rafmacalaba/datause-encoder-data huggingface dataset:
Figure 5 also shows the deciles for the synthetic and composite access indicators. Note that the synthetic indicator is distributed more evenly than the basic WSBI accounts numbers, partly because of the use of a logarithmic transformation of account numbers, which is preferred by the data (as is indicated by regression F) and which is consistent with the plausible idea that, as deposit account numbers grow, access percentages do not grow proportionately. However, the spread of the actual distribution out of sample is likely to be also understated by the deciles shown, given that the fitted value has a much lower standard deviation than the actual within sample.

Despite the numerous shortcomings of the composite indicator, it has the great merit of distilling information from a variety of different sources and placing it on a common basis. No doubt the accuracy of the imputation for any given country may be highly questionable, but it can represent a first approximation to be revised as new data become available for each country. Furthermore, as a cross-country dataset, it has certainly potential for use in cross-country regression analysis, where econometric techniques can minimize the impact of stochastic errors.

be obtained from using the bank data only. As more refined information on specific countries is obtained, it is envisaged that this series will be updated.

28
- 74 -

- De Silva, Dakshina, Timothy Dunne, Anuruddha Kankanamge, and Georgia Kosmopoulou. 2008. ―The Impact of Public Information on Bidding in Highway Procurement Auctions.‖ _European Economic Review_ 52: 150–81.

- Estache, Antonio, Atsushi Iimi. 2009. ―Joint Bidding, Governance and Public Procurement Costs: A Case of Road Projects.‖ _Annals of Public and Cooperative Economics_ 80: 393–429.

- Estache, Antonio, Atsushi Iimi. 2010. ―Bidder Asymmetry in Infrastructure Procurement: Are There Any Fringe Bidders?‖ _Review of Industrial Organization_ 36: 136–87.

- Estache, Antonio, Atsushi Iimi. 2011. _The Economics of Public Infrastructure Procurement in Developing Countries: Theory and Evidence._ Center of Economic Policy Research: London.

- Fay, Marianne, and Tito Yepes. 2003. ―Investing in Infrastructure: What Is Needed from 2000 to 2010?‖ Policy Research Working Paper No. 3102, World Bank, Washington, DC.

- Flyvbjerg, Bent, Mette Skamris Holm, and Søren Buhl. 2002. ―Underestimating Costs in Public Works Projects: Error or Lie?‖ _Journal of the American Planning Association_ 68: 279-295.

- Flyvbjerg, Bent, Mette Skamris Holm, Søren Buhl. 2004. What causes cost overrun in transport infrastructure project? _Transport Review_ , Vol. 24, pp. 3-18.

- Foster, Vivien, Cecilia Briceno-Garmendia. 2010. _Africa's Infrastructure: A Time for Transformation_ . The World Bank.

- Grimm, Veronika, Riccardo Pacini, Giancarlo Spagnolo, and Matteo Zanza. 2006. ―Division into Lots and Competition in Procurement.‖ In _Handbook of Procurement_ , ed. Nicola Dimitri, Gustavo Piga, and Giancarlo Spagnolo, 168–192. Cambridge University Press.

- Guasch, Luis. 2004. _Granting and Renegotiating Infrastructure Concessions: Doing It Right_ . Washington, DC: World Bank.

- Gupta, Srabana. 2002. ―Competition and Collusion in a Government Procurement Auction Market.‖ _Atlantic Economic Journal_ 30: 13–25.

- Haile, P. 2001. ―Auctions with Resale Markets: An Application to U.S. Forest Service Timber Sales.‖ _The American Economic Review_ , 91(3): 399-427.

- Hong, Han and Matthew Shum. 2002. "Increasing Competition and the Winner's Curse: Evidence from Procurement," _Review of Economic Studies_ , Blackwell Publishing, vol. 69(4), pages 871-98, October.

- Hyytinen, Ari, Sofia Lundberg, Otto Toivanen. 2006. Favoritism in public procurement: Evidence from Sweden. _International Public Procurement Conference Proceedings_ , September 2006, pp. 279-327.

- Iimi, Atsushi. 2006. ―Auction Reforms for Effective Official Development Assistance.‖ _Review of Industrial Organization_ 28: 109-128.
# _Table 5: Prosecutions for Bribery (2000)_

|**Number of**|**Average number**|**Standard Error**|**Minimum**|**Maximum**|
|---|---|---|---|---|
|**Observations**|**of prosecutions**||||
||**per 100,000**||||
|54|3.5|9.6|0.01|52.4|

_Source: United Nations, 2012_ There are two major problems with using the bribery data as a measure of corruption. Firstly, there are little data from OECD countries and the definition of bribery varies by jurisdiction, so cross-country comparisons may be extremely problematic. Secondly, prosecution for bribery does not necessarily measure higher/lower levels of overall corruption. Countries may have low levels of prosecution due to the lack of corruption (e.g. Ireland), or due to the poor capacity of the legal system (e.g. Pakistan). Conversely, a high prosecution rate may indicate high levels of bribery (e.g. Romania) but also the use of prosecution as a deterrent (e.g. Hong Kong SAR, China). In short, because the use of prosecution may vary significantly by context (capacity, deterrent, etc.) it is not possible to use this indicator as a valid, cross-sectional measure of increased/decreased corruption (Lambsdorff, 2004).

**Over Time Variation.** Because of changes in the definition of bribery over time and the inconsistent use of the term across jurisdictions, both over time and cross-sectional analyses may be problematic. However, within-country comparisons may be possible: for example, among U.S. states in which issues of judicial capacity and the use of broadly similar definitions of bribery are likely.

## **(ii) Quasi‐Experimental Data: The Behavior of Diplomats**

While individual incidents of corruption may be opportunistic in nature, there is evidence that social, cultural, and institutional norms – which create incentives and expectations – may strongly condition the propensity for individuals to abuse public office for private gain, when the opportunity arises. Fishman and Miguel (2007) exploit the fact that UN diplomats in New York are immune from prosecution and use the 21
selection. The results show that Catholic schools do perform better, while schooling choice is available and affordable for the majority of families.

To what extent is this high achievement due to the institutional features associated with school choice? School choice leads to competition which can manifest itself in schools firing ineffective teachers for instance; or through schools using achievement data to evaluate teachers. School autonomy can also lead to schools taking a more direct role in establishing teachers’ salary increases among other school functions. In addition, parents may be more involved in discussions on budget formulation at the school level. Another variable that might help explain superior performance is attendance at a private school. Given the predominance and growing significance of private schools in the Netherlands, and the ease of entry of new providers, this might be a useful measure of the effect of school choice on outcomes. In the Netherlands, on average, private schools perform slightly better than public schools – despite 100 years of competition and equal funding to both sectors – and all perform significantly above the OECD mean. Figure 3 shows the slight advantage of private schools in PISA 2006.

# **<u>Data and Coverage</u>**

In the analysis that follows, we use data from the OECD’s Program for International Student Assessment (PISA), a student assessment jointly developed by participating countries to assess and compare student achievement based on a standardized and highly reliable framework. The detailed description of assessment framework, cautious procedures of translation and supervision of country specific implementations, and finally careful calibration of student scores based on response items and collected background variables, supports the view that PISA is a valid framework for assessing student achievement. The survey is realized as representative to 11
Figure 10: Average predicted probability of laying off workers across predicted probabilities of adjusting on the intensive margin.

<!-- Start of picture text --> .5 .5 .5<br>.4 .4 .4<br>.3 .3 .3<br>.2 .2 .2<br>.1 .1 .1<br>0 .2 .4 .6 .8 0 .2 .4 .6 .8 0 .2 .4 .6 .8<br>Predicted probability of Predicted probability of Predicted probability of<br>granting leave of absence reducing wages of employees reducing hours of employees<br>laying off workers laying off workers laying off workers<br>Average predicted probability of Average predicted probability of Average predicted probability of<br><!-- End of picture text --> Note: Predicted probabilities from Probit models that control for change in sales, and dummies for country, size, sector, and weeks before and after the peak of the mobility shock. The analysis first conditions on the probability on the x-axis and computes the average probability of businesses in that category in the y-axis.

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# **6 Summary and Discussion of Results**

This research evaluates the impact of overdraft facility policy, for recipient firms by controlling the selection bias with the up-to-date propensity score matching technique. The results are based on an investigation of the differences in the performance indicators from 2005, for the firms receiving overdraft facility policy.

In 2005, growth in the eight new European Union (EU) member States from Central and Eastern European (EU-8) economies had become more broadly based, driven by robust consumption and investment expenditure and strengthening external demand. On the other hand, most Southern and Eastern European economies had preserved a strong growth momentum in 2005. Finally, macroeconomic policy in most of these countries had remained restrictive.<sup>2223</sup> For all of these factors, 2005 have been chosen to establish performance baseline and establish the impact of overdraft facility policy. In this context, the empirical analysis in Eastern European Union industrial firms showed that the overdraft facility policy would be needed to stimulate investment in R&D, which will eventually result in growth in productivity. We could not find sufficient evidence that the overdraft facility has affected positively technical efficiency, production workers trained, and sales export activity.

Since this research was based on relatively short period of time not accompanied with significant changes in overdraft facility schemes, the comparative analyses regarding the changes on the parameters were not possible. The designing of these parameters to reach the optimal level could remain as an interesting further research topic.

On the other hand, comparing with today situation, we have conducted our empirical analysis in a very different economic scenario. In this context, a longitudinal study with new data could provide a greater richness to the results, capturing the effects that persist over time. Extensions of future research could be aimed at addressing the limitations discussed.

> 22 The OECD (2001b) provides a framework allowing policy-makers to identify strong and weak points in their country‘s business environment by comparing their performance and business environment to that of other OECD countries. The OECD report concludes that four micro-drivers (human capital, information and communications technology, innovation and entrepreneurship) are key drivers of productivity and economic growth performance in knowledge-based economies

> 23 The most notable micro-policies highlighted in the OECD report are: (1) increasing access to venture capital corresponds to the driver, ―fostering firm creation and entrepreneurship‖, (2) Enhancing public-private partnerships for innovation relates to the driver ―harnessing the potential of innovation and technology diffusion‖, (3) Promoting Information and Communications Technology (ICT) diffusion to business is the driver ―seizing the benefits of ICT‖, and (4) Developing highly-skilled workers for future industry needs corresponds to the driver ―enhancing human capital and realizing its potential

26
calculates the treatment effect of migration on mental health. Section 6 explores the mechanisms underlying the gain in mental health from migration, while Section 7 concludes.

# **2. Previous Literature**

Since the classic study of Ödegaard (1932), which found higher hospital admission rates for schizophrenia among Norwegians who had migrated to the United States than those who stayed in Norway, it has been generally presumed in the literature that migration is harmful for mental health. For example, according to Pernice et al, (2000, p. 24) “the adverse effects on mental health of migration have been widely researched and established”.

Much of the clinical literature concentrates on schizophrenia, where five hypotheses are suggested for the apparently high incidence rates amongst migrants (Cochrane and Bal, 1987):

1. sending countries have high rates of schizophrenia,

2. schizophrenia predisposes people to migrate, 3. the process of migration produces stress and elevated rates of schizophrenia, 4. certain cultural practices of migrant groups get misdiagnosed as schizophrenia, and 5. some symptoms of schizophrenia are more common amongst the cultures that migrants are from.

There is debate about the strength of each of these hypotheses. According to Bhugra (2004), there is no evidence that either schizophrenia or its symptoms are more prevalent in migrant source countries and cultures. The fact that second generation migrants have higher rates of schizophrenia than their parents also counts against this and against the selection hypothesis that schizophrenics are more likely to migrate. It is also unlikely that stress during the process of migration is a direct cause of mental health disorders because the increased risk of schizophrenia occurs 10-12 years after migration (Bhugra, 2004a). There are also doubts about the evidence for misdiagnosis (Bhugra, 2004).

The evidence is further clouded when attention switches from clinical conditions like schizophrenia to more common mental disorders. According to both Cochrane and Stopes-Roe (1981) and Nazroo (1997) immigrants from South Asia in the United Kingdom had lower rates of emotional disorders and depression than did native-born whites. Similarly, a study of a 4
# **Table 2 - Political Connection and Loan Terms**

The table reports the coefficients of ordinary least squares regressions of loan terms (volume, spread, maturity and collateral) on an indicator variable, _Chairman_ , which equals to one if the firm is headquartered in the state that has a senator serving as committee chairman. The sample period spans from October 2003 to March 2012. A unit of observation is a loan at the firm-bank-quarter level. The dependent variables (ordered by column) are the firm's loan volume (in logarithm multiplied by 100), loan spread (in percent), original maturity (in months) and collateral requirement dummy. Please refer to Appendix A for variable definitions. The yearly state-level GDP growth and unemployment rate are included as controls in all specifications. All regressions include _firm*bank_ and _bank*quarter_ fixed-effects. Standard errors are reported in parentheses and are clustered at the state level. *, **, *** indicate significance at the 10%, 5% and 1% levels.

||Loan<br>|Volume<br>|Loan s<br>|pread<br>|Mat<br>|urity<br>|D(Col<br>|lateral)<br>|
|---|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
|Chairman|0.762**|0.720**|-0.019*|-0.020*|0.106***|0.091***|-0.021***|-0.018***|
||(0.285)|(0.287)|(0.011)|(0.011)|(0.036)|(0.033)|(0.007)|(0.006)|
|GDP growth rate|13.723*|13.686*|-0.250|-0.233|1.961**|1.782**|-0.176*|-0.164*|
||(7.240)|(7.099)|(0.239)|(0.234)|(0.738)|(0.735)|(0.095)|(0.087)|
|Unemployment rate|0.083|0.067|-0.009***|-0.009***|0.021|0.019|-0.002|-0.002|
||(0.116)|(0.118)|(0.003)|(0.003)|(0.017)|(0.016)|(0.002)|(0.002)|
|Bank*Firm FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Bank*Quarter FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Industry*Quarter FE|No|Yes|No|Yes|No|Yes|No|Yes|
|R-Square|0.021|0.021|0.414|0.415|0.201|0.202|0.368|0.372|
|N|594,534|594,534|594,534|594,534|594,534|594,534|594,534|594,534|

36
unprofitable to move rice from surplus to deficit provinces. As a result, surpluses in the lowland along the Mekong River might instead be exported to Thailand and Vietnam, even though there are deficits in the north. Also, in some provinces it is cheaper to import rice from abroad than from other provinces in Lao PDR (WFP, 2014).

In spite of these controls and structural obstacles, prices in Lao PDR follow those in Thailand and Vietnam well, as the cointegration analysis shows.<sup>12</sup> There are periods of a couple of years where Lao prices deviate substantially, but they eventually return to the long-run (equilibrium) path. We do not know if there is a long-run interdependence because the authorities occasionally allow for exports, or because of informal trade, or both.

The current policy of export restrictions is at least in part responsible for keeping prices relatively low in Lao PDR. We have also traced the price spikes back to the discretionary policy interventions of the government, as domestic prices are found to increase in periods where restrictions are relaxed. The 2010 price spike illustrates the dynamics well: In 2009 the harvest was unusually bountiful, so constraints on official exports were relaxed, which subsequently led to large exports. Rice prices rose slowly during the first quarter of 2010, and then rapidly until the 2010 harvest started in October. This story contrasts with FAO (2011), according to which prices rose to unusually high levels due to fears of low production in 2010, both nationally and regionally. The government may not have been able to adequately track the volume of trade over time relative to the size of the harvest, which ultimately led to overly optimistic exports and

> 12 Since the price level continues to be substantially lower in Lao PDR than in the neighboring countries, it appears that there are no arbitrage opportunities that would even out prices; the finding of cointegration only implies that the price difference does not change in the long run.

26
|(8)|-0.00711<br>(0.0114)|-0.00433<br>(0.00695)|0.0209_∗_<br>(0.0114)|Yes<br>Yes|Yes|Yes|Yes<br>Yes|72236|||
|---|---|---|---|---|---|---|---|---|---|---|
|(7)|-0.0182_∗∗_<br>(0.00819)|-0.00819_∗∗∗_<br>(0.00310)|0.0262_∗∗∗_<br>(0.00829)|Yes<br>Yes|Yes|Yes|Yes<br>No|417214|||
|(6)|||0.0216_∗_<br>(0.0114)|Yes<br>Yes|Yes|Yes|Yes<br>Yes|72236|||
|(5)|||0.0283_∗∗∗_<br>(0.00840)|Yes<br>Yes|Yes|Yes|Yes<br>No|417214|||
|(4)||-0.00482<br>(0.00699)||Yes<br>Yes|Yes|Yes|Yes<br>Yes|72236|||
|(3)||-0.00885_∗∗∗_<br>(0.00313)||Yes<br>Yes|Yes|Yes|Yes<br>No|417214|arrival.||
|(2)|-0.00921<br>(0.0111)|||Yes<br>Yes|Yes|Yes|Yes<br>Yes|72236|status.<br>l _×_ Year of||
|(1)|-0.0225_∗∗∗_<br>(0.00793)|||Yes<br>Yes|Yes|Yes|Yes<br>No|417214|gender, marital <br>Canton of arriva||
||Unemployment rate|Network|Attitudes|Current year FE<br>Country of birth FE|Arrival year FE|Canton of arrival FE|Canton of arrival X Current year FE<br>Education FE|Observations|Dependent variable is Employment dummy.<br>All regressions also include age, age squared, <br>Standard errors are clustered at the level of|* p < 0.10, ** p < 0.05, *** p < 0.01|

51
# 5.2. Cooking and black carbon

In recent years, black carbon (BC)<sup>11</sup> has received wide attention because of its impact on global climate change and human health. Several studies have also emerged indicating incomplete combustion of traditional biomass and fossil fuels for residential uses as the important source of BC. At the global and regional levels, BC is considered as short-lived but important climate forcers that has significant influence on the climate system for climate change (UNEP and WMO, 2011). However, the sign and magnitude of the net climate influence (warming or cooling) from BC emissions is not fully known at present and further research and quantitative assessment are needed to reduce these uncertainties (EPA, 2012). Based on the review of health effects of BC, WHO (2012) reports that sufficient evidence suggest association of BC concentrations with short term changes in health including cardiovascular mortality. At the local level, BC emissions vary considerably by region and sector due to variation in local practices and the types of fuels and technologies used in different regions and sectors. Based on scientific studies, Ramanathan and Carmichael (2008) and Gustafsson et al. (2009) find that, in general, developing countries in the tropics and Asia are generally recognized as dominant source regions. These studies also find that about 40% of BC originates from burning fossil fuels, 40% is from open biomass burning, and 20% is from the burning of biofuels. However, all of Asia, including China and India, accounts for 40% to 60% of global BC emissions (Bond et al., 2004; Zhang et al., 2009), while biomass combustion for cooking comprises about 26% of BC emissions globally (Bond, 2009). Besides, BC emissions are also transboundary in nature. Kopacz et al. (2011) explain how prevailing wind patterns draw BC emissions in considerable quantities from Africa and the Middle East to Tibetan Plateau especially during dry months when biomass burning activities are most prevalent. At the national level, Venkataraman et al. (2010) estimate that fossil fuel, open burning and residential biofuel combustion combined account for 25%, 33% and 42% of BC emissions in India, respectively. The study also estimates that switching to improved stoves in India could reduce the country’s total greenhouse gas (GHG) emissions by 4%. Through project _Surya_ , one of the few improved cookstove programs with BC

> 11 Black carbon (BC) is the most strongly light-absorbing component of particulate matter (PM), and is formed by the incomplete combustion of fossil fuels, biofuels, and biomass. The short atmospheric lifetime of BC (days to weeks) and the mechanisms by which it affects climate distinguish it from long-lived GHG like CO2. See Venkataraman et al. (2005), UNEP and WMO (2011) and EPA (2012) for details on BC.

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