rafmacalaba/datause-encoder-data huggingface dataset:
# **Appendix A.4 Trade elasticities**

||sigmaq|sigmat|rhoe|
|---|---|---|---|
|c-agr|1.35|3.375|-7.5|
|c-mining|1.35|3.375|-7.5|
|c-food|1.35|3.375|-7.5|
|c-bevetob|1.35|3.375|-7.5|
|c-garment|1.35|3.375|-7.5|
|c-woodpap|1.35|3.375|-7.5|
|c-fuel|1.35|3.375|-7.5|
|c-metplast|1.35|3.375|-7.5|
|c-machequip|1.35|3.375|-7.5|
|c-enewat|1.35|3.375|-7.5|
|c-constr|1.35|3.375|-7.5|
|c-hotrest|1.35|3.375|-7.5|
|c-transp|1.35|3.375|-7.5|
|c-posttele|1.35|3.375|-7.5|
|c-finance|1.35|3.375|-7.5|
|c-realest|1.35|||
|c-itserv|1.35|3.375|-7.5|
|c-othbusiserv|1.35|3.375|-7.5|
|c-recrea|1.35|3.375|-7.5|
|c-othperserv|1.35|3.375|-7.5|

Note: sigmaq = Armington-elasticity of substitution between imports and domestic output in domestic demand; sigmat = CET-elasticity of transformation for domestic marketed output between exports and domestic supplies; rhoe = constant price elasticity of export demand. For sector (commodity) notation, see Table 3. For a survey of elasticity values, see Annabi, Cockburn and Decaluwé (2006).

41
industries. These factors encourage investment, permit the rapid development of new products and activities by firms, and allow U.S. workers to adjust to new opportunities. While identifying the precise importance of these factors is difficult, a variety of analyses suggest that such market differences could be very important explanations for differences in growth rates.<sup>42</sup> Second, over the twentieth century, the expansion of the education system in the United States outpaced that around the world. The United States pushed to open secondary schools to all citizens. With this came also a move to expand higher education with the development of land grant universities, the G.I. bill, and direct grants and loans to students. More schooling with less learning each year still yielded more human capital than found in other nations that have less schooling but more learning in each of those years.<sup>43</sup> Third, the analysis of growth rates across countries emphasizes quality of the elementary and secondary schools of the United States. It does not include any measures of the quality of U.S. colleges. By most evaluations, U.S. colleges and universities rank at the very top in the world. A number of models of economic growth in fact emphasize the importance of scientists and engineers as a key ingredient to growth. By these views, the technically trained college students who contribute to invention and to development of new products provide a special element to the growth equation. Here, again, the United States appears to have the best programs.

Finally, in terms of endogeneity, it is important to note that our current work (reported below) replicates two of the tests in Hanushek and Kimko (2000). Specifically, in the context of extended growth analyses, we consider potential biases from including the East Asian countries, and we consider the resources-skills relationship.

# **_4.3 Some New Evidence_**

To provide the most up-to-date evidence, we extend the existing evidence in several ways. The new evidence adds additional international student achievement tests not previously available and uses the most recent data on economic growth which allow an analysis for an even longer time period (1960-2000). Furthermore, the new data extend the sample of countries with available test-score and growth information from 31 countries in Hanushek and Kimko (2000) to 50 countries (see the appendix of this section for details on the data). In the subsequent sections, we will also use these data to analyze

> 42 See, for example, Krueger (1974); World Bank (1993); Parente and Prescott (1994, 1999).

> 43 This advantage has clearly ended as many OECD countries have expanded schools to exceed the quantity of schooling found in the United States (see Organisation for Economic Co-operation and Development (2003)).

31
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Policy Research Working Paper 9188

# **Abstract**

Despite informality being the norm in conflict-affected countries, most estimates of the impact of conflict on economic activity rely on formal sector data. Using high-frequency data from Afghanistan, this paper assesses how surges in conflict intensity affect not only the formal sector, but also informal and illicit activities. Nighttime light provides a proxy for aggregate economic activity, mobile phone traffic by registered firms captures fluctuations in formal sector output, and the land surface devoted to poppy cultivation gives a measure of illicit production. The unit of observation is the district and the period of reference is 2012–16. The same dynamic specification and controls are used for the estimation in the three cases, making the results comparable across sectors. Controls include the presence of combat troops and the level of foreign aid at the local level, which both influence local living standards in Afghanistan. The results show that an increase in conflict-related casualties has a strong negative impact on formal economic activity in the following quarter and a positive effect on illicit activity after two quarters. The impact on aggregate economic activity is negative, but more muted.

This paper is a product of the Poverty and Equity Global Practice. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at gacevedo@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
countries). In view of the significance of undocumented migration within Africa and given the lack of official data in many African countries, these figures are likely to be significantly 5 underestimated.

African countries are affected differently by emigration. As evidenced in Ratha et al. (2011), emigration rates are particularly high in countries that have suffered from conflicts, e.g. Eritrea and Liberia, or in countries with a small population, such as Cape Verde, Sao Tome, or Lesotho. Moreover, World Bank’s data suggest also that Egypt, Morocco, Burkina Faso, Algeria and Zimbabwe were the top five African emigration countries in 2010, representing 12.4 percent, 10.5 percent, 4.8 percent, 4.2 percent and 3.9 percent of all African emigrants, respectively.

Turning to destination countries of African migrants, two main stylized facts emerge: their destination countries are not very diversified and most African migrants remain on the continent rather than migrating to other regions. France still appears to be the most attractive destination for emigrants from Africa (almost 10 percent of total emigrants from Africa in 2010), ahead of Côte d’Ivoire, Saudi Arabia, South Africa and the United States (see Appendix A, Figure A.1, and Table A.1).

In 2010, about half of international migrants from African countries still lived in Africa. Almost all migrants from Swaziland, Niger and Lesotho have settled in other countries on the continent (97 percent, 97 percent and 99 percent of migrants, respectively). The countries in Africa attracting most migrants in 2010 were Côte d’Ivoire, South Africa and Burkina Faso (Ratha et al., 2011). Their attractiveness might be explained either by higher wages and work opportunities or by their direct access to the sea.

This tendency of African migrants to move within the African continent is verified all along the period studied. We note, however, that the share of African migrants who stay in Africa has decreased steadily over time (from 59 percent in 1980 to 51 percent in 2010), with a slow diversification of African migrant destinations (for example, a growing share of African migrants, mainly from North Africa, going to the Middle East). The attraction exerted by Europe, the second most popular destination for African migrants, has remained stable over the same period (at around 28 percent).

> 5 About half of African migratory flows are intra-continental and most of them are informal and not included in national official statistics (Ratha et al, 2011).

4
billion per year, i.e., more than twice as much as Crassus—expressed in mean incomes of one’s own time and place. According to The _Forbes’ Magazine_ 2007 list of richest people in the world,<sup>32</sup> four individuals in the United States have wealth above $20 billion which would place them around Crassus’s level.

Tacitus (Book XII, Chapter 53) gives the wealth of Pallas, a freedman, at 300 million HS. This would have been 50 percent more than Crassus’s wealth. Augustius’ household’s private income was estimated (as mentioned above) by Goldsmith at HS 15 million. Using the conventional interest rate of 6 percent, it translates into a wealth of HS 250 million.

Other incomes and wages compiled from Tacitus’ _Annals_ (for comparison and illustrative <u>purposes):</u>

||Amounts in HS|Amounts in<br>terms of the<br>estimated<br>average annual<br>income (orGDP)|Source|
|---|---|---|---|
|Augustus’ donative to each|1000|2.6|Book I, Chapter 8|
|pretorian guardsman (year 14)||||
|Augustus’ donative to each|300|0.8|Book I, Chapter 8|
|legionnaire and soldier of cohorts<br>||||
|(year 14)||||
|Augustus’ donative to people (year<br>14)|43.5 million|0.2% of GDP|Book I, Chapter 8|
|Tiberius dowry to Agrippa’s|1 million|~2600|Book II, Chapter|
|daughter (year 19)|||86|
|Left by the Senate to Senator|5 million|~13,000 (or 5|Book III, Chapter|
|Marcus Piso after his punishment||times the|17|
|(year 20)||senatorial<br>census)||
|Tiberius’ personal loan to the banks|100 million|0.5% of GDP|Book VI, Chapter|
|(who were suffering from shortage<br>of funds; year 33)|||25|
|Tiberius’ donative after a large fire|100 million|0.5% of GDP|Book VI, Chapter|
|in Rome (year 36)|||51|
|Maximal lawyer’s fee (year 47)|10,000|26|Book XI, Chapter<br>7|
|Consular reward for raising a|5 million||Book XII, Chapter|
|pertinent issue in the senate (paid to<br>a senator; year 52)|||53|
|Nero’s guaranteed annual income|500,000|~1300|Book XIII, Chapter|
|for Messala (year 58)|||34|
|Seneca’s average annual earnings|75,000|~200|Book XIII, Chapter|
|(years 55-58)|||42|
|<br>Nero’s average annual gift to the|60 million|~0.3% of GDP|Book XV, Chapter|

> 32 Available at <u>http://www.forbes.com/lists/2007/10/07billionaires_The-Worlds-Billionaires-NorthAmerica_6Rank.html.</u>

67
# **Regional disparities in labor market performance in Croatia: The role of individual and regional structural characteristics**

Xubei Luo<sup>*</sup> The labor market performance in Croatia failed to keep pace with the moderately good overall macroeconomic development in the last few years. With a stagnant total employment rate, the large disparities in employment and earnings across individual groups and regions have become one of the concerns for the long-term sustainable development of the economy.

Using Labor Force Survey (LFS) data from 2002-2004, this paper studies the labor market performance in Croatia at the national and regional levels. The results show that both one’s individual characteristics (including age, education and gender) and where he or she works plays a role in his or her employment and earning. The regional difference in employment and earnings is reduced to a large extent when the difference in individual characteristics is accounted for. The simulations shed light on the effectiveness of the nation-wide education policy and regional specific labor market policy, and suggest that improving human capital endowment and adjusting labor market structure are both important to rebalance regional development and enhance total welfare.

The paper is structured as follows. The first section describes the labor market performance in Croatia, and presents the disparities across individual groups and regions. The second section studies the determination of employment and earnings at the national and regional levels, and examines the role of the individual characteristics and regional structural characteristics. The third section simulates the effects of the nation-wide education policy and regional specific labor market policy on labor market development. The final section concludes.

## **1. Labor market performance in Croatia**

The economy of Croatia has been growing quite satisfactorily in the period of 2002-2004 with a GDP growth rate of 4.4%.<sup>1</sup> However, the labor market performance was stagnant – only 55% of the working-age population was employed, which lagged far behind the employment objective of the Lisbon Agenda. The fruits of economic growth were not shared equally across the society – although wage increased slightly in nominal terms, with a long-term unemployment rate of about 60%,<sup>2</sup> a large percentage of the active

> * The author is indebted to Jean-Jacques Dethier, Bernard Funck, Chorching Goh, Gillmore Hoefdraad, Satu Kristiina Kahkonen, Tracey Lane, Paula Lytle, Sanja Madzarevic-Sujster, Celestin Monga, Russell Pittman, Sarosh Sattar, and Salman Zaidi for helpful comments.

> 1 Data source: Central Bureau of Statistics and Croatian Pension Insurance Fund, cited from Nestic (2006) 2 Long-term unemployment rate is measured as the share of the unemployed population who are unemployed for at least 12 months.

2
Figure 18. Financial Equilibrium Contribution Rate <!-- Start of picture text --> 25%<br>20% 19.2%<br>15%<br>11.7%<br>10%<br>5%<br>0%<br>2018 2021 2024 2027 2030 2033 2036 2039 2042 2045 2048 2051 2054 2057 2060 2063 2066 2069 2072 2075 2078<br><!-- End of picture text --> Source: author elaborations based on administrative data, IESS However, and like most pension systems in the region, the IESS-managed pension insurance has fiscal resources as a financing source, which are equal to 40% of the total expenditure on pensions, as introduced in section two. By taking into account this complementary financing source, the total financial result would be negative over one decade later (approximately by 2045).

At present, Ecuador has just started a term of 40 years with the most favorable population age structure having the largest proportion of working-age population. This phenomenon, called demographic opportunity window, could favor a higher economic growth. Likewise, this demographic opportunity may provide an ideal setting to set the foundations in the long term for sustainable policies and foster economic and social opportunities. On the other hand, there is a certain urgency related to launching an institutional framework and adequate policies taking into account this gradual demographic change. That is due to two reasons: (i) institutional changes take time, and (ii) those people who will be older adults from 2060 on are the ones joining the labor force at present and, therefore, their current decisions are subject to the current game rules. Social and economic institutions, whether current and future, frame the decisions taken by people throughout their adult life, which define economic security in old age. In addition, a political reality normally imposes a longterm transition until full application of a new institutional framework. The more the reform postpones, the greater the need of it and the more radical its implementation will be. Consequently, the cost of 27
including in the control district of Ngoi. Third, enrollment increases do not appear any larger in takeup villages compared to non-takeup villages within the same district. Estimates from the difference-in-difference models (Table 4) confirm this. The first three columns present results from within-district estimates (comparing takeup to non-takeup villages) and the next three columns compare takeup villages in intervention districts to all villages in the control district (Ngoi). The top panel shows all children, followed by age- and sex-stratified analyses. The coefficient of interest is the interaction of takeup village * 2008, the follow-up survey round. We find weak evidence for any impact of school feeding programs on children’s enrollment status. There is a marginally significant effect in take-up villages in Phongsaly district (which provided onsite feeding) of 5 percent when compared with villages in Ngoi. In Nhot Ou district, which provided take-home rations, a significant enrollment boost of 7 percent is observed. The stratified models point to younger girls (ages 6-10) driving this result in Phongsaly (with a 15% increase). In Nhot Ou, however, increases are seen for younger boys and older girls (ages 11-14). However, this finding is not supported by a comparable increase in enrollment in Khua District, which offered both on-site meals and take-home rations, and saw only a marginally significant increase in enrollment of 10% only among younger boys. In addition, school feeding takeup is associated with a significant decline in enrollment among older girls.

It is clear from the results in Table 4 that enrollment increased across the board in 2008 relative to 2006 (note the large and positive coefficients on the 2008 term in most models, particularly for younger children). This finding echoes reports by the World Food Programme in Lao PDR that enrollment has been increasing in districts with school feeding interventions (World Food Programme, 2008, 2009a). It is not clear from the present analyses, however, that these increases can be attributed to school feeding. It should also be noted that the baseline 17
of different components are listed in Table 13. The largest capital cost components are for feedstock pretreatment, simultaneous saccharification and fermentation, and energy utilities.

**Table 13: Estimated investment for a 220 million liters per year cellulosic ethanol**

**<u>plant</u>**<sup>**a**</sup>

|Cost category|Million $|Share (%)|
|---|---|---|
|Feedstock handling (wood or switchgrass)|13.3|5.1%|
|Pretreatment|44|16.8%|
|Xylose fermentation|11.4|4.3%|
|Cellulose production|5.2|2.0%|
|Simulataneous saccharification and fermentation|38.8|14.8%|
|Ethanol recovery|7.5|2.9%|
|Off-site tankage|7.6|2.9%|
|Environmental systems|7.3|2.8%|
|Utilities (steam, electricity, water)|94.5|36.0%|
|Miscellaneous|8.9|3.4%|
|Fixed capital investment|238.5|90.9%|
|Start-up costs|12|4.6%|
|Working capital|11.9|4.5%|
|**Total investment**|**262.4**|**100.0%**|

Source: Solomon, Barnes, and Halvorsen (2007).<sup>a</sup> Inflation adjusted to 2008 A different break-out of the capital investment components was recently presented by Piccolo and Bezzo (2009) for the EHF process, which consists of five major steps: (1) biomass feedstock pre-treatment, (2) cellulose hydrolysis, (3) fermentation, (4) separation, and (5) effluent treatment. The investments needs for the different steps of this process are summarized in Table 14.

In general, based on currently available technology, capital investments for cellulose based ethanol production are estimated to be in the range of $1.06 to $1.48 per liter of ethanol annual capacity (Wright and Brown 2007). Currently, the operation costs associated to these plants are between $0.35-$0.45 per liter depending on assumed feedstocks and corresponding technologies. Anticipated improvements of biofuel conversion technologies are expected to reduce the capital investments to $0.95-$1.27 per liter ethanol annual capacity and to reduce the operating cost to $0.11-$0.25 per liter of ethanol (Hamelinck, Hooijdonk and Faaij 2005). Again, however, there exists significant 28
ZAMBIA’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE

# **Table 12. The** **<u>efficiency gap</u>**

||ICT|Power|Transport|WSS|Total|
|---|---|---|---|---|---|
|Overstaffing|—|—|—|2|2|
|Distribution losses|—|6|—|22|27|
|Undercollection|—|0|0|30|30|
|Undermaintenance|—|—|0|—|0|
|Low budget execution|0|2|39|17|57|
|Underrecovery of costs|—|152|20|25|198|
|Total|0|160|59|96|315|

_Source:_ Briceño-Garmendia, Smits, and Foster 2008. — = data not available.

Undercharging for power services costs Zambia about $152 million per year (more than 2 percent of GDP). As noted above, Zambia’s power tariffs of $0.03–$0.04 per kWh barely cover half of the full economic costs of power production. Overall, the national power utility, ZESCO, covers barely 40 percent of its costs. The associated financial burden is substantial at the macroeconomic level, amounting to more than 2 percent of GDP, and is also several times larger than that found in other resource-rich countries in Africa (figure 15). Underpricing of water services, though significant in absolute terms and substantially higher in Zambia than in other resource-rich countries in Africa, remains less of a macroeconomic issue due to the relatively low turnover of the sector, amounting to 0.4 percent of GDP.

Zambia’s inequitable access to power and water makes subsidized tariffs a highly regressive policy. Zambian power consumers are having the full capital costs of their service (implicitly or explicitly) subsidized by the state. Given that 84 percent of households with access to power belong to the top quintile of the budget distribution—and indeed 99 percent of those with access belong to the top two quintiles of the distribution—this amounts to a highly regressive subsidy (figure 16). Though less significant in absolute magnitude, subsidies to the water sector are equally regressive in nature since the pattern of access to piped water mimics that of access to power.

27
_Note_ : Standard errors clustered at the respondent level. Sampling weights are applied. *** p<0.01, ** p<0.05, * p<0.1. Where applicable, excluded levels are: contract (none), notice (none), hours (30-45), leave (none), Provident Fund (none), and income (no change).

# **Table 6. Marginal Values of Attributes**

||Coefficient<br>(1)|Marginal Value<br>in Terms of %<br>Income (βk/βw)<br>(2)|Labour Law<br>Requirement<br>(3)|Marginal Value<br>x Labour Law<br>Requirement<br>(4)|
|---|---|---|---|---|
|Contract - 6<br>months|0.97|19.0|||
|Contract - 1<br>year|1.37|26.9|||
|Contract -<br>long-term|2.22|43.5|||
|Notice (days)|0.020|0.39|30 days|11.7|
|Hours (median)|-0.021|-0.41|||
|Leave (days)|0.027|0.53|10 days|5.3|
|Provident Fund<br>(Yes)|0.91|17.8|||
|Percent change<br>in income|0.051|1.0|||

_Source_ : Authors’ analysis based on survey data.

_Note_ : Coefficients and associated marginal values of each attribute relative to income. Marginal value is calculated by dividing coefficient on attribute by coefficient on percent change in income. Labour law is based on requirement for typical workers given in the 2006 Bangladesh Labour Law.

45