rafmacalaba/datause-encoder-data huggingface dataset:
-4-

1.15 The ostenasible reason for this poor performance would appear to be at least partly technical. The quality of property tax administration in developing countries is so poor that dramatic increases in revenue would result simply from achieving comprehensive coverage and reasonable levels of collection efficiency. Misguided policy would also appear to be at fault: nominal tex rates are low and the b;uoyancy of the tax is held hostage to the timing of general revaluations.

1.16 Underlying poor administration and low rates, however, are political liabilities inherent in the property tax. From a political standpo.nt, the effectiveness of the property tax in confronting taxpayers with the costs of municipal services is no virtue. Central governments--which exercise veto power over property tax policy and frequently are responsible for its administration-are reluctant to allow the tax to be exploited effectively. Local governments prefer to rely on less politically vulnerable revenue sources, where they are available. Sustainable property tax reform t,Lerefore often requires that these underlying constraints be addreqsed--by reducing central controls and altering the structure of local finance.

1.17 The remainder of thif, report consists of four parts. Chapter 2 and Chapter 3 address the policy and edministrat.ve eonstrair-ts on the property tax. Chapter 4 discusses institutior.l issues. The final chapter discusses strategies for reform and the steps required to address these underlying constraints on performance of the tax.
depends on GDP per capita (Jetter et al. 2015) and how mature the democracy is (Rock 2009). Notwithstanding the mixed nature of these findings, the possibility of a systematic correlation between corruption and democracy cannot be ruled out. If the level and quality of democracy also varies systematically with country size, our main results could be spuriously affected. We guard against this possibility by controlling for the quality of democracy in the country using the “Polity” measure from the Polity IV database.

Next, for civil society to play an important role in the fight against corruption,<sup>9</sup> an enabling institutional environment is needed in terms of freedom of press, ability of citizens to voice their dissatisfaction against the government without any fear of political oppression and unjust imprisonment (see for example, Themudo 2013). Similarly, the quality of bureaucracy and its independence from political pressures, the quality of policy formulation and implementation, the credibility of the government's commitment to such policies can have significant effects on the level of corruption. If the level of regulation also varies systematically with the quality of bureaucracy or protection against political pressure and unjust imprisonment, our main results could suffer from the omitted variable bias problem. Thus, we control for the quality of bureaucracy using the Government Effectiveness measure from Worldwide Governance Indicators. For protection against political pressure and unjust imprisonment, we control for a subcomponent of the Rule of Law measure from Freedom House’s Freedom in the World data set. The sub-component aims to capture if there is protection from political terror, unjustified imprisonment, exile, or torture, whether by groups that support or oppose the system, and if there is freedom from war and insurgencies (Rule of Law (Protection)).<sup>10</sup>

> 9 For the role of civil society in combatting corruption, see for example OECD (2003), World Bank (1997), Themudo (2013), Lambsdorff (2005) and McCoy and Heckel (2001).

> 10 Data for the Rule of Law (Protection) variable are not available for all the years covered by our sample. We use year 2013 values for all the countries. Thus, the variable is not included in our panel estimation results.

18
3 trade ag~.eement and/or further unilateral EC trade concessions. Another is the possible expansLon of the Er to include Au3tria, another important trade partner of Hu.gary.

# A. The Present EC-Hungary trad n_ pattern

In the recent past the USSR has been by far Hungary's main export outlet and took as much as one third of its exports in 1985. In the early 1980's Hungary had been exporting more heavily to the EC, particularly low-priced agricultural goods, but in the mid-1980s there was some re-direction of trade toward the USSR for hard currency and oil. This also coincided with an economic downturn in the EC. However, in the second half of the 1980s, the pattern has begun to change with the EC-12 becoming an increasingly important market for a wide range of Hungarian exports.<sup>3</sup> As TabLe 2 indicates the EC-12's share of Hungarian exports had grown to 22.7 per cent in 1988 while the Soviet share had declined to 27.6 percent (following a 4 6 percent decline in the $ value of this trade).<sup>4</sup> Preliminary data for 198S and the first half of 1990 show the continuation of these trends, and they are expected to continue -- for reasons discussed in succeeding sections of this paper.<sup>5</sup> Table 2. Geographical Distribution of Hungary's Merchandise Exports - 1980, 1985, 1988.

|Partner|19<br>Sm|80<br>%|19<br>Sm|85|1988<br>Sm|%|
|---|---|---|---|---|---|---|
|World|8,667|100.0|8,555|100.0|°,931|100.0|
|Austria|378|4.4|460|5.4|568|5.7|
|EC 12|1,810|20.9|1,373|16.0|2,250|22.7|
|Germany,F.R.|590|6.8|520|6.1|525|5.3|
|USA|113|1.3|198|2.3|293|3.0|
|USSR|2,540|29.3|2,876|33.6|2,742|27.6|

Source: COMTRA-DE 3 The Yearbook of the Hungarian Statistical Office shows that the share of the EC-12 in 1988 exports increased to 24%. On the other hand, Hungary represents less than 1% of EC's imports. It is also worthwhile noting that the relative importance of trade with the EC is almost twice as large as the average of the other CMEA couintries.

4 Although 45.5% was still exported to CMEA countries in 1988, only 41.2% of total exports was settled in transferable rubles. According to Hungarian sources, the share of CMEA is expected to go down to 40% in 1990, something very significant in historical perspective, given that it was more than _70%_ in 1960.

_5_ Exports to the* EC increased by 33.7% in value terms in the first six months of 1989. Eve.a discounting the effect of inflation which on an annual basis was not above 20%, this makes for a very substantial volume increase, at a time when exports to the USSR tended to stagnate if not decline in absolute terms. See EIU nr.4 1989.
# **3. Data and methodology**

## _3.1 Sovereign ratings and relative risk ratings_

Systems of converting alphabetical ratings into numerical scores have been proposed in papers such as Cantor and Packer (1996), Dadush and Dasgupta (2001), Gaillard (2009) and Basu, et al (2012).<sup>3</sup> The empirical analysis relies on long-term foreign currency ratings assigned by Standard and Poor’s as this is often used as the indicator for sovereign creditworthiness by international investors, and S&P demonstrates the least dependence on other agencies when rating sovereigns (Alsakka and ap Gwilym 2010). Sovereign ratings obtained from Standard and Poor’s were converted to numeric ratings using the rating scale in Table 1.

**Table 1: S&P ratings, outlooks and numerical scores**

|S&P<br>Rating|Outlook|Outlook<br>adjusted<br>score|S&P<br>Rating|Outlook|Outlook<br>adjusted<br>score|S&P<br>Rating|Outlook|Outlook<br>adjusted<br>score|
|---|---|---|---|---|---|---|---|---|
|||Does not|||||||
|AAA|P|exist|BBB+|P|40|B|P|19|
||S|60||S|39||S|18|
||N|59||N|38||N|17|
|AA+|P|58|BBB|P|37|B-|P|16|
||S|57||S|36||S|15|
||N|56||N|35||N|14|
|AA|P|55|BBB-|P|34|CCC+|P|13|
||S|54||S|33||S|12|
||N|53||N|32||N|11|
|AA-|P|52|BB+|P|31|CCC|P|10|
||S|51||S|30||S|9|
||N|50||N|29||N|8|
|A+|P|49|BB|P|28|CCC-|P|7|
||S|48||S|27||S|6|
||N|47||N|26||N|5|
|A|P|46|BB-|P|25|CC|P|4|
||S|45||S|24||S|3|
||N|44||N|23||N|2|
|A-|P|43|B+|P|22|C||1|
||S|42||S|21|D||0|
||N|41||N|20||||

Notes: Based on Basu et al. (2012). ‘P’ signifies Positive, ‘S’ Stable and ‘N’ Negative.

The availability of a numerical conversion system for sovereign ratings is useful for the analysis of sovereign ratings and their possible links to global economic and financial factors,

> 3 These classifications have a scale of around 20 ratings. Basu, et al. (2012) build upon this system but introduce an innocuous cardinal change of multiplying each number by 3. This modification allows for a richer analysis by including the outlooks into the numerical rating spectrum (see Table 1). The revised measure is a positive monotone transformation of the earlier measure and therefore does not change its basic characteristics. Each rating is divided into three outlooks, namely, ‘positive’, ‘stable’, and ‘negative’. These outlooks indicate, respectively, the possibilities of an upgrade, remaining at the same rating, or having a lower rating.

9
4. Confirmation process: businesses should ensure that the point at which consumers are asked to confirm a transaction, after which time payment is due or they are otherwise contractually bound, is clear and unambiguous, as should the steps needed to complete the transaction, especially for new payment mechanisms.

# **Post - Purchase**

5. Dispute resolution and redress: consumers should be provided with meaningful access to fair, easy-touse, transparent and effective mechanisms to resolve domestic and cross-border e-commerce disputes in a timely manner and obtain redress, as appropriate, without incurring unnecessary cost or burden. These should include out of court mechanisms, such as internal complaints handling and alternative dispute resolution. Subject to applicable laws, the use of such out-of-court mechanisms should not prevent consumers from pursuing other forms of dispute resolution and redress. (See below sub-section on online dispute resolution and redress.) 6. Privacy and security: businesses should protect consumer privacy by ensuring that their practices relating to the collection and use of consumer data are lawful, transparent and fair, enable consumer participation and choice, and provide reasonable security safeguards (see section on data privacy).

# **Other**

7. Education, awareness, and digital competence: governments and stakeholders should work together to educate consumers, government officials, and businesses about e-commerce to foster informed decision making. They should work towards increasing business and consumer awareness of the consumer protection framework that applies to their online activities, including their respective rights and obligations, at domestic and cross-border levels.

Source: **(OECD, Guidelines for Consumer Protection in the Context of Electronic Commerce 2000)**

**Table 5. Regulation on online consumer** **<u>protection around the world</u>**

|**protection arou**|**nd the wo**<br>**Online**|**rld**<br>**consumer p**<br>**regulation**|**rotection**|
|---|---|---|---|
||None|General|Specific|
|**Albania**||●||
|**Armenia**|●|||
|**Bangladesh**|●|||
|**Burkina Faso**|||●|
|**Canada***|||●|
|**Colombia**||●||
|**France**|||●|
|**Honduras**||●||
|**Indonesia**||●||
|**Kazakhstan**||●||
|**Kenya**||●||
|**Korea, Rep.**|||●|
|**Kyrgyzstan**||●||
|**Malaysia**|||●|
|**Mexico**||●||
|**Moldova**|||●|
|**Pakistan***||●||
|**Senegal**|||●|
|**Tanzania**|||●|

14
- Rice, P. and A. Venables. 2003. ‘Equilibrium Regional Disparities: Theory and British Evidence,’ Regional Studies, vol. 37, no. 6& 7, pp. 675-686

- Sachs, J.,  Varshney, A. Bajpai, N. (eds) (1999).  India in the Era of Economic Reforms. New Delhi:  Oxford University Press

- Stiglitz, J. and A. Weiss. 1981. “Credit Rationing in Markets with Imperfect Information,” American Economic Review, vol.71, no. 3, pp. 393-410.

- Zagha, R. 1999.  “Labor and India’s Economic Reforms.” In Sachs et al. (eds), India in the Era of Economic Reforms. New Delhi:  Oxford University Press.

37
Table 6 Correlation Analysis for the Economies in East Asia 1978-1990

# Pearson Correlation Coefficients / Prob > R I under Ho: Rh0=0 / Number of Observations

||Korea|Philippines|Taiwan|
|---|---|---|---|
||MCY|MCY|MCY|
|M1Y|-0.228|0.448|0.898|
||0.4145|0.0943|0.0001|
||_15_|_15_|14|
|QMY|0.727|0.353|0.787|
||0.0022|0.1963|0.0008|
||15|15|14|
|DCPY|0.62873|-0.479|0.931|
||0.0121|0.0710|0.0001|
||**_15_**|_15_|14|
|GNPCAP|0.886|0.380|0.912|
||0.0001|0.1625|0.0001|
||**14**|15|14|

20
_in μr and μs, increasing in t, decreasing in τ , and_ The expectation of _v_ ( _μr, τ_ ) is often called the value of experimentation. The greater the expected return to the risky project and its precision _t_ , the better off is the agent and the greater the willingness to experiment in the new project. Suppose any signal leads the agent to choose the safe project, then the value of experimentation would be equal to the discounted value of choosing the safe project forever. However, such case becomes uninteresting since there is no room for learning. Now, if for some signals the agent prefers the risky project, it means that the value of the risky project given that signal is greater than choosing the safe project forever. Therefore the value of experimentation (the ex-ante expectation before the signal is realized) is always greater than choosing the safe project since some signals will lead to a greater value whereas the others lead to the same value (the ones where the safe project is preferred to the risky one). Hence, we obtain _β_ E _μ′r_ � _v_ ( _μ_<sup>_′_</sup> _r_<sup>_, τ_+</sup><sup>_t_)</sup> � _> β_ 1<sup>_<u>μ</u>_</sup> _−_<sup>_<u>s</u>_</sup> _β_<sup>.Therefore, there</sup> exists a unique _μ_<sup>_∗_</sup> _< μs_ such that This in turn implies that the agent will experiment with the risky project for any _μr ≥ μ_<sup>_∗_</sup> - as long as the expected return is not “too much” lower than the safe return. It also implies that a bad realization _x_ sufficiently lower than _μ_<sup>_∗_</sup> will lead the entrepreneur to stop investing in the risky project since the new updated expected mean would become lower than _μ_<sup>_∗_</sup> .

The value of information is also increasing in entrepreneurial capital. In the limit, where _τ_ tends to infinity and there is no entrepreneurial capital, prior beliefs are too strong and there will be no learning. In that case the expected value function is simply the discounted value of the greatest expected return. As _τ_ decreases, the value of information becomes less convex since more weight is given to the information yet to be realized. Since the boundary 21
# **F Exploratory Analyses**

Table F.1. Treatment Effects on Generalized vs. Specific Self-reported Trust

||Trust in People<br>(Z-score)|Others Intention<br>to Help<br>(Z-score)|Count on<br>Others<br>(Z-score)|Trust in<br>Venezuelans<br>(Z-score)|
|---|---|---|---|---|
||(1)|(2)|(3)|(4)|
|Game [=1]|0.169*|-0.064|0.187**|0.297***|
||(0.089)|(0.089)|(0.082)|(0.080)|
||[0.132]|[0.364]|[0.089]|[0.002]|
|Video [=1]|-0.004|-0.078|0.153*|0.116|
||(0.096)|(0.097)|(0.088)|(0.090)|
|FDRq-values|[0.568]|[0.364]|[0.144]|[0.202]|
|Observations|827|828|828|828|
|Av. Dep. Var. (Control group)|0.000|0.000|0.000|0.000|
|Diff. p-value|0.082|0.888|0.691|0.033|
|YWYjoint test|0.124|0.664|0.045|0.001|

_Notes:_ This table presents OLS estimates derived from Equation 1. The dependent variables are z-scores derived from a four-point scale ranging from 1 (strongly disagree) to 4 (strongly agree) for the following statements: (i) “One can trust other people,” (ii) “In general, other people have good intentions toward me,” (iii) “One can rely on other people, even if one does not know them well,” and (iv) “One can trust Venezuelan migrants,” All columns control for gender and student status, the stratification variables. The Diff. p-value reports the p-value of a Wald test assessing the equality of coefficients between the game treatment and video treatment. The YWY joint test reports sharpened False Discovery Rate (FDR) q-values. Significance levels are denoted as:<sup>_∗∗∗_</sup> at 1%,<sup>_∗∗_</sup> at 5%, and<sup>_∗_</sup> at 10%.

53
Table 7: Contemporaneous effects of foreign variables on domestic counterparts by countries

||real GDP|inflation|real equity prices|short-term rate|long-term rate|
|---|---|---|---|---|---|
|arg<br>|0.106|-1.701<br>|1.095 ***<br>|2.195<br>||
|austlia<br>|-0.007|0.625 **<br>|0.854 ***|0.468 ***<br>|1.039 ***|
|bra|0.037|6.563 **||-5.525 *||
|can|0.164 ***|0.677 ***|0.950 ***|0.524 ***|1.012 ***|
|china<br>|0.096|0.403 *||0.015<br>||
|chl|0.071<br>|0.091<br>|0.762 ***<br>|0.160 *<br>||
|euro<br>|0.090 *|0.203 **|0.998 ***<br>|0.065 ***|0.694 ***|
|india<br>|0.030|0.335|0.609 ***|-0.045||
|indns|0.114|-0.334||0.649||
|japan<br>|-0.006<br>|-0.202<br>|0.638 ***<br>|-0.040<br>|0.504 ***<br>|
|kor|0.026|0.359|0.988 ***|-0.238|-0.069|
|mal|-0.012<br>|0.447 *<br>|1.350 ***|-0.042<br>||
|mex|0.090|-0.770||0.764 **||
|nor|0.099|0.642|1.071 ***|0.232|0.733 ***|
|nzld|0.037|0.617 ***|0.729 ***|0.811 **|0.439 **|
|per|-0.079|-1.065||0.483||
|phlp|-0.044|-0.323|0.905 ***|0.395||
|safrc|0.012|-0.074|0.813 ***|0.017|0.542 **|
|sarbia|0.332 **|-0.033||||
|sing|0.087|0.419 **|1.317 ***|0.293 *||
|swe|0.399 ***|0.896 ***|1.117 ***|0.481 ***|0.907 ***|
|switz|0.114|0.581 ***|0.899 ***|0.245 ***|0.510 ***|
|thai|0.059|0.294|0.870 ***|0.449||
|turk|-0.032|1.823||1.014 *||
|uk|0.077 **<br>|0.396 **<br>|0.813 ***|0.242 **|0.777 ***|
|usa|0.143 ***|0.357 ***||||

Note: Please refer to Table A1 in the Appendix for a glossary of acronyms. White’s heteroskedasticity robust standard error is used. ***, **, *, represent 1 percent, 5 percent, and 10 percent significance levels, respectively.

Mathematically, it is defined as where _σii,ll_ is the corresponding diagonal element of the residuals’ variance-covariance matrix Σ **u** ; Ω _t−_ 1 is the information set at time _t −_ 1.

GIRFs are different from the standard IRFs proposed by Sims (1980), which assumes orthogonal shocks. The standard IRFs are calculated using the Cholesky decomposition of the covariance matrix of reduced-form errors. Thus, if we calculate the IRFs using different orders of variables, the shape of the IRFs will be different. If a VAR contains two or three variables, we might be able to use the standard IRFs by assuming a relation between the variables inferred from economic theory. However, the same approach is not useful for the GVAR model since it contains a large number of variables. This means that we cannot list a 23
Policy Research Working Paper 8800

# **Abstract**

This study analyzes the impacts of the recent rural road development in Ethiopia on welfare and economic outcomes. The identification of the impacts relies on a difference-in-differences matching approach, taking advantage of the nationally representative household survey and the original road database, both of which are panel data spanning between 2012 and 2016. The results of the econometric analysis overall suggest that Ethiopia’s recent rural road development has substantially increased household welfare and supported households in coping with the recent severe droughts. This study estimates that rural roads increased, on average, household consumption by 16.1 percent between 2012 and 2016 (or 3.8 percent per year). The effects of rural road development were largest in the most remote communities, as it increased household consumption by 27.9 percent. Furthermore, in the communities most affected by the El Niño drought, the likelihood of falling into poverty was 14.4 percent lower between 2012 and 2016 if the community was connected by a rural road. Taken together, the results suggest that, by connecting remote communities to markets, rural roads have substantially increased the welfare and resilience of rural households in shock-prone environments.

This paper is a product of the Poverty and Equity Global Practice. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/research. The authors may be contacted at snakamura2@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
CIS than CEE. Overall, the TFP effect of privatization to domestic owners is weaker than that to foreign owners, takes longer to take a hold, and in the CIS it has been outright negative or insignificant.

For comparative purposes, we have also surveyed the ownership-related studies that have been carried out on data from China. Probably because large scale privatization is a relatively recent phenomenon in China, there have not yet been any studies of great econometric sophistication and this may explain the patchy results. A number of studies, including Jefferson and Rawski (1996), address TFP issues with firm level data but do not examine differences in TFP related to privatization or ownership. Studies that address these issues (e.g. Yifan Hu, Frank Song, and Junxi Zhang, 2004; Shahid Yusuf, Kaoru Nabeshima, and Dwight Perkins, 2006)) find diverse results, with the effect of non-state ownership being mostly positive and often statistically significant.<sup>17</sup> Compared to the D-M survey that found the effect of private ownership to be positive in CEE but insignificant in CIS, we hence find a strong positive effect of foreign ownership in both the CEE and CIS regions, and a quantitatively smaller positive effect of domestic private ownership in CEE and in Ukraine (together with a negative effect in Russia and the rest of CIS). The reason for finding a stronger positive effect than D-M is in part because we are focusing on studies that take into account the problem of selection/endogeneity of ownership, whereas the earlier surveys did not place as much emphasis on this issue. Indeed, the unreported OLS studies, including those in China, generate much more diverse effects in terms of the estimated OLS coefficients. Another reason for our stronger and more uniform findings of positive effects of private ownership may be that more of our studies cover recent years and privatization may take several years to have an effect as strong owners take control and markets start to function. Finally, institutional development is a slow process and more recent data may pertain to a more developed legal and institutional setting in most of the transition economies. The variety of findings about the effects of non-state ownership in China may also be related to the fact that privatization on a relatively large scale is a more recent phenomenon in China.

> 17 For a more detailed discussion of the results of these studies, see Estrin, Hanousek, Kočenda and Svejnar (2007).

20