rafmacalaba/datause-encoder-data huggingface dataset:
# **A Survey of Judicial Effectiveness: The Last Quarter Century of Empirical Evidence**

Erica Bosio<sup>1</sup>

**JEL Classification:** D63 (Equity, Justice, Inequality); K41 (Litigation Process); O43 (Institutions and Growth); P48 (Legal Institutions).

**Keywords** : Access to Justice; Justice (Public Sector Management); Economic Growth Policy.

> 1 Erica Bosio: World Bank Group (ebosio@worldbank.org). The author thanks Matthieu Chemin, Decio Coviello, Klaus Decker, Adrian Fozzard, Manuel Ramos Maqueda, Richard Messick, Rita Ramalho, and Roberto Senderowitsch for helpful suggestions on an earlier draft, as well as seminar participants at the World Bank for comments; and Ana Maria Palacio Jaramillo, Kartik Saboo, and Virginia Upegui for excellent research assistance.
Cambodia. They replicate findings from, e.g., T. Paul Schultz (2004) who finds significant positive effects of Mexico’s PROGRESA cash transfer program on enrollment, but demonstrate that these effects are subject to strongly diminishing returns as the size of the transfer is increased. These findings are consistent with our results for poorer households. However, by targeting a sub-set of poorer households, these programs may implicitly restrict their focus to partial equilibrium effects, whereas we attempt below to examine the potentially off-setting general equilibrium effects of fee abolition. (Note that all three of the mechanisms linking free schooling to education quality outlined above are consistent with downward-sloping demand curves in controlled experiments, but could produce different outcomes when prices fall for all children simultaneously.) In the Kenyan context, Milu Charles Muyanga, John Olwande, Esther Mueni & Stella Wambugu (2010) apply propensity score matching methods to a panel data set of rural households to estimate a 7% increase in net enrollment spread across public and private primary schools with the largest response coming from higher income groups. Adrienne M Lucas & Isaac M Mbiti (2012) examine – – the impact of FPE on primary school retention rates but not the influx of new students and exam performance by comparing districts based on their pre-FPE enrollment rates. They find a significant effect of FPE on completion in public primary schools, but not in private schools, and a small negative effect on test scores, which is mainly explained by changes in student composition. Tessa Bold, Mwangi Kimenyi, Germano Mwabu & Justin Sandefur (2011) find a large, putatively causal test-score gap between Kenyan public and private schools which widened slightly after FPE.

Our paper is also related to the literature estimating spillover effects in enrollment decisions. Both Gustavo J Bobonis & Frederico Finan (2009) and Rafael Lalive & M Alejandra Cattaneo (2009) use random placement of the PROGRESA program to identify large and positive spillover effects in enrollment. Piero Cipollone & Alfonso Rosolia (2007) use exogenous variation in men’s enrollment in the wake of an earthquake in Southern Italy to estimate positive spillovers on women’s enrollment. In contrast, Sarah J Reber (2011) finds that school desegregation in the Southern United States led to negative spillover effects in the form of affluent flight. Again, we would argue that these different findings can be reconciled by viewing them as partial vs. general equilibrium effects.

5
Table A.3: Baseline Parameters (continued)

|Parameter|Description|Units|Value|
|---|---|---|---|
|_a_<sup>_g_</sup>|Share of Agricultural Land in CES function||0.55|
|_ρg_|CES Parameter for Agricultural Land and Fer-<br>tilizers||0.123|
|Livestock F|arming Sector|||
|_θ_<sup>_l_</sup>|Livestock Technology Index||0.69|
|_a_<sup>_l_</sup>|Share of Pasture Land in CES function||0.35|
|_ρl_|CES Parameter for Pasture Land and Animal||-0.33|
||Feed|||
|_c_<sup>_l_</sup><br>0|Livestock Processing Cost|1000USD/ton|0.21|
|_ξ_<sup>_l_</sup><br>0|Adjustment Cost Function Parameter||300|
|Food Proce|ssing Sector|||
|_θ_<sup>_g,y_</sup><br>0|Crop Processing Technology Index in 2004||1.5|
|_κg,y_|Crop Processing Technology Growth Rate p.a.||0.022|
|_c_<sup>_g,y_</sup>|Crop Processing Cost|1000USD/ton|0.081|
|_θ_<sup>_l,y_</sup><br>0|Livestock Processing Technology Index in 2004||1.7|
|_κl,y_|Livestock Processing Technology Growth Rate||0.022|
|_c_<sup>_l,y_</sup>|Livestock Processing Cost|1000USD/ton|0.27|
|Biofuels Se<br>|ctor<br>|||
|_θ_<sup>_b,_1</sup>|1G Biofuels' Conversion Rate|toe/ton|0.283|
|_c_<sup>_b,_1</sup>|Non Land Cost of 1G Biofuels|1000USD/ton|0.442|
|_θ_<sup>_b,_2</sup>|2G Biofuels' CES Production Technology||0.467|
|_c_<sup>_b,_2</sup>|Non Land Cost of 2G Biofuels|1000USD/ton|0.577|
|_a_<sup>_b_</sup>|Share of Fixed Factor in CES function||0.4|
|_ρb_|CES Parameter for Cellulosic Feedstocks and<br>||-1.5|
||Fixed Factor|||
|_φ_0|Fixed Factor Endowment in 2004||0.05|
|_κ_<sup>_φ_</sup>|Growth of Factor Specic Tech. Change p.a.||1.005|
|Energy Sec|tor|||
|_ρe_|CES Parameter for Drop-in Fuels and 1G Biofu-<br>els||0.5|
|_α_<sup>_e_</sup>|Share of 1G Biofuels in CES Function||0.09|
|_θ_<sup>_e_</sup><br>0|Energy Eciency Index in 2004||1.195|
|_κe_|Energy Eciency Growth Rate p.a.||0.016|

9
26 the market, but kerosene continued to enjoy a significant price advantage over diesel. The government eliminated the price difference in November 2008, at which point apparent consumption of diesel rose sharply (Figure 8). Similarly, when Vietnam in March 2005 lowered the price of kerosene by D 600 ($0.038) a liter below that of diesel—against the historical difference of D 50 ($0.003)—the demand for kerosene immediately rose by 30–40 percent (Asia Pulse 2005). In response, the government equalized the prices of these two fuels in July 2005.

Figure 8: Kerosene and diesel pricing and consumption in Nepal <!-- Start of picture text --> 700 14<br>600 12<br>500 10<br>400 8<br>300 6<br>200 4<br>100 2<br>0 0<br>2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011<br>Kerosene consumption Diesel consumption<br>Diesel-open kerosene price difference Diesel-quota kerosene price difference<br>Million liters Rupees per liter<br><!-- End of picture text --> _Source_ : Sales statistics from the Nepal Oil Corporation. _Note_ : The years are fiscal years in Nepal, starting on July 16, and each year represents the year in which the fiscal year ended. For example, fiscal 2000 started on July 16, 1999 and ended on July 15, 2000. The price differences are averaged over each fiscal year.

Multi-tier pricing of gasoline and diesel, targeting specific businesses, is equally problematic. One example is premix in Ghana, which is sold at less than one-third of the price of gasoline to help the fishing industry. Although premix is not an ideal diluent for gasoline, it is diverted on a large scale to adulterate gasoline, leading to frequent complaints of premix shortages ( _Modern Ghana_ 2012). Eliminating subsidies for high-octane gasoline which is used in high-performance cars typically owned by the rich and widening the price difference between different grades of gasoline similarly risks diversion of cheap gasoline to adulterate high-octane gasoline, or fuel switching from high-octane to lower-octane gasoline. Subsidized diesel set aside for farmers and fishing boats can also be diverted, either by suppliers before the fuel reaches the intended beneficiaries, or, especially in the case of fuel for fishing, by the beneficiaries themselves. Fishing boats are particularly suited for transporting large quantities of fuel and hence for diversion. It is for this reason that the government of Malaysia in August 2012 announced a new requirement for fishing boats whereby each catch would be verified by a government representative to ensure that the subsidized fuel is used for fishing and not for some other purpose ( _Borneo Post_ 2012).

LPG is more difficult to store and transport because it is a gas at room temperature and atmospheric pressure and needs to be kept under pressure. Nevertheless, selling LPG in small cylinders for household use at lower unit prices than that in large cylinders invites diversion from households to businesses and industries, as in Indonesia and Thailand, or to the automotive sector, as in Ghana, if the price difference between gasoline and LPG is large enough. Even
**Table 4: Public to private pay comparator analysis, range in total monthly remuneration (kip)**

||**Civil service**<br>**Monthly comp**<br>|**professional**<br>**ensation range**<br>|<br>|**Private sector a**<br>**Monthly c**|**nd SOE comp**<br>**ompensation**<br>|**arator jobs**<br>**range**<br>||
|---|---|---|---|---|---|---|---|
||**Average**<br>|**Lowest**<br>|**Highest**<br>|**(ratio to c**<br>|**Average**<br>**ivil service)**<br>|**Lowest**<br>|**Highest**<br>|
|**Technical**<br>**staff**|721,786|525,161|1,004,987|**Entry-level, clerical,**<br>**secretary, office worker**<br>**SOE, accountant**|1,289,336<br>**(1.79)**|437,750|2,370,000|
|**Deputy**<br>**Head**<br>**Division**|979,277|807,599|1,229,674|**Chief accountant, deputy**<br>**head section SOE,**<br>**supervisor**|1,859,293<br>**(1.90)**|950,000|3,050,000|
|**Head**<br>**Division**|1,092,340|880,114|1,268,749|**Head of section, deputy**<br>**director division small SOE**|2,041,243<br>**(1.87)**|925,136|3,350,000|
|**Deputy**<br>**Director**<br>**General**|1,456,200|1,294,589|1,560,741|**Manager of small business,**<br>**director of division small**<br>**SOE**|2,603,712<br>**(1.79)**|1,021,136|4,282,500|
|**Director**<br>**General**|1,588,758|1,396,565|1,806,157|**Director of division SOE**|4,439,252<br>**(2.79)**|3,827,892|4,850,000|

_Source: World Bank analysis_ Average regular monthly compensation in the civil service appears lower, across all positions, than compensation for similar jobs in the private sector and SOEs, as depicted in Figure 10.

**Figure 10: Public-private average compensation differentials**

<!-- Start of picture text --> CS Comparators CS max<br>5,000<br>4,000<br>3,000<br>2,000<br>1,000<br>0<br>Technical Deputy Head Deputy Director<br>staff head division director general<br>division general<br>Average monthly compensation, '000 kip<br><!-- End of picture text --> In relative terms, the ratio of private to public sector pay is between about 1.8 and 1.9 across all civil service white collar positions, with the exception of the director general. At this top end of the civil service ranks, private sector comparators earn around 2.8 times as much, on average, as directors general. In absolute terms, average salaries for rank-andfile positions in the private sector (e.g., entry level staff, secretaries, clerks) are somewhat higher but roughly comparable to those for civil service technical staff. At higher professional levels, the magnitude of the public-private earnings differential is more pronounced so that the earnings of managers in private firms are significantly more than official earnings of civil service managers.

_Source: World Bank analysis_ A number of other contextual factors must be taken into account, however, when coming to a judgment about the relative adequacy of civil service compensation using the pay comparator survey. It should be noted that for all civil service positions with the exception of the DG, average compensation is always higher than the lowest compensation received by those in comparator jobs—thus it is not the case that all 26
of poverty could decline by 1.5 million. Finally, if CEDEFOP’s employment scenario for 2020 prevails, then our simulations indicate that at-risk of poverty will decline by about 3.2 million people by 2020 relative to the 2008 population.

In most NMSs, a relatively large spread is observed in at-risk-of-poverty rates depending on the employment scenario considered. A particularly large spread is observed in Bulgaria, Estonia, Latvia, and Slovenia depending on the employment trajectory (Table 7). In Bulgaria, at-risk-of-poverty rates are simulated to be six percentage points lower when employment growth rates achieved during the high growth period (2000–08) are assumed, rather than assuming continuation of the employment growth rates achieved during entire 2000–11 period. The Czech Republic, Hungary, Poland, Romania, and Slovenia are expected to achieve more favorable at-risk-of-poverty rates if they reach the Europe 2020 national targets for employment by 2020 than if they reach the high growth years’ employment scenario through to 2020. In the other five NMSs, the simulated at-risk-of-poverty rate is more favorable under the high growth period employment scenario than if the Europe 2020 employment target is reached. Bulgaria, Latvia, and Lithuania are expected to achieve lower at-risk-of-poverty rates under the CEDEFOP’s employment scenario for 2020 than if countries achieved the Europe 2020 national employment targets.

In nine of the ten NMSs the at-risk-of-poverty rate is expected to fall if the Europe 2020 national target for employment is achieved, although in Bulgaria the magnitude of the reduction in the at-risk-ofpoverty rate is small. In Estonia, at-risk-of-poverty rates are expected to increase marginally by 2020 if national targets for employment are met, mostly because the current employment rate is approximately the same as the 2020 employment target and, therefore, the change in the poverty rate is determined by factors such as demographic changes.

**Table 7: Simulated at-risk-of-poverty rates**

||2008|2020<br>[assuming<br>national<br>goals are<br>met]|2020<br>[assuming<br>2000-11<br>growth<br>rate]|2020<br>[assuming<br>2000-08<br>growth<br>rate]|2020<br>(assuming<br>CEDEFOP’s<br>forecast)|2020<br>[assuming<br>2008-11<br>growth<br>rate]|
|---|---|---|---|---|---|---|
|Bulgaria|20.6|20.5|21.1|15.3|19.9|21.3|
|Czech Rep|9.3|8.9|9.4|9.0|9.1|9.9|
|Estonia|19.5|20.3|20.8|18.1|20.3|20.9|
|Hungary|12.4|11.1|13.0|12.7|12.0|13.0|
|Latvia|25.8|20.7|22.1|18.7|20.6|22.3|
|Lithuania|20.2|14.9|15.8|14.7|14.9|17.2|
|Poland|16.9|12.0|13.8|12.9|13.0|15.3|
|Romania|23.4|18.2|19.0|19.0|18.5|20.1|
|Slovakia|11.6|9.9|10.3|9.4|10.0|10.9|
|Slovenia|11.6|10.8|13.4|13.1|11.6|13.4|

_Source:_ World Bank staff calculations using EU-SILC data. _Note:_ We assume no deterioration in employment and education indicators by 2020 relative to their rates in 2008.
(2009) have shown that, in Nepal, subjective welfare is negatively associated with geographical isolation. Census data on total population and population density in each district are used as proxies for urbanization and geographical proximity: the denser the population, the less geographically isolated individuals are likely to be. We also include data on the average elevation in each district. Nepal being a mountainous country, the higher the average elevation of a district, the more costly it is to build roads, raising transport and delivery costs to the district. Ceteris paribus, we expect migrants to seek out districts with a higher population density and a lower elevation.

# 4 Econometric results

## 4.1 Univariate analysis

We now investigate the choice of migration destination. We begin with simple univariate analysis. Variables are of the form ∆<sup>h</sup> is<sup>= x</sup> s<sup>h−xh</sup> i<sup>where i is the district of origin of migrant h and s is each</sup> of 74 possible districts of destination. We examine the average value of ∆<sup>h</sup> is<sup>forthedestination</sup> district and compare it to the value of ∆<sup>h</sup> is<sup>foralternativedestinations.Forinstance,letxh</sup> s be population density in district s. The average value of ∆<sup>h</sup> is<sup>fortheactualdestinationofthe</sup> migrant tells us whether the destination district is more densely populated than the district of origin. The comparison between ∆<sup>h</sup> is<sup>foractualandhypotheticaldestinationstellsuswhether</sup> the actual district of destination is more densely populated than alternative destinations.

Results are presented in Table 3 for all variables used in the analysis. We begin with district log income<sup>�</sup> δ s. We have two estimates of<sup>�</sup> δ s, one obtained using reported income data, and the other based on reported consumption data. Given that most respondents to the NLSS survey are self-employed, measurement error is typically larger for income than for consumption. We see that our estimates of log income and consumption<sup>�</sup> δ s are on average 20% and 8% higher in 20
# **Figure 7.  Courts**

<!-- Start of picture text --> Courts<br>Constraint level, conditional means; quadratic fit; 5% robust CIs<br>PAK<br>MDV<br>BGDAFG<br>BTN<br>IND<br>NPL<br>6 7 8 9 10 11<br>Log(GDP p.c.) at PPP<br>4<br>3<br>2<br>1<br>0<br><!-- End of picture text -->

**Figure 8.  Inadequately educated labour**

<!-- Start of picture text --> Inad. Educ. Labor<br>Constraint level, conditional means; quadratic fit; 5% robust CIs<br>BTN<br>BGD MDV<br>LKA<br>IND<br>PAK<br>NPL<br>AFG<br>6 7 8 9 10 11<br>Log(GDP p.c.) at PPP<br>4<br>3<br>2<br>1<br>0<br><!-- End of picture text --> 69
than one to one) on the propensity to save and that increases in the permanent component of the terms of trade (measured using three alternative detrending techniques and with both weighted and unweighted measures of the terms of trade) tend indeed to lead to higher rates of private savings.

Our interpretation focused on the adjustment of saving to a permanent shock, in circumstances where borrowing constraints are anticipated to bind in the future. It should be noted, however, that the empirical results may be consistent with another interpretation. For example, if the permanent shock is associated with an anticipated increase in future volatility, it would raise the demand for assets needed to be used as an effective buffer stock in the future, increasing thereby saving today. Nevertheless, it can be verified that the logic of our analysis continues to apply in this case-the increase in the demand for the buffer stock is magnified by the anticipation of future borrowing credit constraints and by greater loss aversion.

The analysis developed in this paper can be extended to study the asymmetric effects of terms-of-trade shocks on saving to _oil-exporting_ countries and analyze the response of _public_ savings as well.'" This is important because of the policy concerns that the high degree of commodity price volatility has generated in recent years. The 1998 slump in commodity prices, for instance, generated large terms-of-trade effects. Although the real income effect on primary commodity exporters was moderate (of the order of -0.5 percent of GDP), and net importers of oil and primary commodities actually registered a gain overall, oil exporters registered a negative real income effect of the order of -6.3 percent of GDP (World Bank (2000, Chapter 4)). Because oil exports account for almost all of government revenues in oil-exporting countries, the public sector bore the brunt of adjustment. The ability of each country to smooth public consumption in response to the revenue shortfall was, however, limited by their ability to draw down their official reserves and to borrow, both domestically and abroad. In many cases, constraints on domestic finance and lack of access to international capital markets actually prevented governments from successfully smoothing the impact of the oil price cycle, and economic performance deteriorated." The same study by "<sup>0</sup> An early study that attempted to test for an asymmetric effect of terms-of-trade movements on savings in oil-exporting countries is by Spatafora and Warner (1995). However, the test poerformed by the authors was essentially a standard stability test of the coefficient of the terms-of-trade variable across two sub-periods (1965-80 and 1981-89). "The World Bank report estimated that the effect of the drop in oil prices on the external earnings of oil-exporting countries in sub-Saharan Africa led to a deterioration in 17
# **Annex V: Stamp Tax Revenue Data Across States**

Andhra Pradesh Figure 1a: Annual Percentage Change in Stamp Duty Revenues and Rates Figure 1b: Stamp Duty Revenues and Rates <!-- Start of picture text --> Andhra Pradesh<br>45<br>40<br>35<br>30<br>25<br>20<br>15<br>10<br>5<br>0<br>Year<br>Tax Revenue Stamp Tax Rate<br>Figure 2a: Annual Percentage Change<br>in Stamp Duty Revenues and Rates<br>Delhi<br>100<br>80<br>60<br>40<br>20<br>0<br>-20<br>-40<br>-60<br>Year<br>Tax Rev Stamp Rate<br>1980-81 1985-86 1990-91 1995-96 2000-01<br>1980-81 1985-86 1990-91 1995-96 2000-01<br>% change annually<br>Annual % Change<br><!-- End of picture text --> <!-- Start of picture text --> Andhra Pradesh<br>90000 10.1%<br>80000 10.0%<br>70000 9.9%<br>60000 9.8%<br>50000 9.7%<br>40000 9.6%<br>30000 9.5%<br>20000 9.4%<br>10000 9.3%<br>0 9.2%<br>Year<br>Tax Revenue Stamp Rate<br>Figure 2b: Stamp Duty Revenues<br>and Rates<br>Delhi<br>45000 14.0%<br>40000 12.0%<br>35000<br>10.0%<br>30000<br>25000 8.0%<br>20000 6.0%<br>15000<br>4.0%<br>10000<br>5000 2.0%<br>0 0.0%<br>Year<br>Tax Revenue Stamp Rate<br>3604 7199 15786 37587 76630<br>Rs. Lakhs Rate (%)<br>Rs. Lakhs Rate (%)<br><!-- End of picture text --> Delhi 72
**_Necessity :_** _We proceed by contradiction. Assume that s_<sup>∗</sup> ( _R_ ) _is a Nash equilibrium, but at least one of the following conditions fails to hold_ ∣ _N_<sup>1</sup> ( _s_<sup>∗</sup> ( _R_ ))∣= ∣ _N_<sup>−1</sup> ( _s_<sup>∗</sup> ( _R_ ))∣ _and ci_ ≤ 2<sup><u>1</u></sup><sup>_foralli_∈</sup><sup>_N.Ifthereexisti_∈</sup><sup>_Nsuchthatci_></sup><sup><u>1</u></sup> 2<sup>_,thenthisimpliesthat_</sup> _ui_ ( _s_<sup>∗</sup> ( _R_ ) = 2<sup><u>1</u>−</sup><sup>_ci_< 0 =</sup><sup>_ui_(0</sup><sup>_,s_</sup> −<sup>∗</sup> _i_<sup>(</sup><sup>_R_))</sup><sup>_.Therefore,votericandeviatefroms_∗</sup> _i_<sup>(</sup><sup>_R_)</sup><sup>_toti_= 0</sup><sup>_,_</sup> _which contradicts the fact that s_<sup>∗</sup> ( _R_ ) _is a Nash equilibrium. If_ ∣ _N_<sup>1</sup> ( _s_<sup>∗</sup> ( _R_ ))∣≠∣ _N_<sup>−1</sup> ( _s_<sup>∗</sup> ( _R_ ))∣ _, then let us assume without loss of generality that_ ∣ _N_<sup>1</sup> ( _s_<sup>∗</sup> ( _R_ ))∣> ∣ _N_<sup>−1</sup> ( _s_<sup>∗</sup> ( _R_ ))∣ _. It follows that any voter i_ ∈ _N_<sup>−1</sup> ( _s_<sup>∗</sup> ( _R_ )) _can deviate from s_<sup>∗</sup> _i_<sup>(</sup><sup>_R_)</sup><sup>_toti_=0</sup><sup>_,whichcontradictthe_</sup> _fact that s_<sup>∗</sup> ( _R_ ) _is a Nash equilibrium. We find a contradiction in any case, therefore the conditions_ ∣ _N_<sup>1</sup> ( _s_<sup>∗</sup> ( _R_ ))∣= ∣ _N_<sup>−1</sup> ( _s_<sup>∗</sup> ( _R_ ))∣ _and ci_ ≤ 2<sup><u>1</u></sup><sup>_foralli_∈</sup><sup>_Narenecessaryforthe_</sup> _truthful vote to be a Nash equilibrium._

_Furthermore, s_<sup>∗</sup> ( _R_ ) _is not Pareto-efficient since it is Pareto dominated by the strategy profile ti_ = 0 _for all i_ ∈ _N , where every voter decides not to participate in the election. Indeed, we have that ui_ ( _t_ ) =<sup><u>1</u></sup> 2<sup>></sup><sup>_ui_(</sup><sup>_s_∗(</sup><sup>_R_))</sup><sup>_._</sup>

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