rafmacalaba/datause-encoder-data huggingface dataset:
Objective indicators of corruption are less numerous and less standardized than the composite subjective indicators. Three types of objective indicators have generally been developed: (1) input-output analysis of the anticipated versus actual costs of construction/provision of services; (2) criminal statistics regarding the number of prosecutions for bribery; and (3) natural experimental data on the behavior of policy makers. As the research question of interest pertains to cross-national variation in the policy-making context, it is not possible to consider the efficacy of input-output analysis, as these data do not exist except for very detailed country case studies. However, comparative indicators for the other two objective types of data do exist and it is worth critically reviewing them.<sup>11</sup>

# **(i) The United Nations Survey of Crime Trends and Operations of Criminal Justice Systems**

This is a survey compiled and collected by the Crime Prevention and Criminal Justice Division of the United Nations. The survey began in 1970 and compiles annual data on the incidence of different types of crime in UN member states. The survey asks relevant public authorities in each member state to provide data, from their own national statistics, regarding the incidence of crime. Included in this survey are questions regarding the number of prosecutions for bribery per 100,000 of the population.

**Coding.** The rate of prosecution per 100,000 can, hypothetically, vary from 0 to 100,000 (interval range). As Table 5 indicates, there is significant variation in the number of prosecutions for bribery per 100,000 of the population for the year 2000, while on average there were 3.5 prosecutions for bribery per 100,000 of the population. The standard error is greater than the mean (9.6), the lowest per capita rate of prosecution is 0.01 per 100,000 (Pakistan), while the highest rate was in Romania (52.3 per 100,000).

> 11 Another sub-set of objective indicators not examined are those based on the input-output analysis of major infrastructure projects. This is because such indicators have only been developed at the local/national level and cannot therefore be used to test theories linking changes in national level electoral contexts to the level of corruption.

20
Month of interview is known for each household in the survey. Given that for all expenditures (except durables) the recall period is last month, shifting the time of interview by one month gives the actual period of expenditures reported. Monthly rebased CPI is then applied to deflate the components of welfare aggregate within each year.

Across year deflation is done to keep welfare aggregates in similar prices. Given that expenditure per capita is transformed into USD in 2011 PPPs, welfare aggregates in each year are inflated or deflated to be in 2011 prices using SCI CPI.

# 4.3 Spatial deflation

Spatial deflation is another important step to make sure the welfare aggregate is corrected for differences in prices across space. Two different deflators were constructed for this purpose: food and rent. The food spatial deflator is constructed from unit values of purchased food products (excluding tobacco) and is based on Paashe price index following Deaton and Zaidi (2002). Paashe index is calculated using the formula below where ��� is the share of household h's budget devoted to good k. P0 is reference price vector. Ph is vector of prices household face. For spatial deflator, food unit prices are used to calculate household level deflators, which are then averaged to the regional level separately for urban and rural areas (called subregions further in the text). The whole process is described below.

Unit food prices are calculated by dividing inter-temporally (within-year) adjusted expenditures by purchased quantities. To make sure prices are calculated for consistent units, all quantities are transformed to kilograms. Products with zero or missing quantities are dropped. Products observed less than six times at the national level are dropped as well. Finally, outliers in unit prices at sub-regional level - lower or higher than 2.5 standard deviation from the mean – were identified and replaced by missing values. Missing values were replaced by median values at the lowest possible level starting from sub-regional level, followed by rural/urban and finally the national level. Households with zero expenditure were assigned imputed values using unit prices and quantities.

Household level deflators are constructed for each product by dividing household level prices over median prices at the national level. For each household average deflator is constructed using expenditure shares of different products as the weight. Thereafter, average deflators for each rural and urban area of seven regions are calculated and will be applied to each household for spatial adjustment.<sup>10</sup> Simple illustration of this procedure is shown in table 2.

> 10 Using sub-regional averages as deflators is easier for communication than having household specific deflators and is more robust to extreme values.

9
# **Economic Depreciation**

The economic depreciation rate interacts with the tax depreciation allowance to affect the effective tax rate. Suppose, for example, under our assumption of fully mobile capital and technology that a given type of machinery is depreciated at the same economic rate everywhere around the world. Countries with faster tax depreciation allowances for this type of machinery will then encourage this type of capital investment through a lower effective tax rate.

# **Capital Structure**

A real capital investment generally involves two categories of capital: depreciable and non-depreciable assets. These two categories can be further divided into four types: buildings and machinery (both depreciable) and inventory and land (non-depreciable). Capital investments in different industries are as a rule structured differently. Moreover, under the same statutory tax rate(s), different types of assets may incur different effective tax rate due to the various interactions between tax provision and non-tax parameters discussed above. In the absence of other information, we use the same capital structure by industry, based on the Canadian data, to aggregate these differentiated effective tax rate on various type of capital for a given industry across countries.

23
WPS3784

**Table 9. Impact of Quality-Driven Price Premium**

|Simulation: doubling<br>coffee price:|Headcount<br>P0|% change<br>in P0|Poverty<br>gap (P1)|Change in<br>P1|% change in<br>expenditure|% change<br>in income|
|---|---|---|---|---|---|---|
|**_baseline coffee farmers_**|**_41.88_**|**_0.00%_**|**_0.135_**|**_0.00%_**|**_0.00%_**|**_0.00%_**|
|50% of all coffee farmers|39.86|-4.82%|0.124|-8.25%|2.49%|4.85%|
|all large coffee farmers|41.15|-1.74%|0.131|-2.93%|1.34%|3.15%|
|all medium coffee farmers|<br>40.77|-2.65%|0.132|-2.79%|0.87%|1.92%|
|all medium and large<br>coffee farmers|40.05|-4.37%|0.128|-5.72%|2.21%|5.07%|
|small coffee farmers|39.23|-6.33%|0.120|-11.08%|2.93%|4.85%|

Source: Computed by authors from EICV data

**Table 10. Determinants** **<u>of the Probability of Being a Coffee Farmer (propensity score)</u>**

|**Parameter**|**DF**|<br>**Estimate**|**Standard**<br>**Error**|**Wald 95%**<br>**Lim**|**Confidence**<br>**its**|**Chi-**<br>**Square**|**Pr > ChiSq**|
|---|---|---|---|---|---|---|---|
|Intercept|1|-3.1209|0.0318|-3.1832|-3.0586|9651.42|<.0001|
|Distance to road|1|-0.0353|0.0028|-0.0407|-0.0298|158.7|<.0001|
|Access to electricity|1|1.0688|0.0372|0.9959|1.1418|824.84|<.0001|
|Extension services<br>dummy|1|0.1843|0.0049|0.1747|0.1938|1439.03|<.0001|
|Head's Education|1|0.023|0.0014|0.0202|0.0258|257.34|<.0001|
|Dependency ratio|1|0.0441|0.0011|0.042|0.0462|1687.25|<.0001|
|Land area|1|0.0345|0.0012|0.0321|0.037|763.17|<.0001|
|Livestock heads|1|0.0058|0.0004|0.0051|0.0066|234.77|<.0001|
|Head is male dummy|1|0.1184|0.0058|0.1069|0.1298|411.64|<.0001|
|Use fertilizer dummy|1|0.1508|0.0048|0.1414|0.1602|984.59|<.0001|
|Dummy Butare|1|1.7102|0.0247|1.6617|1.7587|4779.86|<.0001|
|Dummy Byumba|1|1.1653|0.0251|1.1162|1.2145|2158.49|<.0001|
|Dummy Cyangugu|1|2.5895|0.0252|2.5401|2.639|10529.5|<.0001|
|Dummy Gikongoro|1|1.4111|0.0253|1.3615|1.4606|3114.6|<.0001|
|Dummy Gisenyi|1|1.3244|0.0253|1.2748|1.3739|2740.1|<.0001|
|Dummy Gitarama|1|1.5732|0.0246|1.5249|1.6214|4078.33|<.0001|
|Dummy Kibungo|1|1.2965|0.0249|1.2477|1.3453|2712.76|<.0001|
|Dummy Kibuye|1|1.3999|0.0255|1.35|1.4498|3023.54|<.0001|
|Dummy Kigali|1|1.0657|0.0255|1.0158|1.1156|1752.61|<.0001|
|Dummy Ruhengeri|1|0.2822|0.0273|0.2288|0.3357|107.17|<.0001|

25
# **1. Introduction**

Social cohesion is an essential foundation of inclusive and sustained development. Over the past 10 years, there have been various forms of social unrest in countries around the world. While the specific events that spurred each of these episodes are unique, the perception of growing economic inequality and the limited scope for participation in decision making have been common themes underlying these movements. Although strong economic management and social expenditure are necessary, they have proven insufficient in contexts where social cohesion is low or deteriorating (OECD 2011). As a result, identifying effective instruments to foster social cohesion remains an essential objective for policy makers across the world.

In urban areas, public spaces are an important aspect of social life and can play a vital role in delivering better economic, health, social, and environmental value for its residents (CABE 2004). Better designed, multi-use, green public spaces can encourage people to walk more, interact with others, and benefit from a natural landscape (Das 2008). Public spaces are also crucial sites for the interplay of social relations among different groups. While urban designers have traditionally pursued instrumental goals in designing cities, such as bringing order to densely populated areas, a fundamental function of public spaces is also to promote informal, spontaneous, non-transactional social interaction among its residents (Stevens 2007). In recent years, more attention has been given to social and cultural factors in developing public spaces. However, there is little empirical documentation of whether, and to what extent, urban design attracts users from diverse backgrounds and facilitates social contact, and whether these interactions promote social cohesion.

This paper presents the result of a Virtual Reality (VR)<sup>3</sup> experiment, conducted with youth in the city of Karachi, Pakistan, in April-May 2019. The experiment aimed to assess the impacts of improvements in the design of a public park and people’s exposure to social diversity on their intention to use the public space and their attitudes towards other social groups. Specifically, the study focused on two questions: (i) whether improvements in the physical design of a public park had any impact on participants’ willingness to use the park, and on their trust, stereotypes and willingness to interact with other social groups; and

> 3 While the first form of VR goes back to 1957, the first head mounted display attached to a computer which enabled the user to see a virtual world was created in 1968 (Virtual Reality Society, 2018). Since then, helped by several technological breakthroughs in the last five years that have made VR hardware highly affordable, VR has been developed in an endless list of applications such as the military, arts, and health care.

2
During the global recession, the average export growth of incumbent exporters was negative in the most developed LAC countries - Chile, Colombia, Costa Rica, Colombia, Mexico, Peru - (as well as in South Africa) but was positive in the LAC processing countries (as well as its comparators) and in Ecuador. Incumbent products explained most of the export decline for incumbent exporters in Chile, Colombia, Mexico, and Peru and most of the export growth for incumbent exporters in Ecuador and Nicaragua. Interestingly, during the crisis new products became the major contributor to export growth of incumbent exporters in the Dominican Republic, El Salvador, and Guatemala and they also accounted for a large share in Nicaragua and Ecuador. The participation in the CAFTA might have partially insulated the LAC processing countries’ incumbent exporters from the crisis (despite the important decline in U.S. demand) by fostering entrepreneurship through the introduction of new products. This pattern of the crisis fostering entrepreneurship was also verified in Ecuador.

Analyzing further the role of new products for export growth of incumbent exporters, Panel E of Figure 7 shows that during the steady growth period the contribution of new products was mostly accounted for by the products with higher durability in the market, i.e., those that continued to be exported by incumbent exporters in _t+1_ in all LAC countries.<sup>26</sup> This was also the case in some comparator countries though not so clearly in Bangladesh.

# **_6. Export Entrepreneurship across LAC Countries along the New Destination Dimension_**

Export entrepreneurship is also present when incumbent exporters start exporting to new destinations. To examine this dimension of entrepreneurship, we focus on incumbent exporters (as defined in Table 1) in each country and consider their export destination markets.<sup>27</sup>

# **_6.1 Destination Entry, Exit and Survival Rates_**

Panels A and B of Figure 8 shows the destination entry rates for incumbent exporters during 2005-2007 and 2008-2009.<sup>28</sup> Incumbent exporters in LAC engaged in a high degree of experimentation along the destination dimension during the steady growth period: on average a

> 26 The decomposition of the contribution of new products during the crisis period is not presented due to the lack of data for 2010 for most LAC countries which would be necessary to calculate the survival of new products into the year following 2009. 27 Brazil, New Zealand, and Spain are not used in this section given that we do not have access to the raw exporter-level customs data for those countries necessary to compute the measures used here.

> 28 The average number of destinations served per incumbent exporter in LAC countries ranges from 2.6 in Mexico and Nicaragua to 4.7 in Chile, as shown in Appendix C.

27
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**Figure A1: The growth of international private equity investment**

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# **8 Sensitivity Analyses**

Sensitivity analyses check the robustness of the calculations and conclusions drawn. The recent collapse in the world price of oil is one example of what could significantly alter the findings by reducing costs in countries heavily dependent on oil-based generation. The discount rate used in annualizing capex can also be changed to test sensitivity to the cost of capital. Changing the discount rate has the same effect as changing capex at a fixed discount rate.

## 8.1 Oil product prices

Thirty-three countries generated a portion of their electricity from oil products in the reference year. During the reference years in this study, spot prices of liquid fuels spanned a wide range (Table 12). Kenya and Tanzania are the only countries for which the reference year includes the first six months of 2015 (see annex 2), which were the months following the collapse of the world oil price. The reference years for all other countries are 2010 (Lesotho), 2012, 2013, or 2014, when oil prices were high. Some generate 90 percent or more of electricity from diesel and fuel oil, and five are 100 percent dependent on oil for all their domestic electricity generation. In these countries, fuel purchases account for a high proportion of operating costs, as much as 90 percent in The Gambia. As a result, the cost of electricity generation would have fallen significantly in 2015 and may fall even further in 2016, depending on the world oil price movement.

**Table 12: Annual average spot prices of liquid fuels**

|**Yr**|**Brent**|**Diesel**|**Heavy fuel oil**|
|---|---|---|---|
|**ea**|US$/barrel|US$/liter|US$/metric tonne|
|2010|80|0.56|427|
|2011|111|0.78|592|
|2012|112|0.79|612|
|2013|109|0.76|573|
|2014|99|0.70|507|
|2015|52|0.40|237|

_Source_ : industry sources. _Note_ : Diesel is the grade with 0.1% sulfur and fuel oil has 3.5% sulfur, and both prices are from Europe.

This sensitivity analysis assesses the hypothetical impact of the annual average oil price in 2015 prevailing in the reference year. For the purpose of this illustration, the nominal prices of diesel and fuel oil in 2015 were taken to calculate the reductions on the cost of service and the QFD in the countries with oil-based power generation by both public and private generation companies (Table 13). The starting points for fuel prices are the spot prices of US$0.40 per liter of diesel and US$237 per tonne of HFO in Table 12, to which are added shipping and transportation costs. Heat rates are consistent with those used in section 6.6. The simplified analysis assumes that shipping and land transportation costs are fixed and do not change in the short run as a result of falling world oil prices. The analysis assumes that fuel price reductions are fully passed onto utilities, which may not be the case. If, for example, fuel prices were subsidized in the reference year—through price discounts, VAT exemption, and other means—the changes in the prices charged to utilities with falling world oil prices may be smaller, as governments attempt to reduce fuel price subsidies. As such, the fuel cost savings calculated here may represent an upper bound.

57
thus imply a welfare loss equal to the benefits not received due to later retirement. Under this assumption, if men receive higher wages, they suffer a greater loss of welfare, on average. The contrary might in fact be closer to the truth. **A** study in Ecuador (Rama and MacIsaac **1999)** on the impact of downsizing on workers earnings and welfare show that women suffer higher income loss than men, but these difference did not show up when the questions is about welfare loss. In other words, women **did** not mind withdrawing from the labor force, as they valued their time **highly. A** retirement age increase is just the opposite. As a result, our calculation of the net wealth changes, which does not take into account women's valuation of leisure, probably underestimates the overall negative impact of the pension reform on women.

# **IV.** Results

4.1 In this section, the three variables constructed above are first used to estimate the gender differences nestled within the systems in each country. We follow a sequential approach to isolate the three gender relevant aspects of the pension reforms. First, we look at how the ratio of women's benefits to men's benefits would change with the reform, assuming both have a full work history and both earn the average wage. In this case, any observed gender difference in replacement rates highlights the pure gender differences in benefit rules. 4.2 Next, we compute the gap in the implicit rate of return on contributions. Here the systems' subsidy to women based on longer life expectancy emerges. In a second stage, we look at what happens to women if they continue to experience the labor market outcomes historically observed in these countries **-** lower wages and interrupted work histories. Again we first isolate the benefit gender gap, and then we use the implicit rate of return on contributions to see the combined impact of behavior, benefit rules, and life expectancy. We do a final analysis of the full relative impact of the reform using the net wealth variable.

4.3 Lastly, we check the sensitivity of the analysis to assumptions on the financial rate of return of the fully-funded pillar. We look at how our results change for poorer workers, picking up the effect of the minimum benefit, which lowers the gender gap. We also analyze

**18**
_(Table C.4 continued.)_

|Purchase clothing (In-<br>dex)|Index of woman’s involvement in the following decision in the house-<br>hold: Decision to purchase clothing and jewelry for herself. For each<br>decision, an ordinal variable was coded such that<br>4=Woman decides alone without needing permission,<br>3=Woman decides alone, but needs permission,<br>2=Woman is involved in joint decision-making,<br>1=Others decide, but need the woman’s permission,<br>0=Woman is not involved.<br>I create dummy variables for each ordinal category, and then com-<br>pute a standardized index following Anderson (2008) and O’Brien<br>(1984).|
|---|---|
|House repairs (Index)|Index of woman’s involvement in the following decision in the house-<br>hold: Decision about large house repairs. For each decision, an ordi-<br>nal variable was coded such that<br>4=Woman decides alone without needing permission,<br>3=Woman decides alone, but needs permission,<br>2=Woman is involved in joint decision-making,<br>1=Others decide, but need the woman’s permission,<br>0=Woman is not involved.<br>I create dummy variables for each ordinal category, and then com-<br>pute a standardized index following Anderson (2008) and O’Brien<br>(1984).|
|Purchase appliances (In-<br>dex)|Index of woman’s involvement in the following decision in the house-<br>hold: Decision to purchase large household appliances. For each de-<br>cision, an ordinal variable was coded such that<br>4=Woman decides alone without needing permission,<br>3=Woman decides alone, but needs permission,<br>2=Woman is involved in joint decision-making,<br>1=Others decide, but need the woman’s permission,<br>0=Woman is not involved.<br>I create dummy variables for each ordinal category, and then com-<br>pute a standardized index following Anderson (2008) and O’Brien<br>(1984).|

64
**The Development Impact of the Prohibition of the Drug Trade***

Philip Keefer Norman V. Loayza Rodrigo R. Soares _The World Bank The World Bank PUC-Rio and University of Maryland_

**Key Words:** Drug trafficking, narcotic policy, prohibition, organized crime, violence, corruption, expropriation, developing countries

> * Naotaka Sugawara provided excellent research assistance. For insightful comments and discussions, we are grateful to Ximena Del Carpio, Gabriel Demombynes, Claudio Raddatz, Peter Reuter, and Luis Servén.