rafmacalaba/datause-encoder-data huggingface dataset:
**Figure 2.2: Original Altman Z-score model**

## **Z-Score = 1.2A + 1.4B + 3.3C + 0.6D + 1.0E**

Where: A = Working Capital / Total Assets B = Retained Earnings / Total Assets C = Earnings Before Interest & Tax / Total Assets D = Market Value of Equity / Total Liabilities E = Sales / Total Assets _Source_ : Altman, 1968.

When based on internal data to be estimated, a statistical model can capture specific risks the government is exposed to and be based on actual performance by beneficiary entities. Calibrating a model based on internal data requires a significant amount of historical information.

# **_Scenario analysis_**

In scenario analysis, various scenarios for key risk drivers (e.g. macroeconomic variables, revenue drivers, cost drivers, etc.) are constructed. Risk managers then estimate how scenarios impact financial performance of beneficiaries and their ability to service debt.<sup>19</sup> A scenario analysis model has to make assumptions about how key risk drivers interact with each other and how they affect an entity’s performance. Also, risk managers may need to define a threshold beyond which financial performance translates into a credit event, such as a default.

Scenarios can be constructed using deterministic or stochastic processes. Scenario analysis based on deterministic processes usually uses a smaller number of discrete scenarios and often no probabilities are attached to the respective scenarios. Risk managers may define a base case (most likely scenario) and several risk scenarios defined by an adverse development in individual risk drivers or a combination of a few risk drivers. If stochastic processes are constructed, a probability distribution for key risk drivers and their dependent relationships are estimated. A large number of scenarios can be simulated (e.g. using Monte Carlo simulation).<sup>20</sup> The outcome of scenario analysis can be an estimate of the frequency of default events. A default probability distribution may be estimated if stochastic modeling of risk drivers has been employed.

The Swedish National Debt Office has implemented scenario analysis using simulation models as a tool to evaluate more complex guarantees. Also the Turkish Treasury developed a macroeconomic simulation model in the past (see below for both country examples).

> 19 Stress testing is one approach to scenario analysis. In stress tests macroeconomic, financial, and idiosyncratic shocks beneficiaries may be exposed to are designed. Next, the impact on beneficiaries is estimated and an assessment of credit risk in stress scenarios is deduced. 20 In debt management scenario analysis is usually employed to assess the impact of market risks (interest rate and exchange rate risks) on debt portfolios.

11 | P a g e
Policy ReseaRch WoRking PaPeR 4934

# **Abstract**

Innovation is key to technology adoption and creation, and to explaining the vast differences in productivity across and within countries. Despite the central role of the entrepreneur in the innovation process, data limitations have restricted standard analysis of the determinants of innovation to consideration of the role of firm characteristics. The authors develop a model of innovation that incorporates the role of both owner and firm characteristics, and use this to determine how product, process, marketing, and organizational innovations should vary with firm size and competition. They then use a new, large, representative survey from Sri Lanka to test this model and to examine whether and how owner characteristics matter for innovation. The survey also allows analysis of the incidence of innovation in micro and small firms, which have traditionally been overlooked in the study of innovation, despite these firms comprising the majority of firms in developing countries. The analysis finds that more than one-quarter of the microenterprises are engaging in innovation, with marketing innovations the most common. As predicted by the model, firm size has a stronger positive effect, and competition a stronger negative effect, on process and organizational innovations than on product innovations. Owner ability, personality traits, and ethnicity have a significant and substantial impact on the likelihood of a firm innovating, confirming the importance of the entrepreneur in the innovation process.

This paper—a product of the Finance and Private Sector Team, Development Research Group—is part of a larger effort in the department to study entrepreneurship and innovation. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The author may be contacted at dmckenzie@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
relatively small. Aggregated at industry or sector level, the distribution reveals the importance of mineral exports in value, even if they represent only 4% of transactions compared with 52% for textiles.

**Table 1 – Distribution of export value by destination region and by sector over the period (in**

||**millio**<br>|**ns USD)**|||
|---|---|---|---|---|
|Asia-Pacific|EU||R.O.W.|Thailand|
|$201|$267||$87|$1,230|
|11%|15%||5%|69%|
|Minerals|Textiles<br>A|gricultur|e<br>Wood|Misc.|
|$927|$579|$151|$85|$60|
|51%|32%|8%|5%|3%|

_Source: Own calculations based on Lao PDR customs data_ In addition to this skewed landscape faced by potential exporting firms, it appears that the economic performance of Lao exporters does not compare well to nonexporters. Moreover, they face a heavier regulatory burden, even after controlling for firm characteristics such as size, ownership and sector. About 20% of manufacturing firms in Laos export some part of their output, but data from a 2009 World Bank enterprise survey indicate that annual value added per worker is much lower for exporters. Similarly, larger firms, which are more likely to have the scale necessary to serve export markets, generate less value addition than smaller firms. This suggests that the domestic market, buoyed by large FDI inflows and investment in construction, offers greater returns than the more competitive export markets, where high production and trade costs undermine Lao competitiveness (World Bank 2011).

To put the characteristics of Lao exports into perspective, the World Bank’s Export Dynamics Database provides a useful framework for international comparison (Cebeci, et al. 2012). That analysis shows a high similarity between Laos and Malawi, another small, landlocked, developing economy, albeit without such large and developed neighbors. Overall, Laos is very close to trend on several measures given its GDP, including mean exports per exporter and exit rates. It has higher than average entry and survival rates, but its number of exporters (for its GDP) and of products and destinations per exporter is below average. In conclusion, it can be said that exporting is quite a difficult venture in Laos, in particular for small firms seeking to grow outside the dominant markets and products.

7
# **Table A6:  Business Environment Risk Intelligence (BRI)**

**Data Provider Description Website Data Source**

**Type Respondents Frequency Coverage Public Access Description**

BERI S.A.

Commercial risk rating agency headquartered in Geneva, Switzerland www.beri.com Political Risk Index and Operational Risk Index (BRI), Quantitative Risk Measure in Foreign Lending (QLM) Expert assessments Permanent panel of experts convened by BERI Quarterly since 1980 for BRI and annual since 1996 for QLM Global sample of countries Full dataset is commercially available. Averages of sub-indicators are publicly available in this spreadsheet.

PRI measures eight causes and two symptoms of political risk on a 7-point scale. ORI measures 15 obstacles to business development on a 5-point scale. QLM measures risk factors in foreign lending on a 100-point scale.

|**Voice and Accountability**|2008|2007|2006|2005|2004|2003|2002|2000|1998|1996|
|---|---|---|---|---|---|---|---|---|---|---|
|NA<br>**Political Stability and Absence of Violence**|..|..|..|..|..|..|..|..|..|..|
|PRI: External Causes of Political Risk : Dependence on/Importance to a Hostile Major<br>Power|X|X|X|X|X|X|X|X|X|X|
|PRI: External Causes of Political Risk : Negative Influences of Regional Political Forces|X|X|X|X|X|X|X|X|X|X|
|PRI: Internal Causes of Political Risk: Social Conditions: Wealth Distribution, Population|X|X|X|X|X|X|X|X|X|X|
|PRI: Internal Causes of Political Risk: Fractionalization of political spectrum and the power<br>of these factions.|X|X|X|X|X|X|X|X|X|X|
|PRI: Internal Causes of Political Risk: Fractionalization by language, ethnic and/or religious<br>groups and the power of these factions.|X|X|X|X|X|X|X|X|X|X|
|PRI: Internal Causes of Political Risk: Restrictive (coercive) measures required to retain<br>power.|X|X|X|X|X|X|X|X|X|X|
|PRI: Internal Causes of Political Risk: Organization and strength of forces for a radical<br>government.|X|X|X|X|X|X|X|X|X|X|
|PRI: Symptoms of Political Risk: Societal conflict involving demonstrations, strikes, and<br>street violence.|X|X|X|X|X|X|X|X|X|X|
|PRI: Symptoms of Political Risk: Inst ability as perceived by non-constitutional changes,<br>assassinations, and guerilla wars.<br>**Government Effectiveness**|X|X|X|X|X|X|X|X|X|X|
|ORI: Bureaucratic delays|X|X|X|X|X|X|X|X|X|X|
|**Regulatory Quality**|||||||||||
|NA|..|..|..|..|..|..|..|..|..|..|
|**Rule of Law**|||||||||||
|ORI: Enforceability of contracts|X|X|X|X|X|X|X|X|X|X|
|QLM: Direct Financial Fraud, Money Laundering and Organized Crime|X|X|X|X|X|X|X|X|X|X|
|**Control of Corruption**<br>PRI: Internal Causes of Political Risk: Mentality, including xenophobia, nationalism,<br>corruption, nepotism, willingness to compromise.|X|X|X|X|X|X|X|X|X|X|
|QLM: Indirect Diversion of Funds|X|X|X|X|X|X|X|X|X|X|
|**Country Coverage (PRI, ORI)**|**101**|**101**|**101**|**101**|**101**|**101**|**101**|**101**|**101**|**101**|
|**Country Coverage (QLM)**|**115**|**115**|**115**|**115**|**115**|**115**|**115**|**115**|**115**|**115**|
|**Year of Publication**|**2008**|**2007**|**2006**|**2005**|**2004**|**2003**|**2002**|**2000**|**1998**|**1996**|

45
approach and thus do not consider behavioral responses, second-round effects, dynamic issues, or macroeconomic balances.

Using information from the _Continuous Household Survey_ (Encuesta Continua de Hogares - _ECH_ ) from 2017, the study documents that both income inequality and poverty decline as a result of Uruguay’s fiscal policy, with the highest reduction of the Gini coefficient due to in-kind transfers (i.e. the monetary valuation of public spending in health and education). In addition, the paper finds that the reduction in inequality from fiscal policies seen in 2017 is similar to the one from 2009 for most policies, although in-kind transfers have a stronger effect in 2009.

Immervoll et al. (2006) analyze the redistributive properties of tax benefit systems in countries of the EU-15 and find that the different sizes and structures of the systems are major determinants of differences in income inequality among countries. Higher reductions in inequality are achieved through non means-tested benefits (unemployment benefits, pensions to non-elderly, and universal benefits) and taxes. Newer work (De Agostini et al., 2016; Euromod, 2019) compares the effects of policy changes in several European states, with varying results depending on policies, periods and countries. Fiscal policy studies in Latin America highlight the importance of cash and in-kind transfers for reducing inequality, and of both transfers and taxes for reducing poverty (Arancibia Romero et al., 2019; Goñi et al., 2008; Lustig et al., 2014).

Uruguay stands out in regional comparisons as among the most egalitarian economies, and fiscal policies contribute to that result as it is the most redistributive country of those analyzed by Arancibia Romero et al. (2019) and among the top performers according to Bucheli, et al. (2013). The latter also find that the country ́s taxes and transfers reduce inequality and poverty when considered as a whole. In addition, direct taxes are progressive while indirect taxes are regressive, and social spending is quite progressive. Nevertheless, in-kind transfers _via_ higher education are found to be pro-rich in the country (Lustig et al., 2014). None of these have worked with data newer than 2014, which is the main reason for the current update in the analysis.

The paper is structured as follows. Section II presents a summary of the fiscal policies in place in Uruguay for the year 2017, with particular attention to those that are included in this note. Section III presents the methodology applied, as well as the data sources used. Section IV presents the results and the last section concludes and suggests policy implications. The appendix includes more detail on the application of the methodology to the Uruguayan case.

# **II. Taxes and Social Spending**

In this section we present a description of the taxes and public benefits comprised in this analysis.

3
In summary, a larger percentage of glaciers was noted to be retreating with highly variable retreat rate. Some glaciers notably in upper Indus were reported to be advancing. Available a few studies suggest negative mass balance in high altitude glaciated region.

Following Kargel et al. (2009) glacial dynamics in the Hindu Kush-Himalaya region could be summarized by dividing the region into four zones. Zone-1 covers Afghanistan’s Hindu Kush region (Kargel et al., 2009) where glaciers are relatively stable or show very slow retreat (Scherler et al., 2011). In contrast, Zone-2 glaciers (Northwestern Himalaya in India, Karakoram, and Pamir) show both retreat and advances: retreat is generally observed in the Pamir Mountains, while Karakoram glaciers have retreated, advanced, and surged (Hewitt, 2005). Zone-3 glaciers (covering Himalayas in western India, southwestern part of Xizang province of China and western Nepal) show variable rates of retreat over different periods of time (Table 2). Zone-4 glaciers (eastern Nepal, Bhutan, Sikkim and southeast part of Xizang province of China) are characterized by the formation of many large glacial lakes since 1960s (Watanabe et al., 2009) and rapid disintegration of many glaciers. Many Zone-4 glaciers are not retreating but are rapidly losing mass (Bolch et al., 2011).

As glacier retreat generally does not respond to year-to-year climate variation, it may not be an indicator of the climate warming and change. The negative mass balance, shrinking areal extent and expanding glacier lakes and increasing temperature observed widespread in most parts of the Himalayas, however, may be due to climate warming as suggested in the literature.

# **2.5 Mixed streamflow trends**

Streamflow trends can result from both climate and land use changes, and thus requires precipitation and temperature data for climate change attribution. Either upward or downward streamflow trends can have big implications for water availability and flood risk. Bhutiyani et al. (2007) note a significant increase in the number of high-magnitude flood events in rivers of the northwestern Indian Himalayas in the last three decades, as well as increasing trends in annual maximum flood in three of four basins. Khattak et al. (2011) find an increasing trend in winter and spring streamflow at 100% and 50% of eight considered hydrometric stations in the upper Indus Basin, Pakistan, respectively. With no observed trend in winter precipitation but a positive trend in winter maximum temperature, Khattak et al. (2011) attribute observed streamflow trends 4
#

Illustration of Industrial Policy Strategies in Selected Countries

|Country|Program<br>In the Small|In the Large|
|---|---|---|
|Brazil|PITCE (2003-2007)<br>Productive<br>Development<br>Policy<br>(PDP)-2008-2010<br>Plano<br>Brasil<br>Maior<br>2011-2014<br>X<br>X<br>X|X<br>X<br>X|
|Colombia|Vision Colombia 2019<br>and 2032<br>National<br>Competitiveness<br>Policy<br>National<br>Development<br>Plan<br>2010-2014 (PNP)<br>Program to Promote<br>World Class Sectors<br>(included in PNP)<br>X<br>X<br>X<br>X|X<br>X<br>X|
|Chile|2007<br>and<br>2008<br>National Strategy for<br>Innovation<br>Competitiveness<br>Agenda 2010-2020*<br>X<br>X|X<br>X|
|Dominican Republic|National<br>plan<br>for<br>Systemic<br>Competitiveness<br>30 Year Development<br>Strategy<br>X<br>X||
|Ecuador|National<br>Development<br>Plan<br>2007-2011<br>X||

11
# **Combination of Incidence and Intensity: The Multidimensional Poverty Index (MPI)**

Using a single measure of multidimensional poverty based on the five dimensions specified, in 2015 the incidence of multidimensional poverty (the proportion of the population that experiences multiple overlapping deprivations) was estimated at 29.1 percent. This is quite similar to the incidence of monetary poverty. In addition to the incidence of multidimensional poverty, the intensity of deprivations reflects its severity. The intensity, calculated as the average proportion of deprivations that multidimensionally poor households report, held more or less constant over the entire time period. Finally, the MPI, defined as the product of incidence and intensity, shows a slightly decreasing trend as the incidence of multidimensional poverty has declined.

**Figure 21. Armenia’s MPI: A Combination of Incidence and Intensity, 2015, Percent**

<!-- Start of picture text --> 50%<br>45%<br>41%<br>40%<br>35%<br>34%<br>30% 31% 31% 32%<br>29%<br>25%<br>20%<br>15%<br>10%<br>5%<br>0%<br>2010 2011 2012 2013 2014 2015<br>MPI Intensity Headcount<br><!-- End of picture text --> _Source_ : ILCS data.

Breaking down the MPI by location of residence shows significant variations. On the horizontal axis, Figure 22 shows the share of the population considered multidimensionally poor (head count, incidence) for each of the eleven marzes in Armenia. Both dimensions show considerable heterogeneity: headcount ranges from 0.21 percent in Vayots Dzor to almost 50 percent in Shirak; intensity is lowest for Armavir and highest for Shirak. However, the figure also illustrates that Yerevan has the largest number of multidimensionally poor households (almost 270,000 individuals). In combination with Kotayk, north of the capital, this regional view suggests that multidimensionally poor households are concentrated in urban areas in the center of the country.

24
# **HEALTH, DEMOGRAPHIC TRANSITION AND ECONOMIC GROWTH**<sup>**1**</sup>

Ole Hagen Jorgensen<sup>2</sup> LCSPE _JEL Classification_ : H55, I18, J13, O11, O16, O41 _Keywords_ : Health, Mortality, Endogenous Fertility, Demographic Transition, Endogenous Economic Growth

> 1 I am grateful to Jocelyn Finlay and Svend E. Hougaard Jensen for excellent comments and valuable discussions. Furthermore, I thank Michele Gragnolati, Jan Walliser, Tito Cordella, Makhtar Diop, Rodrigo Chaves, Dino Merotto, Oded Galor, David Bloom, and David Canning for helpful comments.

> 2 The World Bank, Poverty Reduction and Economic Management, LCSPE, 1818 H Street, N.W., Washington, D.C., 20433, USA, Email: ojorgensen@worldbank.org, Tel.: 202-458-0532, Fax.: 202-522-2119.
|Experienced<br>outages(0-1)||||-0.02<br>(0.01)**|0.02<br>(0.01)**||0.02<br>(0.01)**|-0.01<br>(0.03)|
|---|---|---|---|---|---|---|---|---|
|Experienced inf.<br>competition(0-1)||||-0.10<br>(0.01)***|-0.10<br>(0.01)***||-0.10<br>(0.01)***|-0.09<br>(0.02)***|
|Metropolitan<br>area-level|||||||||
|Lack of mobility||||0.14<br>(0.24)|0.10<br>(0.25)||-0.45<br>(0.52)|-0.24<br>(0.54)|
|Pollution (PM 2.5,<br>μgm-3/10)||||0.05**<br>(0.02)|0.03<br>(0.02)||-0.02<br>(0.03)|0.02<br>(0.04)|
|Abs. difference<br>from ideal<br>temperature||||0.00<br>(0.01)|0.00<br>(0.01)||0.00<br>(0.02)|0.01<br>(0.01)|
|Average<br>precipitation(m)||||0.01<br>(0.01)|0.01<br>(0.01)||-0.00<br>(0.01)|0.00<br>(0.01)|
|Number of firms|51828|51828|51828|38526|38526|51828|38526|38526|
|Number of<br>metropolitan areas|649|649|649|356|356|649|356|356|
|Metropolitan-level<br>average of firm-<br>level variables|NO|YES|YES|YES|YES|YES|YES|YES|
|Country fixed-<br>effects|YES|YES|YES|YES|YES|YES|YES|YES|
|Industry fixed-<br>effects|YES|YES|YES|YES|YES|YES|YES|YES|
|Year fixed-effects|YES|YES|YES|YES|YES|YES|YES|YES|
|Anderson<br>canonical corr. LM<br>(p-value)||||||0.00|0.00|0.00|
|Cragg-Donald<br>Wald F Statistic||||||2284.67|922.25|1015.43|
|Stock-Yogo<br>(critical value; 10%<br>maximal IV size)||||||19.93|19.93|19.93|
|Hansen/Sargan (p-<br>value)||||||0.09|0.66|0.25|
|Durbin-Wu-|||||||1.00|0.28|
|Hausman(p-value)|||||||||
|Standard errors in par|entheses. *|p<0.10; **p|<0.05; ***|p<0.01 (Clu|ster-robust s|tandard for|2SLS estim|ation).|

All controls have expected signs suggesting a positive association between labor productivity and the following set of firm characteristics: size, age, human capital, capital investment, managerial experience, exporting status, and foreign ownership, while state ownership and sole proprietorship are negatively associated with labor productivity. We find evidence of agglomeration diseconomies in the form of crime and informal competition.

When we re-estimate our full specifications using a Mundlak IV-estimator (Table 1, Columns 5 and 6) and 2SLS (Table 1, Column 7), the coefficient on the density variable increases, but the effect becomes (highly) insignificant. In both the Mundlak IV and 2SLS estimations, we tested whether the urban density variable is econometrically endogenous using the Durbin-Wu-Hausman 17
20 Nevertheless, it is striking that indicators of competition, openness and market-orientation in overall financial sector and economic policy such as the Heritage Foundation index of banking freedoms and of media freedom are strongly correlated access barriers.

As far the contractual and the informational infrastructures are concerned, better credit registries are associated with lower access barriers, but there is no evident correlation with creditor rights, suggesting a greater importance of information infrastructures compared to stronger legal enforcement. Interestingly, government ownership of banks is associated with lower barriers on the deposit side (as is foreign ownership of banks), but with higher barriers on the loan side. Indicators of the approach to regulating banking are also correlated with access barriers. Countries that rely more on empowering markets (by enforcing accurate and timely information disclosure and providing the right incentives for market participants) tend to have lower barriers to accessing loan services. To the contrary, creating all too powerful regulators (who may be subject to corruption and political and industry capture) is associated with higher access barriers, particularly for deposit services. Not too much should be read into these simple partial correlations and much more research is needed to understand them. They do, however, have important policy implications for broadening access.

# **_4.4.  Impact of Access_**

How does improved access to finance affect poor households and small and micro enterprises which employ majority of the work force in developing countries? It is by now well established that in more developed financial systems, deserving firms have easier access to external finance, with positive implications for their growth and performance (Demirguc-Kunt and Maksimovic, 1998). However, more recent research has also identified a distributional impact. Using industry level data for 44 countries and 36 industries and difference-in-difference approach,