rafmacalaba/datause-encoder-data huggingface dataset:
Figure 6 shows on the world map which countries currently have public credit registries, private credit bureaus or both. Many countries started their public or private credit bureaus during our sample period, which allows us to evaluate the impact of institutional reforms in credit reporting systems on corporate debt maturities. For instance, Peru, Portugal and Thailand established their first private bureaus in 1995, 1996 and 1999, respectively. After these dates, the average short-term to total debt ratios for firms in these countries dropped by 8%, 8% and 9%, respectively, compared to the years prior to the establishment of credit bureaus. In addition, according to the Loanware/Bondware Capital Markets database, the average maturity of loans and bonds for borrowers domiciled in these countries appears significantly longer after the establishment of credit bureaus than before (the increases in average maturities of loans and bonds for borrowers domiciled in each of these countries were 130%, 70% and 40%, respectively, comparing the period before and after the establishment of credit bureaus). This anecdotal evidence seems to confirm the above documented correlations between credit information quality and debt maturity. In order to control for other concomitant factors which may also be affecting corporate debt maturity, we attempt to disentangle the impact of better credit information by estimating the following panel regression: _STD/TD_ = f _(Credit Information Quality, Macroeconomic Environment, Firm Characteristics, Country Fixed Effects, Year Fixed Effects)_ where the dependent variable _STD/TD_ is the average short-term debt to total debt ratio of all available listed companies for each country in each year. Our estimation sample contains country-year observations for 45 countries over 11 years. Panel A of Table 2 presents summary statistics for the variables used in our regression analysis and Panel B provides more details on variable definitions and data sources.

> We proxy for _Credit Information Quality_ using indicator variables capturing the existence of private and public credit registries. Furthermore, GDP Growth and Inflation are included to

> control for the _Macroeconomic Environment._ We also include four variables to account for specific _Firm Characteristics_ : Log of Total Assets, Return on Assets, the Fixed Assets to

> Total Assets ratio, and the Dispersion of Default Risks _._ We introduce both _Country Fixed Effects_ and _Year Fixed Effects_ to control for unobserved heterogeneity across countries and over time.

19
Figure _7_ : Correlation between turnover (% of new hires) and the share of migrants in each occupation <!-- Start of picture text --> 911<br>941<br>912 932<br>816512 921<br>133134142112312313121132111831753233143421261222215331334731122211812232541322813722521321141241242411226263432752723221231814311721742815325214741265431412515315732611213264332754712252216834352250833514235351333441251751343821713516511711243818832522341962531532 422342961524413523933 931 513<br>21 33561231 234 314410<br>610130120613140220230 740260210750310330240810540720830430340710520910510 930<br>0 .1 .2 .3 .4<br>Share of new hires (<12 months)<br>.4<br>.3<br>.2<br>.1<br>Share of foreign workers per occupation<br>0<br><!-- End of picture text --> Source: EU-LFS (2018).

Note: Correlation based on the 136 ISCO-08 3-digit occupations that represent more than 0.1% of the total employment in the EU. The size of the circles represents the share of each occupation in the total employment in the EU. In grey, 95% confidence intervals are reported for comparison.

Figure 2: Correlation between turnover (% of new hires) and the share of income-risk jobs in each occupation <!-- Start of picture text --> 141 513<br>753750810720812813722752723814721815 514751821818 816912512 932523 941<br>510<br>312 742754 520 930 524<br>521<br>740 540731 741515 713 911 933<br>313 265732712834710 910711522<br>140 310 931<br>132 432311 333 422<br>321 962<br>541 611<br>143 830430214314 343516511<br>226 352 832 921<br>610130120613220335131342142612112 3 1210 26021011183123333024042123426122221533133412221123322241411 2 221231633431412 242 315213340 2 332252216645410250251833235351441243341531532 342961413<br>0 .1 .2 .3 .4<br>Share of new hires (<12 months) per occupation<br>1<br>.9<br>.8<br>.7<br>.6<br>.5<br>.4<br>.3<br>.2<br>Share of income-risk jobs per occupation<br>.1<br>0<br><!-- End of picture text --> Source: EU-LFS (2018).

Note: Correlation based on the 136 ISCO-08 3-digit occupations that represent more than 0.1% of the total employment in the EU. The size of the circles represents the share of each occupation in the total EU employment.

39
assets. However, although these are reported among fund operating expenses, they are in fact properly deducted from the investment income of timber assets.

At the end of fiscal 2006, the private equity program had not yet been set in motion. It is not clear if the NZSF intends to act as a passive investor in private equity funds organized by other investment groups or whether it will follow the example of the CPPIB and become involved as a principal investor.

Fund performance, net of investment management expenses, averaged 14.5 percent since its inception. This exceeded the risk-free rate by 8.3 percentage points and was much better than the target excess return of 2.5 percent over the risk-free rate. The fund projects a nominal return of 9 percentage points over the next 30 years and an excess return of 3.5 percent. Annual results show the usual fluctuation from year to year (Table 5.4).

|Table 5.4:InvestmentReturns,20<br>|04-2006<br>||
|---|---|---|
|percent<br>2004|2005|2006|
|Return on Assets<br>10.4|14.1|19.2|
|Risk Free Rate<br>5.3|6.3|6.8|
|ExcessReturn<br>5.1|7.8|12.4|

Source: NZSF Because of the young age of the fund and its small size as well as the need to establish computing and information systems, operating expenses relative to average total assets are high by comparison with other large public pension funds. Operating expenses rose to 71 basis points in 2006, up from 28 points in 2004 (Table 5.5). Investment management fees account for the lion's share of expenses. As already noted, the fund deducts these expenses from reported investment income.

|Table 5.5: Operating|Expenses,|2004-20<br>|06<br>|
|---|---|---|---|
|basis points|2004|2005|2006|
|Manager Fees|11.9|28.8|54.1|
|Custodian|5.4|9.6|8.5|
|Other fund expenses|1.9|2.5|3.1|
|Guardian Expenses|9.2|6.6|5.3|
|Total Expenses|28.3|47.5|71.1|

Source: NZSF

# **5.7 Concluding Remarks**

The NZSF has been created as a state entity with its own independent board and a long investment horizon. The board of directors consists of six professionals who are appointed by the government after a proposal by an independent nominating committee. The board is responsible for investing the fund on a commercial and prudent basis in order to maximize investment returns without incurring undue risks. The NZSF operates with a high level of transparency and public accountability.

60
36

# Table 12. ExRenditures on nutrition interventions. UN agencies and USAID.

<!-- Start of picture text --> Agency S Spent an $ Spent on<br>Direct Nutrition Health<br>UN Agencies: FY 1987<br>FAO 450,000<br>UNICEF 31,660,000 165,000,000<br>WHO 6,800,000 138,000,000<br>UNDP 180,000 2,072,000<br>TOTAL UN AGENCIES 39,090,000 305,072,000<br>USAID FY 1988<br>USAID Child Survival 26,000,000 170,000,000<br><!-- End of picture text --> Source: reproduced from Huffman and Steel, 1990.

Direct nutrition is defined as projects having a direct impact on the nutrition status of the individual. Examples include direct feeding projects, growth monitoring and nutrition surveillance. Not included are health or food security.
**23 |** P a g e

Landesman and Stehrer (2003) divide two‐digit SITC product groups into three broad categories: (1) **_low technology and labor intensive_** activities including among others agricultural products, textiles and clothing, footwear, some chemicals, tires; (2) **_resource intensive_** activities including, among others, manmade fibers, wood and wood products, coke, refined petroleum products and nuclear fuels, mineral products, and chemicals; and (3) **_medium‐ to high‐technology_** production activities which include, among others, machinery and equipment, transport equipment, and electrical and optical equipment, pharmaceuticals, etc. Exports of resource‐intensive products posted the largest increase over 2003‐07, whereas exports of low‐tech, labor‐intensive products recorded the slowest growth. Their share in total declined 7 percentage points, while the share of resource intensive exports increased 7 percentage points in 2007 over 2003 (Table 14).

**Table 14: Technology content of BiH foreign trade in 2003 and 2007 (in percent)**

||Share i<br>exp<br>2003|n total<br>orts<br>2007|Index 2007<br>2003=100|Share i<br>imp<br>2003|n total<br>orts<br>2007|Index 2007<br>2003=100|Exports i<br>of im<br>2003|n terms<br>ports<br>2007|
|---|---|---|---|---|---|---|---|---|
|Low‐tech, labor intensive|31|24|77|29%|24%|85|23%|33%|
|Resource intensive|46|53|116|38%|46%|119|40%|53%|
|Medium to High‐tech|23|22|97|33%|30%|91|28%|36%|

Source: Own calculations based on BiH's trading partners reporting to UN COMTRADE database.

Interestingly, the composition of imports has moved in the same direction indicating slower growth in import demand for medium‐ to high‐ tech products and low‐tech labor intensive products. The latter appears to be the result of domestic production substituting imports, as the export coverage of imports of low tech products has posted the largest increase despite the contraction in their share in total exports. Yet, the export coverage of low‐tech imports has remained lower than those of other product groups The share of resource intensive products reached almost half of total imports while their export coverage also significantly increased by around one‐third to 53 percent in 2007.

The contraction in the share of medium‐ to high‐tech products in total imports, albeit a very small one, raises some concerns about future competitiveness of BiH exports. The share of these products in total imports fell from 33 percent in 2003 to 30 percent in 2007. These products include among others capital equipment goods, whose share in total BiH imports also fell from 18 percent in 2003 to 17 percent in 2007. Note that a 17 percent share is rather low: in Bulgaria’s imports, for instance, the share of machinery was on average 20 percent in 2001‐03.
Policy Research Working Paper 5215

# **Abstract**

This paper estimates the impact of the 2008 food price spike and the 2009 contraction in global growth on undernourishment rates. The analysis is based on a methodology that uses a calorie-income relationship and income distribution data. The authors find that the 2008 global food price spike may have increased global undernourishment by about 6.8 percent, or 63 million people. Moreover, they show that the sharp slowdown in global growth in 2009 could have contributed to 41 million more undernourished people compared with what would have happened if the economic crisis had not occurred.

This paper—a product of the Poverty Reduction and Equity Unit, Poverty Reduction and Economic Management Network—is part of a larger effort in the department to assess the links between crises, nutrition and food security. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The author may be contacted at hzaman@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
and Israel among the industrialized countries, and even poor China and India that experienced a "take off' that dramatically diverges from the median trajectory observed in the global data.

Second, we ask whether the success of several of these countries was due partly to their deviation from the standard path, suggesting that developing countries need to greatly upgrade their R&D efforts, or were these innovation "over-achievers" engaged in wasteful spending? To approach these questions we follow an emerging literature that estimates the social rates of return to R & D. Virtually all studies have used U.S. industry and firm level data and found extremely high social rates of return ranging from 71% (Griliches and Lichtenberg 1984) to over 100% (Terleckyj 1980 and Scherer 1982). Only three studies to date use cross country data, thereby presumably capturing intra-country spillovers. Coe and Helpman (1995) estimate rates of return to R&D of 123% for the G7 and 85% for the remaining 15 OECD countries; van Pottelsberghe de la Potterie and Lichtenberg (2001) find returns of 68% in the G7 and 15% for a subset of the remaining OECD countries. At the long run US cost of capital of 7%, these estimates imply that the optimal levels of R&D should be multiples of their present levels.

To date, the literature relative to developing countries is extremely thin. Lichtenberg (1994) works with a cross section of 53 countries and argues that the return to private R&D is seven times larger than to fixed investment. Coe, Helpman and Hoffmaister (1997) and a sub-sequent literature (Keller 2001) estimate the impact of _foreign_ R&D on manufacturing TFP growth in developing countries. These authors argue that because developing countries' own R&D expenditures are so low, they can be ignored. The data employed here suggest that developing-country R&D is not necessarily insignificant relative to the size of their economies, and more importantly, the returns are substantial. In fact, the returns to R&D in developing countries are above those for industrialized countries.

> Our estimation strategy attempts to deal with several issues raised in the existing literature, particularly those employing single cross sections, related to unobserved country heterogeneity and the likely endogeneity of R&D. For instance, Barro and Sala-

3
# B. Impact on nonmonetary dimensions of poverty

||_Electricity_|_Sewerage_|_Water_|_Health_|_Incomplete_<br>_elementary_|_Illiteracy_<br>_15+_|_Illiteracy 6–_<br>_14_|_Floor_|_Restroom_|
|---|---|---|---|---|---|---|---|---|---|
|FAIS ∞|−2.055***|−9.008***|−6.424***|2.401***|7.329***|4.192***|0.644***|−4.459***|−1.506***|
||(0.119)|(0.386)|(0.335)|(0.511)|(0.204)|(0.133)|(0.0614)|(0.267)|(0.212)|
|Own revenues ∞|0.114|2.296***|0.214|−2.395***|−0.311**|−1.755***|−0.0394|−0.0706|1.975***|
||(0.0759)|(0.246)|(0.213)|(0.325)|(0.130)|(0.0846)|(0.0391)|(0.170)|(0.135)|
|Unconditional<br>revenue ∞|0.244*|4.376***|6.037***|10.64***|−2.469***|−2.084***|−0.522***|7.692***|−1.800***|
||(0.146)|(0.472)|(0.409)|(0.624)|(0.249)|(0.162)|(0.0750)|(0.326)|(0.259)|
|Conditional<br>revenue ∞|1.952***|4.538***|1.765***|8.324***|−3.747***|−1.410***|−0.503***|3.140***|0.573**|
||(0.153)|(0.497)|(0.431)|(0.657)|(0.263)|(0.171)|(0.0790)|(0.344)|(0.273)|
|Direct workers ‡|14.44***|14.88***|19.04***|13.26*|−7.233***|−2.726|−3.236***|20.85***|8.782***|
||(1.612)|(5.222)|(4.530)|(6.906)|(2.760)|(1.795)|(0.830)|(3.611)|(2.868)|
|Indirect workers<br>‡|−23.90***|−124.9***|−54.03***|56.00***|−2.075|32.82***|−1.342|−76.36***|2.167|
||(4.967)|(16.09)|(13.96)|(21.28)|(8.503)|(5.531)|(2.557)|(11.13)|(8.838)|
|Productivity per<br>occupied ‡‡|−0.375***|0.704*|−0.433|−1.157**|0.180|−0.993***|0.0544|0.476*|−0.227|
||(0.112)|(0.361)|(0.313)|(0.478)|(0.191)|(0.124)|(0.0574)|(0.250)|(0.198)|
|Private business<br>assets ‡‡|0.213***|2.518***|1.377***|4.027***|−2.339***|−0.594***|−0.163***|1.904***|0.328**|
||(0.0744)|(0.241)|(0.209)|(0.319)|(0.127)|(0.0829)|(0.0383)|(0.167)|(0.132)|
|Semiurban|−1.091***|2.267**|−3.148***|3.997***|−0.508|0.414|1.052***|5.020***|−5.415***|
||(0.274)|(0.888)|(0.770)|(1.174)|(0.469)|(0.305)|(0.141)|(0.614)|(0.488)|
|City|−1.020***|1.952*|−5.831***|2.443|−0.587|1.324***|1.637***|3.761***|−7.083***|
||(0.360)|(1.165)|(1.011)|(1.541)|(0.616)|(0.401)|(0.185)|(0.806)|(0.640)|
|Constant|93.47***|33.92***|62.93***|−66.62***|83.11***|27.38***|7.793***|27.33***|99.26***|
||(0.922)|(2.986)|(2.590)|(3.949)|(1.578)|(1.026)|(0.475)|(2.065)|(1.640)|
|N|6,143|6,143|6,143|6,143|6,143|6,143|6,143|6,143|6,143|
|R-2|0.175|0.430|0.234|0.213|0.593|0.566|0.124|0.397|0.133|
|Municipalities|2,048|2,048|2,048|2,048|2,048|2,048|2,048|2,048|2,048|

_Note:_ The mean income corresponds to the average household income per capita in each municipality in logarithms. Monetary values are expressed in real terms in August 2014. Pooled OLS. Semiurban refers to municipalities with a population between 2,500 and 15,000. City refers to municipalities with more than 15,000 inhabitants.

∞ Fiscal revenue items in the logarithm of the average monthly per capita value three previous years. ‡ Occupied per inhabitant, employees of the economic units or indirect workers, reported in the economic census of the circa year. ‡ ‡ Productivity by the total of occupied and active of the economic units of each municipality. Standard error in parentheses. *** p < .01 ** p < .05 * p < .1 30
- 82 -

For the other industries in our sample we made no adjustment for this problem because we did not think it was too serious. There is obviously still some unit value error in our results but its size is likely to be smaller than with cars and its direction is not clear.

# ------------------------------------------------------------------

|<br>Number of Unit Values Available<br>Brazil<br>and Mexi|TABLE 6.<br>and Ma<br>co (1975)|1<br>tched in<br>and U|Our 17<br>SA (1977)|Industr|__---------__--<br>y<br>Sampl|e,|
|---|---|---|---|---|---|---|
|Br|azil|M|exico|Un|ited Stat|es|
|total|matched|total|matched|total|matc|hed|
||(a)||(b)||Brazil/<br>USA|Mexico/<br>USA|
|GrainMill Products<br>21|6|55|10|37|19|18|
|Sugar &Confectionery<br>Products 10|5|7|3|13|9|9|
|<br>Malt and Malt Beverages<br>3|2|7|2|20|17|17|
|<br>Tobaccoand Tobacco Products<br>6|4|4|3|21|21|16|
|Textiles<br>49|21|209|37|54|51|49|
|Footwear and Leather Products<br>65|*9|163|23|48|18|22|
|Pulp and Paper<br>24|12|133|16|68|47|53|
|<br>Soap and Detergents<br>13|7|43|9|38|20|23|
|<br>Paints<br>26|6|63|10|54|31|30|
|Agricultural<br>Fertilizers<br>9|6|32|6|22|22|22|
|Petroleum Refining and Products59|19|56|11|44|17|13|
|<br>Tires and Inner Tubes<br>14|7|22|4|16|8|6|
|Cement<br>5|1|10|4|7|1|2|
|Bricks<br>23|4|31|5|24|4|2|
|Iron and Steel<br>94|27|114|21|76|29|27|
|Radio and TV Receivers<br>22|13|92|12|21|17|18|
|Motor Vehicles<br>100|12|393|16|101|41|15|
|Total17Industries<br>543|171|1,434|192|674|372|342|

(a) in the Brazil/USA comparison; (b) in the Mexico/USA compa'3son; Source: see industry tables in Statistical Appendix.

Strengths of the Industry of Origin Approach The disadvantage we suffer in our approach from potential unit value error is offset by certain strong advantages of the census material, as follows: 1) The census is not a sample, but covers the vast bulk of activity in manufacturing in the year specified. This means that the problem of representativity is much milder for us than it is in the expenditure approach. With the census one can judge the representativity of the "unit values" to be matched from a much wider range of information than ICP had at its disposal. Table 6.1 shows that our 17 industry sample yielded 1,434 Mexican unit values from which 192 were chosen to match with the USA, and 543 unit values for Brazil of which 171 were matched with the USA. The ICP, by contrast, had to live with what it got from national statistical offices (at least for consumption goods). For Mexico, it received only 284 of the much larger number of consumer prices it
# **_South Asia is a negative outlier in investment in Infrastructure_**

In order to examine the role of fiscal stimulus, we examine how much South Asian economies are spending on physical and social infrastructures, and what is the potential of this stimulus in overcoming the crisis. Is South Asia spending too much or too little on education, health, roads, power and water compared to the rest of the world?

|**Table 7: Cross Cou**<br>**Pan**|**ntry Com**<br>**el A**|**parison of Pu**<br>|**blic Expenditure on Edu**<br>**Pan**|**cation an**<br>**el B**|**d Health**<br>|
|---|---|---|---|---|---|
|||(% of Total|||(% of Total|
||(% of<br>GDP)|<br>Government<br>Expenditure)||(% of<br>GDP)|<br>Government<br>Expenditure)|
|_Public Expenditure on_<br>_Education_|2003|2003|_Public Expenditure on Health_|2004|2004|
|log (real GDP per capita)|0.56|0.95|log (real GDP per capita)|-1.51*|-2.51|
|log (real GDP per capita)^2|-0.005|-0.99|log (real GDP per capita)^2|0.15***|0.25*|
|India|0.88|-4.83**|India|-1.41**|-6.10***|
|Bangladesh|-0.73*|-0.45|Bangladesh|-1.34***|-2.53***|
|Pakistan|-1.34***|-9.43***|Pakistan|-1.96***|-7.34***|
|China|.|.|China|-0.91*|-8.94***|
|Sri Lanka|.|.|Sri Lanka|-0.75***|-0.81|
|Control for Size|Yes|Yes|Control for Size|Yes|Yes|
|Observations|85|62|Observations|116|115|

_Source_ : WDI

*** represents significance at 1%, ** represents significance at 5%, * represents significance at 10%. Country size is measured by population.

Table 7 reports cross-country regressions on public expenditure on education and health both as a share of GDP and total government expenditure. As evident, South Asia is a big negative outlier controlling for the level of income, non-linearities in development and country size (proxied for by population). In terms of expenditure on health, all countries in South Asia are big negative outliers. Pakistan is a large negative outlier on expenditure on education. This global downturn provides an opportunity to increase spending on education and health. This will not only help in stimulating the economy but also serve to bring them at par with the rest of the world.

What about physical infrastructure? Is South Asia investing too little or too much? The biggest problem in answering this question is the extreme paucity of comparable data for a large number of countries. The data reported for different countries use very different definitions thus limiting the scope of our exercise.

33