rafmacalaba/datause-encoder-data huggingface dataset:
measures are more focused on client satisfaction and the efficiency of BDC, but are consistent with a bank that has a well defined mandate.

Finnvera’s performance targets are defined by the Ministry of Employment and Economy in the annual corporate plan and include the following indicators: Minimum amount of guarantees and loans; minimum lending to startups; minimum lending to growing and internationalizing companies;<sup>16</sup> distribution of lending by risk category (minimum percentage of lending to riskier segments of the market); employment creation (typically estimated by the companies); minimum ratio between loans and guarantees; maximum lending to affiliates; ECA objectives; efficiency cost ratio; and capital adequacy ratio. Finnvera’s corporate goals provide clear guidance to the administration regarding the segments of the market on which the management needs to focus. The Ministry of Employment and Economy follows the accomplishment of these objectives on a regular basis.

In the case of BancoEstado, the corporate plan highlights the bank as a universal (commercial) bank that provides all types of financial services to nationals. The government does not participate in the formulation or approval of the corporate plan. Performance indicators are more aligned with those of any other commercial bank, with only a hidden attention to the social objectives of the company. As shown in Table 4, the 2008 balanced scorecard included three components: financial results (30 %); client satisfaction (42%); and organizational capability (28 %). Only one social objective is included in the client satisfaction area, but refers to the liability management side of the bank (increasing the network of points of sales ( _caja vecina_ )). The balanced scorecard makes no references to other social objectives such as the development of products for micro and SME lending, and mortgages for low-income people.

**Table 4 BancoEstado Balanced Scorecard 2008**

|**Financial Results**<br>(30%)|**Client Satisfaction**<br>(42%)|**Organizational**<br>**Capacity**<br>(28%)|
|---|---|---|
|Efficiency ratio|Sales through<br>remote channels|Operational costs|
|Average ROE of<br>commercial banks|Multiple products to<br>clines|Internal control|
|Diversification of<br>sources of revenues|Index of client<br>satisfaction<br>Access through<br>points of sales|Engagement of<br>employees|

> 16 Internationalizing companies are companies that are looking for euro or world scale

15
linkages were also high with manufactured exports of China and those of the United States at 34% and 25% respectively that year. At 8%, the region’s linkages with India’s manufactured exports were smaller but still substantial. By contrast, West European manufactured exports have had relatively few linkages with Africa and the four external comparators.

Western Europe’s linkages to Germany’s manufactured exports were predominantly downstream but included a large upstream component. The region's linkages to manufactured exports of China, the United States, and India were almost evenly divided between upstream and downstream components with upstream linkages being slightly higher. Upstream and downstream linkages of Western Europe’s to US manufactured exports and the upstream linkages to Germany’s manufactured exports have declined significantly in the past 20 years, during which the downstream linkage to German exports has grown by an even larger amount, and the region’s linkages with manufactured exports of China and India developing over the period.

# **6. Establishment-Level Analysis of GVC Participation**

Global or otherwise, a value chain defines a series of transactions taking place at the level of the firm or business establishment. Typically, we consider a business establishment to be part of a global value chain if it is exporting downstream goods that it has produced using imported inputs. To identify instances of participation in GVCs from cases of non-participation in the production activities of the population of establishments in an economy, we analyze the factors that affect participation at the firm level. For this purpose, we use data from the World Bank Enterprise Surveys and estimate a linear probability model of firms’ GVC participation: (3) PP(yy= 1 | XX) = X where equals 1 if a firm operates along a GVC and 0 otherwise. We adopt two definitions of GVC participation. The first one is a standard definition in which a given firm is considered active yy in GVC if it is exporting and importing. Alternatively, we follow a less exclusive definition where a manufacturer is deemed to be operating along a GVC either if it exports any part of its output

18
exports. Table 6 lists countries for which the value of non-agricultural preferences exceeds 5% of dutiable non-agricultural exports to the US, after incorporating utilization. This list is nearly the same as the list in table 5. For 17 of these 29 countries values exceeded 10%. With a few exceptions, most of this value is attributable to apparel preferences. Other significant products include petroleum-related products,<sup>18</sup> chemicals, jewelry, and electrical machinery. Table 7 shows 23 countries for which the value of agricultural preferences exceeded 5% of dutiable agricultural exports to the US, after incorporating actual utilization. Most of these countries do not appear in table 5. Five of these beneficiaries had values exceeding 10%. Of the total preference value of all agricultural products, 50 % was accounted for by 5 products in 2003: melons, fresh cut flowers, frozen orange juice, raw cane sugar, and fresh asparagus. There are at least three reasons why apparel accounts for such a large share of the value of preferences for many countries: apparel often accounts for a large share of exports; apparel exporting countries had high apparel utilization rates; apparel exports had relatively high preference margins. However, the removal of QRs on apparel in January 2005 (with the completion of the Agreement on Textiles and Clothing) reduced the relative prices of apparel imports from China (prior to the reimposition of QRs), South Asia, and ASEAN countries, where QRs had been highly restrictive. Thus, US apparel imports from CBTPA, ATPA and AGOA countries were likely to fall relative to their 2003 levels. Even if tariff preference margins in 2005 remained similar to those in 2003, these margins would be applied to a smaller value of apparel imports, thus reducing the value of the preferences below those in tables 5 and 6. Data from the Dept. of Commerce suggest that US 2005 apparel imports from SubSaharan Africa dropped by 3.1%, from CBI dropped by 0.2%, and from ATPA actually rose by 35.9% compared to 2003.<sup>19</sup> These aggregate figures do not suggest a radical drop in the value of apparel

> 18 Although the value of preferences for some AGOA countries comes exclusively from petroleum-related exports, none of these countries showed values exceeding 1% of dutiable exports to the US.

> 19 Major Shippers Report, <u>http://www.otexa.ita.doc.gov/msr/cat1.htm, downloaded June 2006.</u>

20
## **Figure 2. Share of Rice Expenditure in Total Expenditure by Deciles, 1998**

<!-- Start of picture text --> Share of Rice Expenditure-1998<br>45%<br>40%<br>35%<br>30%<br>25% Rural<br>20% Urban<br>15%<br>10%<br>5%<br>0%<br>1 2 3 4 5 6 7 8 9 10<br>Deciles<br><!-- End of picture text -->

**Figure 3. Share of Rice Expenditure in Total Expenditure by Deciles, 1993**

<!-- Start of picture text --> Share of rice expenditure-1993<br>50%<br>45%<br>40%<br>35%<br>30%<br>Rural<br>25%<br>Urban<br>20%<br>15%<br>10%<br>5%<br>0%<br>1 2 3 4 5 6 7 8 9 10<br>Deciles<br><!-- End of picture text -->

# **Net seller-net buyer**

Net exposure is cited in the literature as a major factor that determines the impact of a price change on household welfare. Thus, we group the households into net seller11
51

|Dependent variable|Log<br>exp/capita<br>(excl. health<br>expenditure)|Log (asset and<br>consumer<br>durable value)|Disaster<br>relief|Domestic<br>remittance<br>s|Remittance<br>s from<br>outside VN|Herfindahl<br>index|
|---|---|---|---|---|---|---|
||ML|OLS||Tobit||OLS|
||(1)|(2)|(3)|(4)|(5)|(6)|
||parameter/|parameter/|parameter/|parameter/|parameter/|parameter/|
||t‐value|t‐value|t‐value|t‐value|t‐value|t‐value|
|> 25,000 people)|||||||
||‐8.10|‐3.58|1.72|‐1.67|‐0.83|‐0.01|
|Log (1+tenths of hours to urban area<br>of > 100,000 people)|‐0.0426***|0.0482**|46|43|‐1286|0.0023|
||‐6.03|2.22|0.35|0.91|‐1.45|1.1|
|Log (1+tenths of hours to urban area<br>of > 500,000 people)|‐0.1190***|‐0.0751***|276*|‐62|‐152|‐0.0022|
||‐11.73|‐3.28|1.74|‐1.15|‐0.15|‐0.99|
|**_Regional characteristics_**|||||||
|1 if urban; 0 otherwise|0.27923***|0.4571***|‐469|30|2260|0.0247***|
||34.94|10.56|‐1.52|0.25|1.44|5.48|
|Region 2: Northeast|0.0860***|0.1445**|1004*|304*|1642|‐0.0226***|
||5.25|2.46|1.67|1.93|6.20E‐01|‐3.49|
|Region 3: Northwest|0.0335|0.0545|1637**|‐454|9462*|0.017|
||1.31|0.43|2.14|‐1.29|1.87|1.45|
|Region 4: Northcentral Coast|‐0.0504**|0.0366|1516**|613***|7550**|‐0.0072|
||‐2.41|0.48|2.32|3.51|2.46|‐0.91|
|Region 5: Southcentral Coast|0.1409***|0.2884***|5111***|515**|8506**|0.0254**|
||5.55|2.8|4.44|2.15|2.08|2.49|
|Region 6: Central Highlands|0.0687***|0.1144|1936**|811***|5428|0.0605***|
||2.75|0.87|2.32|3.24|1.18|5.24|
|Region 7: Southeast|0.2347***|0.2871***|3449***|835***|12333***|0.0825***|
||12.07|3.88|4.2|4.32|3.87|10.37|
|Region 8: Mekong Delta|0.2157***|0.2418**|2840***|686***|13412***|0.0927***|
||9.86|2.29|3.6|2.91|3.64|8.84|
**Figure 5. GDP Per Capita and Real Exports for Agricultural Exporters**

<!-- Start of picture text --> (a) GDP Per Capita (1950=1) (b) Export Volume (1960=1)<br>1.10 1.3<br>1.2<br>1.00<br>1.1<br>0.90 1<br>0.9<br>0.80<br>0.8<br>0.70<br>0.7<br>0.60 0.6<br>0.5<br>0.50<br>0.4<br>Argentina Uruguay New Zealand Argentina Uruguay New Zealand<br>19501952195419561958196019621964196619681970197219741976197819801982198419861988199019921994199619982000<br>19601961196219631964196519661967196819691970197119721973197419751976197719781979198019811982198319841985198619871988198919901991199219931994199519961997199819992000<br><!-- End of picture text --> Note: Countries’ GDP per capita and real exports are divided by the world’s. _Source: Maddison Project and WDI._
Policy ReseaRch WoRking PaPeR 4479

# **Abstract**

This paper takes stock of labor market developments in Bosnia and Herzegovina over the period 2001-2004, using the panel _Living Standards Measurement Study/Living in Bosnia and Herzegovina survey_ . The analysis estimates a multinomial logit model of labor market transitions by state of origin (employment, unemployment, and inactivity) following the specification of widely used models of transition probabilities, and analyzes the impact of standard covariates. The results provide strong evidence that there are indeed significant differences in labor market transitions by gender, age, education, and geographic location. Using the panel structure of the multi-topic survey data, the authors find that these transitions are related to welfare dynamics, with welfare levels evolving differently for various groups depending on their labor market trajectories. The findings show that current labor market trends reflecting women’s movement out of labor markets and laid-off male workers accepting informal sector jobs characterized by low productivity will lead to adverse social outcomes. These outcomes could be averted if the planned enterprise reform program creates a more favorable business environment and leads to faster restructuring and growth of firms.

This paper—a product of the Poverty Reduction and Economic Management Sector Unit, Europe and Central Asia Region—is part of a larger effort in the department to understand the economic transition in former centrally planned economies. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The author may be contacted at etiongson@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
(Bolivia, Mexico, Brazil, Peru, and Chile).<sup>4</sup> The coefficients of temperature (and temperature squared) provide an estimate of the long-run relationship between temperature and welfare (i.e. the output-climate elasticity) inclusive of adaptation. The estimated relationships are then used to simulate the impact of the climate changes that the IPCC projects for the next 50 years. Their poverty analysis is, however, crude. The authors do not attempt to estimate the poverty-output elasticity; they simple assume that a negative relationship exists between per capita income and poverty. As previously explained, income per capita and population growth projections are needed in order to be more precise about the number of poor people fifty years from now. Because of this, the authors are careful to warn that their simulation results should not be interpreted as forecasts but as simply indicative of the direction and magnitude of the effects that might be expected from climate changes. Table 1 below summarizes the estimated impacts of increased temperature on the mean level of welfare along with the _likely direction_ of the effects of anticipated future climate change on poverty and income inequality.

A couple of points are worth highlighting. First, the presented estimates (derived from the country specific elasticities and climate projections) refer to the percentage change in per capita income as a result of climate change relative to a world without it. Second, the direction of the poverty impacts due to climate change is derived by assuming a distribution-neutral change in the mean level of welfare. Third, as in the case of per capita income changes, the increase or decrease in poverty refers to a future situation without and with climate change, and not relative to the current situation. Therefore, a prediction that poverty will increase in Brazil does not imply that poverty will be higher relative to the present, but that it will be higher relative to the no-climate change scenario in 2058. Finally, caution should be applied when looking at the reported effects on poverty and inequality since they are based on the distribution of income (per capita) between municipalities and not households.

> 4 In four of the five countries the dependent variable in the analysis is income per capita, whereas in Bolivia consumption per capita is used. Four explanatory variables are included in the regression models—temperature, rainfall, education, and urbanization rates.

5
<!-- Start of picture text --> 15<br>py<br>& © Costa Rica<br>a 6§<br>a<br>foi<br>= @ Colombia q@ominica<br>Bos | se * Republic<br>% araguay Peru — Chile<br>iB) Brazil<br><!-- End of picture text -->

<!-- Start of picture text --> 8 -15 ado uatemala Mexico<br>=. ® Venezuela<br>rn ; © 4 Nicaragua<br>PI ~25 Argentina Panama<br>| o,- °<br>O = Uruguay<br>-50 @ Ecuador<br>-0.10 -0.05 0.00 0.05 0.10<br>Change in Gini (2002-13)<br><!-- End of picture text -->
around 0.70, whereas the marginal propensity to consume out of reduction in taxes and tax rebates was only about 0.13 (Feldstein 2009). When separating revenue changes resulting from legislation from other changes, such as those related to changes in prospective economic conditions, it is estimated that a tax cut equivalent to 1 percent of US GDP raises output by about 1 percent within a year, but the magnitude rises in the following periods and reach about 3 percent after three years (Romer and Romer, 2010). A survey of empirical evidence suggests that for the 2008-9 stimuli by the G20, the low set of multipliers was 0.3 for revenue, 0.5 for capital spending, and 0.3 for other spending; the high set of multipliers was 0.6 for revenue, 1.8 for capital spending, and 1.0 for other spending (Spilimbergo, Symansky, and Schindler 2009).<sup>2</sup> <mark>For a sample of 102 developing countries, th</mark> e one-year government spending multiplier resulting from borrowing from official creditors is estimated to be around 0.4 (Kraay, 2012).

**Table 2 Estimates of fiscal multipliers in China and other settings**

|_Entity_|_Period_|_Size of multiplier_|_Source_|
|---|---|---|---|
|China|2008 global crisis|Short term: 0.84<br>Medium term: 1.10|He, Zhang, and Zhang (2009)|
|United States|Since 1990|Government spending: 1.5–2<br>Tax-cut: 0.5–1|Nallari (2010)|
|United States|Since 1980|Tax-reduction and tax-rebate: 0.13|Feldstein (2009)|
|United States|1950-2006|1% within a year, and up to 3% after three years|Romer and Romer (2010)|
|G7|Since mid-1970s|High set: 0.6 for revenue, 1.8 for capital spending, 1.0<br>for other spending<br>Positive spending shock: 1.22 (negative output gap);<br>0.72 (positive output gap)|IMF Fiscal Monitor (April 2012)|
|G20|2008 global crisis|Low set: 0.3 for revenue, 0.5 for capital spending, 0.3<br>for other spending|Spilimbergo, Symansky, and<br>Schindler (2009)|
|Developing||||
|countries|1970-2010|Government spending 0.4|Kraay (2012)|

_Source_ : Authors’ compilation.

Whether developing or advanced economies have larger multipliers depends on several factors. On the one hand, multipliers may be larger in developed countries because they are better able than developing countries to maintain increased government consumption. Ilzetzki, Mendoza, and Vegh (2011) examined quarterly data on 44 countries (20 high-income and 24 developing 9
Policy Research Working Paper 11050

# **Abstract**

Nitrogen fertilizer is essential for boosting agricultural yields and food production. However, agricultural subsidies often drive the inefficient application of fertilizer, leading to significant costs for farms, the environment, and economies. Scientific evidence indicates that nitrogen pollution has exceeded safe planetary boundaries, making it one of the world’s largest externalities. Yet, the global economic costs and consequences of subsidized nitrogen fertilizer use remain poorly understood. This paper combines data on subsidies, satellite-derived measures of crop productivity, nitrogen usage, water quality, and spatial and administrative data sets to provide globally comprehensive empirical estimates of the long-term costs of fertilizer use and the role of subsidies. The results show that in regions with large input subsidies, nitrogen overapplication diminishes crop productivity returns and increases nitrogen runoff into waterways, with lasting implications for human health and labor productivity. More than half of global agricultural production occurs in areas with high subsidized nitrogen use, where the marginal benefit of additional fertilizer is negative. This indicates significant potential to reduce fertilizer use without adversely affecting crop yields. Globally, up to 17 percent of nitrogen pollution in water is linked to inefficient input subsidies, contributing to hypoxic zones and harmful algal blooms. Conversely, decoupled subsidies not tied to production reduce these harmful spillovers. These findings underscore the enduring consequences of nitrogen fertilizer, how well intentioned but poorly designed subsidies can aggravate nitrogen waste, and the potential of policies to pave the path to reform.

This paper is a product of the Office of the Chief Economist, Planet Vertical. 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 author may be contacted at ezaveri@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
portfolios.<sup>17</sup> Pension fund providers need to be incentivized to be responsible for good pension design which delivers adequate pension incomes.<sup>18</sup>

# **III. Creating Benchmark Portfolios**

24. The proper approach to pension design, including for DC schemes, is to start calculating back from a desired/required pension level and setting a contribution rate, investment return target and consequent long term investment strategy adequate to reach this goal. Given, say, a 60% replacement rate target at the age of 65 and a contribution rate of 10%, one needs to calculate expected annuity prices based on mortality data and estimates about improvements, and then use asset return and wage curve modeling to see what sort of investment performance and portfolio may be necessary to reach the pension capital required.

25. It may turn out that the set of replacement rate / retirement age /contribution rate does not allow for a realistic investment performance, in which case one or more of those parameters may have to be adjusted - though some of these decisions, such as the contribution rate, may well be outside the control of the supervisory authority (e.g. in mandatory systems where they are set in legislation). Indeed, some of the problems with second pillar systems have been driven more by unrealistic expectations beyond the specific short-term investment challenges outlined in this paper.

26. This approach is fundamentally different from the standard one of ‘contributions are defined and benefits will be as much as investment strategies succeed in delivering’. From day one a long term investment strategy and portfolio must be utilized, whose only objective is to lead to adequate pensions.

27. Given this goal, an alternative approach to controlling investment risk is required. Berstein et al (2013) argue that it is necessary to define an appropriate variable on which to measure pension risk, one that includes all relevant sources of risk faced by members of the pension system. **“** _The target variable that best represents the member’s position on retirement is the replacement rate...The appropriate measurement of risk is given by the marginal effect on the expected replacement rate of carrying out a particular investment strategy, the scatter around that expected value and the shape of its density.”_ The best way to align regulation with the long-term goal of delivering adequate pension income is through the use of benchmarks or reference portfolios. These are designed to deliver a targeted pension income within the parameters of the greatest probability and the least risk.

> 17 See (World Bank 2013), (Bazak and Makarov 2009), (Castañeda and Rudolph 2010), (Rudolph et al 2010)

> 18 According to Blake et al. (1999), Ibbotson and Kaplan (2000), and Iglesias and Walker (2010), strategic asset allocation explains more than 90 percent of the variability of pension fund long term returns. .

7