rafmacalaba/datause-encoder-data huggingface dataset:
Figure A.3: A Partial Population Design <!-- Start of picture text --> Sample of clusters<br>q 0 q 1 q 2 q 3<br>Pure control 20% treated 50% treated 80% treated<br>0.8 0.2 0.5 0.5 0.2 0.8<br>C C T C T C T<br><!-- End of picture text --> _Notes_ : In a partial population design, clusters are first randomly assigned to different treatment intensities or saturations. Within each cluster, units are randomly assigned to treatment with a probability equal to their cluster saturation. The figure above shows an example of a partial population design with four saturations, including pure control clusters with no treated units.

Figure A.4: Timeline of the randomized communication campaign 25,000 letters delivered

|September 28|October 7|October 9|Timeline|
|---|---|---|---|
|**First day**|**Last day**|**October 2020**|2020|
|**of campaign**|**of campaign**|**bill is due**||

# **A.2 Sample Sizes and Descriptive Statistics**

Table A1: Sample sizes

|||Blocks|Control Obs|Treated Obs|
|---|---|---|---|---|
|_Tg_ =|0<br>Pure control|1_,_102|19_,_103|0|
|_Tg_ =|1<br>20% treated|1_,_100|15_,_060|3_,_853|
|_Tg_ =|2<br>50% treated|680|5_,_905|5_,_897|
|_Tg_ =|3<br>80% treated|1_,_100|3_,_677|15_,_311|
|Total||3_,_982|43_,_745|25_,_061|

_Notes_ : This table shows the final sample sizes used in our experiment. We limit the analysis to clusters of size ranging between 8 and 50 property tax accounts per street-block.

A-4
**Figure 5: Estimated Volume of Stolen and Assumed Production in Shut-in (2003-2008)**

**Figure 6: Value of Stolen and Lost Production (2003-2008)**

<!-- Start of picture text --> 900 40<br>800 35<br>700 30<br>600 25<br>500 20<br>400 15<br>300 10<br>200 5<br>100 0<br>0 2003 2004 2005 2006 2007 2008<br>2003 2004 2005 2006 2007 2008<br>Value of Oil Stolen (USD)<br>Volume Stolen Assumed Production in Shut-in bpd Value of Production Shut-in (USD)<br>Sources: Davis 2009 Sources: Davis 2009<br>Thousand Barrels per Day USD Billions<br><!-- End of picture text -->

# _i. Small-Scale Petroleum Theft versus Organized Crime_

37. Losses occur in different ways and volumes. Small-time operators who tap into oil pipelines are not confined to the Niger Delta. They are spread all over the country where the pipelines pass. Criminals trying to steal refined petroleum products for distribution through informal retail outlets have caused periodic pipeline accidents in Lagos and other areas outside the Niger Delta.<sup>13</sup> Numerous small-scale crude oil thieves also steal the oil for refining in illegal local refineries. Between June and July of 2008, the military Joint Task Force (JTF) had destroyed 111 illegal refineries that were located ―in almost every village in Bomadi and Burutu Local Government areas‖ (This Day 2008, 1).

38. Although bunkering is a large problem for the government, those involved seem to defend the crime by reference to low political accountability. Asari Dokubo, leader of a militant gang in Rivers State, is alleged to steal crude from Shell pipelines for refining locally and for sale to barges and ocean tankers. He claims that ―the oil belongs to us, we‘re not stealing it. It is the Nigerian state stealing our oil from us‖ (IRIN 2008, 1). Neverheless, the resource control conflict also provided opportunistic excuse for militants and criminal gangs to engage in oil theft, the proceeds of which they used to procure arms to perpetuate themselves and resist the security agencies.

39. Those engaged in large-scale oil theft break into pipelines in the Delta‘s mangrove swamps. They load stolen oil into smaller barges and ships that then go on the high seas to load huge ocean tankers. Another form of large-scale bunkering is the loading of crude in excess of approved or rightful quotas by licensed operators (Azaiki 2006, 268). Given the highly visible nature of oil theft, it would appear that there must be either payoffs to or participation of security forces.

40. As the bunkering gets large and organized, it has a clear demand side. Large-scale stealing of oil by large ocean tankers involves international organized crime groups, which buy the oil and take them to refineries abroad for refining. The payoff from this type of bunkering must be exceedingly profitable to keep people investing in such a risky venture. For instance, in September 2007, the 19
In addition to generating awareness for the program, its potential target group, aand the characteristics of demand, the process of beneficiary selection also provides a basis for local authorities to integrate land reform into a broader program of capacity building and social assistance at the municipal level. This could contribute to the resolution of at least some aspects of the potential conflict between the dual objectives of equity and efficiency which is to some extent unavoidable if land reform is to make a longterm sustainable contribution to poverty reduction.

# **Creating the basis for a functioning land market**

The availability of large amounts of unutilized or underutilized land in large holdings implies that, in a reasonably fluid land market there would be plenty of supply to enable potential beneficiaries to choose the most suitable lands and negotiate to obtain a competitive price. In practice, however, land markets have found to be thin, highly segmented, characterized by high transaction costs, and often pushed into inforrnality (FAO, 1994). Credit market imperfections, lack of market information by potential sellers, and the lack of farmn models suited to the specific needs and factor endowments of small agricultural producers, have prevented such an outcome and contributed to the fact that beneficiaries under the oldstyle reform program often acquired marginal lands at highly exaggerated prices without making productive use of it.<sup>2 5</sup> In the pilot municipios, a procedure similar to the identification of demand for land is being followed on the supply side as well. The first step is to determine ecologically suitable zones and, based on cadastral information, establish an inventory of the land according to- size classification that could be used to identify target areas for agrarian reform. Areas where land reform would result in environmental hazards, where soil fertility is insufficient, or where the existing ownership structure is already characterized by small to medium-sized holdings, are thus eliminated a _priori._ This gives beneficiaries a better idea of where to focus their efforts, helps to set realistic goals, and puts into perspective the potential contribution of land reform for solving the social problems of a given _municipio._ It increases not only awareness of the scope for land reform as compared to other options aiming at overall development of the municipio (and the degree to which the success of land reform will depend on such complementary measures), but also forces local governments to think about potential levers (from land taxes to land price information systems and training to increase the productive capacity of potential beneficiaries) that they can utilize to

> 25 As highlighted earlier, even under the new land reform law, rationing was largely by non-price means-including outright corruption-and the main challenge for landowners willing to sell was to obtain clearance from the regional and central INCORA offices regarding the availability of funds to purchase their land which was seen as the precondition for proceeding with negotiations.

14
We then assess these income effects empirically by estimating beneficiaries’ WTP for electricity in three low‐income countries with pockets of households living in extreme poverty ‐ Burkina Faso, Senegal, and Rwanda. Using survey data from unelectrified rural areas in each of these three countries collected between 2010 and 2013, we calculate stated WTP for different electricity access technologies, such as a solar lamp, a solar home system, and a grid connection. We then analyze its determinants by regressing stated WTP on the household income level, household characteristics, and other control variables.

We find that households put a high priority on having electricity and are willing to dedicate more than 10 percent of their monthly expenditures to paying for electricity. Consistent with the theoretical model, we also find that the household WTP increases with household income. A 1 percent increase in households’ expenditures increases their WTP for electricity access by around 0.2 percent. Moreover, we see that the WTP increase with households’ income is nearly twice as large for grid electricity than for a low‐cost off‐grid technology, such as a solar lamp.

Nonetheless, the overall WTP for electricity access by the rural households covered in the study is low enough that it is not sufficient in itself for covering operational and capital costs of extending on‐grid electricity supply to poor rural households over the short‐ and medium‐term. These results suggest concentrating efforts to bring electricity service to very low‐income households in rural areas on ultra‐low‐ cost off‐grid technologies, such as solar lanterns and solar home systems. This recommendation is consistent with providing service that meets the basic needs of low‐income rural households, as shown previously by Samad et al. (2013) and Grimm et al. (2017, 2018) in the context of solar home systems and even Pico‐PV.

As noted above, if aggregate gains to households from improving provision of public services are high, subsidized grid electrification to improve community electricity access may be desirable from a social planner’s perspective. Estimating indirect effects of grid electrification is beyond the scope of this paper. However, other 4
WPS3803

# **How Substitutable Is Natural Capital?**<sup>**1**</sup>

**Anil Markandya FEEM, The World Bank and University of Bath**

## **Suzette Pedroso-Galinato The World Bank**

### World Bank Policy Research Working Paper 3803, December 2005

_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 view of the World Bank, its Executive Directors, or the countries they represent. Policy Research Working Papers are available online at http://econ.worldbank.org._

> 1 This paper is drawn from a larger study on the ‘Where is the Wealth of Nations?’ (World Bank, 2005a).  We wish to thank Giovanni Ruta, Kirk Hamilton, Giles Atkinson and a number of others who have provided useful comments on earlier drafts.
8 non-daily metered nominations by 2:00 p.m.. All shippers' nominations must be approved by BGT.

- On the basis of the approved nominations, BGT schedules the gas flows in the pipeline system to maintain safety and reliability of supply and to minimize costs. If the system cannot transport all nominated gas, BC;T contacts shippers and asks for revised nominations.

- Renominations can be made between 6:00 p.m. on the day before the gas day and 3:59 a.m. on the current day.

- Shippers able to adjust their supply and demand according to the expected system price post their flexibility bids for selling or buying gas from BGT on the U.K. Link. Flexibility bids become available to BGT at about 6:00 p.m. (see the section below on the flexibility mechanism).

- ? Shlippers trade gas in the on-system market and include information about the trade in their nominations or renominations (see the section below on on-system trading).

_Stage 3: Processes performed during the gas day_

- 3 BGT operates the network according to the schedule and responds to any problem in the system through central dispatch of entry and exit ooints and the gas flows.

- BGT monitors the system balance. If the system runs an imbalance, BGT buys or sells the missing or excess gas in the flexibility market. If it cannot eliminate an imbalance through the flexibility mechanism, it uses available storage facilities or, in serious cases, asks shippers to stop shipments to interruptible sites.<sup>2</sup> In an emergency BGT issues flow orders<sup>3</sup> .

- Shippers are responsible for maintaining a balance between their intake and offtake according to their nominations. If they run an imbalance, they can avoid penalties by trading in the on-system market.

# _Stage 4: Processes performed on the day after the gas day_

- BGT collects data about the volume and calorific value of gas at entry and exit points and in storage. At 4:00 p.m. metering data become available for the allocation process.

> 2 BGT's choice of interruptible sites must depend entirely on operational necessity. But on average over the year, all shippers with interruptible sites must be treated equally.

> 3 Flow orders are orders issued by pipeline companies that require shippers to inject or withdraw natural gas at a specific point to ensure continued flow of natural gas through the system during an emergency.
# **1. Introduction**

The government of your country announces a certain policy change. How do you know if it will benefit you or not? There are many things you may take into account. For example, what do the media and different political parties say about it? Who do you trust? What are your beliefs about the government’s capacity to deliver? The list of factors to consider could go on. But what happens when your assessment is wrong? Sometimes winners may believe they are losers. This paper explores empirically one such case. More specifically, it analyzes the determinants of the citizens’ satisfaction about a reform of the gas subsidy in El Salvador, a reform that was expected to improve the welfare of around three-quarters of the population but which was initially unpopular.

The theoretical literature has long recognized the potential importance of such scenario. If individuals are uncertain about the benefits of a policy change it can lead to a status quo bias, in which a policy that would benefit the majority of the population is not be adopted (Fernandez and Rodrik [5]). Under individualspecific uncertainty, an increase in the number of expected winners could reduce the probability that a reform is approved, until it reaches a critical threshold and becomes an overwhelming majority (Jain and Mukand [12]). More generally, the inability of the policymaker to persuade the electorate of the benefits of a policy change has featured in a large number of political economy models (reviewed by Drazen [4]). Recently, the literature has turned its attention to the importance that the dynamics of learning about reform outcomes may have on support for reforms (van Wijnbergen and Willems [15]). Others have pointed out that political support for reforms can vary dramatically over time, which may alleviate the problem of status quo bias because governments may be able to withstand long periods of low popularity, as long as political support recovers before election day (Veldkamp [16]). While the theoretical literature is abundant, it has proven difficult to identify these effects empirically.<sup>1</sup> This paper contributes to the literature in three ways. First, it documents a case of a reform that benefited the majority of the population but was initially unpopular. Second, it uses new survey data to identify the factors that help explain this puzzle. Third, it analyzes how the main factors driving the popularity of the

> 1One of the few examples in the literature is represented by Harrison [8] who discusses the British Columbia carbon tax reform.

2
used as a variable in other studies including Fan et al. (2009), Duvanova (2014), and Amin and Soh (2020).

Firm-level responses to the bribery question cannot be used directly in our regressions as they are likely to be endogenous to the firms’ growth experience (reverse causality problem) and firm characteristics (omitted variable bias problem). For instance, large firms are more visible and have higher ability to pay bribes. Thus, these firms may be targeted by corrupt public officials. At the same time, growth rate is also known to vary systematically with firm-size. One solution suggested in the literature is to proxy the level of corruption faced by a firm by the average level of corruption experienced by all other firms (that is, other than the firm in question) in the countryindustry cell.<sup>2</sup> Using country-industry cell average also helps to control for potential measurement error if some firms choose not to respond or misreport the regulatory burden (Pounov 2016). Thus, we define our main explanatory variable, _Overall Corruption_ , as the average of the bribe rate reported by the firms “to get things done” where the average is taken over all firms in the countryindustry cell excluding the firm in question. For later reference, the term “CI avg.” denotes the cell average at the country-industry. To ensure adequate thickness within the cells, all cells with fewer than 5 firms are excluded from the sample. As mentioned above, there are 712 country-industry cells in our baseline sample for overall corruption. In this sample, the mean value of _Overall Corruption_ equals 1.18 and the standard deviation equals 1.81.

# 2.4.2 _Petty corruption_

The ES also contains information on instances of corruption that firms experience in soliciting the following: obtaining electricity connection, obtaining water connection, obtaining construction

> 2 Reverse causality from a firm’s own growth rate to the bribe payments experienced by the other firms in the countryindustry cell is highly unlikely, although it cannot be ruled out completely (see for example, Aterido et al. 2011).

12
Table 9 (Contd.)

# Wage Premium for Public Sector Jobs

|Study|Estimation<br>technique|Burkina<br>Faso|Cameroon|Cote<br>d'Ivoire|Mali|Senegal|
|---|---|---|---|---|---|---|
|Levy and<br>Newman<br>(1989; Table<br>6, page 106)|OLS<br>with dummy<br>variable<br>for public<br>sector jobs|||- 17.3 %<br>[ONFP, 1984;<br>N = 10,835]|||
|Miller and<br>Vallee<br>(1995; Table<br>6.5, page 163)|OLS<br>with dummy<br>variable<br>for 100%<br>private**firms**||11.1 %<br>[RPED, 1994;<br>N = 517]||||
|Van der Gaag<br>_etal._<br>(1989; Table<br>5, page 28)|FIML<br>with<br>switching<br>regression<br>model|||-26.5 %<br>[CILSS, 1985;<br>N = 513]|||

Note: Depending on the studies, the public sector may or may not include state-owned enterprises. All studies control for individual characteristics, but the set of controls varies across studies. The wage premium is based on the coefficient (c) multiplying the public sector dummy variable, except in the case of Berth6lemy and Bourguignon (1992), where it is based on the difference between coefficients for government and other formal sector jobs. For large values of the c coefficients, in absolute terms, the percent premia is approximated as 100[exp(c)-1]. Data sources and sample sizes are reported in brackets. CILSS is the Cote d'Ivoire Living Standards Survey, a household survey with national coverage. IIES is a series of labor force surveys carried out by the Institut International d'Etudes Sociales in the capital cities. ONFP is a plant-level survey covering large firms, but reporting data on individual workers. RPED is the Regional Program for Enterprise Development, a plant-level survey which includes data on up to 10 workers in each firm.

_45_
||(0.079)|(0.102)|(0.110)|(0.113)||
|---|---|---|---|---|---|
|Q0.25|0.148**|‐0.052|0.012|‐0.057||
||(0.073)|(0.095)|(0.107)|(0.122)||
|Q0.5|0.067|‐0.041|‐0.032|0.044||
||(0.067)|(0.081)|(0.100)|(0.100)||
|Q0.75|‐0.071|‐0.072|‐0.015|‐0.052||
||(0.054)|(0.080)|(0.092)|(0.080)||
|Q0.9|‐0.041|0.157|0.074|0.119||
||(0.047)|(0.105)|(0.144)|(0.127)||
|Observations|780|780|780|770||
|Month FE|YES|YES|YES|YES||
|State FE|YES|YES|YES|YES||
|Controls|YES|YES|YES|YES||
|Interacted Controls|YES|YES|YES|YES||
|Robust<br>standard<br>errors<br>Column (1) is measured on the ho|in<br>parentheses<br>usehold level. Columns (|(White,<br>1980):<br>***<br>2‐4) refer to per capita OECD ad|p<0.01,<br>**<br>ult equivalents.|p<0.05,<br>*|p<0.1.|

# <u>Robustness:</u>

In line with hardly credible low consumption levels, misreporting could be considered to be more prevalent at the tails of the distribution, hence, among the extreme values. On the one hand, it makes, thus, sense to consider those outliers. On the other hand, it is problematic to base the inference mainly on those extreme values. Ideally, one would know how to distinguish the intentionally misreported outliers and the ones that are caused by errors in reporting or data entry. The log normalization in the main analysis is chosen as a compromise of keeping most data possible, but making estimates less susceptible to outliers. This suggests two natural robustness checks: (i) in a more liberal setting, the outcomes in levels are used and (ii) in a more conservative setting, the outliers at the 5<sup>th</sup> and 95<sup>th</sup> percentile are discarded. Regression results using the levels are depicted in Table 7.<sup>18</sup> _Table 7: Quantile Regressions – outcomes in levels._

||(1)|(2)|(3)|(4)|
|---|---|---|---|---|
|Outcome Variables|Cons. Num.|Cons. Quant.|Cons. Val.|Cons. Cal.|
|Q0.1|0.544**|0.741***|9.440|229.126*|
||(0.254)|(0.173)|(13.305)|(136.013)|
|Q0.25|0.298*|0.675**|60.585***|179.447|

18 As scaling of the outcome variables is different – e.g., the outliers with regard to consumption quantity in kilograms might not correspond to the consumption quantity in calories – the outliers for one measure do not always correspond to outliers in the other measure. In order to guarantee that we still base the inference on the same observations, outliers from all corresponding variables are dropped, which explains that the resulting sample is smaller than 90 percent of the full sample.

17