rafmacalaba/datause-encoder-data huggingface dataset:
(5) Analogous to the case of the BTIO model, the _ij_<sup>_th_</sup> element of the _m_ × _m ZMR_ matrix represents emissions in country _i_ necessary to support demand of final consumption goods in country _j_ . Unlike the BTIO model, virtual carbon flows from _i_ to _j_ include emissions not directly associated with trade between _i_ and _j_ . In fact, these virtual flows include emissions that have been exported from _i_ to regions other than _j_ and from there where exported to _j_ .

The key difference between MRIO and BTIO is that endogenizing intermediate trade results in full measurement of all indirect flows of carbon through trade of intermediate goods. So, for example, the diagonal entry in the _ZMR_ matrix includes domestic emissions linked to production of an intermediate good that is exported to another country, which in turn produces an intermediate good that is imported back into the original country. Offdiagonal row entries represent total emissions virtually exported to a given country (county _j_ for example), including emissions to produce intermediate goods that are used in a third country, which are then exported as intermediate goods to country _j_ . In contrast, the BTIO approach measures only the domestic emissions required to supply domestic final demand in the diagonal entry of the _ZBT_ matrix, while the off-diagonal entry for country _j_ represents only the emissions in the producing country associated with exports, both intermediate and final, to country _j_ .

In principle, therefore, the MRIO approach yields a more complete accounting of the flows of virtual carbon between countries than BTIO. The cost of this more complete accounting is that it is difficult to associate virtual carbon flows with bilateral trade flows, which is where the BTIO approach would be more appropriate. Accordingly, in what follows we use MRIO where complete accounting is needed, and BTIO where accounting linked to specific trade flows is needed.

# **4. Data**

The data that we use in what follows are from version 7 of the Global Trade Analysis Project (GTAP) database for the year 2004 (Dimaramam, 2006). The GTAP data, widely used for computable general equilibrium (CGE) modeling, are available for 106 countries and regions and 57 sectors covering agriculture, industry and services. It includes IO, trade and CO2 emissions data, among others. In the analysis that follows, however, these GTAP data are aggregated to 15 countries and regions and 19 sectors. Arguably this allows for enough detail without overcomplicating the analysis unnecessarily. Abbreviations for the countries and the regions discussed in tables, figures and results are outlined in an annex to this paper.

While the data in GTAP differentiates imports by use (i.e. final demand vs. intermediate consumption), these do not specify the country of origin. Because of this, in this paper, we use the GTAP trade data to estimate the proportion of intermediate (and final consumption) goods and services that come from a particular country assuming the share 9
_L. Fox and A.M. Oviedo_ _Are Skills Rewarded in Sub-Saharan Africa?_ countries still lags behind the rest of the world in average levels of education, we find that in some countries the average education level of manufacturing workers is as good as it was in Asian countries 20 years ago.

Not all countries have followed the same patterns in expanding access. In a number of countries - primarily former British colonies- the “universal primary education” (UPE) model was adopted beginning in the mid-1990s. In this model, a large push was made towards access for all, but often with a quality decline in the initial period, at least in terms of inputs per student.<sup>4</sup> Other countries have focused on expanding both primary and secondary school while trying to maintain quality.

In this paper we focus attention on how the expansion of education has affected the relation between skills and labor market outcomes, specifically wages in the manufacturing sector. We are also interested in how skills with positive returns for wages relate to productivity at the firm level.

There is an ample literature on returns to education in Africa, especially using household survey data from the 1980s and early 1990s. Appleton _et al_ (1996) review some of them, citing 14 studies that analyze the returns to education for wage workers. Most used basic “Mincerian” specifications, although a few had additional information measuring other, usually unobservable, characteristics. All found positive and significant returns to education, but in contrast to what is observed in OECD countries, most researchers find that rates of return curves are still convex in Africa, especially for higher education, implying that skill shortages persist at this level. The cost of expanding higher education, combined with the brain drain (which has, in effect, made the market for skilled professions in some areas global) imply that this skill shortage is likely to persist for some time. Most authors have also found that returns to education are lower for women (see Table 2), and a few studies also find evidence of age effects (see Söderbom _et al.,_ 2006).

# INSERT TABLE 2

Recent analyses of the determinants of wages using repeated cross section data also suggest that returns to education are declining for new generations, especially for primary education (see Fox and Gaal, 2008). This is not surprising given how fast the labor force has been growing compared with the growth of physical capital. In addition, as education systems move from elite systems to broad ones, the selectivity factor associated with education (especially post-primary education) decreases.<sup>5</sup> Finally, there is no doubt that in some countries, enrollment expanded faster than the supply of trained teachers and other inputs, lowering quality.

> 4 See Avenstrup (2006)

> 5 In other words, returns to education were over-estimated for older cohorts as education was correlated with other, unobservable, individual characteristics.

3
21 industry have decreased.19 This is surely a result in favor of trade liberalization, but not the one we have stressed.

Several alternative explanations also<sup>ccme to mind for the</sup> technical efficiency findings. For example, the positive correlation between PROTEC and DISPER might have been<sup>generated by a reduction in</sup> product diversity within certain 3-digit<sup>industries, making intra-</sup> industry technology more uniform across plants.<sup>This, too, would reduce</sup> observed deviations from the estimated<sup>technology. As for the</sup> association between PROTEC and EFFIC,<sup>it might reflect a downward shift</sup> of the efficient cost functiz-n for<sup>industries exposed to world</sup> competition. If this explains table<sup>3, dispersion about the average</sup> isoquant need not have fallen most in<sup>the industries</sup> with the greatest average efficiency improvement. Given that the rank correlation between DISPER and EFFIC is only -.13 (t=-.55),<sup>this interpretation</sup> of the results appears to have some merit.

Finally, despite our very encouraging<sup>findings, it should be</sup> stressed that they reflect a number<sup>of somewhat arbitrary decisions</sup> regarding data preparation and model<sup>specification. First, there was</sup> some specification search involved<sup>in choosing the capital stock</sup> instrument. Initially electricity consumed<sup>was used, and when it became</sup> clear that this led to very inaccurate<sup>estimates, we switched to</sup>

> 19/ Using the same data, de Melo and Urata<sup>(1986) found that price-cost</sup> margins decreased slightly between<sup>the two census years. They</sup> attribute this to increased competitive pressures from liberalization.
# **The Impoverishing Effect of Adverse Health Events: Evidence from the Western Balkans**

by Mariapia Mendola <u>mariapia.mendola@unimib.it</u> Caryn Bredenkamp <u>cbredenkamp@worldbank.org</u> Michele Gragnolati <u>mgragnolati@worldbank.org</u>

*The authors wish to thank Ruslan Yemtsov and Rodica Cnobloch for their helpful advice on the use of the LSMS data and poverty lines.
the actual value of the tangible assets we use Braun’s measures of tangibility which has the same numerator but is related to market value of total assets or total sales rather than to the book value of total assets. In both cases the estimated coefficient is positive, highly significant and almost identical in magnitude.

To summarize, Table 13 shows that when using alternative proxies for external dependence from finance and tangibility our results slightly change in terms of magnitude but remains almost which reinforces our conclusions and the robustness of our results.

# **4.7.3 Do the proxies measure something else?**

Another concern with our measure of the external finance dependence is that it might potentially capture other industry characteristics that are not inherently related to finance. We have already shown that even though the high dependent RZ industries are pro-cyclical, durability of goods produced is not what is driving our results. Another possibility is that the financial needs of an industry might be driven by the sophistication of the industry. Therefore, other possible drivers could be capital or R&D intensity of the industry. We obtain proxies for both measures from Kroszner, Laeven, and Klingebiel (2007) and include their interaction with the financial crisis dummy as additional controls in our baseline regression. As shown in column one and two in the top panel of Table 14 inclusion of none of them weakens our main result. Three additional industry characteristics that the RZ could potentially capture are reported in the same table. The first two, the share of 20 largest intermediates and the Herfindahl index are both capturing the dependence on intermediates and are taken from the work of Cowan and Neut (2007). The reason to include these two proxies is their relation to the complexity of the product. The higher complexity, the higher the number of tasks involved in production and potentially the higher are the working capital and the external finance requirements (Raddatz 2006). The third variable, the product homogeneity is based on the Rauch (1999) classification of industries. Rauch classifies a good as homogeneous if it is sold in organized exchanges or if there is a reference price for it. A heterogeneous product on the other hand requires building up a trading relationship. Therefore, we can expect that a differentiated product will have a higher fixed cost of entry into a foreign market. If the inclusion of this variable weakens the coefficient on the main interaction it would suggest that the Rajan and Zingales (1998) measure is a proxy of entry cost rather than external finance dependence. Again inclusion of any of these variables does not affect the coefficient on the main interaction in any substantial way.

We do a similar set of tests for tangibility. In addition to the capital-labor ratio, R&D intensity and product homogeneity we also include the interactions of physical capital intensity, natural resources intensity and human capital intensity with the crisis dummy. The thought behind 18
Weber, E.U., & Hsee, C. (1998). Cross-Cultural Differences in Risk Perception, but Cross-Cultural Similarities in Attitudes towards Perceived Risk. _Management Science_ 44:1205-17.

Weber, E. U. & Johnson, E. J. (2006). Constructing preferences from memory. In: Lichtenstein, S. & Slovic, P., (Eds.), _The Construction of Preference_ (pp. 397-410). New York NY: Cambridge University Press.

Weber, E. U. & Johnson, E. J. (2009). Mindful judgment and decision making. _Annual Review of Psychology, 60_ , 53-86.

Weber, E. U., Johnson, E. J., Milch, K., Chang, H., Brodscholl, J., & Goldstein, D. (2007). Asymmetric discounting in intertemporal choice: A query theory account. _Psychological Science, 18_ , 516-523.

Weber, E. U. & Kirsner, B. (1996). Reasons for rank-dependent utility evaluation. _Journal of Risk and Uncertainty, 14_ <u>, 41-61.</u> Weber, E. U. & Lindemann, P. G. (2007). From intuition to analysis: Making decisions with our head, our heart, or by the book. In: H. Plessner, C. Betsch & T. Betsch (Eds.), _Intuition in judgment and decision making_ (pp. 191-208). Mahwah, NJ: Lawrence Erlbaum.

Weber, E. U. & Morris, M. W. (2010). Culture and judgment and decision making: The constructivist turn. _Perspectives on Psychological Science, 5_ , 410-419.

Weber, E. U., Shafir, S., & Blais, A.-R. (2004). Predicting risk-sensitivity in humans and lower animals: Risk as variance or coefficient of variation. _Psychological Review, 111,_ 430-445.

Weber, E. U., Siebenmorgen, N., Weber, M. (2005). Communicating asset risk: How name recognition and the format of historic volatility information affect risk perception and investment decisions. _Risk Analysis, 25_ , 597-609.

Weber, E.U. & Stern, P. (2011). The American public’s understanding of climate change. _American Psychologist, 66_ <u>, 315-328.</u> Weber, M. (1921, 1984). _Soziologische Grundbegriffe._ Tuebingen, Germany: J.C.B. Mohr.

Weitzman, M. L. (2007). A review of the Stern review on the economics of climate change. _Journal of Economic Literature_ , 45, 686-702.

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47
Policy Research Working Paper 5096

# **Abstract**

The authors use recent data from the 2006 National Family Health Survey of India to explore the relationship between religion and demographic behavior. They find that fertility and mortality vary not only between religious groups, but also across caste groups. These groups also differ with respect to socio-economic status. The central finding of this paper is that despite their socio-economic disadvantages, Muslims have higher fertility than their Hindu counterparts and also exhibit lower levels of infant mortality (particularly female infant mortality). This effect is robust to the inclusion of controls for non-religious factors such as socio-economic status and area of residence. This result has important policy implications because it suggests that India's problem of “missing women” may be concentrated in particular groups. The authors conclude that religion and caste play a key role in determining the demographic characteristics of India.

This paper—a product of the Poverty and Inequality Team, Development Research Group—is part of a larger effort in the department to understand the relations between religion and demography. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The author may be contacted at pflewitt@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
**Figure 2. Outsourcing in the U.S. Automotive Industry, Assembly and Parts Employment, 1958-2002**

<!-- Start of picture text --> 600<br>500<br>400 Parts<br>Assembly<br>300<br>200<br>100<br>0<br>19581960196219641966196819701972197419761978198019821984198619881990199219941996199820002002<br><!-- End of picture text --> Note: Assembly includes SIC 3711 (Motor Vehicles and Car Bodies) and Parts includes SIC 3714 (Motor Vehicle Parts and Accessories). Source: Employment, Hours, and Earnings from the Current Employment Statistics Survey, SIC basis (U.S. Bureau of Labor Statistics).

As automakers set up final assembly plants in new locations and tried to leverage common platforms over multiple products and in multiple markets, they pressured their existing suppliers to move abroad with them. Increasingly, the ability to produce in all major production regions has become a precondition to be considered for a project. However, what is emerging in the automotive industry is more complex than a seamless and unified global supply base, given the competing pressures of centralized sourcing (for cost reduction and scale) and regional production (for just-in-time and local content). The need for full co-location of parts with final assembly varies by type of component, or even in stages of production for a single complex component or sub-system. Suppliers with a global presence can concentrate volume production of specific components in one or two locations and ship them to plants close to their customer’s final assembly plants where modules and sub-systems are built up and sent to nearby final assembly plants as needed.

6
Policy Research Working Paper 5122

# **Abstract**

This paper examines the characteristics and performance of female-owned firms in Latin America. Data from firm surveys show that female-owned firms tend to be smaller than male-owned firms in terms of employees, sales, costs, and physical capital. Female-owned firms also have lower profits than male-owned firms, but for larger firms this difference disappears after controlling for labor and capital inputs. Medium-size and large femaleowned firms are as productive as male-owned firms of the same size, although micro and small female-owned firms are less productive than male-owned firms. There is no evidence that the differences between female and maleowned firms are due to differences in access to finance or regulatory burdens. However, this paper finds a negative correlation between child care and household obligations and female-owned firm size and performance.

This paper—a product of the Finance and Private Sector Development Team, Development Research Group—is part of a larger effort in the department to understand the role of gender-related issues in private sector development. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The author may be contacted at mbruhn@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
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disaggregted to allow for detailed coidparisons between air and ocean freight rates, as well as between freight costs<sup>of the Caribbean countries</sup> and those of their competitors. 18/ The first four columns in the<sup>table show the number</sup> of tariff line items exported exclusively from the Caribbean by one mode of transport (either air or vessel) and also gives<sup>the average ad valorem freight</sup> rate for each Caribbean country and<sup>all other exporters of these products.</sup> Columns 5 through 8 provide similar<sup>information</sup> for products exported from the Caribbean, some shipments of which are<sup>by air and others by vessel. These</sup> entries allow cross-country comparisons of freight rates and also<sup>permit</sup> comparisons of nominal air and vessel freight rates<sup>for the same goods.</sup> Although there are one or two exceptions, Table 6 shows the Caribbean countries have a general advantage<sup>over their competitors</sup> for air transport, and that the average favorable air freight differential can be as large as 8 percentage points (Belize). Antigua<sup>and Barbuda, for example, employ air</sup> transport exclusively for 38 tariff<sup>line products exported to the United</sup> States and these shipments have an average ad valorem freight<sup>rate of 7.9 per</sup> cent. This nominal freight rate is more than<sup>3 points below the average for</sup> 18/ The table disaggregates all exports of Caribbean countries<sup>into three</sup> distinct product groups: G1 -products exported from Caribbean<sup>countries</sup> only by air freighc. The table (columns 1 and 2) show the number<sup>of items falling in this group and</sup> also indicate their average nominal<sup>freight rate for Caribbean countries</sup> and their competitors.

C2 -are products exported solely by<sup>ocean vessels. Columns 4 and 5</sup> provide information on their number and their average<sup>freight rate.</sup> C3 -are products which are exported<sup>both by air and vessel from the</sup> Caribbean country. Columns 5 through<sup>8 show the number of items falling</sup> in this category as well as their average<sup>transport rate for each type of</sup> carrier. These data permit two<sup>transport cost comparisons:</sup> between the Caribbean country and its competitors; and bewteen air and ocean freight costs for a common group of products.

Appendix tables to this report provide the<sup>detailed freight cost data upon</sup> which Table 6 is based.
# Tables

|**Table 1: Survey Data Used**........................................................................................................................ 18|
|---|
|**Table 2: Kazakhstan District-level Results 2017**...................................................................................... 33|
|**Table 3:Uzbekistan District-level Results 2018**....................................................................................... 40|
|**Table 4: Kyrgyz Republic District-level Results 2016**............................................................................. 46|
|**Table 5:Tajikistan District-level Results 2015**......................................................................................... 48|
|**Table 6: Fay-Herriot Model for Poverty Rate at $5.5/day and $3.2/day**............................................... 74|
|**Table 7: Fay-Herriot Model for share below middle-class line, and average consumption in 2011 PPP**<br>.................................................................................................................................................................... 75|

4