rafmacalaba/datause-encoder-data huggingface dataset:
supply inclusive of the “distributional cost”. We estimated cost of supply of electricity to domestic consumers by taking the lower bound of the price range DISCOs paid while importing/purchasing the electricity (NIS 0.53) and adding a 20 percent margin to cover distributional costs, surcharges, and recoveries.<sup>55</sup> This results in an estimated average cost of supply of 0.636 NIS per kWh.<sup>56</sup> We then calculated the subsidy for each consumption slab by taking the difference between the cost of supply (0.636 NIS per kWh) and the tariff in that consumption slab. Table 4-2 describes the subsidies.

**Table 4-2 The estimated electricity subsidy per kWh ranges from 0.178 to -0.0371 in the West Bank**

**and Gaza**

Electricity subsidy <u>per kWh by consumption slab, West Bank and Gaza</u>

||**Quantity**<br>|**Subsidy**<br>|
|---|---|---|
|**Slab**|(kWh per<br>month)|(NIS per<br>kWh)|
|1|1-160|0.18|
|2|161-250|0.14|
|3|251-400|0.07|
|4|401-600|0.03|
|5|More than 600|-0.04|

Source: Authors’ estimates based on Prime Minister’s Office (2017)

# **We performed the following steps to simulate direct effects from the domestic electricity subsidy:**

- (i) Identified the amount of households’ expenditure on electricity based on the self-reported “electricity charges” in the PECS.

- (ii) Estimated the quantity of electricity consumed by dividing total expenditure on electricity by the tariff (which varies by consumption slab).

- (iii) Calculated the direct effect as the subsidy amount per kWh (between 0.178 and -0.0371) times the (estimated) electricity quantity (kWh) used domestically consumed per the PECS.<sup>57</sup>

- The analysis focuses on direct effects and assumes no indirect effects since the domestic electricity subsidy is only provided to households and not to electricity producers/distributors.

# **4.5.2. Fuel subsidies**

# **Simulation of gasoline and diesel fuel subsidies is based on self-reported PECS households’ expenditure**

**data and subsidy calculated from administrative data (0.0686 and 0.1962 NIS per liter for gasoline and diesel, respectively).**<sup>58</sup> The simulation calculates the direct effects of fuel subsidies for residents in the West Bank and Gaza, and indirect effects for residents in the West Bank only. The direct effects of subsidies on gasoline and diesel were calculated by multiplying the subsidy per liter of fuel by the

> 55 Estimation of distribution margin (inclusive of all surcharges and costs) of 20 percent in the electricity sector is provided by the World Bank Energy Team based on regional average distribution margins.

> 56 Cost of supply after 20 percent margin calculated as NIS 0.53/ KwH * 1.2.

> 57 Note that due to the data limitation, the subsidy estimation is based on assumption of “volumetric consumption” instead of Increasing block tariff (IBT). Furthermore, the estimation only includes “Tariff Differential subsidy” and not” cross subsidy” within the tariff structure.

> 58 The per liter subsidy for gasoline and diesel was calculated by taking the difference between the retail price and GDoP price for the year of analysis.

32
Figure 12: Transmission to Non-Oil Developing Countries of Clinton Deficit Reduction Program and Maastricht Agreement <!-- Start of picture text --> LDC Trade Balance<br>0.10<br>0.08<br>0.02 - --------<br>-0100<br>-0.22-S<br>-0.23 - . - - -E -E eastrlaht<br>-0.30 . 19g4 ._ 19M 19ig8 2000 2002 - 2004 2006 2008 2010 2012--- Combined<br>Year<br><!-- End of picture text --> <!-- Start of picture text --> LDC Exports<br>.m 0.4-<br>0.0<br>:; 0.2<br>L~0.1<br>El 3 _ 83- Maastricht<br>-0.2.- -A-- Combined<br>1994 1996 1996 2000 2002 2004 2006 200S 2010 2012<br>Yeor<br><!-- End of picture text --> 23
minimizes net bias and allows us to distinguish anchoring effects that arise from simply “repeating” the same decision from explicit choices that reveal true WTP. The second measure is to model each binary choice in the MPL with a latent utility model and apply an appropriately scaled random-effects probit estimation to estimate WTP. This approach accommodates inconsistency (MSB) as well as dominated choices, and thus avoids selection biases that arise when dropping or recoding MSB or NSB choices. In addition, it provides identification of average WTP, dispersion of individual WTP, and errors in subjects’ choices, and allows us to explicitly estimate biases that arise from order effects.<sup>8</sup> This ability to learn about both the distribution and mean of individual errors is a key advantage of the MPL method relative to alternative preference elicitation methods.

We show that both non-systematic errors and systematic biases play a significant role in individual choices, using a WTP elicitation example from South Africa. This finding is in line with the literature; in a survey of MPL studies, 17.1 percent of observations included inconsistencies (Filippin and Crosetto, 2016; Crosetto and Filippin, 2016). Research in populations with low literacy and numeracy may be particularly affected: for example, Dave et al. (2010) show that subjects with low math ability make significantly more inconsistent choices. Moreover, while most researchers do not randomize the order in which MPL questions are asked, others have found significant order effects (e.g. Channa et al., 2021). In our application, the order of choices within MPL is an important source of bias in measured WTP, but the order of options within choice has little effect on reported valuations.

We compare our WTP estimates with those we would have obtained using MPL design and estimation approaches found in the literature, and show important differences. First, WTP estimates obtained from non-randomized MPL cannot differentiate true WTP from existing order bias, and some MPL even create new sources of bias (such as MPL elicitation that stops after the first “switch”). Second, not accounting for choice error alters the estimated outcome variance. Together, these issues lead to markedly different conclusions about the mean and dispersion of WTP in the study population.

To facilitate use of our proposed MPL design and estimation approach, we provide a template WTP elicitation module implemented in the survey software SurveyCTO that carries out order randomization within the MPL. In addition, we provide template analysis files in Stata that carry out the random-effects latent utility estimation. The template

> 8Note that this methodology can be applied to measures of the sample average WTP, or to subgrouplevel measures of WTP, but not WTP at the individual level. Based on the high rate of choice errors in typical MPL data from LMIC contexts, we caution against approaches that calculate an individual-specific WTP from MPL data, for use, for example, as an explanatory variable.

3
# **Figure 3**

# **Seemingly growing correlation between G7 and EM’s growth cycle**

This figure presents average 5-year correlation between growth in EM countries and G7 growth. First, 5-year rolling correlations between individual countries and G7 were obtained, and then the simple average across emerging countries was taken. Source: IMF WEO April 2011.

<!-- Start of picture text -->
1.6<br>1.4<br>1.2<br>1.0<br>0.8<br>0.6<br>0.4<br>0.2<br>0.0<br>-0.2<br>-0.4<br>-0.6<br>-0.8<br>EM  EM+1 SD  EM -1 SD<br><!-- End of picture text -->
indicates the importance of conditional cooperation focusing on all single countries individually summarizing the results of 30 regressions reporting only the coefficient for conditional cooperation. In 27 of the 30 countries, the coefficients are highly statistically significant and have a negative sign. This is strong evidence of conditional cooperation in most Eastern and Western European countries. The more individuals expect others members of society to cooperate, the higher the individual‘s intrinsic motivation to pay taxes.

# **V. EVIDENCE FROM A EUROPEAN COUNTRY: A CASE STUDY ON SPAIN**

To better understand potential developments of tax morale in transition countries, one may focus on a European country that has a history of being part of the European Union and that has experienced substantial changes over time. It is therefore valuable to focus on Spain; the country has undergone fundamental changes in the public sector since its transition to a democratic system after the death of General Francisco Franco in 1975. The previous two sections of this report would suggest that such changes should affect tax morale in Spain. These very significant changes in Spanish society during the last 30 years offer an opportunity to examine citizens‘ attitudes toward paying taxes and the ways in which these attitudes are affected by changes in government policies and institutions. Martinez-Vazquez and Torgler (2009) use survey data from the World Values Survey (WVS) and the European Values Survey (EVS) to observe the evolution of tax morale in Spain at four benchmark years: 1981, 1990, 1995, and 1999/2000. Although these years do not coincide with any particular key event in the recent history of Spain, they cover a period long enough to incorporate many of the important reforms of the Spanish public sector. The study of the evolution of tax morale in modern Spain is particularly interesting because of the constitutional and political changes after Franco died in 1975. The advent of a fully democratic state, deep tax policy and tax administration reforms, a significant push for decentralization, joining the European Community and so on provide excellent benchmarks for institutional changes that are expected in the compliance literature to change tax morale. The issue of tax morale in Spain so far has attracted little attention in the literature (for exceptions, see Prieto 33
operating expenses, donated goods and services, loan write-offs, loan loss reserves and loan loss provisioning. In addition, the MIX reclassifies some long-term liabilities as equity, and reverses any interest income accrued on non-performing loans. We further adjust the data to reflect ideas consistent with economic definitions of profit.

The MIX Market presents a calculation of profitability: i.e., the financial self-sufficiency (FSS) ratio. This notion of financial self-sufficiency is meant to indicate whether an organization can continue operations without external donor funding, but the FSS ratio falls short of accounting for inputs at their opportunity costs. The MIX Market reports that they make a costof-funds adjustment to account for the impact of “soft loans.” The MIX Market calculates “the difference between what the MFI actually paid in interest on its subsidized liabilities and what it would have paid at market terms.” To do that, the MIX Market uses data for shadow interest rates from the IMF’s _International Financial Statistics_ database, using the country’s deposit rate as the benchmark.<sup>6</sup> Yaron (1994) and Shreiner and Yaron (2001) argue that this adjustment is inadequate and that the FSS thus over-states financial self-sufficiency. The deposit rate provides a benchmark for the cost of borrowing by microfinance banks that is too low: The interest rate spread (the difference between the interest rate charged by banks to private sector customers when lending and the interest rate that the private sector offers to its depositors) is generally over 5 percentage points. (2014 World Bank data, for example, show that the interest rate spread for low income countries as a group was 11.2 percentage points and 6.4 percentage points for middle income countries as a whole.)<sup>7</sup> Moreover, many institutions, are not legally able to collect deposits, and even those

> 6 From MIX Market, “Benchmarks Methodology”

> http://www.themix.org/sites/default/files/Methodology%20for%20Benchmarks%20and%20Trendlines.pd f.

> 7 The 2014 World Bank _World Development Indicators_ Table 5.5 (http://wdi.worldbank.org/table/5.5).

6
hered to the very strong patterns of occupational segregation in Ghana. Data from a nationally representative survey show that female apprenticeships were limited almost exclusively to garment making and cosmetology. In contrast, just over a third of male apprenticeships were in construction trades (Ghana Statistical Service, 2014).

Participants were matched with a training provider, often referred to as a master craftsperson (MCP). Participants would work in the MCP’s firm and obtain skills through learning by doing in an unstructured environment similar to a traditional apprenticeship The NAP was to last one but in train- program. training period supposed year, practice ers generally kept their apprentices for 18 months to almost 4 years, depending on the district and trade. The length of training was ultimately decided by each trainer. Because most trainers considered one year to be too short, they pushed back on COTVET’s suggested duration. Since the program was decentralized, COTVET could not enforce the one-year training term. The program was meant to pay trainers 150 GHS to train an apprentice, an amount equivalent to the traditional apprenticeship entrance fee. As a result of the government’s fiscal crisis, however, COTVET was unable to pay this fee. But the research team was able to secure donor funds to 100 GHS to each pay participating trainer. The program was also supposed to provide participants with a tool kit relevant to their trade (for example, a sewing machine for garment makers). But most tool kits were never delivered. While the program provided no subsidy to apprentices, firm owners typically paid apprentices small wages or “chop money” (about 20 GHS a month in our midline surveys of firm owners), which increased with seniority and varied with firm productivity or revenues. Thus the program essentially functioned as a subsidized version of a traditional apprenticeship with training timelines of around three years and limited government monitoring or additional support.

# **3 Research Design**

## **3.1 Participant Recruitment and Randomization Procedure**

We use a randomized control trial to rigorously evaluate the effectiveness of the NAP in Ghana. Starting with the entire list of 78 program districts, we chose a set of 32 districts for the evaluation using population-weighted random sampling, stratifying by north and south. This resulted in a representative set of evaluation districts across all 10 regions of the country. Starting in July 2012, COTVET announced the NAP on radio stations. Program materials and fliers were distributed by local officials, including the Technical and Vocational Education Training (TVET) coordinators of the Ghana Education Ser8
# **<u>Table 4: Lease Fee Collection in Urban Kigali, 2015</u>**

||<br>**Residential**|**Agricultural**|**Others**|
|---|---|---|---|
|Parcels with lease fee obligation|112,143|454|3,496|
|Mean size (m<sup>2</sup>)|726|49,925|4,781|
|Parcels with declared amount|44,985|245|1,130|
|Parcels paying lease fees|37,025|189|942|
|Mean size (m<sup>2</sup>)|677|19,201|3,932|
|Lease fee obligation (US$)|6,742,827|7,548|743,042|
|Lease fees declared at RRA (US$)|2,444,347|14,774|205,640|
|Leasefees paidtoRRA(US$)|1,947,081|10,215|122,046|

_Source_ : LAIS and RRA, 2015. _Note:_ 40,887 agricultural parcels smaller than 2 ha are exempt.

18
find that consumers are willing to pay an additional $430–$4,200 for the distinctively designed Toyota Prius. Our finding is also consistent with the findings from the eco-labeling literature that eco-labeling can help guide consumer purchasing decisions and encourage behavioral change of consumers and producers towards sustainability (Teisl et al., 2002; Bjørner et al., 2004; Mason, 2013).

# **5.3 Charging Infrastructure**

As in many new technology markets, the EV market is characterized by indirect network effects in that the demand for EVs depends on the availability of publicly-accessible charging stations and the supply of charging infrastructure depends on the installed base of EVs. An inherent challenge in the development of this type of market is the coordination problem whereby one group of market participants tends to wait for the other group to act before taking their own action.<sup>36</sup> Policies such as subsidies that strengthen one side of the market could help the development of the other side. From the perspective of the government expenditure, the government could subsidize EV purchases or the investment of charging stations. Whether the policies on either side of the market are equally effective or not in promoting EV sales (i.e., the symmetry/neutrality of the policies) is an empirical question that has important implications on effective policy design.

The preferred specification in Table 4 shows that an increase of 1,000 charging ports is equivalent to ¥12,700 consumer subsidies in promoting EV sales. To examine the issue of neutrality, we examine the average cost of per induced EV sales from charging station investment, and from consumer subsidies separately during our data period. The total consumer subsidies during our data period amount to ¥55 billion, and the total sales induced by these subsidies were 561,495 (or 55% of all EVs sold). These numbers imply an average government cost of ¥98,000 to induce consumers to buy one extra EV through consumer subsidies.

To calculate the total investment cost of charging ports, we assume that average cost of an AC charging port is ¥10,000 while that of a DC charging port is ¥100,000 during our data period.<sup>37</sup> There are 132,207 AC charging ports and 65,902 DC charging ports constructed during our data period. The total cost would have been ¥7.92 billion. Since our data only capture

> 36More broadly, the consumer’s benefit from adopting the primary good (i.e., EVs) depends on the availability of complementary goods (i.e., charging stations) while an investor’s benefit from supplying the complementary goods depends on the installed base of the primary good. The interdependence is referred to as an indirect network effect. Examples of this type of market include computer hardware and software, CD players and CDs, video game consoles and games, and eReaders and eBooks.

> 37 Source: `https://zhuanlan.zhihu.com/p/20800474` . The cost has reduced by about 20-30% by 2020. `https://www.shangyexinzhi.com/article/547872.html` .

27
|Table 2:<br>Non-g|vernance deter|minants of project<br>Spec|returns (ERRs):<br>ification:|1974-1987|
|---|---|---|---|---|
||A<br>Exogenous|B<br>Exogenous<br>with region<br>durnmies|C<br>Exogenous<br>and policy|D<br>Exogenous,<br>policy, and<br>region<br>dummies|
|Exogenous|||||
|ln(capital/labor)|-1.28<br>(.029)a|-1.73<br>_(.050)_|-1.34<br>(.024)|-1.39<br>(.122)|
|Dummy for project<br>complexity|-5.80<br>(.0001)|. -5.81<br>(.0001)|-4.99<br>(.0003)|_-5.06_<br>(.0002)|
|Terms of trade|0.086|0.096|0.064|0.077|
|shock|(.278)|(.218)|(.417)|(.316)|
|Policy|||||
|Black market<br>premia|||-0.046<br>(.0001)|-0.040<br>(.0001)|
|Fiscal deficit|||0.178<br>(.194)|0.229<br>(.116)|
|GDP growth|||0.233|0.056|
||||(.272)|(.799)|
|Regional Dummiesb|||||
|East Asia<br>___________|______|-0.61<br>(.791)||-2.69<br>(.246)|
|Latin America||-3.85<br>(.140)||-4.90<br>(.065)|
|EMENA||-6.13<br>(.036)||_-5.51_<br>(.071)|
|Sub-Saharan Africa||-8.54<br>(.0001)||-9.12<br>(.0001)|

Notes: a) p-levels in parenthesis, b) based on World Bank regional classifications. Sample size = 761. Source: Authors' calculations _-9-_
3 useful complement to the ‘harder’ evidence from consumption‐smoothing regressions, particularly in situations like ours where panel data are not available, but may also be useful when they are given Chetty and Looney’s (2006) observation that a zero coefficient on shocks could simply reflect low‐ income households being highly risk‐averse and using whatever methods they can to avoid a drop in consumption, even if they have consequences for future welfare.

Finally, we seek to make a third contribution to the literature on shocks. Analyses of non‐health shocks routinely inquire about the asset losses associated with shocks, e.g. the lost or damaged equipment and infrastructure associated with weather‐related shocks. Analyses of health shocks, by contrast, do not, focusing instead on the consumption and sometimes labor‐market consequences of health shocks (Gertler and Gruber 2002; Wagstaff 2007). Yet a health shock is likely to result in some depletion of an individual’s health ‘capital’. The pure neoclassical model of health capital would rationalize this in terms of people choosing to reduce their health stocks following an unexpectedly high user cost of health capital; see Grossman (1972) and the articles reviewed in Grossman (2000). An alternative explanation is that, contrary to the traditional neoclassical model, people are unable to instantaneously adjust their health stocks to the optimal level (Wagstaff 1993). In this paper, we are agnostic on the reason for any decline in health status following a health shock; we aim instead simply to shed light on its magnitude and on the factors affecting its size. We hypothesize that some groups are likely to be especially disadvantaged, e.g. those living far from health facilities and other amenities that are conducive to people recovering their health following a health shock. This aspect of the paper builds on recent research in the US on the health impacts of health shocks (Smith 1999; Goldman and Smith 2002).

Our paper, like that of Heltberg and Lund (2009), uses a cross‐section retrospective survey instrument designed specifically to get the incidence and consequences of the full gamut of shocks
target scenarios: girls have the same inactivity rate as adult women (Target 1) and girls have the same inactivity rate as boys (Target 2).The following equation is used to calculate the cost as foregone output to the economy: where _IRy_ is young girls’ inactivity rate; _IR_<sup>_*_</sup> is the target inactivity rate; _WAPy_ is the working age population of young females; and _wy_ is real annual female youth wage. The opportunity cost scenario is where girls’ inactivity rates are the same as that of a counterfactual group, where IR* is the counterfactual rate. Thus, (IRy – IR*)xWAPy gives us the number of additional girls who would be working if girls’ inactivity rates were the same as IR*. Since most girls are not searching for jobs, we assume that the lost productivity per year is equivalent to the annual wage of all those girls who ―could‖ be working. _Cost_ is then divided by total GDP in order to obtain an estimate of the cost of youth inactivity for males and females as a share of GDP.

Data for the number of female youth labor force participants is drawn from the ILO’s KILM database, which projects employment data based on country-specific models. GDP is drawn in current US dollars from the WDI database (World Bank). As there are no cross-country comparable wage data for youth, the methodology relies on using the GDP per labor force participant (PPP adjusted). The computation assumes that the share of wage earnings is 60% of GDP.<sup>9</sup> Since females earn less than males and this ratio differs widely across countries, we introduce three adjustment factors to simulate the gender wage gap: 25%, 50%, and 75% of average wages (based on Saba Arbache, Kolev, and Filipiak, 2010). Further, since youth earn less than adults, we introduce three adjustment factors to the average wage to account for the age wage gap: 50%, 70%, and 90% of average earnings.<sup>10</sup> The ILO’s KILM ―inactivity‖ rates that we use in the above estimates include students, which would lead to an over-estimate of the joblessness issue. A more appropriate estimate would be to generate the foregone output only of those girls who are

> 9 These assumptions are based on cross-country estimates elaborated in Cunningham, and García-Verdú, (forthcoming).

> 10 The 70% adjustment factor is based on a comparison of mean youth and adult wages in several Latin American countries (Cunningham et al., 2008).

9