rafmacalaba/datause-encoder-data huggingface dataset:
perceptions of corruption) to study the effects on confidence in public institutions. Seligson (2002) uses survey data for four Latin American countries to test the effects of corruption experiences on perceptions of the legitimacy of the political system at the individual level. He finds that exposure to corruption erodes belief in the political system and reduces interpersonal trust. Bratton (2007) uses survey data from 18 African countries to document that perceptions of corruption are negatively correlated with respondents‟ satisfaction with public services, but somewhat surprisingly, personal experience with bribery is _positively_ associated with user satisfaction. However, these two papers also do not recognize or try to address the identification problem that we have highlighted in the introduction.<sup>3</sup> Finally, Cho and Kirwin (2007) and Lavallée, Razafindrakoto and Roubaud (2008) use a set of African countries covered by the Afrobarometer survey to investigate directly the links between confidence in public institutions and both corruption perceptions and corruption experiences questions. Unlike the rest of the literature surveyed so far, these papers are the only ones to acknowledge the potential for reverse causality. Cho and Kirwin (2007) in particular explicitly stress the possibility of vicious cycles: corruption undermines confidence in public institutions, and this in turn increases the acceptability of offering bribes to obtain public services, increasing the prevalence of corruption. Both papers propose using instrumental variables drawn from the same survey in order to address this identification problem. However, as we explain in more detail below in our discussion of identification, this strategy depends on the validity of in our view highly implausible exclusion restrictions that the authors make no effort to justify.

In summary, the existing literature on the effect of corruption on confidence in public institutions has been based on small samples of countries, and has for the most part failed to recognize or address the difficulty of isolating the direction of causation between corruption and confidence. In the remainder of this paper we show how we can use the

> 3 The identification problem is compounded by the fact that, despite having record-level data for many countries, Bratton (2007) does not appear to include country fixed effects in his specifications.  This opens the possibility that unobserved country-level effects are confounding the relationship between corruption and satisfaction with public services that he studies.

6
the OECD and non-OECD sample. In the non-OECD sample, the tail hypothesis has stronger explanatory than the mean hypothesis for both secondary and tertiary education dynamics. In addition, as in the full sample, the explanatory power of the model is strongly increased for tertiary enrollment when both hypotheses are jointly accounted for.

In contrast, the estimates reported in table VIII suggest that the explanatory power of the mean hypothesis is stronger in the OECD sample. For tertiary enrollment, the R-squared of the model under the mean hypothesis only (column 2 and 4) is larger than that of the tail hypothesis only (column 1 and 3). In addition, accounting for both hypotheses jointly in column 3, 6 and 7 only marginally increases predictive power. Results for secondary completion reported in the lower panel are similar. The mean hypothesis has stronger explanatory power individually, and accounting for the tail hypothesis only marginally increases predictive power. The tail hypothesis parameter remains individually highly statistically significant in all specifications for tertiary enrollment, but turns insignificant in some specifications for secondary completion once the mean hypothesis parameter is also accounted for.

# **6.4 Magnitude of the parameter estimates**

In our preferred specification that also controls for GDP per capita and where both hypotheses’ parameters are accounted for, estimates for the male-to-female variance ratio vary between 1.12 and 1.36.<sup>19</sup> In the OECD sample, which is closest to that of Machin and Pekkarinen (2008), estimates for _σ_<sup>2</sup> range between 1.12 and 1.30. The magnitude of the model estimates is therefore close to the female-to-male variance ratio in test scores estimated by Machin and Pekkarinen (2008) using PISA 2003, which is 1.21 for reading and 1.20 for mathematics.<sup>20</sup> In addition, the tail hypothesis parameter estimates are quite similar when derived from the secondary school completion or tertiary enrollment time-series. The internal and external consistency of our estimates provides further comfort on the predictive power and validity of the tail hypothesis. One should note, however,

> 19The female-to-male variance ratio is the square of the estimate for the tail hypothesis parameter, which is the ratio of the female and male standard deviation in scholastic performance.

> 20In the latest PISA 2015 data, estimates average female-to male variance ratios is 1.17 for both mathematics and reading, in a larger sample of 67 countries.

27
**Figure 34 Groups of Firms According to Technological Capability**

<!-- Start of picture text --> High<br>Type 4 Firms<br>High capability and<br>absorptive capacity<br>Type 3 Firms<br>‘Know what, but not<br>Awareness always where and how’<br>of What<br>and How<br>to Change T ype 2 Firms<br>‘Kno w they don’t know,<br>but d on’t know what’<br>Type 1 Firms<br>‘Don’t know that<br>Low<br>they don’t know’<br>Awareness of the<br>Low High<br>need to change<br><!-- End of picture text --> _Source:_ Arnold et Al., 2000; World Bank, 2002 At the lowest level are firms that have no capacity for technological change. At the highest level are firms such as Intel, Boeing, Siemens and Microsoft which have the capacity to absorb technologies from around the world, innovate, and produce leading edge high tech products. None of the leading Korean firms, for example, is in the top tier. Firms such as Hyundai, LG, and Samsung are only in the third category. They can produce and assemble high tech products using technology imported from abroad, but they cannot innovate or generate their own leading edge technologies.

This analysis of firm-level technological capability suggests that Latvia’s goal of becoming a global innovator may be overly ambitious, at least in the immediate short run. Latvia has very few enterprises that can compete with a Samsung or Hyundai in the production of globally competitive mass produced consumer or capital goods, most Latvian enterprises do not have the high levels of technological capability required to compete with other globally competitive innovative firms, and the Latvian NIS is far from being in robust good health. Addressing these issues will be critical if Latvia wishes to become a truly innovative economy.

**RECOMMENDATION 10.  Audit a representative sample of Latvian enterprises to assess their technological absorption and development capacity, using the tools and methodology developed for the Korean study.  Based on the needs identified by the survey, develop specific policies, based on international lessons of experience,  to help Latvian enterprises at each stage in the development process to improve their technological capabilities and attain higher levels of technological sophistication.** Researchers have developed a fairly simple audit tool to assess the technical capability of

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# **Table A6 continued**

|**Category**|**What we look at?**|**Why we look at it?**|**Characteristics**|
|---|---|---|---|
|National law|This data category requires all national<br>laws and statutes to be available online,<br>although it is not a requirement that<br>information on legislative behaviour e.g.<br>voting records is available.This data<br>category draws on work by the National<br>Democratic Institute (NDI) and<br>theDeclaration of Parliamentary<br>Openness.|Access to open data on a<br>country's legal code (i.e.<br>national law) supports<br>compliance with law, enables<br>to keep track of legal<br>changes, and also enables<br>public deliberation around a<br>law.|Following data must be online to qualify for<br>assessment:<br>•<br>Content of the law / status<br>•<br>Date of last amendment<br>•<br>Amendments to the law (if<br>applicable)|
|Air quality|Data about the daily mean<br>concentration of air pollutants,<br>especially those potentially harmful to<br>human health. Data should be available<br>for all air monitoring stations or zones in<br>a country, including at least 3 major<br>cities. The Index evaluates the openness<br>of key pollutants as defined by<br>theWorld Health Organisation (WHO).|Air quality is a key factor for<br>human health and<br>environment.|Following data must be online to qualify for<br>assessment:<br>•<br>Particulate matter (PM)<br>•<br>Sulphur oxides (SOx)<br>•<br>Nitrogen oxides (NOx)<br>•<br>Carbon monoxide (CO)<br>•<br>Ozone (O3)<br>•<br>Available per air monitoring<br>station (at least for 3 major cities<br>Following data is assessed optionally (if<br>available):Volatile organic compounds<br>(VOCs)|
|Water quality|Water quality data by water source. The<br>data category regards the quality of<br>designated drinking water sources. If<br>data on designated drinking water<br>sources is not available, it refers to<br>environmental water sources (lakes,<br>rivers, groundwater). Data per each<br>water source is desirable. But for this<br>year the Index also accepted if a country<br>only published country-wide aggregated<br>reports. As the review shows, we either<br>find local and granular data or<br>aggregated national reports.|This information is essential<br>for both the delivery of<br>services and the prevention<br>of diseases.|In order to satisfy the minimum<br>requirements for this category, data should<br>be available on level of the following<br>chemicals:<br>•<br>Fecal coliform<br>•<br>Arsenic<br>•<br>Fluoride levels<br>•<br>Nitrates<br>•<br>Total Dissolved Solids<br>•<br>Data per water source<br>•<br>Available for the entire country|

49
I.B5 -

-

# **Figure** 1.C.18 FARM INCOME UNDER POPULATION. INCOME AND WAGE GROWTH

(ItLtlfao of 196? CedIs) <!-- Start of picture text --> 900<br>700<br>-<br>600-<br>300 \\<br>200<br>1992 2002 2012 2022 2032<br>PERIOD MDPOINTS<br>0 0 + 140 o mI I SO<br><!-- End of picture text -->
# **1. Citizens’ Scorecard**

## **_1.1 Introduction_**

**_This study seeks to promote understanding of citizens’ experience with public services and of the institutional challenges in public service delivery, and to contribute to enhancing governance for public service delivery in China_** .  It concentrates on urban services and employs an innovative approach to gauge citizens’ experience.  Chapter one briefly introduces the national policy context for public service delivery in China, explains the rationale and use of the citizen score card survey, and presents the its findings from five cities. On the basis of citizen scorecard survey and several case studies, chapter two explores the institutional challenges of public service delivery in China’s urban areas.  To conclude, chapter three outlines the main recommendations to help enhance governance for public service delivery.

### **National policy context**

**_China has set for itself an overarching development objective to create a Harmonious Society that emphasizes sustainability, equity and citizens’ satisfaction_** .  The government has recognized that achieving this development objective would be impossible without making public services work for all citizens.  China’s leadership has accelerated efforts to develop “service-oriented” government.  In this perspective, top policymakers have emphasized the importance of increasing transparency, expanding public participation and improving accountability in government and public service delivery.   Accordingly, important laws and regulations have been enacted in recent years, including the Administrative License Law, Civil Servants Law, and Provisions on the Disclosure of Government Information<sup>2</sup> .

**_The central government recognizes that the new strategic emphasis on public service delivery in China needs to rely on a strong institutional framework involving all subnational levels of government and entities involved in service delivery, financing and regulation_** .  The Government of China has already launched a government reorganization process that is to contribute toward comprehensive institutional reforms to enhance policy coordination and public sector governance.  In terms of public sector governance, the broad objective has been to gradually move from administrative control toward a new governance mode, one that would be more appropriate for China’s open and dynamic socialist market economy and rapidly evolving society.  In this context, it is indeed very appropriate for the government to continue strengthening transparency, participation and accountability as the building blocks of good governance.  While clear in their broad objectives, the evolving

> 2 _Provisions on the Disclosure of Government Information_ (unveiled in April 2007 and in effect since 2008) require the central and all sub-national governments to release information that affects the immediate interests of citizens, legal persons and other organizations, and information for which transparency is desirable. Citizens have the right to file a written inquiry for information not included in official announcements.

11
18 as the Rand insurance study in California in the 1970s22)<sup>w,l.d have high pay-</sup> off.

Even where health systems are highly decentralized with heavy reliance on the private sector, the need to implement some forms of cost<sup>control seems</sup> reasonable. Alternative cost control mechanisms include use of fees and insurance copayments and deductibles; (on the demand side;<sup>and on the supply</sup> side rationing of access to medical school, to expensive<sup>technologies and to</sup> drugs; centralized controls on the prices and number<sup>of procedures, including</sup> diagnostic-related group pricing; and use of preferred providers and other related mechanisms by those who are financing health, such as<sup>employers or</sup> insurance systems. Though these mechanisms are increasingly<sup>employed in</sup> developing countries, especially in Latin America,<sup>23</sup> and greater recourse to them is widely discussed, little empirical work has been<sup>done on their effects</sup> in reducing costs relative to any effects on health outcomes<sup>in developing</sup> countries.

Adult health. Additional work on the above three topics will<sup>be</sup> dependent on improvement in data and methodology for study of<sup>adult health,</sup> including adult mortality but especially adult morbidity.<sup>Figure 1 provides a</sup> rough suimmary of this author's knowledge of the extent of the literature relating health and development; it compares work on leterminants and consequences of child (including infant) and adult mortality and<sup>morbidity.</sup> Under determinants (our concentration in this section),<sup>I know of little</sup> work in developing countries on adult mortality<sup>and virtually no work on adult</sup> 22 Manning et. al. (1987).

23 See World Bank (1989a) for a summary at<sup>the country level of use of</sup> these mechanisms, covering chiefly North and South<sup>America and Europe.</sup>
|(1)<br>PCE<br>Coef.|se|(2)<br>PCE +<br>vignettes<br>Coef.|se|(3)<br>PCE +<br>household<br>controls<br>Coef.|<br>se|(4)<br>PCE +<br>household<br>controls +<br>vignettes<br>Coef.|se|
|---|---|---|---|---|---|---|---|
|RRP Rural||||-0.210***|0.075|-0.263***|0.076|
|Gbao Urban||||0.370***|0.098|0.439***|0.097|
|Gbao Rural||||-0.079|0.076|0.003|0.079|
|_Vignette 1 (positions 3 to 6 omitted)_||||||||
|vign1==1||0.139|0.134|||0.161|0.145|
|vign1==2||0.168|0.132|||0.152|0.144|
|_Vignette 2 (position 1 omitted)_<br>vign2==2||0.497***|0.179|||0.498**|0.198|
|<br>Vign2==3||0.207|0.182|||0.226|0.201|
|<br>Vign2==4||-0.353*|0.205|||-0.311|0.225|
|<br>Vign2==5||-0.075|0.433|||-0.195|0.416|
|<br>_Vignette 3 (positions 1 and 2 omitted)_||||||||
|<br>Vign3==3||-1.143***|0.290|||-1.137***|0.314|
|<br>Vign3==4||-1.739***|0.293|||-1.722***|0.318|
|Vign3==5||-1.778***|0.297|||-1.772***|0.321|
|<br>Vign3==6||-2.110***|0.437|||-2.258***|0.446|
|<br>_Vignette 4 (positions 1 to 3 omitted)_||||||||
|<br>Vign4==4||-0.112|0.388|||-0.212|0.399|
|<br>Vign4==5||-0.137|0.390|||-0.309|0.402|
|<br>Vign4==6||-0.172|0.392|||-0.351|0.403|
|Number of observations<br>4,771||4,771||4,771||4,771||
|Pseudo R<sup>2</sup><br>0.017||0.075||0.042||0.101||

Note: *** p<0.01, ** p<0.05, * p<0.1 25
<!-- Start of picture text --> Firms adopting clean technology (G<4)<br>v<br>0.16<br>0.14 ~---= p=0.05<br>at ae : y=0.5<br>0.12 whe —— p=0.95<br>0.10 wos<br>-<br>0.1 0.2 0.3 04 0.5 0.6 07 z<br>Industry Expected Profits (G<4)<br>E(n)<br>0.60<br>0.55<br>----- p=0.05<br>0.50 “ p=0.5<br>0.45 . —— Bp=0.95<br>0.40 ;<br>0.1 02 0.3 04 05 0.6 07 “<br>Firms adopting clean technology (G>d)<br>v<br>0.15 Sone.<br>on ----- p=0.05<br>0.10 aren . u=0.5<br>oe —— 1=0.95<br>0.05 a<br>0.2 04 0.6 0.8 1.0 1.2 14 z<br>Industry Expected Profits (G>Q)<br>E(n)<br>0.9<br>08 ----- p=0.05<br>. u=0.5<br>07 : : : oo —— p=0.95<br>06 PTT<br>0.2 04 0.6 0.8 1.0 1.2 1.4ra z<br><!-- End of picture text -->
# **1.  Introduction**

This paper analyzes alternative models for conflict prediction. We work with a country-year panel dataset covering 114 developing countries since 1977, and define a set of conflict episodes based on the frequency of battle deaths, the presence of UN peacekeeping operations, and the proportion of the population forcibly displaced as refugees across international borders. In this dataset, we assess alternative prediction models that seek to distinguish these conflict episodes from complementary nonconflict episodes, using data on a number of country-level covariates observed prior to the start of the episodes. Our interest in this prediction exercise is motivated by the desire of aid donors and multilateral organizations such as the World Bank and the United Nations for a framework that can be used to anticipate the outbreak of conflict.<sup>1</sup> Ideally, the signals of the risk of conflict generated by such a framework open the possibility of policy interventions to reduce the risk of conflict before it occurs, rather than simply reactions to conflict after the fact.

In this paper, we contribute methodologically and pragmatically to the extensive empirical literature on conflict prediction. Our methodological contribution is based on the observation that conventional approaches to predicting conflict, whether based on fitted values from binary response regression models or machine learning algorithms such as random forests, suffer from an internal inconsistency: the objective function that is optimized when the prediction model is fitted to the data is different from the objective function that typically is used to evaluate the quality of the resulting predictions. Consider for example predictions of conflict based on the fitted values from a probit or logit regression model. Estimating the regression model involves maximizing a likelihood function which typically weights all observations equally, and in particular does not assign any particular priority to fitting conflict or non-conflict observations well. The same is true for random forest algorithms, where the branches in the underlying classification trees are chosen to maximize the homogeneity of observations in the resulting subgroups, again weighting all observations equally.

In contrast, forecast performance typically is evaluated using a prediction loss function that assigns specific weights to Type 1 error rates (i.e. the proportion of conflict observations incorrectly classified as non-conflict) and Type 2 error rates (i.e. the proportion of non-conflict observations

> 1 For example, in the latest donor replenishment of resources to the International Development Association (IDA, the part of the World Bank that provides concessional loans and grants to the world’s poorest countries), the World Bank committed to “adopt a risk-based approach for identifying fragility”, where “fragility” is understood to mean vulnerability to the outbreak of conflict.

2
# **Table 4: Impact of risk-weights and capital quality on bank risk**

|VARIABLES|(1)|(2)<br>Z-score|(3)<br> [4year]|(4)|
|---|---|---|---|---|
|ln (Assets in 2012 USD)|-0.972|-1.351|9.497**|-7.943***|
||(0.726)|(0.971)|(4.677)|(1.387)|
|Loans/TA|-0.15<br>(0.105)|0.105<br>(0.096)|-0.428***<br>(0.084)|-0.211**<br>(0.085)|
|Short-term funding/TA|-0.287***<br>(0.079)|-0.252***<br>(0.073)|-0.400***<br>(0.116)|-0.406***<br>(0.106)|
|Liquid assets/TA|-0.849***<br>(0.142)|-0.753***<br>(0.189)|-1.077***<br>(0.159)|-1.044***<br>(0.185)|
|ROA|6.365***|6.992***|7.278***|7.925***|
||(1.516)|(1.500)|(1.577)|(1.519)|
|Regulatory capital/RWA|1.174***|1.053***|1.357***|1.227***|
||(0.169)|(0.168)|(0.161)|(0.170)|
|Tier 1/Regulatory capital|-1.158<br>(0.947)||1.367***<br>(0.422)||
|(Tier 1/Regulatory capital)* Tier 1 stringency|0.279*<br>(0.146)||||
|(Tier 1/Regulatory capital)* ln(TA)|||-0.116**<br>(0.057)||
|RWA/TA||1.284***<br>(0.287)||-1.111***<br>(0.164)|
|RWA/TA * Tier 1 Stringency||-0.249***<br>(0.037)|||
|RWA/TA * ln (TA)||||0.103***<br>(0.018)|
|Observations|61,090|59,789|134,748|127,062|
|R-squared|0.038|0.039|0.048|0.051|
|Number of Country_Year|870|942|1,943|1,759|
|Marginal Effect at mean|2.026|-0.524|0.669|-0.491|

Note: *, **, and *** represent statistical significance at 10%, 5%, and 1% two-tailed level, respectively. Standard errors clustered at country level appear in parentheses. Off-shore financial centers included in the computations.

It is particularly easier for larger banks to reduce risk-weights (for instance, through securitization) and to issue lower quality securities that can be used as Tier 1 capital (for instance, subordinated debt). We would, therefore, expect the _RWA/TA_ and _Tier1/Regulatory Capital_ variables to have a lower impact in reducing risk for large banks. We test this hypothesis by interacting these two variables with bank size. The results are reported in columns (3) and (4) of Table 4. For larger banks, it is indeed the case that the relationship between risk-weights and the Tier 1 ratio and bank risk is significantly weaker. Our results highlight the importance of higher quality capital and the dangers of over-reliance on risk-weights. These findings are especially important for larger banks which have the capacity and resources to have greater 33
|**Education**|Mode 1||
|---|---|---|
||Higher education|0.15|
||Mode 2||
||Higher education|0.15|
||Mode 3||
||Higher education|0.40|
||Mode 4||
||Higher education|0.30|
|**Health**|Mode 1||
||Medical and dental services|0.15|
||Mode 2||
||Medical and dental services<br>Mode 3|0.15|
||Medical and dental services|0.40|
||Mode 4||
||Medical and dental services|0.30|

Notes: The sector weights are used for constructing country STRI, there are no sector weights reported for education and health services, because these sectors were not covered in 2008 surveys and for comparison with 2008 country level STRI, the 2012 country level STRIs don’t aggregate STRIs for education and health. The STRI for midwives and physical therapists services via mode 4 was aggregated with medical dental services via mode 4 with equal weights.

For most sectors the deviations between the set of weights used and weights representative of low-income countries are not large; if anything, scores in the retailing sector are underweighted whereas professional services scores are somewhat overweighted.

8