rafmacalaba/datause-encoder-data huggingface dataset:
2

# Constitutional Law

The constitution of a country sets the most basic rules on the structure and roles of the government and the economic system. On December 23, 1991, Slovenia adopted a new constitution,3 one year after the public referendum overwhelmingly voted in favor of an independent sovereign Slovenia. This ia the culmination of a series of constitutional developments promoting evergreater dissolution from Yugoslavia.

# The Historical Settina

In contrast to the United States, with its one constitution in 200 years, constitutions in Eastern Europe chan e regularl7. Post-war Yugoslavia has had 4 conetitutions--1946,4 1953,' 1963, and 1974. The 1946 constitution introduced central planning, while the 1953, 1963, and 1974 constitutions introduced and later revised the concept of worker selfmanagement. Yugoslav constitutions also tend to be long, with extensive 3 Ustava Republike Slovenije [Constitution of the Republic of Slovenia (hereinafter "Slovene Constitution")], Uradni list Republike Slovenije [Official Gazette of the Republc of Slovenia (hereinafter "Official Gazette RS")], No 33 (1991), pages 1.73-1386.

4 Ustava Federativne ljudske republike Jugoslavije [Constitution of the Federal Republic of Yugoslavia], Uradni list Federativne ljudske republike Jugoslavije [Official Gazette of the Federal People's Republic of Yugoslavia (hereinafter "Official Gazette FPRY")], No 10 (1946).

5 Ustavni zakon Federativne ljudske republike Jugoslavije [Constitut:onal Law of the Federal People's Republic of Yugoslavia], Official Gazette FP'.. No 3 (1953).

6 Ustava Federativne ljudske republike Jugoslavije [Constitution of the Federal People's Republic of Yugoslavia], Official Gazette FPRY, No 14 (1963), with most recent amendments Uradni list Socialisticne federativne republike Jugoslavije [Official Ga'ette of the Socialist Federal Republic of Yugoslavia (hereinafter "Yugoslav Official Gazette")], No 29 (1971).

7 Ustava Socialistibne federativne republike Jugoslavije (Constitution of the Socialist Federative Republic of Yugoslavia], Yugoslav Official Gazette 9 (1974), with most recent amendments Yugoslav Official Gazette No 70 (1988).
stability of emerging markets and the global economy. The paper starts with a selective review of the literature on financial globalization and growth. It then continues with the response of both the industrialized and developed countries to financial globalization, pointing out the stark differences. This discussion is organized around the macroeconomic trilemma, or the idea that countries can choose at most two out of the following three: an open capital account (financial integration); independent monetary policy; and a fixed exchange rate.

Next comes a review of the large accumulation of international reserves (IR) by emerging market countries. BW II is only one explanation. Arguably, the global crisis has chipped away at the credibility of the BW II, US-centric interpretation of global financial intermediation. We also discuss other explanations for IR accumulation like the buffer stock argument, mitigation of real exchange rate volatility, mercantilism reflecting concerns about relative competitiveness, and self-insurance against foreign and domestic shocks. Self-insurance with an added emphasis on financial stability and putting the fiscal house in order emerges as the most convincing explanation in a second-best world.

The discussion on IR accumulation is followed by outlining a public finance approach to financial integration based in large part on the actual response of emerging markets to their crises of 1997-2001. A provocative hypothesis is that the global crisis of 2008-09 means that the financial sector in OECD countries may have grown beyond socially desirable levels, pitting those who would reform it with a view to restricting its untrammeled growth against those with vested interests in a minimum of regulation and publicly funded bailouts; while the comparative resilience of developing countries is a tribute to the corrective measures they took after their own crises of 1997-2001. The paper ends with a summary of policy conclusions.

# **LITERATURE SURVEY**

This selective survey is divided into two parts: the links between financial globalization and growth on the one hand and macroeconomic crises on the other.

# **Financial Globalization and Growth**<sup>4</sup>

Expectations and optimism were rife at the beginning of the 1990s that growing financial integration would speed up growth in developing countries and help with income convergence. The channel through which this would happen would involve global savings being channeled into investment in capital-scarce developing countries. Aizenman, Radziwill and Pinto (2004, 2007)— APR—examined whether there was any increase in the extent to which the domestic physical

> 4 This section draws on chapter 7 in Pinto (Forthcoming).

3
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mean less time spent in making a correct diagnosis and offering a correct treatment. Conversely, a knowledgeable doctor may take more time in being more thorough.

The data suggest two patterns. First, greater competence is associated with higher effort. For instance, in India we divided doctors into four categories: with and without an MBBS, and private versus public hospital-based (or clinic-based) doctors. In all four categories, the more competent the provider, the more effort exerted. Indeed, the increased effort from greater competence looks similar between the groups, except for the deficient effort in public clinics that stands out. The finding that effort and competence are positively correlated is confirmed in the data from Tanzania as well (Leonard and Masatu, 2005).

Second, despite the positive correlation between effort and competence, there is a large and significant gap between what doctors know they should do (as measured by testing doctors) and what they actually do (as measured by watching doctors). In both India and Tanzania, for patients reporting fever, cough or diarrhea, observers were asked to note whether the doctor asked certain questions and performed certain examinations that had been included in the vignettes as essential tasks. Since these cases had also been covered under the vignettes, we can directly compare what doctors said they would do and what they actually did when faced with a similar patient. How large is the gap between knowledge and practice?

For the data from India, we considered the percentage of essential tasks completed in the vignettes concerning diarrhea and “cough without fever” and in direct observation of patients reporting these conditions for three types of doctors—private doctors with an MBBS degree; private doctors without an MBBS degree (recall that doctors with an MBBS degree are more competent) and; public doctors, all of whom have an MBBS degree. There are several noteworthy features of the comparison. First, private doctors without an MBBS complete just 23
invariant, then the estimates should not vary substantially across these alternative empirical models. This provides a way to gauge the importance of unobserved time-varying factors in our application.

For implementation of the above discussed empirical strategy, we use alternative comparison groups. We exclude the households which were members of MFIs in both years from our sample, because no pretreatment benchmark is available for them. There are two groups who can serve as comparison groups: households which had not been members of MFI on both survey years (termed as “never member”) and households who were members in 2000 but not in 2007 (termed as “drop-outs”). The drop-outs are considered by many to be more comparable to the new members as both of these groups are MFI clients. We also put together the ‘never members’ with the ‘drop-outs’ as an additional comparison group, as failure to include the drop-outs may overestimate the effects of MFI membership on household outcomes (Alexander-Tedeschi and Karlan (2009)).

# **3.2. DATA**

The household level panel data for two rounds (2000 and 2007) from the BIDS-BRAC surveys are used for our analysis. These two rounds of the surveys have complete information on 1599 households. The sample used for estimation is however a bit smaller (1365), as we exclude the households (234) who had been MFI members in both survey years and thus lack observations on pretreatment period(s). Out of the sample of 1365 households, 376 households are new members, 142 are drop-outs and rest (844) were never member in MFI institutions. The MFI participation rate in 2007 is 38 percent which is consistent with evidence from representative national surveys such as Household Income and Expenditure Survey 2010 (According to HIES 2010, MFI participation rate in rural Bangladesh is about 30 percent). In the full sample, about 7.11 percent (97) households are new borrowers from the informal sources in 2007. About 4 percent of new MFI members borrow from informal sources compared with 8.3 percent among non-members.

# **3.3. EMPIRICAL RESULTS**

Table 4 reports the estimation results for the effects of MFI membership on propensity to borrow from informal sources. The upper panel shows the results when the comparison group is defined to include only those who have not been MFI members in both survey years. The comparison group in middle panel consists of drop-outs who were MFI members in 2000 but not in 2007. The comparison group in the final panel combines both the drop-outs and never members. We begin by presenting the DID-FE estimate of the effect of MFI membership which is reported in column (1) of Table 4. This specification (equation 5) does not include any household or region level controls. The estimates in 22
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Sectors not only vary in reliance on external but also in ability to access external finance. For a given technological need for external finance, raising outside finance is likely to be easier for firms in sectors structurally characterized by a high level of tangible assets. These assets can be pledged as collateral,<sup>20</sup> reducing in that way the adverse selection and moral hazard problems that lenders face. To capture this second dimension of financial vulnerability, we use the Kroszner et al. (2007) measure of asset tangibility (TANG). It corresponds to the sector-specific ratio of fixed assets to total assets and it has been calculated for the period 1980-1999 using the same methodology as Rajan and Zingales (1998). While the TANG measure is not specific to FDI activities, firms are more likely to have access to external finance, at home or abroad, when their existing assets, or the assets they wish to acquire in a foreign country, are tangible.<sup>21</sup> Table 3 in Appendix A provides the values of the two measures of vulnerability for the thirteen manufacturing sectors present in the _FDI Markets_ database. Sectors vary both in their dependence on external finance and their ability to access external finance.<sup>22</sup>

# **3.3 Financial development**

FDi) is the domestic credit allocated to the private sector by banks and other financial intermediaries, normalised by GDP (CREDIT/GDP ). This financial development measure, which reflects the actual use of external debt financing in the economy, has

> 20Unlike intangible assets, tangible assets can be easily liquidated in case of default.

> 21Given that our FDI variable corresponds to capital expenditures, i.e. investment in collateralisable tangible assets, it could be argued that asset tangibility is not an appropriate measure of financial vulnerability. This may have been true if external finance was only sought to cover the costs of building a new manufacturing facility. However, outside capital is also likely to be required to invest in intangible assets complementary to the tangible assets. Engaging in FDI may then be conditional on the ability of firms to finance the intangible assets that they need to operate successfully in foreign markets. This constraint should be particularly strong for firms operating in sectors where intangible assets are at the core of their business. It is also possible that sector-specific asset tangibility matters if the loan financing the FDI project is backed by the tangible assets of the parent company. That may be the case if a domestic bank does not want to deal with the seizure of assets in a foreign jurisdiction. For these reasons, TANG ought to remain a valid measure of financial vulnerability, including in the context of this paper.

> 22

10
14 development of new products and processes. A high share of public funding of environmental costs can indicate a lower impact on industry competitiveness, although the cost will ultimately be more widely shared via higher taxes.

As environmental policies and related expenditures are not new to most industrial countries, the use of data starting in 1970 is justified. In many OECD countries, environmental policy making started in earnest in the early 1970s with a main focus on national problems. 't reached a new peak in the late 1980s and early 1990s with greater emphasis on global problems. Available data on the costs of these policies to industry or to the ecor -my at large is patchy and subject to many definitional and measurement problems. The following expenditure estimates, nevertheless, show a number of interesting features among countries on how the policies may or may not have affected competitiveness (see Table 8).

**Table 8: Environmental expenditures as a share of GDP, industrial countries, 1980-90**

||1980|1990|
|---|---|---|
|(O)|||
|**Industrial**|**0.9-1.8**|**0.9-1.9**|
|**Austria**|**1.2**|1.9|
|**Finland**|1.3|1.1|
|_Norway_|**_1.3_**|0.6|
|Sweden|1.0|0.9|
|Genrany|_1.5_|1.7|
|Japan|**1.8**|1.0|
|US|1.6|1.4|

* Range in mid-1985, as total industrial country estimates **are** not available for 1980 or 1990. Source: OECD (1990), Blazejczak (1993).

The overall level of environmental expenditures in industrial countries is moderate and, from country to country, not radically different. Although, as mentioned, considerable caution has to be exercised in making cross-country comparisons with the available data. Existing data suggests that higher standards are not forcing industries to much higher costs than their competitors in other countries. Estimates of expenditures range between 1 and 2% of GDP. Over the past ten years these expenditures have increased in some countries and decreased in others as share of GDP, reflecting differences in timing and types of policies. Some of the
over time; they include all sectors of the U.S. economy from 1960-2016. The data further serve to test the empirical relevance of our mechanism for an industrialized economy with more developed financial markets.<sup>22</sup> The Compustat sample is based on publicly traded firms. While these are relatively few compared to the total number of firms, they include the largest firms in the U.S. economy. In fact, the data account for one-third of total U.S. employment and about 41% of total U.S. sales (De Loecker and Eeckhout, 2017). While detailed measures of innovation in terms of technology choice (product and processs innovation) are not available, Compustat allows to measure the intensive margin of firms’ innovation activity: we proxy firms’ exposure to the risky advanced technology by their R&D intensity as measured by R&D expenses relative to total assets. We also use firms’ total investment and balance sheet information on cash holdings as discussed earlier. Further, we include the stock of total assets and retained earnings as firm-level control variables.

The descriptive statistics are summarized in Table 5. The average (median) inflation rate in the sample is 3.6% (3.0%); and firms’ average (median) share of R&D in total assets amounts to 26% (5%). Importantly, the data reveal that even in countries with relatively developed financial markets such as the U.S. firms hold a large amount of cash, accounting on average for 11% of firms’ total assets (median of 4%). In line with earlier findings (Opler, Pinkowitz, Stulz, and Williamson, 1999), our model suggests that the principal motive for these cash holdings is to provision for liquidity needs arising due to expenditure shocks associated with the advanced technology, that is, with more productive but risky investment.

Table 5: Descriptive statistics

||Observations|mean|s.d.|min|max|
|---|---|---|---|---|---|
|**Compustat and WDI:**||||||
|R&D/assets|125,709|.261|8.59|0|1|
|Corp. liquidity/assets|320,325|.113|.179|0|1|
|Inv/assets|357,136|.641|.230|0|1|
|ln(corp. liquidity)|326,943|2.22|2.05|-6.91|13.0|
|ln(total assets)|382,007|4.77|2.55|0|15.1|
|ln(retained earnings)|237,585|3.54|2.46|-6.91|12.9|
|infation|428,482|3.64%|2.53|-.36%|13.5%|
|ln(real GDP pc)|428,482|6.38|.231|6.16|6.99|

> 22Our Compustat results are also better comparable to those from the empirical corporate finance literature discussed in the Introduction.

25
||||||||Root-|mean|-squar|e erro|r|||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
|||||n=|400|n=|1,000|n=|3,000|n= 1|0,000|n= 3|0,000|
|pT|pY|∆ATE|∆LAT E<br>∆AT E|BP|IV|BP|IV|BP|IV|BP|IV|BP|IV|
|0.1|0.1|0.08|<br>1.47|0.38|3.03|0.32|1.88|0.23|0.22|0.11|0.12|0.06|0.07|
|0.1|0.3|0.15|1.12|0.36|4.93|0.33|4.81|0.25|0.34|0.15|0.18|0.09|0.10|
|0.1|0.5|0.16|0.90|0.37|6.65|0.34|4.22|0.26|0.38|0.17|0.20|0.10|0.11|
|0.1|0.7|0.12|0.70|0.41|5.30|0.37|2.99|0.28|0.34|0.19|0.19|0.11|0.11|
|0.1|0.9|0.05|0.47|0.32|3.40|0.41|2.67|0.40|0.23|0.25|0.12|0.16|0.07|
|0.3|0.1|0.07|1.19|0.27|1.38|0.20|0.21|0.11|0.11|0.06|0.06|0.03|0.04|
|0.3|0.3|0.14|1.11|0.31|1.58|0.24|0.32|0.15|0.16|0.09|0.09|0.05|0.05|
|0.3|0.5|0.16|1.02|0.32|2.16|0.24|0.34|0.16|0.18|0.09|0.10|0.05|0.05|
|0.3|0.7|0.13|0.91|0.31|1.78|0.24|0.31|0.15|0.16|0.09|0.09|0.05|0.05|
|0.3|0.9|0.06|0.73|0.32|1.29|0.26|0.21|0.16|0.11|0.08|0.06|0.04|0.04|
|0.5|0.1|0.06|0.96|0.24|0.50|0.18|0.18|0.10|0.09|0.05|0.05|0.03|0.03|
|0.5|0.3|0.14|1.03|0.29|0.89|0.22|0.28|0.13|0.14|0.08|0.08|0.04|0.04|
|0.5|0.5|0.16|1.05|0.30|1.08|0.23|0.29|0.15|0.15|0.08|0.08|0.05|0.05|
|0.5|0.7|0.14|1.03|0.29|0.83|0.21|0.26|0.13|0.14|0.08|0.08|0.04|0.04|
|0.5|0.9|0.06|0.96|0.25|0.54|0.18|0.17|0.10|0.09|0.05|0.05|0.03|0.03|
|0.7|0.1|0.06|0.73|0.32|1.19|0.25|0.23|0.15|0.11|0.08|0.06|0.04|0.04|
|0.7|0.3|0.13|0.91|0.31|1.82|0.25|0.33|0.15|0.16|0.09|0.09|0.05|0.05|
|0.7|0.5|0.16|1.02|0.32|1.31|0.25|0.37|0.16|0.18|0.09|0.10|0.05|0.05|
|0.7|0.7|0.14|1.11|0.31|1.25|0.24|0.31|0.15|0.16|0.09|0.09|0.05|0.05|
|0.7|0.9|0.07|1.19|0.27|0.72|0.20|0.21|0.11|0.11|0.06|0.06|0.03|0.04|
|0.9|0.1|0.05|0.47|0.34|2.73|0.41|1.28|0.40|0.24|0.27|0.12|0.15|0.07|
|0.9|0.3|0.12|0.70|0.42|4.12|0.37|1.90|0.29|0.35|0.19|0.18|0.11|0.10|
|0.9|0.5|0.16|0.90|0.38|4.81|0.34|2.07|0.26|0.38|0.17|0.20|0.10|0.11|
|0.9|0.7|0.15|1.12|0.38|4.24|0.34|2.85|0.25|0.35|0.16|0.18|0.09|0.10|
|0.9|0.9|0.08|1.47|0.39|3.12|0.34|2.45|0.23|0.22|0.12|0.12|0.07|0.08|

Table 3: Root-mean-square error of ∆<sup>ˆBP</sup> ATE<sup>andˆ∆IVfortrueATEasafunctionofpTand</sup> pY , in bivariate probit model simulations with no covariates and ρ = 0.3.

26
# **1. Motivation and overview**

1.1 About a third of development projects fail to achieve satisfactory outcomes, according to agencies’ independent evaluation units. (Figure 1)  To a large extent, these outcomes appear to be baked into projects at their inception. This is evident in Figure 2, which shows a strong correlation between project outcome<sup>1</sup> and quality at entry. Quality at entry, in turn, is closely associated with quality and relevance of project design, the focus of this paper<sup>2</sup> .

**Figure 1 Proportion of international donor agency projects with successful outcomes**

<!-- Start of picture text --> World Bank<br>IFC<br>IFAD<br>EBRD<br>ADB: Nonsovereign<br>ADB: Sovereign<br>AfDB<br>0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%<br><!-- End of picture text -->

**Note:** ADB = Asian Development Bank; AfDB = African Development Bank; EBRD = European Bank for Reconstruction and Development; IFC = International Finance Corporation; IFAD = International Fund for Agricultural Development. “Outcome” is an index combining efficacy, relevance, and efficiency. “Success” is a rating of four or better on a six-point scale (IFAD, IFC, World Bank); three or better on a four-point scale (ADB, EBRD); or 2.5 or better on a four-point scale (AfDB). Based on most recent available reviews of independent evaluations or independent validations of self-assessments.

**Source: World Development Report 2016**

1.2 This paper addresses the question: are better-designed and/or better-performing projects more likely to be emulated?  One would hope so, but the answer is by no means obvious.  The development business is subject to fads: hype and hope are piled onto fashionable interventions, which are widely replicated before evidence of efficacy is available. Evaluations by the World Bank’s Independent Evaluation Group (IEG) have found pervasive, structural failures that inhibit learning from experience (IEG 2014, 2015).  But the impact on diffusion of project ideas has not been quantified.

1.3 To address this question, this paper introduces a novel approach: a network model of the diffusion of project ideas.  The motivation is as follows.  Investment projects consist of

1 “Outcome” is a rating that incorporates achievement of objectives, relevance of objectives, and efficiency.

2 According to evaluation guidelines, quality at entry captures the degree to which project identification, preparation and appraisal supported the achievement of planned outcomes in a way consistent with the Bank’s fiduciary role. It includes but goes beyond the relevance of project design.

1
**Table 7: Fay-Herriot Model for share below middle-class line, and average consumption in 2011 PPP**

||Below<br>Middle<br>Class||Average<br>Cons.|
|---|---|---|---|
||(1)||(2)|
|Population count|1.0000**|Air temperature max|0.9071***|
||(0.0000)||(0.0270)|
|Grid Sum GDP|1.0000***|Max precipitation|1.0278***|
||(0.0000)||(0.0104)|
|Grid GDP Min|1.2266***|Nighttime lights|1.0000***|
||(0.0940)||(0.0000)|
|Vegetation Index max|1.0000**|Grid GDP Min|0.0142***|
||(0.0000)||(0.0225)|
|Max precipitation|0.9987***|No local service road max value|1.0000***|
||(0.0004)||(0.0000)|
|Vegetation Index average|0.9999***|Grid GDP Mean|1.5187***|
||(0.0000)||(0.1485)|
|No local service road max|1.0000***|Population count|1.0000**|
||(0.0000)||(0.0000)|
|||Grid GDP Sum|1.0002***<br>(0.0001)|
|||500m Elevation|0.9977***|
||||(0.0006)|
|Constant|2.8220***|Constant|9,759***|
||(0.1133)||(5755)|
|Observations|331|Observations|331|
|R-squared|0.4902|R-squared|0.5593|

*** p<0.01, ** p<0.05, * p<0.1; selected country/region dummy variables not shown

*** p<0.01, ** p<0.05, * p<0.1; selected country/region dummy variables not shown

75