _The impact of disasters and crises on agriculture and food security: 2021_ . Rome: FAO. doi:10.4060/cb3673en.

FAO (no date) _Drought Intensity: Uganda_ , _Earth Observation_ . Available at: http://www.fao.org/giews/earthobservation/country/index.jsp?lang=en&type=11111&code=UGA.

Freudenreich, H. and Kebede, S. (2019) ‘Experience of shocks and expectation formation – Evidence from smallholder farmers in Kenya’, in. _168th Seminar, February 6-7, 2019, Uppsala, Sweden_ , European Association of Agricultural Economists. doi:10.22004/ag.econ.289587.

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25 <!-- Start of picture text --> Chart IV<br>KOREA<br>70<br>60<br>50 -<br>40<br>3u<br>20 -<br>1965 1 1967 I 1969 I 1971 I 1973 1 1975 I 1977 I 1979 I 1981 4 1983 4 1985<br>1966 1968 1970 1972 1974 1976 1978 1980 1982 1984<br>Year<br>O Priv.invt./GDP + Dom.Cred./GDP<br><!-- End of picture text -->
In Table 2, we show the mean child standardized height-for-age and proportion of children suffering from stunting in 1997 and 2000 over the two survey rounds. We observe a positive but insignificant change in child standardized height-for-age from 1997 to 2000 among children between 6 and 60 months of age. More striking, however, is the significant reduction in stunting. Specifically, the proportion of 12-24 months and 24-60 months children who were stunted decreased by 8.3 and 2.8 percent, respectively, from 1997 to 2000 and this is consistent with descriptive evidence presented in Strauss et al (2004).

While we will first present results using a binary measure of exposure to the feeding program, ����, in community �, we also construct a time-varying individual level indicator of exposure, ������, by combining information on child age, program introduction and program rules.<sup>15,16</sup> In particular, let ���, ��, ��� be the date of the survey, date in which the program is first introduced in community �, and birth date of a child, respectively. Children differ in exposure to the feeding program by date of birth and the program start and end dates within the community. For example, a child born six months or more before the introduction of the program will be exposed for �� ��� years at the time of the survey. We calculate ������ as the share of life after age six months that child � in community � was exposed to the program, where ���� ���� �0.5�, the program exposure variable as calculated as:<sup>17</sup>

> 15 In IFLS3, the community informant was asked about details of program implementation, including the beginning and the ending dates of programs, the kinds of food given to the recipients, and the identity and position of the program administrator.

> 16One might use program duration in the community as an alternative measure, but this would assign the same measure of exposure to each child living in the community regardless of age and fail to capture heterogeneity in exposure related to child age and program design. Second, it is important to distinguish program effects across children within communities because duration of exposure at the community level is likely to be endogenous if timing of implementation was based on criteria that were not observed by the program evaluator.

> 17Remember that program exposure starts at six months of age, or at half a year.

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unanticipated mining revenues, where all the regressors were interacted with an urban–rural dummy<sup>39</sup> . Changes in unanticipated mining revenues, for either region had no statistically significant differential effect on changes in access to public health centers for rural areas.

The survey over-sampled individuals in poor areas and by design excluded the wealthiest 5% of the sample in order to focus more on poorer groups and ethnic minorities. Hence it is not a nationally representative sample, although it is representative of the Peruvian population in a broad array of socio-economic indicators (Escobal et al. 2008).

The Young Lives study has a modest attrition rate. The non-response rate was 4.39% for the younger cohort<sup>40</sup> . Tests for selection bias due to this attrition reveal almost no statistically significant differences between the sample of children who dropped out of the study during the second round and the children who continued in the sample as shown in **Table 1**<sup>41</sup> . The only significant differences at a 5% level of means are for mother’s height and the probability of having an immunization program in the child’s community<sup>42</sup> . I also test if attrition can be explained by the spending patterns within regions<sup>43</sup> . I find no statistically significant effect of

> 38 An additional older cohort of 860 children aged between 7.5 years old and 8.5 years old—and not used in this analysis—was also sampled in 2002.

> 39 I controlled for regional and time-fixed effects, as well community characteristics (the average household wealth score in the community, population size, a dummy for living in the highlands, and a poverty index) and regional variables (public spending in the region and debt payments). I cluster my standard errors at the region-year level.

> 40 The survey team made sure to track migrating children between survey rounds.

41 This table contains mean difference tests for a wide range of child, household, community, and region characteristics.

42 Three other variables show small but statistically significant differences at a 10% level: gender, native tongue and the probability of living in the highlands.

43 I run a Probit using information from the first round. The dependent variable is the probability of dropping out from the sample and the independent variable is the level of public spending in the region. The specification includes regional fixed effects, child characteristics (age, gender, ethnicity, and health status), maternal characteristics (education), household characteristics (number of children under 5 years of age, availability of drinking water, household size, and wealth score), community characteristics (rice price, population size, a dummy for belonging to the highlands, a dummy for being in an urban area, and a poverty index), and regional variables (debt payments).

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- 13 -

in Table 4 and the marginally significant likelihood ratio, which suggests a structural break after 1981. This year also coincides with the last period of observation of most previous studies of creditworthiness. Hence, it may be misleading to attempt to extrapolate the findings of such studies to cover recent developments without incorporating recent data in the estimations.

# 6. The Secondary larket for **LDC** Debt

Several authors have recently explored the theoretical and policy implications of debt overhang, a situation in which external debt amassed in the past exceeds the normal abilities of a country to repay.IO/ Krugman (1987) builds models showing that it may be in the interest of international lenders to keep refinancing such loans balc xnderly4ng 4 F **ivrne.** The creditor's hope is that the debtor's fortunes may unexpectedly turn dramatically for the better, in which case the loans will be repaid. This is a formalization of Keynes' famous dictum that "if you owe your banker one hundred pounds you are at his mercy; if you owe him a million, _he_ is at your mercy. " A conflicting consideration, however, is that the interest rate on the refinanced loans should not rise to discouragingly high levels. Amassed external debt that exceeds by far the ability of a country to repay has been blamed by several authors for introducing possibly severe distortions in the operation of a country's domestic economy. For example, according to Sachs (1986), a very serious consequence of debt overhang is that it discourages domestic investment. Public investment declines because of the bad state of governmental finances, and private investment is then caught in a vicious circle with dwindling public investment causing a deterioration oI infrastructure necessary for private 10 See primarily Krugman (1987) and Sacns (19b6).
reports information for the United Kingdom as a whole. This paper follows the World Bank’s policy with respect to country identification.

# **D. Estimation strategy**

This paper uses a logistic regression approach to estimate the effect on PISA participation of a country’s level of economic development, region, prior participation in national, regional or international assessments, and OECD membership. The logistic regression analysis estimates the increased odds of a country’s participation in PISA at any time, based on these country characteristics. Two models are estimated, one with controls for the six PISA cycles and one without these controls. In addition, a final model is estimated for only the PISA 2015 cycle.

# **RESULTS**

# **A. Descriptive statistics and bivariate comparisons**

**PISA participation** . Since 2000, country participation in PISA<sup>4</sup> has increased by about 70%, from 20 percent of countries participating in the first cycle to 34 percent of countries participating in the sixth cycle of the assessment. Thus, approximately one-third of all countries with populations over 30,000 are participating in the sixth cycle of 2015 (table 2).

**Table 2: Share of countries participating in PISA, by PISA cycle**

||I<br>(2000/01)*|II<br>(2003)|PISA Cycl<br>III<br>(2006)|e (Year)<br>IV<br>(2009/10)*|V<br>(2012)|VI<br>(2015)|
|---|---|---|---|---|---|---|
|PISA participants|20.3%|19.1%|27.01%|33.5%|29.3%|33.95|
|All countries|207|209|211|212|215|215|
|Note: * includes PISA|Plus countries||||||

_Participation by level of economic development._ A higher share of high income and upper-middle-income countries have participated in PISA, as compared with lower-middle income countries and low-income countries. Overall participation rates increase dramatically with the level of a country’s income, with greater levels of participation among higher income countries in all cycles.

The gap in participation rates between higher income and lower income countries has increased over time (figure 1). The most rapid growth in PISA participation has occurred for

> 4 Including PISA Plus, which was carried out in 2001 and 2010 with a number of OECD “observer” or “partner” countries.

7
Overall, we opt for the 40 km results because they incorporate many more degrees of freedom, while exhibiting no deviation from the 30 and 20 km results in signs and little deviation in magnitudes. The results for salinity are particularly stable.

# _<u>Assessment of Implications for Public Health in the Coastal Region</u>_

To illustrate the implications, we use our 40 km results to predict mortality probabilities for the case presented in Table 4: a female child, one month old, of a mother with no formal education living in a poor, male-headed household with no toilet facilities, access to a community water tap and wood fuel for cooking. We predict probabilities with salinity at the 5th and 95th percentile measures for the sample (0.52 and 7.83 dS/m, respectively), and for maternal ages 30 and 17. As the table shows, increasing salinity from the 5th to 95th percentile increases the child’s mortality probability by about 30% (33.3% for maternal age 30 and 29.1% for age 17). In comparison, the 13-year difference in age accounts for a change of about 40% in mortality probability (43.3% for 5th-percentile salinity; 38.8% for 95thpercentile salinity).

**Table 4:  Salinity impact illustration**

|Child||Household||
|---|---|---|---|
|Gender:|Female|Head:|Male|
|Age:<br>|1 month|Wealth class:|Bottom 60%|
|Birth month:<br>|June|Toilet facility:<br>Water access:|None<br>Community tap|
|Mother||Cooking fuel:|Wood|
|Education (years):|0|||

|||Mor<br>Mothe|talityPr<br>r Age|obabilit|y (%)|
|---|---|---|---|---|---|
|Salinity<br>Percentile|Salinity<br>(dS/m)|30|17|Diff.|% Ch.|
|5|0.52|6.0|8.6|2.6|43.3|
|95|7.83|8.0|11.1|3.1|38.8|
|Diff||2.0|2.5|||
|% Ch.||33.3|29.1|||

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(2.71-2.73) X - (1+4w)*X_1 ; X = T fb,T fpoWRFinally, there are some variables that we project exogenously. These ares DFI , PR , P&Lcl P&Ld, iDD and iR.

# The Normative Model

The purpose of this model closure is to find the fiscal, monetary and exchange rate policies that are consistent with a given set of macroeconomic policy objectives.

The first step is to set up tarlets for the inflation rate (X), full employment growth rate (g), the real exchange rate (q), and foreign reserves as a certain number of months of imports(res). This allows us to calculate p, YF, E and R *d:

- (2.74) p (1+)*p_ 1

(2.75) YF - (l+g)*YF

- (2.76) E - q(p/pIM *)

(2.77) R d * reso[(IM/E)/12] Once we have set these targets, the model solves for the values of policy variables that would be consistent with this objectives, given our assumptions. Figure 3 shows our proposed closure rule for a normative type model closure.<sup>5</sup> We start with the goods market. Note first, that the growth equation, (2.14), and the definition of total investment, (2.15), determine the S5 This type of closure is consistent with the Fund's financial programming approach as presented in IMF Institute (1981).

29
|**Appendix tabl**|**e A3: Frequen**|**cies of the FE**|**used by regio**|**n in out-of-sam**|**plepredictio**|**n**|||
|---|---|---|---|---|---|---|---|---|
|**Region**|**Total**|**Cad. block**|**Cad. zone**|**Community**|**Village**|**Old rayon**|**Rayon**|**Oblast**|
|Center|2,664,807|785,649|705,679|618,335|484,531|56,100|14,513|0|
|East|730,452|96,780|23,220|293,970|158,136|91,267|45,822|21,257|
|North|1,355,948|317,615|335,137|343,133|331,968|10,457|17,638|0|
|South|1,132,229|295,911|106,980|473,511|167,701|71,770|16,356|0|
|West|1,565,201|151,624|113,964|918,466|221,800|133,967|18,063|7,317|
|Country|7,448,637|1,647,579|1,284,980|2,647,415|1,364,136|363,561|112,392|28,574|
|Percentage||22.1%|17.3%|35.5%|18.3%|4.9%|1.5%|0.4%|

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51 Table A1.11: Distribution of the change in oil share of energy by income and region for 1999– 2009 (percentage of countries in each category except for the median)

|**Category**|**Below -5%**|**-5 to 0%**|**0 to 5%**|**Above 5%**|**Median**<sup>**a**</sup>|
|---|---|---|---|---|---|
|**Income**||||||
|Low|3|48|45|3|0.0|
|Lower middle|2|45|53|0|0.0|
|Upper middle|0|56|42|2|-0.2|
|High|0|66|32|2|-0.1|
|**Region (excluding high-income c**|**ountries)**|||||
|East Asia & Pacific|0|63|38|0|-0.1|
|Europe & Central Asia|5|30|60|5|0.6|
|Latin America & Caribbean|0|46|54|0|0.0|
|Middle East & North Africa|0|58|42|0|-0.3|
|South Asia|0|57|43|0|-0.5|
|Sub-Saharan Africa|2|53|42|2|0.0|

_Sources:_ Table A1.8 and author’s calculations. a. The median is in percentage of GDP.

Table A1.12: Distribution of the change in energy intensity by income and region for 1999–2009 (percentage of countries in each category except for the median)

|**Category**|**< -15%**|**-15 to -5%**|**-5 to 0%**|**0 to 5%**|**>5%**|**Median**<sup>**a**</sup>|
|---|---|---|---|---|---|---|
|**Income**|||||||
|Low|0|6|61|29|3|-0.2|
|Lower middle|0|4|68|23|4|-0.4|
|Upper middle|4|4|60|29|2|-0.6|
|High|0|0|80|20|0|-0.3|
|**Region (excluding high-incom**|**e countries)**||||||
|East Asia & Pacific|0|0|63|31|6|-0.2|
|Europe & Central Asia|5|15|75|5|0|-2.6|
|Latin America & Caribbean|0|4|64|32|0|-0.3|
|Middle East & North Africa|0|8|58|25|8|-0.8|
|South Asia|0|0|57|43|0|-0.4|
|Sub-Saharan Africa|2|2|60|30|5|-0.2|

_Sources:_ Table A1.8 and author’s calculations. a. The median is in percentage of GDP.

Table A1.13: Distribution of offsetting coefficients by income and region for 1999–2009 (percentage of countries in each category except for the median)

|**Category**|**< -20%**|**-20–0%**|**0–10%**|**10–25%**|**>25%**|**Median**<sup>**a**</sup>|
|---|---|---|---|---|---|---|
|**Income**|||||||
|Low|19|16|16|26|23|9|
|Lower middle|21|19|17|21|21|6|
|Upper middle|10|15|19|29|27|15|
|High|7|9|16|43|25|18|