rafmacalaba/datause-encoder-data huggingface dataset:
# **Table 1a: Estimates of Adjusted Net National Income Using a Marginalist Approach**

||**2000**|**2001**|**2002**|**2003**|**2004**|**2005**|**2006**|**2007**|**2008**|**2009**|
|---|---|---|---|---|---|---|---|---|---|---|
||**_aNNI: $m, 2000_**<br>|**_Percentage_**|**_growth in (re_**|**_al) aNNI per_**|**_annum_**||||||
|Angola|$20,746|-40.7%|195.1%|-75.1%|100.4%|4.0%|41.5%|674.3%|-75.8%|-2.2%|
|Azerbaijan|$4,328|5.4%|1927.1%|-93.9%|13.7%|41.1%|56.4%|51.9%|54.1%|-7.7%|
|Ecuador|$14,823|21.6%|36.6%|-8.6%|11.0%|-8.7%|-20.6%|-7.5%|840.7%|-79.3%|
|Kazakhstan|$14,079|621.3%|-81.3%|22.2%|35.2%|32.4%|24.6%|25.2%|21.5%|-11.2%|
|Norway|$151,636|-2.7%|-4.2%|26.0%|10.3%|20.3%|-10.7%|27.8%|4.2%|-12.5%|
|Russian Federation|$237,601|35.4%|26.0%|-2.8%|23.5%|31.7%|-1.5%|50.8%|47.2%|-34.6%|
|Saudi Arabia|$171,079|-6.9%|3.5%|9.9%|21.3%|13.3%|11.0%|3.4%|20.1%|-17.9%|
|Sudan|$17,885|-29.4%|-5.8%|966.3%|-86.1%|19.7%|47.9%|10.8%|13.2%|-6.7%|
|Trinidad & Tobago|$7,142|19.8%|10.2%|-45.9%|63.4%|43.9%|6.3%|42.2%|-5.4%|-23.3%|
|Venezuela|$102,341|7.7%|-32.6%|-9.4%|67.6%|3.4%|115.4%|37.2%|319.6%|-69.5%|
|||**_Percentage_**<br>|**_contribution_**<br>|**_of (discovery_**<br>|**_-adjusted) d_**<br>|**_epletion to (_**<br>|**_real) aNNI g_**<br>|**_rowth per a_**<br>|**_nnum_**<br>||
|Angola||-39.8%|179.0%|-80.3%|58.6%|-22.6%|0.0%|628.7%|-81.2%|0.0%|
|Azerbaijan||0.0%|1920.4%|-94.7%|0.0%|0.0%|0.0%|0.0%|0.0%|0.0%|
|Ecuador||-8.1%|21.5%|-18.6%|0.0%|-20.2%|-32.6%|-17.7%|805.0%|-78.1%|
|Kazakhstan||599.6%|-83.1%|0.0%|0.0%|8.5%|-6.4%|0.0%|0.0%|0.0%|
|Norway||-3.2%|-14.4%|8.6%|-2.8%|4.7%|-19.0%|14.0%|-8.6%|9.3%|
|Russian Federation||18.7%|17.2%|-18.6%|-9.8%|5.9%|-18.3%|18.6%|12.4%|-8.6%|
|Saudi Arabia||-0.2%|0.7%|-0.7%|9.9%|-8.7%|0.9%|-0.6%|-1.1%|3.3%|
|Sudan||-33.3%|-13.4%|948.9%|-88.1%|-6.5%|21.9%|-7.0%|-6.9%|0.0%|
|Trinidad & Tobago||13.4%|9.1%|-62.4%|36.3%|19.0%|-1.3%|26.9%|-28.7%|12.4%|
|Venezuela||5.7%|-7.5%|2.5%|37.7%|-18.6%|93.2%|23.8%|303.7%|-69.8%|

Source: authors’ own calculations and adapted from BP (2011), World Bank (2011a), Gelb _et al_ . (2012) 14
median unit value in a given product category suggest that this ratio was relatively constant over time. Rather, the deterioration of the median unit value of total Costa Rican exports was due to new FPs moving into products (within broad industrial categories, as discussed earlier) with lower median unit values.<sup>7</sup> Furthermore, if unit values are declining and iceberg transport costs affect exports, then it is possible that the marginal effects of geographic distance to export markets became a tougher constraint on exports.<sup>8</sup> In their study of U.S. data, Bernard et al. (2007) argued that the distribution of products, exports values, and unit values should vary across destinations depending on distance if transport costs are relevant. To assess this hypothesis, we estimated the gravity-model equations presented by Bernard et al. (2007, Table 6), where the authors estimate the marginal effects of distance from the United States on its total export value, the number of exporting firms, the number of exported products, and export value per product per firm. This model can be written as: (2) Log(X)i = α + β*Log(GDP)i + γt*Log(Distance from Costa Rica)i + εi, where i indexes Costa Rica’s trading partners (export destinations), t indexes the years, GDP is the Gross Domestic Product (PPP adjusted from the World Bank’s World Development Indicators database, base year of 2005), and Distance is the number of kilometers between San José, the Capital of Costa Rica, and the capital cities of its trading partners. The data for the latter variable comes from the French Research Center on International Economics (CEPII).<sup>9</sup> X denotes any of the aforementioned dependent variables. The coefficient of interest is γ, which is allowed to vary over time during 19972007.

For the case of the United States in 2000, Bernard et al. (2007) estimated the following coefficients  : -1.36 for the total export value, -1.14 for the number of exporting firms, -1.06 for the number of exported products, and +0.84 for the export value per product per firm. The first three coefficients indicate that distance tends to reduce the value

> 7 Results from regressions of the log of the ratio of new FP unit values over median unit values in the same HS10 product category as a function of year dummies are available upon request.

> 8 More intuitively, Hummels (2007, p. 136) explains that “Ad-valorem transportation costs for a particular product depend on how far the good is shipped, the quality of the transport service offered, and the weight/value ratio of the good. Because all three factors vary considerably across shipments, transportation costs significantly alter relative prices and patterns of trade.”

> 9 <u>http://www.cepii.fr/anglaisgraph/bdd/distances.htm)</u>

14
**Figure 5 Distribution of Firm Population, Employment, and Value-added, 2007**

<!-- Start of picture text --> Firm population Employment Value added<br>million firms millions of individuals billions of 2005 US$<br>2.6 5.6 8.3 3.1 0.7 0.3 23.9 46.7 35.5 15.4 5.9 2.0 1,715 3,099 1,583 332 53 32<br>1% 9% 0%1 % 9% 0%1 5% % 0% 1% 4% 0% 2% 9% 0% 11%2% 0% 43% 38% 20% 31% 29% 22% 45% 43% 28% 43% 41% 27% Large(250+)<br>14%<br>19% 24% 26% 17% 29% Medium<br>18% 23% (50–249)<br>90% 89% 94% 95% 89% 87% 16% 15% 18% 19% 24% 21% 23%<br>23% 27%<br>19% 22% 17% 19% 15% 20% 26% Small(10–49)<br>44%<br>35% 31%<br>22% 23% 24% 24% 20% 19% 21% 16% 17% Micro(1–9)<br>EU15 EU12 EU15 EU12 EU15 EU12<br>North Continental South Continental South North North Continental South Continental South North North Continental South Continental South North<br><!-- End of picture text --> _Source:_ World Bank staff calculations based on Eurostat.

The EU15 South also suffers from unfavorable domestic business environments. In comparison to rest of the EU15, the EU15 South rated consistently lower in regulatory indices and in outward FDI (Figure 6). Successful countries in Northern and Continental Europe were able not only to nurture domestic firms that invested abroad, but also to attract foreign firms to invest domestically, as reflected in their FDI stocks. This situation in turn allowed greater access to foreign markets and increased demand (Antras and Helpman, 2004). This finding implies that Southern Europe may realize productivity improvements by loosening restrictive regulatory requirements, encouraging competition, and supporting both outward and inward FDI.

**Figure 6 Policy Variables in the EU15**

<!-- Start of picture text --> Business<br>Operations Skills of Quality of Access to Inward FDI Outward FDI<br>Index workforce infrastructure credit stock stock<br>2012 Percentage with Index by World Credit to private Manufacturing and Manufacturing and<br>tertiary education, Economic Forum, sector, percentage services, services,<br>2009 2008 of GDP, 2008 percentage of percentage of<br>GDP, 2008 GDP, 2008<br>Finland 88 35 6 86 18 32<br>Sweden 84 30 6 127 38 45<br>France 82 29 7 108 28 38<br>Spain 73 32 6 203 25 21<br>Italy 75 16 4 105 7 6<br><!-- End of picture text --> 18
- 33 -

# Notes

1. Export restrictions have included prohibitions based on economic or safety grounds, restrictive licensing, export quotas, export taxes, and regulations limiting foreign exchange retention.

2. Quantitative restrictions have included import prohibitions, quotas, and restrictive licensing of various sorts. Other restrictions include foreign exchange licensing and control, advance import deposit requirements, and restricted import channels (as in the case of a state trading monopoly.) 3. In addition to customs duties, customs charges include customs surcharges, surtaxes, stamp taxes, and taxes on foreign exchange.

4. The sources were reports on recommendations for loans, country memoranda, country briefs, audit reports, mission reports, background work for World Bank (1988), DMF reports, and the Ford Foundation project on trade policy and the developing world. Sufficiently large differences were detected to permit such a broad classification.

5. When 'secondary trade restrictive intent' is included, the figure rises to 27.2 percent; estimates for imports "affected,' rather than covered, are higher still -- 48 percent instead of 27.2.

6. These proposals are grounded In the conceptual and empirical work of many trade policy analysts; see, for example, Balassa (1988); Bhagwati (1978); Corden (1974); Krueger (1978); and Little, Scitovsky, and Scott (1970).

7. While the achievement of a real depreciation clearly depended on macroeconomic adjustments (fiscal, monetary, and wage), this chapter focuses only on exchange rate policy identified under trade adjustment lending.

8. When quantitative restrictions are binding before and after the depreciation, however, the depreciation increases the price of exportables relative to importables.

9. For most indicators a positive change is an improvement. For resource balance/GDP, external debt/exports, and debt service/exports, a positive change is a worsening and is indicated in the table by a minus. For the real exchange rate, a greater real depreciation for recipients between periods than that for comparators is an improvement.

10. Sample selectivity bias is likely to come into play here in that changes in performance attributed to the receipt of a loan may reflect conditions that systematically led to the receipt of the loan.
**Table 6: Maximum Likelihood Estimates of the Reduced Form Tobit and Partial Effects at Mean Values**

|**of Exogenous Regressors.**<br>Regessors=x|**Dependent va**<br>All Industries***|**riable: Log(exports**<br>*<br>Garment/textiles|**)=y.**<br>****|Partial Effects|(all industry)on|
|---|---|---|---|---|---|
|(industryand time dummies included)||<br>|E(y|x)|<br>E(y|x, y>0)***|<br>P(y>0)***|
|International distance|-0.412|-0.287|-0.087|-0.005|-0.082|
||(10.53)**|(4.85)**|(11.79)**|||
|Land locked|-12.691|-18.854|-0.022|-0.106|0.084|
||(3.85)**|(1.71)|(3.65)**|||
|Area|-7.262|-26.158|-1.784|-0.100|-1.684|
||(0.83)|(9.89)**|(1.07)|||
|Country population (lagged)|23.661|32.453|4.717|0.266|4.451|
||(3.57)**|(3.00)**|(3.77)**|||
|Local GDP (1995)|0.286|0.754|0.060|0.003|0.057|
||(2.18)*|(2.70)**|(2.45)*|||
|Percent paved (road network)|0.109<br>|0.419<br>|0.020<br>|0.001|0.019|
||(2.35)*|(5.77)**|(2.30)*|||
|Openness to trade|39.978|73.586|11.280|0.635|10.645|
||(0.64)|(0.85)|(0.97)|||
|Rule of law|21.445|12.795|4.087|0.230|3.857|
||(9.03)**|(1.88)|(9.13)**|||
|Control of corruption|13.298|18.417|2.521|0.142|2.379|
||(6.89)**|(5.65)**|(6.93)**|||
|Bangladesh|21.186|-53.532|9.554|0.330|9.224|
||(1.26)|(7.75)**|(1.44)|||
|Malaysia|18.189|26.680|10.129|0.349|9.78|
||(1.01)|(1.18)|(1.41)|||
|China|-82.252|-7.541|-5.253|-0.318|-4.935|
||(2.31)*|(0.33)|(2.50)*|||
|India|-82.935|-37.769|-8.496|-0.445|-8.051|
||(3.21)**|(1.81)|(3.43)**|||
|Brazil|-56.811||-4.213|-0.273|-3.94|
||(2.55)*||(2.81)**|||
|Cameroon|64.746|56.590|48.612|0.801|47.811|
||(3.26)**|(2.26)*|(3.72)**|||
|Cote D'ivoire|41.131|12.459|24.561|0.651|23.91|
||(1.83)|(0.43)|(2.11)*|||
|Ghana|22.270|-15.087|10.381|0.353|10.028|
||(2.39)*|(1.93)|(2.71)**|||
|Mozambique|-1.765|-15.526|-0.138|-0.109|-0.029|
||(0.04)|(0.22)|(0.27)|||
|Nigeria|-26.832<br>**|-34.525<br>**|-1.112<br>**|-0.148|-0.964|
||(8.65)|(4.09)|(6.97)|||
|Kenya|43.411|31.044|22.889|0.636|22.253|
||(6.77)**|(2.77)**|(7.16)**|||
|Tanzania|19.885|46.375|9.570|0.327|9.243|
||(2.42)*|(2.77)**|(2.87)**|||
|Uganda|54.940|3.731|34.726|0.766|33.96|
||(3.60)**|(0.32)|(4.01)**|||
|Zimbabwe|67.671|80.067|48.562|0.801|47.761|
||(3.48)**|(4.42)**|(3.80)**|||
|Constant|-254151|168035||||
||.<br>(1.70)|.<br>(2.88)**||||
|sigma [standard error]<br>|25.18 [0.22]|21.71[0.30]||||
|Observations|39002|11608||||
|Loglikelihood|-54418|-19279||||

z statistics in parentheses based on bootsrapped standard errors (with 50 replications per estimate) * significant at 5%; ** significant at 1%, ***standard errors same as corresponding item in column (3), ****Pseudo Rsquared=0.09 32
**Table 10. Decompositions of changes in survival to 2015**

|_Scenario_||_Value_|_Implied_<br>_U5MR_|_Percentchangeon_<br>_1998 value_|
|---|---|---|---|---|
||_Top3_||||
||_quartiles_||||
||-ln2" 8|15.86|33.97||
||_(X15-X9 8)f098_|1.95|21.55|-59%|
||_(6i154-98)X15_|0.44|30.69|-41%|
||-Ir)L5|18.25|19.46|-63%|
|A-poorestquartile'sgrowthand|_Bottom_||||
|coefficients|_quartile_||||
||-hl)<br>98|14.17|50.19||
||(X]5-x9 8)fl98|-4.20|129.50|127%|
||_0615-f498)XI5_|1.11|38.87|-32%|
||-1nAls|11.08|101.28|78%|
|B-poorestquartile'scoefficientsbut|_Bottom_||||
|topthreequartiles' growth|_quartile_||||
||-nis 8|14.17|50.19||
||_(X15-x 98)098_|-1.41|69.24|21%|
||_0615-l8)XI5_|0.92|40.63|-29%|
||<br>-hlnA5|13.68|56.16|-1%|
|C-poorestquartile'sgrowthbuttop|_Bottom_||||
|threequartiles'coefficients|_quartile_||||
||-lIu98|14.17|50.19||
||(X15-X98)fi98|1.64|34.37|-40%|
||_8154-1 98)X15_|1.11|38.87|-32%|
||<br>-InA15|16.92|26.57|-53%|
|D-coefficientsandgrowthoftop|_Bottom_||||
|three quartiles|_quartile_||||
||**-hi,98**|14.17|50.19||
||(X15-x98)/398|2.56|27.75|-51%|
||_(f 1S-4698)X98_|0.92|40.63|-29%|
||_-hlt15_|17.65|22.41|-61%|

_Note:_ assumes IMR in 1990 is<sup>**40**</sup> **%o** and 36%6o for the poor and nonpoor respectively, and that the U5MR figures for 1990 are 57%o and 529%o for the poor and nonpoor, respectively.

# **_Key points_**

Achieving a reduction of two thirds of the 1990 levels of child mortality in Vietnam as implied by IDTs is very challenging. Even under quite optimistic assumptions about annual income growth, as well as the evolution of water, sanitation and health services, projected levels of child mortality are likely to be higher than the targets by 2015. The key problem seems to be the slow recent progress among Vietnam's poor. Central to tackling this problem would seem to be: reversing the backward moves along the survival demand curve among the poor (especially vis-a-vis women's education and birth-related health services); and ensuring that it is not just the better-off who benefit from improvements that increase the impact of health determinants on child survival.

28
# **Table 3 (Cont'd)**

|_Variables_|T|he Level M|odel||The Growth M|odel|
|---|---|---|---|---|---|---|
||OLS|FE|RE|OLS|FE|RE|
|W_ELASTICITY: Firm-level pay sensitivity|0.289***<br>(0.060)|0.155***<br>(0.048)|<br>0.143***<br>(0.048)|0.274***<br>(0.060)|<br>0.131***<br>(0.048)|0.118**<br>(0.048)|
|Dummy_(central_NewMan):_the firn had a new manager|-0.140**|0.006|-0.011|-0.036|0.074|0.062|
|& was govemed by the_central_gov't|(0.061)|(0.050)|(0.049)|(0.092)|(0.065)|(0.066)|
|_central_NewMan_*year||||-0.028<br>(0.020)|-0.005<br>(0.015)|-0.013<br>(0.015)|
|Dummy_(province_NewMan):_the firm had a new<br>manager & was governed by_aprovincial_gov't|0.134**<br>(0.059)|0.102**<br>(0.047)|0.128***<br>(0.047)|-0.023<br>(0.095)|-0.007<br>(0.066)|-0.002<br>(0.067)|
|_province_NewMan_*year||||0.053**<br>(0.024)|0.064***<br>(0.018)|0.063***<br>(0.018)|
|Dummy_(prefeture_NewMan): the firm had a new<br>manager & was govemed by_aprefecture_gov't|-0.045*<br>(0.025)|-0.032<br>(0.024)|-0.042*<br>(0.023)|-0.061*<br>(0.033)|-0.028<br>(0.025)|-0.038<br>(0.025)|
|_prefecture_NewMan_*year||||0.007<br>(0.007)|0.022***<br>(0.007)|0.013**<br>(0.007)|
|Dummy(countyNewMan):the firm had a new manager<br>&was govemed by a_county_gov't|0.039<br>(0.057)|-0.010<br>_(0.051)_|0.021<br>(0.049)|-0.099<br>(0.086)|-0.I11 *<br>(0.063)|-0.101<br>(0.063)|
|countv_NewMan*year||||0.041**<br>(0.019)|0.066***<br>(0.016)|0.061***<br>(0.015)|
|R Square|0.330|See|note c|0.332|Se|e note c|
|Sample Size|6544|6544|6544|16544|6544|6544|

Source.-- Based on _A Survey of State Enterprises: 1980-1989._

**a** In all OLS and RE estimations of this table (column 2, 4, 5, and 7), we also control the following variables: 9 year dummies, 3 provincial dummies, 9 industry dummies, 4 governance dummies (the firm governed by the central, provincial, city, county government), and the missing indicators for

W_ELASTICITY, marginal retention rate, and share of output under the mandatory plan of the government.

b In all fixed effects estimations of this table (column 3, 6), we also control the following variables: 9 year dummies, and missing indicators for firm-level pay sensitivity, marginal retention rate, and share of output under the mandatory plan of the government.

C For the level model: (1) the F statistic for FE vs. OLS is 14.8 with a P value of 0.000; (2) the Breusch-Pagen test statistic for RE vs. OLS is 6224 with a P value of 0.000; (3) the Hausman (chi square) test statistic for FE vs. RE is 386.5 with a P value of 0.000. For the growth model: (1) the F statistic for FE vs. OLS is 15.01 with a P value of 0.000; (2) the Breusch-Pagen test statistic for RE vs. OLS is 6288 with a P value of 0.000; (3) the Hausman (chi square) test statistic for FE vs. RE is 271.9 with a P value of 0.000.

specification(columns 2 to 4), reforms are assumed to have _level effects only._ In the growth specification (columns 5 to 7), besides level effects the reforms are assumed to have a impact on the growth rate of the firm: we allow the grow rate of a firm to depend 19
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----average income tax rate<br><!-- End of picture text -->
cognitive, noncognitive, and technical abilities, using a broad categorization (high, medium, low). The predictive power of these three proxy measures can be compared with the predictive power of the detailed skills measures, an issue we turn to in section 4.

# **2.4 Randomization of the survey instrument and fieldwork**

To understand the drivers of measurement error, an important focus of the study was the extent to which the order of answers, of questions, and of modules, or any unobserved enumerator effects might affect answers in an important way. The data collection was done using mini laptops, and a program specifically designed to randomize the different components of the questionnaire. The order of the three main sections (cognitive, noncognitive and technical) was randomized, which allows to control and test for potential survey fatigue and to assess whether some tests tend to modify the responses of the following questions. The order of the questions within a section was randomized to control for a potential learning caused by the preceding questions. And in all multiple-choice questions, the order of the answers was also randomized. In order to test for enumerator effects, we also randomly assigned respondents to enumerators. For the re-test 40% of households was assigned to the same enumerator while the rest varied. Survey teams were allowed to deviate from the random assignment for logistical reasons. Overall compliance with the enumerator assignment was about 75%. Finally, we randomized the order in which the villages were surveyed to evaluate effects related to enumerators learning or changing how they administrate the survey across time.

# **2.5. Training and data collection**

Prior to survey implementation, all enumerators and field personnel participated in an intensive two-week training, with both classroom and field training and extensive practice to guarantee fluent and correct implementation of the different skill measurements. The first round of the survey started January 20<sup>th</sup> 2014– and took approximately 3 weeks. The retest survey was conducted in the following 3 weeks. A small household and farm survey was implemented in parallel and provides the agricultural outcome variables. All survey activities, including tracking of harder to reach respondents were finished by the end of March. Almost all surveys were conducted before the start of the main agricultural season. Additional surveys were implemented at the end of the following four agricultural seasons with information on production outcomes and practices, and are used to investigate which skills best predict these economic outcomes.

11
<!-- Start of picture text --> 30<br>25<br>20<br>15<br>10<br>5<br>0<br>UA RU EE LV PL RO SI SK CZ BG HU DK NO FI SE ES GR IL IT CY PT BE CH DE UK FR NL IE AT<br>East North South West<br>Employees with contracts Informal self-employed Employees without contracts<br>% over-qualified<br><!-- End of picture text -->

**Figure 11 Over-qualification among formal and informal workers, 2004-2009.** _Source:_ Calculation with ESS data.

**_Sectoral composition._** Previous research on undeclared work (Riedmann and Fischer, 2008: p.23; Williams and Renooy, 2008: p. 9-10; Pedersen, 2003: Figure 4.2)  has revealed construction, household and personal services, trade, and hospitality as the most popular activities among informal workers, as well as the ones with the highest proportions of all workers involved in undeclared work; yet the same studies suggest that countries and European regions might substantially differ from each other in terms of sectoral distribution of undeclared work. For instance, Williams and Renooy (2008) report that construction activities account for only 3% of undeclared work in Southern Europe, whilst in the Nordic countries this proportion is 27%. However, given the sensitive nature of the questions and relatively small samples in the underlying surveys, country-specific findings should be subject to caution. ESS-based results, reported in Table 6 by categories similar to the ones used in Williams and Renooy (2008), suggests a much smaller geographical variation in sectoral distribution of informal work; in particular, the share of construction is about 10% in the South and 13% in the North. On the other hand, within European regions we find substantial differences between undeclared employees and informal self-employed. In particular, the former are much more concentrated in education and health-related services, as well as in industry, whilst the latter – in agriculture, and (in Eastern and Southern Europe) also in trade, auto repair, and hospitality sector. The differences in findings between ESS and _Eurobarometer_ survey is likely to be driven by various factors. ESS does not cover secondary jobs; moreover, employed respondents are not asked about their past activities, while the _Eurobarometer_ questions refer to the last 12 months. The seasonal factor might play a role, too: most of the ESS field work has been conducted during autumn and winter months, while

31
39 Notes

# **a)**

Unit costs source. February All costs 1989, OffIce, per recipLent were transferred 1989. per '000 calorLes Washington: to US daily S US Government Printing of 1988 using for a year vere calculated the producer Office, pice 1989.using index of consumter foods information on costs per recipLent, from Economic teport cation sLze of the President: and number of feeding transmiLted days to Congress in original b) Foo4 costs Irclude external transport costs where appicable.

- c) ehLaugse rate from IPF InternatloQal Financial Statistics was used to convert from Rupees to US $ of 1976-7. d) For data approxImately In Mateus the _same_ (1989) adminLstrative for aLl MCM-type programs costs vere within only avaLlable one country, for and for several aLl program school feeding combined within programs each cuunrry. within one country.It was assumed that admLnistrative costs were

- **a)** Data on bFP programs were calculated by this author from W?P documents. Pon-recurrent costs In UFP budgets oere excluded.

S School feeding.