rafmacalaba/datause-encoder-data huggingface dataset:
variables are statistically significant at the 1% level. The positive parameter on _Surplus_ suggests that prices in maize-surplus markets are more volatile than prices in maize-deficit markets. Previous findings also indicate that price volatility tends to be higher in maizesurplus markets than in maize-deficit markets ( **Kilima et al., 2008** ). The intuition is that a local supply shock arising on a maize-surplus market does not necessarily lead to a reduction in maize supply on this market through a transfer of quantities to maize-deficit regions, essentially because of the lack of sufficient market integration. This results in price volatility in maize-surplus markets. We also find that the coefficient on time distance to urban centers is significant. For example, results from the ARCH model indicate that on average remoteness from main consumption centers led to an increase of maize price volatility to 0,01%. The positive sign shows that remote markets tend to exhibit higher price volatility. Indeed, the urban center draws food production over the year, which ensures an adequate equilibrium between supply and local demand, thus stabilizing prices. This confirms the theoretical model we have been drawing in section 2, and the expected effects derived from equation (10). The negative and significant impact of _border_ on price volatility indicates that prices in markets close to the main maize border-crossing points are more volatile than in other markets. The statistical significance of the coefficient on _border -_ independently from the coefficient on remoteness - suggests that _border_ captures additional information: market isolation should not only be comprehended as simple geographic remoteness from domestic urban centers. Remoteness is also expressed through high transport costs, export prohibitions and non-tariff barriers to crossing the border, which all hamper maize marketing abroad. Tackling remoteness by reducing or eliminating non-tariff barriers is essential to promoting regional integration and maize marketing in neighboring countries **(Kaminski et al., 2013)** . Opening of borders is indeed crucial in reducing spatial price volatility and ultimately fostering food security in Burkina Faso ( **World Bank, 2012** ).

# 5.3 **Robustness checks**

The robustness of the previous results is tested in three ways. First, alternative measures of market remoteness are used; second, we carry out the same analysis with nominal price and lastly, we allow a change in the estimator used (GARCH model).

18
The British Public Works Loan Board is 100% Government controlled, but its membership is drawn equally from the ranks of local government and City finance institutions.

# Sources of Funds

24. At Appendix B I have sought to update a tabulation of sources of finance for MDFs compiled by the United Nations in 1972.

25. In Japan and Western Europe the MDFs have been very largely funded by the financial markets. Initial subscription of share capital by either central or local government has been important in establishing control and credibility, but insignificant as resources for lending. The mechanisms for tapping the market have varied,however. Direct private savings deposits have been important in Austria, Belgium, Germany, Italy and Portugal, attracted in competition with commercial outlets. Otherwise financial institutions - pensions funds, insturance companies, commercial banks - have been the major sources, either through bond issues or directly negotiated deposits.

26. By contrast, the third world MDFs have been largely financed from public funds. Most of the autonomous MDIs have the power to offer bonds publicly, but few do so. Where private savings are tapped, it is normally through loans from, or deposits by central banka or state controlled pension and insurance funds. The very
[7 groups], while we also estimate specifications with country fixed effects for robustness (143 groups).<sup>14</sup> In additional specifications, we also include control variables for financial inclusion (having ownership of an account at a bank or formal financial institution), having an outstanding housing loan (as a proxy for home ownership), and generic savings by country (having any savings during the last year, as a proxy for cultural factors shaping the propensity to save). In additional regressions, we also estimate multinomial models of saving for old age by financial inclusion status, and type of savings (formal versus informal/semi-formal<sup>15</sup> ).

Next, we estimate regressions that incorporate country-level macroeconomic characteristics to our baseline specifications. From the World Development Indicators, we include control variables for: the logarithm of life expectancy, the logarithm of per capita GDP (2011 PPP-constant levels), GDP per capita growth, the real interest rate (e.g. James and Song, 2001), and the WDI legal rights index. In additional specifications, we also include control variables for legal origin (La Porta, et al. 2008), the housing-affordability index (as a proxy for potential home ownership<sup>16</sup> ) and indicators for explicit deposit insurance and the safety net/moral hazard index (Demirgüç-Kunt, et al. 2014).

Finally, in the third stage of our analysis we incorporate control variables accounting for country pension-system characteristics, in addition to the baseline individual and macroeconomic variables. We utilize two sources of data capturing country pension systems. Specifically, we incorporate the following variables from the Pension Watch/HelpAge's Social Pensions Database (2014):<sup>17</sup> (i) the logarithm of the benefit (in PPP international dollars), (ii) the benefit as a percentage of GDP per capita, and (iii) the benefit as a percentage of the $1.25 a day poverty line. In addition, we incorporate variables

> 14 Out of the total 146 countries in the Global Findex 2014 data, the following countries are excluded from these specifications due to missing data on saving for old age: Liberia, Paraguay and South Sudan. 15 Across the developing world, only about 4% of adults − 160 million people − are unbanked but save by using a savings club or a person outside the family. But in Sub-Saharan Africa the share is three times that size. On average in the region’s economies, 13% of adults are unbanked and save semi-formally (DemirgüçKunt, et al., 2015).

> 16 The housing affordability index is from Numbeo (2015). The data and description are available at: http://www.numbeo.com/property-investment/rankings_by_country.jsp?title=2014. 17 The data and detailed description are available at: http://www.pension-watch.net/pensions/about-socialpensions/about-social-pensions/social-pensions-database/.

12
- Autor DH. 2015. Why are there still so many jobs? the history and future of workplace automation. _Journal of Economic Perspectives_ **29** : 3–30.

URL http://www.aeaweb.org/articles.php?doi=10.1257/jep.29.3.3

- Autor DH, Dorn D. 2013. The growth of low-skill service jobs and the polarization of the us labor market. _American Economic Review_ **103** : 1553–97.

URL http://ideas.repec.org/a/aea/aecrev/v103y2013i5p1553-97.html

- Autor DH, Katz LF, Kearney MS. 2008. Trends in u.s. wage inequality: Revising the revisionists. _The Review of Economics and Statistics_ **90** : 300–323.

URL http://ideas.repec.org/a/tpr/restat/v90y2008i2p300-323.html

- Autor DH, Levy F, Murnane RJ. 2003. The skill content of recent technological change: An empirical exploration. _The Quarterly Journal of Economics_ **118** : 1279–1333.

URL http://ideas.repec.org/a/tpr/qjecon/v118y2003i4p1279-1333.html

- Basu S, Fernald JG, Oulton N, Srinivasan S. 2003. The Case of the Missing Productivity Growth: Or, Does Information Technology Explain why Productivity Accelerated in the US but not the UK? NBER Working Papers 10010, National Bureau of Economic Research, Inc.

URL http://ideas.repec.org/p/nbr/nberwo/10010.html

- Bloom N, Sadun R, Van Reenen J. 2012. Americans Do IT Better: US Multinationals and the Productivity Miracle. _American Economic Review_ **102** : 167–201.

URL http://ideas.repec.org/a/aea/aecrev/v102y2012i1p167-201.html

- Bridgman B. 2014. Is Labor’s Loss Capital’s Gain? Gross versus Net Labor Shares. BEA Working Papers 0114, Bureau of Economic Analysis.

URL http://ideas.repec.org/p/bea/wpaper/0114.html

- Brynjolfsson E, Hitt LM. 2003. Computing productivity: Firm-level evidence. _The Review of Economics and Statistics_ **85** : 793–808.

URL http://ideas.repec.org/a/tpr/restat/v85y2003i4p793-808.html Caselli F, Feyrer J. 2007. The marginal product of capital. _The Quarterly Journal of Economics_ **122** : 535–568.

URL http://ideas.repec.org/a/tpr/qjecon/v122y2007i2p535-568.html Champagne J, Kurmann A. 2012. Reconciling the divergence in aggregate us wage series. Working Paper.

URL http://www.andrekurmann.com/files/wp_files/CESpaper_27July2012.pdf Christensen LR, Jorgenson DW. 1969. The measurement of us real capital input, 1929–1967. _Review of Income and Wealth_ **15** : 293–320.

Colecchia A, Schreyer P. 2002. ICT Investment and Economic Growth in the 1990s: Is the United States a Unique Case? A Comparative Study of Nine OECD Countries. _Review of Economic Dynamics_ **5** : 408–442.

URL http://ideas.repec.org/a/red/issued/v5y2002i2p408-442.html

- Cortes GM, Jaimovich N, Nekarda CJ, Siu HE. 2014. The micro and macro of disappearing routine jobs: A flows approach. Working Paper 20307, National Bureau of Economic Research.

URL http://www.nber.org/papers/w20307 35
The Ministry of Health in Poland established a Task Force in 1993 to address a totally different kind of problem – namely, to find ways and means to improve the delivery of primary care in Poland. The recommendations of this Task Force led to the establishment of the family practice model, which has had interesting implications for informal payments. The 1993 Task Force review recommended, inter alia, a shift to the family medicine model and suggested increasing the professional prestige and status of primary care providers in order to attract the best medical students and physicians. The Task Force also suggested shifting emphasis on health care from treatment of the sick to health promotion and prevention of illnesses, redefining primary health care and rejecting the multi-specialist model in favor of the family physician and ancillary staff. Following the Task Force recommendations, family practice was introduced in Poland and since then, over 10,000 physicians have been trained and are serving as family doctors. A College of Family Doctors has been established, and over time membership in this college has become a matter of prestige and is much sought after.

Surveys have shown that prestige is a big issue for family physicians, who take great pride in their job and in their practice (Chawla et al, 1998). The introduction of family medicine as a specialization certainly seems to have had a positive effect both on perceived prestige of the profession and self-esteem of the physicians in family medicine. Independent reviews and surveys have shown that the incidence of informal payments for health services in Poland is lowest among family physicians (Chawla, 2002).

# **5. Conclusion**

This paper has applied a model of incentives to the problem of physician effort in the workplace, and in doing so has drawn upon the theories of opportunistic behavior as well as on industrial psychology literature. The intuition behind the model is straightforward: employees respond to a set of factors, both economic and psychological, in determining how much effort they would put in their jobs. In 16
7

# **Figure 2**

# Household Consumption and Fiscal Policy Volatilty

<!-- Start of picture text --> 24<br>Consumvol = 2.32 + .45 FPvol<br> (9.21)<br>20 R2= .49 D.W.= 1.76<br>16<br>12<br>8<br>4<br>0<br>0 5 10 15 20 25 30<br>Fiscal Policy Volatility<br>Household Consumtion Volatility<br><!-- End of picture text --> Source: Author’s calculations Before quantifying the welfare loss associated with consumption volatility, we discuss the more direct impact of policy volatility on growth and capital formation. Fiscal policy volatility is systematically associated with higher output volatility; which, in turn, is associated with lower growth.<sup>3</sup> This relationship is stronger in poorer countries. Researchers have verified econometrically this negative association, estimating an almost one-to-one relationship: a one standard deviation increase in volatility (.75 percentage points) is associated with lower growth by .75 percentage points. (Fatas and Mihov, 2003 and 2005)<sup>**4**</sup> The effect of volatility on growth is mostly through its effect on capital formation, but the relationship has to be interpreted with caution as there is heterogeneous response across countries: the negative impact of volatility on growth is stronger in poorer countries with more underdeveloped institutions, and has become stronger through time (Hnatkovska and Loayza, 2003).

> 3 A survey of the research linking volatility with growth can be found in Loayza, et. al. (2007). 4  Fatas and Mihov define the volatility of the discretionary component of public spending as the standard deviation of the residual of a regression of the growth rate of public consumption spending and the growth rate of output with other control variables including the country’s income level and time trends. They use a sample of 91 countries over 1960-2000.  The ranking of the countries by their volatility level using the Fatas-Mihov data (available in Fatas’ homepage) is very similar to the ranking obtained this crude measure: the rank correlation coefficient is .85
# **TABLE OF CONTENTS**

||||**Page**|
|---|---|---|---|
|1.|Intro|duction|4|
|(i).|Coun|try Context|4|
|(ii)|The|Labor Market in Timor-Leste|8|
|2.|Findi|ngs of the Timor-Leste Urban Enterprises Survey, 2004|10|
|(i)|Char|acteristics of Urban Enterprises|11|
|(ii)|Beha|vior of Urban Enterprises|16|
|(iii)|Wag|es and Wage Setting|22|
|(iv)|Train|ing and Skills in the Urban Labor Market|27|
|3|Conc|lusion and recommendations|31|
|Annex|1:|Sampling and Survey Documentation for the UES|36|
|Annex|2:|Definition of Worker Categories in UES|43|
|Annex|3:|Definition of Skills in UES|44|
|Annex|4:|Key Macroeconomic Indicators|45|

3
Table 1. Global inequality (in Gini points) in 1990s, according to various authors

|Author|Year|Gini value|National mean<br>incomes from:|National<br>income<br>distributions<br>from:|
|---|---|---|---|---|
|Milanovic<br>(2005)|1993|66|Household<br>surveys|Household<br>surveys|
|Milanovic<br>(2205)|1998|65|Household<br>surveys|Household<br>surveys|
|Bourguignon<br>and Morrison<br>(2002)|1990’s|66|GDI<br>(Maddison)|Household<br>survey<br>estimates|
|Sala-i-Martin<br>(2002)|1998|61|GDI<br>(PennWorld<br>Tables)|Ginis and<br>quintiles from<br>HS|
|Bhalla (2001)|2000|65|GDI<br>(PennWorld<br>Tables and<br>WDI)|Ginis and<br>quintiles from<br>HS|
|Dikhanov and<br>Ward (2001)|1999|68|National<br>consumption<br>(WDI)|Ginis and<br>quintiles from<br>HS|
|Dowrick and<br>Akmal (2001)|1993|71|GDI|Ginis and<br>quintiles from<br>HS|
|Sutcliffe (2003)|2000|63|GDI<br>(Maddison)|Ginis and<br>quintiles from<br>HS|
|Chotikapanich et<br>al. (1997)|1990|65|GDI<br>(PennWorld<br>Tables)|Ginis for HS|

Note: HS=household survey; GDI=Gross Domestic Income. WDI=World Development Indicators (World Bank).

20
**Figure 3. Labor force participation, % of population aged 15-64, 2012-2018 a. By age and gender b. By educational level**

<!-- Start of picture text --> 70 70<br>60 60<br>50 50<br>40 40<br>30 30<br>20 20<br>10<br>10<br>0<br>0<br>2012 2013 2014 2015 2016 2017 2018<br>2012 2013 2014 2015 2016 2017 2018<br>All, 15-64 Female, 15-64 Male, 15-64 Age 15-24 Primary or less Secondary Post-secondary<br>Percent Percent<br><!-- End of picture text --> _Source: Own estimates based on 2012-2018 Kosovo LFS. Note: Secondary refers to upper secondary._

**Figure 4. Unemployment rate, % of labor force aged 15-64, 2012-2018 a. By age and gender b. By educational level**

<!-- Start of picture text --> 70 70<br>60 60<br>50 50<br>40<br>40<br>30<br>30<br>20<br>20<br>10<br>10<br>0<br>0<br>2012 2013 2014 2015 2016 2017 2018<br>2012 2013 2014 2015 2016 2017 2018<br>All, 15-64 Female, 15-64 Male, 15-64 Age 15-24 Primary or less Secondary Post-secondary<br>Percent<br>Percent<br><!-- End of picture text --> _Source: Own estimates using the 2012-2018 Kosovo LFS. Note: Secondary refers to upper secondary._

**As a result of low labor force participation and high unemployment, employment rates were significantly lower in Kosovo than in EU peer countries.** In 2018, the employment rate was only 29.4 percent compared with 52.5 percent across the Western Balkans, 69 percent in Hungary and 73 percent in Austria (World Bank and wiiw, 2020). The female employment rate in 2018 was only 12.6 percent compared with 45.7 percent among men. Figure 5 shows trends in employment rates by age, gender and education level. In 2018, 28.5 percent of young people 15-24 years old were not in employment, education or training (NEET), which is higher than in most OECD countries.

12
# **3.2 Sample Descriptive Statistics**

We begin by reporting basic pre-treatment descriptive statistics regarding demographics, education, skills and labor market outcomes for our sample in Table 2.<sup>18</sup> We do not distinguish between treatment and control groups in this section, but we show statistics for our pooled sample and separately for the sample in Argentina and Colombia. We do this to get a sense of how our sample compares across countries and, within country, to the overall target populations in each location. Following a similar criterion of the program targeting of beneficiaries, for the overall population in Argentina, we compute descriptive statistics for women between 18 and 40 years old, living in Buenos Aires city and Greater Buenos Aires Area. We get this data from the 2018 Permanent Household Survey ( _Encuesta Permanente de Hogares_ , INDEC). Equivalently, for Colombia we compute descriptive statistics for women between 18 and 50 years old in Bogotá. We get data from the 2018 National Household Survey ( _Gran Encuesta Integrada de Hogares_ , DANE).

2. Several facts are worth describing from Table Regarding demographics of our sample, the average age of eligible women in our sample is about 29 years old. Even though only 19 percent of our sample are mothers, the mean age in our sample is remarkably similar to the mean age of mothers at first childbirth, which is 29.5 years old for Buenos Aires (Direccion General de Estadistica y Censos, 2019), and 27 years old for Bogota (OECD, 2019). About 14 percent of women in our sample are married. The household income distribution in our sample is somehow skewed towards upper deciles of the income distribution, with 44 percent of our individuals having a household income in deciles 4-7 and 38 percent in the upper 3 deciles. Age and marital status of eligible candidates are extremely similar across bootcamps locations. Our sample in Argentina has a somewhat smaller share of mothers relative to Colombia, and a household income distribution relatively skewed to the right.

To summarize education, we show the percentage of individuals who have a college degree in our sample. This is 61 percent, and is very similar between Argentina and Colombia. Last, employment variables show that 83 percent of our sample is employed at baseline, with 22 of them being self-employed, and about 60 percent of them being employed in the formal sector.<sup>19</sup> On average, individuals in our sample work about 27 hours a week, and their wage and salary earnings are US$ 392 a month. While the employment figure

> 18Data Appendix A.1 details how we construct each variable in our data.

> 19Our self-employed and formal sector dummies assign zeros for those not employed.

11
WPS3719

# **Cross-Country Empirical Studies of Systemic Bank Distress:**

# **A  Survey**

by Aslı Demirgüç-Kunt and Enrica Detragiache*

## Abstract

A rapidly growing empirical literature is studying the causes and consequences of bank fragility in contemporary economies. The paper reviews the two basic methodologies adopted in cross-country empirical studies, the signals approach and the multivariate probability model, and their application to study the determinants of banking crises. The use of these models to provide early warnings for crises is also reviewed, as are studies of the economic effects of banking crises and of the policies to forestall them. The paper concludes by identifying directions for future research.

Keywords: Banking crises; financial fragility. JEL classification: E44, G21

## World Bank Policy Research Working Paper 3719, September 2005

_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 view of the World Bank, its Executive Directors, or the countries they represent. Policy Research Working Papers are available online at http://econ.worldbank.org._

*Development Research Group, World Bank, and Research Department, International Monetary Fund, respectively.  We would like to thank Robert Cull for useful comments and Baybars Karacaovali for excellent research assistance.
The yield of these perennial species in terms of biofuel per ton of feedstock (and thus by hectare) will depend on their theoretical potential, and the conversion efficiency of the process. For switchgrass, the theoretical potential is around 100 gallons (377 liters) per dry ton (see Table 3). Liebman and Heggenstaller (2008) used yields of 79 gallons (300 liters) per ton for both switchgrass and miscanthus. As mentioned above, targets of 340 liters per ton of cellulosic feedstock have been set forth (NREL 2007).

# _2.1.3.2 Woody energy crops_

Broadly referred to as woody energy crops, some fast growing tree species have also shown promise for biofuel production. Important attributes include the relatively high yield potential, wide geographical distribution, and relatively low levels of input needed when compared to annual crops (Smeets et al 2007). Their versatility as a source of solid and liquid energy is also a plus according to these authors. Poplar ( _Populus_ spp.), willow ( _Salix_ ssp.), and eucalyptus are among the species most frequently mentioned for this end.

Dedicated energy crops as feedstocks for biofuel production have some advantages over the feedstocks currently used to that end. These energy crops are in general less demanding in terms of inputs, reduce erosion and improve soil properties, and provide better wildlife habitat. Additionally more energy per unit of land can be obtained from these crops as a higher proportion of the biomass can be utilized.

On the other hand, while highly yielding, dedicated energy crops do not entirely escape the food versus fuel debate as additional land is needed for their production. In order not to compete for land with food production, these crops (woody or forages) should only be installed in lands where neither food crop production nor grazing pastures are feasible activities, or that are not needed.

As with the case of crop and forest residues, the logistics of feedstocks obtained from dedicated energy crops is still a challenging issue to be resolved. These feedstocks are simply bulky and difficult to transport. For the case of switchgrass, Epplin et al. (2007) indicated that the corresponding infrastructure for harvest, storage, transportation, and spot markets still do not exist. In addition, a narrow harvesting window and yield 10