rafmacalaba/datause-encoder-data huggingface dataset:
projects, which increased the size of its total investments. As a share of GDP, the PPP investments in Sub-Saharan Africa have been small, but the rate of early termination has been the highest of any region, with almost 5.8 percent of projects failing. The region with the next highest risk of early termination is LAC, with a 4.4 percent early termination rate (table 1).

**Table 1 Sectoral distribution of public-private partnership projects with financial closure in developing countries, by region**

|_Region_|_Item_|_ICT_|_Energy_|_Transport_|_Water and_<br>_sewerage_|_Total_|
|---|---|---|---|---|---|---|
|East Asia and<br>|Number of projects|22 (8)|1,133 (19)|517 (22)|659 (27)|2,331 (76)|
|Pacific|Investment|13,686|267,089|202,962|50,659|534,395|
|Europe and Central|<br>Number of projects|28 (2)|358 (6)|93 (2)|46 (2)|535 (12)|
|Asia|Investment|2,033|71,851|62,683|6,416|142,983|
|Latin America and<br>|Number of projects|27 (2)|1,114 (20)|583 (45)|309 (22)|2,033 (89)|
|Caribbean|Investment|5,092|318,260|249,872|43,806|617,032|
|Middle East and<br>|Number of projects|16 (1)|110 (0)|40 (3)|28 (1)|194 (5)|
|North Africa|Investment|281|45,606|10,213|5,262|61,362|
|South Asia|Number of projects|16 (3)|669 (3)|580 (30)|23 (0)|1,288 (36)|
||Investment|1,816|211,739|130,414|1,764|345,732|
|Sub-Saharan Africa|Number of projects|24 (3)|264 (11)|113 (7)|33 (4)|434 (25)|
||Investment|742|51,619|24,050|1,151|77,562|
|Total|Number of projects|133 (19)|3,658 (59)|1,926 (109)|1,098 (56)|6,815 (243)|
||Investment|23,649|966,164|680,195|109,058|1,779,067|

_Source_ : PPI database.

_Note_ : The number of canceled projects is in parentheses. Investment totals are in millions of 2020 US dollars. ICT = Information and communications technology More than half of all PPP projects in the developing world are in the energy sector (3,658 out of 6,815). Other important sectors are transport (1,926 projects) and water and sewerage (1,098 projects). The remaining 133 projects are in the information and communications technology (ICT) sector.

Almost 3.6 percent of the infrastructure PPPs initiated in the developing world were canceled. The transport sector saw the largest number of cancelations (109), and the ICT sector saw the largest share of cancelations (14 percent of projects terminated early). The early termination rate in the energy sector is the lowest (1.6 percent). Early termination rates were higher in the transport (5.7 percent) and water and sewerage (5.1 percent) sectors.

8
also applies to survey instruments aimed at capturing other subjective measures of well-being (see Di Tella and Mac Culloch, 2006, for a review of the literature on the economics of happiness).

Nevertheless, as opposed to happiness and life satisfaction scales, mental health measures can be validated against well-defined objectives; in particular, their ability to predict clinical depression, anxiety or other mental disorders can be tested. Such an exercise was conducted in the context of three rounds of Bosnia and Herzegovina’s Living Standards Measurement Study (LSMS) survey. Radloff’s (1977) Center of Epidemiological Studies Depression (CESD) Scale was administered to a nationally representative sample of the population. To validate the CESD, a sample of 184 patients who visited primary health care facilities in a canton of Middle Bosnia and Herzegovina were administered the survey module and underwent a psychological diagnosis. A 1.86 threshold used to assess depression was found to have 97.5 percent sensitivity and 75 percent specificity rates (Kapetanovic, 2004).<sup>7</sup> These CESD scales will constitute the main outcome variables in the analysis we will conduct in Section 3.

While such validations are crucial in assessing the accuracy of mental health measures from surveys, formal validations in clinical setting do have some limitations. They are performed on a selected sample of care-seekers, and validation gives a threshold with associated specificity and sensitivity numbers that apply to the population as a whole, while there might be considerable heterogeneity across groups. Consequently, the measurement of well-being by eliciting preferences or feelings (as opposed to observing actions, such as actual consumption, investments or savings decisions) still has numerous caveats that call for a careful interpretation of results; further validation studies should nonetheless be encouraged. A more systematic inclusion of mental health scales in multi-purpose household surveys will certainly facilitate efforts towards that aim.

# **2.2 Measuring Exposure to Violent Conflict**

A large number of studies in the medical literature have relied on individual self-reported conflict exposure. An issue that has interested researchers is the association between mental health status

> 7 Sensitivity is the probability that a binary test delivers a positive result when the individual is actually sick (one minus the probability of a false negative or type II error); specificity is the probability that a binary tests delivers a negative result when the individual is actually not sick (one minus the probability of false positive or type I error).

6
21

**Figure 10**

<!-- Start of picture text --> Simulated changes in poverty<br>using regional rates with inequality adjustment<br>1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002<br>Year<br>Mainland Tanzania Dar es Salaam<br>Other urban areas Rural areas<br>.45<br>.4<br>.35<br>.3<br>.25<br>Fraction below poverty line<br>.2<br><!-- End of picture text -->

# **5.7 Simulation using “Two Survey” Approach**

Finally, simulations were carried out using the “two survey” approach. This was done in two ways. In the first, the national distribution of consumption was used, and national GDP per capita growth rates from the national accounts were applied. In the second, changes in the distributions were simulated separately for each of the three strata: Dar es Salaam, other urban, and rural areas. For this second approach, urban sector national accounts growth rates were used for Dar and other urban areas, while rural sector national accounts growth rates were used for the rural areas stratum. Results from these two simulations are shown in Appendix Tables A11 and A12, and Figures 11 and 12 below. As expected, the simulated values for 2001 closely match the estimates from the 2000/01 HBS.<sup>10</sup> These simulations show broadly the same patterns as the simulations using the Datt and Walker approach: rising or flat poverty in the initial years, followed by small declines. Unlike the Datt and Walker simulation, these are able to capture the divergence between the rate of declines in poverty in Dar es Salaam and the rest of the country.

Unlike the simulation using the national distribution and national growth rates (Figure 11), the simulation using within stratum distributions and urban/rural growth rates (Figure 12) shows small increases in poverty in 1997 and 1998. This is due to the fact that per capita rural output declined in those two years, even while per capita output for the country as a whole increased.

> 10 They do not match exactly because the simulated changes are based on an approximation of the true distributions using 50 quantiles. The maximum number of quantiles used was limited due to issues associated with weighting, as discussed in Section 3.2.
decision-making; transfers among family members and inter-generational mobility; and participation in community activities. Secondly, the IFLS collects detailed information from the communities in which IFLS households are located including data on: physical and social environment, infrastructure, employment opportunities, food prices, access to health and educational facilities, and the quality and prices of services available at health and educational facilities. Unfortunately, the 1997 data on sectoral employment and migration were not published – thus for our analysis we will use only data from 1993 and 2000.

Our main aim is to understand to what extent _sectoral_ and _locational_ shifts determine movements out of poverty. For this reason, we restrict the panel used for our analysis to the individuals who were between the ages of 15 and 55 in 1993 and were working in both years. For these individuals we have an estimate of their income<sup>7</sup> , individual, household and community characteristics, as well as whether they shifted sector of employment and whether they moved location.

There are two main caveats associated with our choice of sample. First, the sample used for analysis does not account for the unemployed or for newcomers to the labor market after 1993. Thus if a key pathway out of poverty is the entrance into the labor force of people who were out of the labor force in 1993, we will not capture this effect. Similarly if individuals fall into poverty as a result of leaving the labor force (for example through death, illness or old age) this will not be observed. If the aim of our study was to capture all poverty dynamics, these would be serious omissions. However, our aim is to understand the extent to which changes in sector and location of existing workers drives poverty entrance and exits. Moreover, evidence from other developing countries suggest that, although entrance and exits from the labor force can be important, it is usually changes in the income status of existing working household members that drives most poverty entrance or exit (McCulloch and Cao, 2007).

> 7 The IFLS collected data on individual income.  This is calculated as the sum of gross profit, net profit, and wages.  Combining these attributes too much income to individuals since, for some households, multiple members will contribute toward the profits from a household enterprise.  We therefore tested for the possible biases which this might impact.  In addition, calculations were done using individual income sources (wages, net profits, gross profits) to test the robustness of the results. The main findings were not altered substantially.  The results are available on request.

15
WPS3945 labor decisions for children, conditional on inherited household wealth and the set of demographic controls included in the second stage.

Speci fi cation 4 presents the OLS estimates with village fi xed effects. As expected, cleaning out unobserved village characteristics reduces the effect of migration on all three schooling outcomes, indicating that part of the observed migration effect arises from village level unobservables.

Speci fi cation 5 presents IV estimates with village fi xed effects. The joint explanatory power of our instruments, conditional on the village fi xed effect and included household and child characteristics, is high in all cases. The instruments also easily pass the overidenti fi cation test. All statistics are reported at the end of each table. The fi rst stage is reported in appendix table A3 for the school enrollment.

In Table 3, speci fi cation 6 checks for any migration induced changes in returns to schooling among high school age children. Speci fi cation 7 examines accumulated schooling, conditional on having enrolled in school, and speci fi cation 8 examines progress through school among children, age 5-17, conditional on current enrollment.

The control variables are jointly signi fi cant and three facts stand out. First, parental education and inherited wealth both have a positive effect on enrollment as expected. Second, extended families have a negative effect on enrollment and three, the total number of siblings of school age has a positive effect on enrollment while the number of siblings 0-5 does not appear to have any signi fi cant negative effect on enrollment.

# 4.1 School Enrollment, Dropout Rates and Accumulated Grades for Age

The migration effect is positive and signi fi cant for all three schooling outcomes. Children in migrant households are not only more likely to attend school, they are also more likely to stay in school in the age range when school dropout rates are at their peak levels, have higher completed grades in their age cohort and progress through school at a rate that is signi fi cantly better than their counterparts in non-migrant households in the same village. Accounting for selection into migration only strengthens these results indicating that the selection bias is negative.

We also fi nd evidence to support migration induced gender differentials in enrollment rates, school retention and progress through school. Enrollment rates increase by 54% for girls in migrant households (from .35 to .54). In comparison, being in a migrant household raises enrollment rates for boys by only 7% (from .73 to .78). As a result, the gender gap in enrollment rates in migrant households is quite a bit smaller (at .24 as compared to 16
# **Ecosystems  - Burden or Bounty?**

Richard Damania<sup>1</sup> ,<sup>2</sup> World Bank Pasquale Lucio Scandizzo Center for International Economic Studies, University of Rome “Tor Vergata” A.J Glauber World Bank

**Key Words:** Renewable resources, bioeconomic modeling, tourism

**JEL Codes:** Q57; Q59; Q20; Q29

> 1 Author affiliations: Corresponding author  - R Damania World Bank Africa Region, <u>rdamania@worldbank.org.  Pasquale Scandizzo,  , scandizzo@economia.uniroma2.it,  Ann Jeannette</u> Glauber World Bank Africa Region aglauber @worldbank.org.

> 2 The authors are grateful to Michael Toman, Luc Christiaensen, Jacques Morisset and Anders Skonhoft for comments on earlier drafts of this paper.
# 2.7. Japan1<sup>3</sup>

As in many other countries, the fiscal relations between the central and local governments in Japan are markedly a vertical financial imbalance. In recent years, the central government has received tax revenues that exceeded its expenditure, while the local governments have received less than the amount needed to perform their functions. This imbalance can be seen from Table 2.5, which presents the relative share of all tax revenues and expenditures of the two levels of government. As suggested by the table, the central government has collected more than 60 percent of the total tax revenue every fiscal year since 1970, while it has expended less than 35 percent of the total tax revenue.

Table 2.5. Vertical Fiscal Imbalance (in percent)

||Tax Revenue Re|ceived by|Tax Revenue Sp|ent by|
|---|---|---|---|---|
||National|Local|National|Local|
|1970|67.5|32.5|33.7|66.3|
|1980|64.1|35.9|23.1|76.9|
|1989|64.2|35.8|34.9|65.1|

Source: Yonehara (1993).

Transfers from the central governmen **t** o the local governments are the primary means to address the vertical imbalance, i.e., the gap between local governments' tax revenues and their expenditures. In Japan, there are five types of transfers from the central government to local governments: the local allocation tax, central government disbursement, local transfer taxes, special traffic safety disbursements, and transfers as a substitution for fixed-assets tax. Of these transfers, the local allocation tax and central government disbursements are the most important, and comprise about 90 percent of the total transfers from the central govermmen **t** o local governments. The local allocation tax is allocated to local governments to equalize their fiscal capacity and to ensure sufficient funds for the public services that local governments are required to provide. The number of central government disbursement programs exceeds one thousand. These disbursements cover almost all fields of local government activities including education, social welfare, public works, transportation, and regional development. The local transfer taxes are levied by the central government, which imposes them as local rather than as central taxes. The central government collects these taxes on behalf of local governments because of advantages in assessment and collection. In a sense, local transfer taxes can be viewed as local taxes that are delegated to the central government for their collection. The remaining part of this subsection discusses in detail how local allocation tax, central government specific purpose disbursements, and local transfer tax are implemented.

# Local Allocation Tax: An Equalization Scheme

This section is based on Ma (1994), Yonehara (1993), Fujiwara (1992), and Ishi (1993).

22
**Table A- 3: Apparel Country Exporter Capabilities**

|**Country**|**Country Capabilities**|**Firm Ownership**<br>**& Size**|
|---|---|---|
|China|Full Package (ODM), vertical capabilities within country with<br>full supply chain geographic clusters<br>MMF and cotton; world’s largest cotton producer, importer, &<br>consumer. Upgrading to higher-end clothing.<br>Primary supplier to global buyers: major buyers have local<br>sourcing offices. Strong domesticmarket as well(OBM).|Foreign<br>direct<br>investment (FDI)<br>approx. 45%;<br>state-owned<br>enterprises (SOE)<br>2%|
|Turkey|Full Package (ODM), vertical capabilities within country<br>Intricate, high-quality garments; Cotton and MMF fiber<br>production. More knitted apparel~70% (t-shirts, pullovers,<br>socks) thanwoven 20% (outerwear, shirts, blouses).|Many small and<br>medium<br>enterprise (SME)<br>firms|
|Bangladesh|OEM Package Contractors (OEM) (knit apparel only)<br>CMT Assembly: woven apparel: woven fabrics: industry is not<br>developed; import 85% of needed materials from China, India,<br>Pakistan, Hong Kong and Taiwan<br>Major buyers tend to have sourcing offices<br>Products: cottonapparel;~50/50knitted (t-shirts) and woven|FDI dominates<br>SOE: < 5%|
|India|Full Package (ODM): vertical: cotton to cut/sew final products<br>Strong design skills<br>Mostly cotton apparel: medium quality and relatively high<br>fashion ready-made garments for export and domestic markets|Local dominates;<br>foreign<br>firms<br>must be a joint<br>venture.<br>Small<br>firm size|
|Vietnam|CMT Assembly; limited OEM; lack domestic textile industry<br>Major buyers tend to have sourcing offices<br>Products: low cost, volume production<br>Cottonand cottonblends; primarily wovengarments|FDI: 45%<br>SOE: 10%|
|Indonesia|OEM Package Contractors: garment manufacturers source the<br>bulk of fabrics from the U.S. and Europe. Do not take full<br>advantage of domestic upstream production for apparel exports.<br>Vertical capabilities; strong, well-integrated materials and<br>accessories base with strong textile and apparel export markets.<br>Products: low cost, volume, synthetics: fabric and apparel;<br>second strongestin MMFbehind China|Foreign and local<br>firms|
|Mexico|OEM and CMT capabilities<br>Products: commodity cottondenimtrousers,imagewear|Foreign and local<br>firms|
|Pakistan|Vertical production for cotton: (cotton, spinning, weaving,<br>knitting, finishing, & cut/sew; focus more on home textiles than<br>apparel products<br>Cottonapparel;nearly 50/50knitted and woven|Foreign<br>firms<br>important<br>Woven<br>apparel:<br>small-scalefirms|
|Cambodia|CMT Assembly; lack domestic textile industry<br>Lessimportant supply country;mostly basics (t-shirts)|FDI: 90%<br>Local: 7%|
|Sri Lanka|OEM Package Contractors and ODM for knitted apparel<br>Niche products: particularly women’s underwear and bras;<br>specialize in knitted intimate apparel, and activewear<br>Several lead firms have long term strategic relationships with<br>firms (Victoria Secret, Nike, Gap)||

Source: Compiled by authors from various trade journals and online sources.

33
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33
# **3** **<u>The cross-sectional distribution over time</u>**

## **3.1** **<u>Summary statistics</u>**

Since we are interested in analysing the world distribution of income, a first question to ask is how much of the world is represented by the surveys included in our database (Table 1). Because high-income countries are more likely to have a survey which can be included in our data, our coverage is higher when measured in terms of GDP than in terms of population. Our data represent 95% of world GDP on average and more than 90% in all benchmark years. On average (and in all years since 1993), our data also cover 90% of the world’s population.

There are, however, substantial differences across regions. The coverage of Sub-Saharan Africa and Russia/Central Asia/SE Europe has improved markedly, in particular after 1988. Our coverage of the Middle East and Northern Africa region, appears to have declined, particularly in the most recent benchmark year and more so in terms of GDP than population.<sup>51</sup> In the latter part of the analysis we focus on the period from 1993 to 2008, because 1988 has such a poor coverage of Sub-Saharan Africa and Russia/Central Asia/SE Europe.

## **3.2** **<u>The Gini index of the global distribution of income</u>**

Table 3 presents our main results on the inequality in the global distribution of income calculated across the unbalanced panel of country-deciles. Compared with within-country distributions, we find a very high level of inequality as measured by the Gini index: between 70.5% and 72.2%.<sup>52</sup> The global Gini index has virtually remained unchanged. Changes between benchmark years have been around 0.5%, with the exception of the period 2003 to 2008, when the Gini decreased by 1.89% or 1.35 Gini points. The Lorenz curves for 1988 and 2008 (not shown here) intersect.

> 51 This appears to be driven by the dropping out of Iran and Tunisia in 2008, which together represent 26% of the region’s GDP and 23% or the region’s population in 2008. Coverage in this region remains low because we miss big countries such as Saudi Arabia or the United Arab Emirates which account for 17% and 10% of regional GDP respectively.

> 52 For example, the Gini indices at the country-level reported in PovcalNet (the full sample, not the sample used in this paper) range from 19.4% to 74.3%, with an average of 42.2%. Only Jamaica (70.81%) and Namibia (74.33%) have Gini coefficients exceeding 70%. This dataset excludes rich countries, which tend to have lower inequality, so the average Gini is probably upward-biased.

24