rafmacalaba/datause-encoder-data huggingface dataset:
fatalities per VKT.<sup>23</sup> Likewise, the percentage of population living in urban areas (URBAN) has an insignificant effect on the occupant fatality rate.<sup>24</sup>

**_Motorization._** Increases in the size of the motor vehicle fleet (VEH) increase

occupant fatalities per VKT, as the formal model suggests, but this result is significant only in some specifications. Moreover, the negative coefficient on the vehicle-time trend interaction (ln(VEH)*t) indicates that the effect diminishes over time. For example, in the Base Model the elasticity of occupant fatalities with respect to the size of the vehicle fleet decreases from 0.38 in the mid-1960s to approximately zero by the mid-1990s. This result is consistent with newer, safer vehicles providing additional protection to vehicle occupants.

The percentage of motorized two-wheelers in the vehicle stock (2WHEELERS) has no effect on the occupant fatality rate (Model 7). This is somewhat surprising given the increased vulnerability of motorcycle accident victims to bodily injury.

The results suggest that the occupant fatality rate increases with a country’s rate of motorization. In the Base Model in Table 7, the coefficient on the annual growth rate of the motor vehicle fleet (VEHGROWTH) is found to be 0.587 ( _0.278_ ). Since VEHGROWTH enters the model in linear form, the magnitude of this effect is not large. Considering that VEHGROWTH fell from approximately 0.06 to 0.02 in several

> 23 Few other studies have examined the effect of this age cohort on occupant and pedestrian fatalities separately.  Noland (2003a) did find an increase in the percentage of population aged 65 and over to be associated with an increase in total traffic deaths but the result loses significance as he adds other variables (medical treatment proxies) to the model. Using U.S. county level data for 1970 and 1980, Keeler (1994) also found a positive but insignificant effect of elderly on the total traffic deaths per capita. 24 This result differs somewhat from Noland’s (2003a) finding that increasing population reduces total fatalities.  Noland attributes negative coefficient on total population to increasing congestion.

19
Table D3: Effect of Placebo Module Position on SI Effects for Placebo Questions

||(1)|(2)|(3)|(4)|
|---|---|---|---|---|
||Farm work<br>(past year)|Market visit<br>(past week)|Maize/Rice<br>(next week)|Meat<br>(next week)|
|**Panel A. Malawi**|||||
|SI _×_ Placebos First (_β_)|0.01|0.08**|0.11***|0.11***|
||(0.02)|(0.03)|(0.04)|(0.04)|
|SI _×_ IPV First (_γ_)|-0.04**|0.08***|0.05|0.12***|
||(0.02)|(0.03)|(0.04)|(0.03)|
|Placebos First|0.00|0.00|0.01|0.05|
||(0.02)|(0.03)|(0.04)|(0.03)|
|FTFI _×_ IPV First mean|0.94|0.46|0.54|0.26|
|_p_-value (_β_ =_γ_)|0.036|0.992|0.326|0.895|
|Observations|1,718|1,713|1,345|1,228|
|**Panel B. Liberia**|||||
|SI _×_ Placebos First (_β_)|0.03|0.10***|-0.03*|0.01|
||(0.03)|(0.04)|(0.02)|(0.04)|
|SI _×_ IPV First (_γ_)|-0.01|0.07**|-0.04**|-0.03|
||(0.03)|(0.04)|(0.02)|(0.04)|
|Placebos First|-0.01|-0.04|-0.01|-0.04|
||(0.03)|(0.04)|(0.01)|(0.04)|
|FTFI _×_ IPV First mean|0.81|0.69|0.98|0.70|
|_p_-value (_β_ =_γ_)|0.356|0.633|0.610|0.446|
|Observations|1,259|1,260|1,226|1,101|

Note: Regressions include individual controls (including all variables in Table 1) and the order between IPV module and PHQ-9 module. Standard errors clustered at individual level in parentheses. ***, **, and * represent significance at 1%, 5%, and 10%, respectively.

37
16 moving to update and replace these laws with legislation that **4.s** more in line with market-based international norms.' On the institutional side, in June 1991 it established the Agency for the Protection of Industrial Property,64 which is supposed to assume the functions previously carried out by the analogous Yugoslav federal agency. Registrations previously made in the - federal agency remain valid in Slovenia.

The applicable law for patents and trademarks in Slovenia until March 1992 was the Yugoslav industrial property law of 1981--the Law on the Protection of Inventions, Technical Improvements and Distinctive Siinr.65 When adopted, this law was a step backward from its predecessor in terms of the legal protections it provided. For example, patents and trademarks were protected for only 7 years, with the possibility of extension for 7 more (Article 51), and many items (such as pharmaceuticals) were excluded from protection altogether (Articles 20, 23). Internal and external pressure led to amendments in 199066 that improved patent and trademark protection, and Yugoslavia simultaneously moved to expand its participation in relevant international conventions.

Slovenia passed a new patent law in March 1992.' The new law is similar in structure to the amended federal law of 1990 but broader in coverage." It provides patent and trademark protection closely in line with modern international standards and existing international conventions. The period of patent protection is extended to 20 years (Article 37), the first 10 upon request without examination as to novelty or applicability69 and the 62 Pravilnik o patentnih uradih (Statute on Patent Offices), Yugoslav Official Gazette, No 25 (1963).

63 Much of the information on the new legal framework is taken from Bojan Pretnar, "Protection of Intellectual Property in Slovenia," unpublished manuscript, 1991.

64 Zakon o organizaciji in delovnem podrocju republiike uprave [Law on Organization and Working Field of the Administration of Republic], Official Gazette RS, No 27 (1991).

- 65 Supra note 60.

- **66** Yugoslav Official Gazette, No 9 (1990) and No 20 (1990).

67 Zakon o varstvu industrijske lastnine (Law on Protection of Industrial Property], Official Gazette RS, No 13 (1992), pages 816-826.

**68** For example, the law covers plant and animal varieties and all drugs and chemical compounds (Article 28). However, in the case of drugs applications can be filed only after December 31, 1992 (Article 121).

69 The application is examined only to be sure that it meets formal requirements and that exclusion of cther users is feasible (Article 54). Although the patent office does not go further, any person may oppose the
International Survey of Integrated Financial Sector Supervision Jose de Luna Martinez and Thomas A. Rose Jos6 de Luna Martinez (email: JdeJunarnartinezQworldbank.org) and Thomas A. Rose (Trose@worldbank.org) are respectively Senior Financial Economist and Program Manager at the World Bank. The authors express their gratitude to the senior officials of the fifteen unified agencies that kindly accepted to respond the survey on integrated supervision, share their valuable experiences, and provide comments to early drafts of this paper. They also thank Clive Briault, Alex Fleming, Arpad Kiraly, Kenneth Mwenda and Ruth Neyens for their helpful suggestions.
**Table 3: Public Expenditures in the Security Sector (% of GDP)**

||**Total**<br>**Expenditures**|**Defense**|**Public Order**<br>**and Safety**|
|---|---|---|---|
|Afghanistan a/|56.5|13.2|6.4|
|Belarus|18.4|1.2|1.2|
|Bolivia|18.1|1.7|1.8|
|Bulgaria|20.6|2.3|1.9|
|India|16.7|2.6|n/a|
|Jordan|31.7|6.0|2.9|
|Kazakhstan|13.7|0.6|1.3|
|Kyrgyz Republic|18.0|1.7|1.1|
|Moldova|21.2|0.4|1.3|
|Pakistan|22.8|4.5|0.3|
|Philippines|19.6|1.0|1.4|
|Romania|16.4|1.3|1.8|
|Senegal|13.1|1.6|n/a|
|Sri Lanka|25.6|4.5|1.1|

a/ includes "External Budget", cf. World Bank (2005). _Source: IMF (GFS), World Bank (2005)._ A second guidepost is to assess the **rationale for using public resources in the sector** . A long tradition of welfare economics has discussed the role of the public sector in implementing various policies. The general lesson is that public expenditures are justified as a correction for market failures (such as externalities, public goods, imperfect information) or for redistribution. Nevertheless, as noted by Devarajan et al (1996), “neither economic theory nor empirical evidence provides clear-cut answers to how the composition of expenditure affects economic growth.” On this basis, Paternostro, Rajaram, and Tiongson (2005) provide a framework to assess the issue of allocative efficiency, in three steps.

The first question is to assess **whether public intervention—as opposed to private intervention—is necessary** . The discussion above has shown that dealing with securityrelated risks is often a public good. Defense against external security threats is essentially a pure public good at the national level. Internal public security, supported by the police and justice system, comes close to being a pure local public good for the locality concerned (although access may well be inequitable). Justice is also a public good in that its existence benefits everyone. However, any particular justice transaction benefits (or harms) individuals, and moreover, in many countries people in need of recourse may have a considerable degree of choice as to whether to go through the formal justice system or instead rely on traditional informal mechanisms for this purpose. Landmine clearance has elements of both a public and a private service; the private benefits are high if the land demined is under private ownership, whereas the public benefits are high if it is publicly owned/used land (e.g. roads or irrigation canals). Border management and intelligence are also essentially national public goods.<sup>9</sup> Nevertheless, all these functions might not necessarily be provided by the public sector. For instance, effective rule of law is a public good as it allows enforcement of contracts. However, Dixit (2004) provides a number of examples where public provision might not be needed to ensure contract enforcement, for instance if contract enforcement is based on relations instead of rule of law or where there are private incentives for the enforcement of

> 9 There are also a number of functions that have important linkages with security risks, such as diplomacy, customs, safety nets, etc.  We do not explicitly review these functions in this paper.

13
the limit by buying unlimited amounts of euros. The SwF did not appreciate beyond this limit but SNB’s FX reserves ballooned from about SwF 200 billion prior to September 2011 to SwF 424 billion in October 2012, turning Switzerland into one of the biggest FX reserve holders in the world.<sup>7</sup>

# **3.6.   Modalities of FX Intervention and Volatility of FX Reserves**

Is there an association between the way central banks intervene and the volumes of FX intervention? Although systematic knowledge of central bank practice in FX markets is often limited, differences in volumes of FX intervention (scaled by GDP) can be significant, as suggested by a cursory look at the countries reviewed above. Comparison of volumes of FX intervention across countries is difficult because of the paucity of central bank intervention data. A common practice in empirical studies has been to use changes in gross FX reserves as a proxy for FX intervention. This approximation is imperfect because, in addition to FX intervention, changes in FX reserves also reflect valuation effects from changes in exchange rates, income flows (accrual or payment of interest), and FX operations of the central banks on behalf of other government entities. Correlations of actual volumes of intervention (when available) with changes in gross FX reserves are generally low at high frequency data (daily or weekly). However, at lower frequencies (monthly data) changes in FX reserves provide a reasonable approximation of FX intervention (Adler and Tovar, 2011). Correlations between monthly FX interventions and changes in FX reserves for the countries reviewed above are significant: India=0.84; Colombia=0.77; Turkey 0.43.

The volatility of changes in gross FX reserves—measured by the standard deviation of monthly changes over 2000-12 scaled by GDP—varies broadly across countries (Figure 3.5). Volatility ranged from 0.2 percent of GDP in South Africa, Colombia, and Mexico, to around 1.3 percent of GDP in Uruguay and Malaysia, with an average of 0.6 percent. A stylized fact in this comparison is that where central banks practiced some form of rules-based FX intervention (Mexico, Colombia, Guatemala, Chile); or preannounced intervention (Turkey), the volatility of changes in FX reserves was below the average for the 22 countries considered.

> 7 The top ten FX reserve holders, based on 2011 data, were: China; Japan; Saudi Arabia; Russia; Brazil; South Korea; Hong Kong SAR, China; Switzerland; India; and Singapore. The SNB now holds 60 percent of its FX reserves in euros.

17
In order to explore action at the extensive margin, we also consider the simple number of exporters of good _k_ to OECD countries.

Our data are COMTRADE import data for OECD countries (either taken as a bloc or disaggregated by importer) at the product level. Our preferred product classifications are SITC4. The alternative, HS6, is more disaggregated (with 4,990 to 5,016 lines depending on the year against 1,158 to 1,300 for SITC4), but the sample period is longer with SITC4, which also underwent fewer revisions. In terms of country coverage, SITC4 data covers 210 countries between 1962 and 2006 (44 years); HS6 coverage is nominally available starting 1988, but with only 12 countries (9 of which are OECD members) expanding gradually to 116 countries in 1995 and 140 in 2006. Descriptive statistics for our sample are shown for our indices in Table 1.

||<br>Descrip|Table 1<br>tive sta|<br>tistics|||
|---|---|---|---|---|---|
|Variable|Obs|Mean|Std. dev.|Min|Max|
|_k_<br>_n_|54030|144|53|12|253|
|All exporters<br>_OECD_<br>_k_<br>_n_|54030|57|35|1|223|
|Herfindahl|54030|0.19|0.13|0.03|1|
|Theil|54030|2.68|0.48|0.86|4.87|
|Extra-OECD expo<br>_OECD_<br>_k_<br>_n_|rts only<br>53769|35|31|1|194|
|Herfindahl|53769|0.35|0.22|0.03|1|
|Theil|53769|3.05|0.60|0.13|5.30|

Note: All variables are defined at the product (SITC4) level. The number of observations should be interpreted as follows: The number of potential exporters of good _k_ , _nk_ , is observed for each of 1‘034 products and each year, even though its value is, by construction, constant across years.

# **2.2   Intensive and extensive margins: Prima-facie evidence**

Figure 1 shows the evolution of simple averages over all products of our two concentration indices (Herfindahl and Theil) expressed as indices relative to the sample‘s initial year. That is, for Herfindahl, Figure 1 shows 6
- 29 -

state, but In this case there Is no breakdown among rural areas, rural towns and large urban centers. Footnote 13 describes the allocation mathods used.

48. We estimated the model separately for rural areas (RNFY), rural towns (RTNFY), and the expanded rural region (RRTNFY) encompassing both. This provides a useful Indlcatlon of the spatial daiierslon of the rural demand llnkages. We also estimated the model by both OLS and 2SLS. The latter seemed necessary to correct for potential endogenelty problems with some of the right-hand side varlables. For example, It could be argued that the rural nonfarm economy has Its own stimulatory effects on agriculture (the urban growth pole model), In which case RNFY and AGY would be simultaneously determined. Also, population and Infrastructure may be concentrated In regions with higher agricultural potential, leading to selectively bias problems.

49. The econometric estimates are reported In AppendIx Tables A.12 - A.15. Because of multlcolilnearity problems, the IrrigatIon varlable was never si2nificant, hence we dropped It from the fInal runs. As expected, the Breuch-Pagan test revealed heteroskedastlcity In the district data. Consequently, all district regressions were run using the Prals-Houthaker adjustment to correct the problem.

# B. hcome Multipliers

50. The Income multipliers from these regression coefficients suggest three major conclusions. First, on average, a one hundred rupee Increase In agricultural Income will generate about an additlonal 64 rupees In rural nonfarm income, 25 rupees In rural ar3as and 39 In rural towns (Table 13).14 14 Although this **Is** our best **estimate, the** actual value probably rang-o anywhero **between** 64 **and** 79 **rupees. This** interval **is** that computed from **the** state-levol **regressions.** They offer **an advantage** in that farm **and nonfarm** income **are available** for **each** state, whi **lo we must extrapolat based** on **wage** earnings **for the** districts. Yet with **the state** data, **a** different difficulty **emerges; we** must partition *tate-wide nonfarm **income among** rural **areas,** rural towns **and large** urban **centers. There are several ways** to do **this. The** upper bound multiplier **arises** by taking rural **nonfarm** income **as** proportional to **its share** in **state nonfarm** employment, that **is** RNFY x RNFL*(state NFY/state NFL). Tho **lower** bound **takes per** capita rural **nonfarm** income an **equal** to earnings in agriculturo, that **is** RNFY a RNFL * (state AGY/atate **AGL).**
privatization.<sup>33</sup> The coefficient is negative but statistically insignificant -- the large negative coefficient might indicate that only PJ governors are affected by provincial deficits (i.e. we are unable to reject the null hypothesis that the sum of the deficits and interaction coefficients is zero). However, we are also unable to reject the null hypothesis that all governors are affected equally.

**Table 5: Proportional Hazard Model for Bank Privatization (Buenos Aires Included)**

||(1)<br>**1**<br>**Pr**<br>|<br>(2)<br>1<br>**oportional Ha**<br>|<br>(3)<br> <br>**zard Model**<br>|1<br>(4)<br> <br>**(Monthly Dat**<br>|1<br>_(5)_<br>**a)**<br>|
|---|---|---|---|---|---|
|**_Prov. de Buenos Aires Included_**|Yes|Yes|Yes|Yes|Yes|
|_# of_**_Banks_**|23|23|23|23|17|
|_# of Privatizations_|16|16|16|16|12|
|_# of_**_Months at Risk_**_of_**_Privatization_**|1010|1010|1010|1010|825|
|**_Does the PJ control the_**_assembly_|-0.732|-0.625|-0.598|-0.235|-1.935|
|**_and the executive?_**_(l=yes,O=no)_|(-0.91)|(-0.78)|(-0.75)|(-0.28)|(-1.45)|
|**_Can either_**_UCR_**_or independents_**||-1.865**|-1.822**|0.117|-2.055|
|**_block?_**_(1=yes,O=no)_||(-2.19)|(-2.19)|(0.09)|(-1.52)|
|**_Can the UCR block in assembly or_**<br>_executive? (f=yes,O=no)_|-1.466<br>(-1.53)|||||
|_Can independents block in assembly_<br>_or executive? (1=yes,Ono)_|-2.553**<br>(-1.92)|||||
|_Bank net worth over liabilities_|-16.989**|-16.622**|-17.09**|-17.240**|-12.337*|
|**_(lagged)_**|(-2.63)|(-2.64)|(-2.83)|(-2.80)|(-1.90)|
|**_%_**_of_**_bank loans to public sector_**<br>**_(lagged)_**|5.368**<br>(2.09)|5.645**<br>(2.22)|5.552**<br>(2.20)|5.409**<br>(2.18)|1.400<br>(0.30)|
|**_%_**_of_**_bank loans considered_**|-7.321**|-7.289**|-7.423**|-7.501**|-7.884**|
|**_"normal" (either definition) (lagged)_**|(-3.34)|(-3.22)|(-3.32)|(-3.19)|(-2.61)|
|**_%_**_of_**_bank loans considered -_**|2.807**|2.499*|2.514*|2.456*|3.413*|
|**_"normal"_**_(old definition)_**_(lagged)_**|(2.04)|(1.90)|(1.90)|(1.77)|(1.89)|
|**_Gov't deficit as share_**_of_**_revenues_**|8.016**|8.038**|8.018**|9.525**|8.830**|
|**_(lagged)_**|(2.44)|(2.46)|(2.46)|(2.80)|(2.39)|
|**_Gov't deficit (lagged) * blocking_**||||-17.552||
|**_dummy_**||||(-1.43)||
|_Change in net worth over liability_|-2.841|-2.080||||
|**_(lagged)_**|(-0.35)|(-0.27)||||
|**_If the governor is a member_**_of_**_the_**|||||2.575**|
|**_PJ, can he be re-elected?_**|||||(2.01)|
|**_Log Likelihood_**|_-25.56_|-25.84|-25.88|-24.54|-14.67|
|t-stats in parentheses.||||||

* indicates significance at 10% level ** indicates significance at 5% level.

33 We also included an interaction term between net worth over liability and whether a non-PJ party can block privatization in some estimations (both including and omitting the interaction term with the deficit variable). The coefficient on the interaction between bank performance and the blocking variable was negative but never approached significance.

17
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policy and supervisory mandates; (2) the reputational risk, as poor <mark>supervisory performance could damage the credibility of monetary policy makers; (3) the</mark> possible moral hazard effect, as banks can become less risk averse if the lender of last resort is also the supervisor; and (4) the potential that the bureaucratic powers of the central bank could become too big ( <mark>Gerlach et al. 2009;</mark> Cecchetti 2008; Masciandaro 2009). The literature thus produces two alternative hypotheses for empirical work: the possible synergetic and positive effect on financial stability from placing bank supervision in the central bank versus the possible negative effect from the same arrangement because it lacks checks and balances.

The empirical literature that addresses the pros and cons of placing bank supervision in the central bank is only just emerging but is gaining importance. In one of the first studies, Masciandaro, Pansini, and Quintyn (2011) find that the degree of central bank involvement in supervision (with the highest involvement occurring when the central bank is the unified supervisor for all financial subsectors) did not significantly affect economic resilience (growth of real GDP, during 2008–09). They also find that unifying microprudential supervision, either in the central bank or in the financial supervisory authority, negatively affected the measure of economic resilience. For those reasons, Masciandaro, Pansini, and Quintyn (2011) argue for a supervisory architecture with adequate checks and balances that separates macroprudential supervision—typically in the central bank—from microprudential supervision of banks by placing the latter in an agency at arms’ length from the central bank. Eichengreen and Dincer (2011), analyzing the experience of 140 countries during 1998–2006, find that banking systems overseen by independent supervisors other than the central bank had lower ratios of nonperforming loans to GDP and were required to hold less capital against assets, suggesting superior efficiency of this arrangement. Boyer and Ponce (2012), using a formal model, argue that concentrating supervisory authority in the hands of a single supervisor could make the capture of the supervisor by banks more likely. Hence, full integration might not be the supervisory arrangement of social preference.<sup>2</sup>

> 2 There is also a complementary empirical literature on the effect of microprudential regulation on bank soundness. In a banklevel study of EMDEs (Emerging Market and Developing Economies). Klomp and de Haan (2015) show that stricter regulation and supervision (especially on capital) reduce bank riskiness. Caprio et al. (2014) examine determinants of the 2007–09 banking 4