rafmacalaba/datause-encoder-data huggingface dataset:
13 and 28 (i.e. Kirama‐Katuwana WSP, Valachchenai Water Supply Kalpitiya WSP) scored higher on SEI but not the FEI. These projects are considered medium priority projects.

By design, some of the medium priority projects could still be implemented, since the budget is not completely exhausted after executing all high priority projects. In fact, the five high priority projects constitute only 51% of the available budget. Implementation of the medium‐ priority projects should be subject to expert discussion for selection, preferably under the framework of decision rules used to guide deliberation and selection from among the medium‐priority set (e.g., the relative importance of each index, selection by some key policy goal, etc.). These decision guidelines should be discussed and agreed to prior to comparison and selection.

# 4.1.5. Sensitivity Analysis

To evaluate how sensitive the results are to the different possible weighting schemes (e.g., PCA, subjective rule, simple average), additional calculations were carried out using alternative weights based on the subjective criteria (only for SEI) and equal weighting of criteria (for both, SEI and FEI). Individual SEI and FEI rankings under these different scenarios can be found in Annexures VII, VIII and IX.

In this section, two additional prioritization matrices are presented using the results of the SEI from the subjective and simple average weighting approaches. These are presented below in Figures 8 and 9.

The sensitivity analysis reveals that the results of the IPF analysis remain consistent across all three scenarios. Specifically, Projects 12, 21 and 25 (i.e. Palugaswewa Water Supply, Yan Oya Water Supply project and Construction of Treatment Plant at Kethhena) are high priority regardless of what weightage system is utilized. Similarly, Projects 14 and 15 (i.e. Dankotuwa Water Supply project and Greater Galle Stage III) tend to remain low on the SEI, as these projects are in areas with high existing safe water coverage, consistent levels of water supply with water supply hours between 18‐24, and low poverty levels and, compared to other projects, will generate fewer jobs.

There are, however, some noticeable differences between the sensitivity analysis scenarios and the IPF scenario based on PCA. Firstly, Project 27 that ranks high in the IPF analysis, becomes a medium priority project in the subjective weight and simple average scenario. This is because compared to the PCA weights, in both other approaches, the jobs created and beneficiaries per million dollars invested indicators have comparatively low weights, while these factors are key to the projects' high priority status in the PCA scenario. Similarly, Project 11 is high priority in both the PCA and subjective weight scenario, but a medium priority in the simple average. This is because the slight change in individual rankings resulted in Project 23 (Greater Mannar WSP) – a higher budget project (USD 109 million) – ranking higher, leaving Project 11 just outside the available budget.

28
Policy Research Working Paper 10901

# **Abstract**

This paper presents an evaluation of a tax enforcement program conducted in Indonesia where officials from the tax authority visited properties to engage directly with owners about their property tax obligations. Through these visits, auditors explained outstanding debts and payment processes, aiming to improve tax compliance and revenue collection. The paper uses an administrative data set and a new set of machine learning–based techniques to assess the program’s effectiveness. The program was responsible for increasing tax compliance on the extensive margin by 4.3 percent and on the intensive margin by 5.1 percent in the first year it was implemented. These effects are particularly strong as they persist in the following period. The findings show that the visited properties had better compliance history, lower value, smaller area, and were more likely to have some construction on them. A key finding from the analysis is that higher-value properties are less sensitive to the visits. In other words, if a data-driven tax-enforcement strategy is to be applied, then it may focus resources on enforcing taxation at the poorest part of the population in this case. This opens up the discussion of the distributional consequences of an algorithm-based enforcement strategy, which is increasingly important as machine learning techniques are used by tax authorities.

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

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
Table 9: Simulated peer effects on test scores in Spanish

|F|ull integrega|tion<br>Actual level<br>Fu|ll segregation|
|---|---|---|---|
|||_National distribution_<br>_(32, 39, 15, 14)_||
|mean L|1.25|0.73|-1.15|
|mean LM|4.83|5.00|6.45|
|mean UM|6.27|7.44|12.85|
|mean U|4.61|6.67|10.83|
|**overall mean**|**3.86**|**4.21**|**5.55**|
|**gap U - L**|**3.4**|**5.9**|**12.0**|
||_City _|_of Buenos Aires distribu_|_tion_|
|||_(12, 36, 25, 27)_||
|**overall mean**|**6.70**|**6.96**|**8.26**|
|**gap U - L**|**5.7**|**7.2**|**12.0**|
|||_Santiago del Estero_||
|||_(48, 34, 10, 8)_||
|**overall mean**|**2.23**|**2.53**|**3.79**|
|**gap U - L**|**1.9**|**4.5**|**12.0**|

Note: Figures in the table represent the marginal effect of peers’ background on pupil test scores at various levels of socioeconomic segregation. The effects are calculated using the parameters from table in the appendix. As an example the upper-left cell shows that with full integration a pupil from the lowest socioeconomic group will receive a positive test score gain of 1.25 through peer effects, whereas with full segregation she or he will suffer a loss of 1.15 points.

27
15. Allen and others (2012) show that such policies can expand account ownership especially among the groups most likely to be unbanked, such as poor people and those living in rural areas.

16. CGAP 2014.

17. The Global Findex survey does not cover international remittances. While these remittances are economically important for some countries, the share of adults in developing economies who reported sending or receiving domestic remittances is on average three to four times the share who reported sending or receiving international remittances (Gallup World Poll, 2014).

9 THE GLOBAL FINDEX DATABASE
tics, customs, packaging, pricing); 3) marketing (trade fairs, exporter and importer missions, follow-up services offered by representatives abroad); and 4) market research and publications (general, sector and firm level information, such as market surveys, on-line information on export markets and electronic bulletin, publications encouraging firms to export, importer and exporter contact databases). Figure 6 provides a view of the share spent on each of these activities by region. The largest share is generally spent on marketing and market research and publications. Another item which shows a large median –but also a much larger variance– is other activities not related to export promotion, except in the OECD, where the bulk (more than 75 percent of them) spent less than 10 percent on activities not related to export promotion. At the opposite end, in SSA other activities not related to export promotion represent between 10 and 25 percent of the budget of most agencies (at the median). The importance of export support services is also much larger in SSA than in other regions. In LAC, EEA and MENA the distribution of the budget allocated to each activity is very similar.

In terms of client orientation, the data cover the percentage of expenditures spent on large versus small and medium size firms, and established versus new and occasional exporters. Results are quite clear across regions. A very small share of total expenditure is spent on large firms, whereas a relatively large share is spent on established exporters. Thus, in all regions the focus of the agencies is on small and medium size firms that are established exporters. As shown in Figure 7, LAC and SSA agencies are at opposite ends when it comes to the share of expenditure on established firms (with LAC having the highest share and SSA the lowest).

In terms of representation abroad, 41 percent of the agencies have offices abroad (22 percent of the agencies in SSA, 33 percent of the agencies in MENA, 35 percent of the agencies in LAC, 47 percent in EEA and 67 percent in the OECD). In most regions agencies spend a small amount of their budget on offices abroad, with the exception of the OECD where on average 39 percent of the EPA budget is dedicated to offices abroad. In other regions, the average is 7 percent in LAC and EEA, 4 percent in SSA, and 1 percent in MENA. In terms of the geographic coverage of the agencies abroad, Figure 8 highlights the importance in the 11
10 If both (regional) opening of markets and discoveries are driven by a (third) outside factor, omitted variables might also pose a threat to our identification. We try to address this in several ways. First, directly controlling for a lagged, distance-weighted measure of discoveries in other countries (as just discussed) addresses not only the regional spill-over problem but also the problem of omitted variables related to an unknown deep driver (such as US foreign policy perhaps or the fall of the Soviet Union) to some degree. Secondly, we show that the results are a general phenomenon, which holds for different time periods and regions – no one region or time period (which might be affected by a specific omitted variable) is responsible for the results. More generally, the rich fixed-effects structure we use, in particular the year-byresource effects, rule out a number of alternative stories such as one in which by coincidence the arrival of a new technology for discoveries of a particular resource happens concurrently with a wave of opening up of markets. The robustness section discusses this in more detail.

# **III.   DATA AND STYLIZED FACTS**

Here we discuss the various datasets that we use. We focus on the novel data on major hydrocarbon and mineral deposits as well as the data on market orientation (see Appendix B for a more comprehensive list of data and sources as well as additional summary tables).

## **Discoveries**

Discoveries are our main dependent variable in our empirical model (I). The oil and gas discovery dataset is from Horn (2014). Horn reports discoveries of giant oil (including condensate) and gas fields which we refer to jointly as hydrocarbon or simply oil discoveries. A giant discovery is defined as a discovery of an oil and/or gas field that contains at least 500 million barrels of ultimately recoverable oil equivalent. Ultimately recoverable reserves refer to the amount that is technically recoverable given existing technology.

The data on mineral deposits discoveries is from MinEx. The list of minerals included in the dataset is comprehensive and includes precious metals and rare earths. As in the case of hydrocarbons, we only capture discoveries above a certain threshold, corresponding roughly to a mineral deposit which has the capacity to generate an annual revenue stream of at least
compared to gasoline, based on the wholesale pricing mechanism between fuel ethanol producers and gasoline wholesaler<sup>3</sup> .

**Table 4: Typical cost and related assumptions by energy crop**

|Items|Unit|Cassava|Sweet sorghum|Sweet potato|
|---|---|---|---|---|
|**Feedstock cost**|**Yuan/tonne (ethanol)**|**2,857-4,643**|**3,840-4,364**|**2,727-3,636**|
|Assumed feedstock pricing<br>mechanism||Market-based<br>pricing<sup>a</sup>|Cost-based<br>pricing<sup>b</sup>|Market-based<br>pricing<sup>a</sup>|
|Assumed feedstock price|Yuan/tonne (feedstock)|400-650<sup>c</sup>|240<sup>d</sup>|300-400<sup>e</sup>|
|Starch (sugar) content|%|25|10-16|20|
|Conversion efficiency|tonnes<br>ethanol/tonne<br>feedstock|0.14|0.055-0.0625|0.11|
|**Non-feedstock cost**<sup>**f**</sup>|**Yuan/tonne (ethanol)**|**2,160**<sup>**g**</sup>|**1,609**<sup>**h**</sup>|**2,160**<sup>**i**</sup>|
|Conversion technology||Traditional<br>fermentation|Advanced solid<br>state<br>fermentation<br>(ASSF)|Traditional<br>fermentation|
|Investment cost<sup>j</sup>|Yuan/tonne (ethanol)|4325<sup>k</sup>|n.a.|4325<sup>i</sup>|
|Ethanol total production cost|Yuan/tonne|**5,017-6,802**|**5,444-5,968**|**4,887-5,796**|
|Share of feedstock cost|%|56.95-68.25|70.54-73.12|55.8-62.74|
|Designated<br>fuel<br>ethanol<br>wholesale price from January<br>1, 2008 to April 7, 2011|Yuan/tonne||||
|-<br>Min<br>-<br>Average<br>-<br>Max||4,956 (January<br>6, 238<br>7,816 (April 7,|15, 2009)<br>2011)||

Note: a. as mentioned in Table 2, there are other utilizations for cassava and sweet potato in China, therefore their assumed prices are equal to current average market prices, which are not only for energy use, but also for the use of food, feed, starch, and so on; b.as mentioned in Table 2, there is almost no sweet sorghum market in China, therefore, the pricing mechanism of sweet sorghum is cost-based pricing involving farmer’s profit; c. Feedstock price is assumed to be the average value from 2007 to 2008, data from Sun et al (2008); d. data from Wei, et al. (2010); e. data from Cai et al (2011); f. non-feedstock cost includes capital costs, fuel costs, operation & maintenance costs, and auxiliary material costs; g. Data from EB (2008); h. data from Han et al. (2010); i. Costs assumed to be the same as those for cassava; j. Investment cost is for the conversion process only (excluding the investment cost to establish a planting base); k. Survey data based on the cassava project of Guangxi Bioenergy Company with total investment as 0.865 billion Yuan.

# **3.1.3 Challenges**

# **Economic supply of feedstocks**

Ensuring low and stable feedstock costs is one of the major challenges facing the development of starch- and sugar-based biofuels in China. Since crops used for biofuels

> 3 st Wholesale price of fuel ethanol is the ex-factory price of 90# Gasoline multiplied with a factor as 0.911 before Mar.1 , 2011, and is the ex-factory price of 93# Gasoline multiplied with the factor of 0.911 after Mar.1<sup>st</sup> , 2011.

8
**Figure 12: Chile Foreign Currency Bond Issuance by Year**

<!-- Start of picture text --> 1600<br>14<br>AA‐ AA‐<br>1400<br>A+<br>12<br>1200<br>10<br>1000<br>8<br>800<br>6<br>600<br>400 4<br>200 2<br>0 0<br>2010 2011 2012 2013 2014 2015 2016<br>EUR USD LT FX Credit Rating<br>Millions in USD Eq.<br><!-- End of picture text --> Source: Bloomberg, Standard & Poor’s Based on the econometric analysis, the authorities determined a basket of currencies closely correlated with the copper price. The rationale for finding such a basket is the high correlation between the Chilean peso and the copper price and the importance of copper exports as a source of budget revenues, which was initially estimated at 7%. The analysis showed good correlation with NZD, AUD, CHF, and EUR with results somewhat dependent upon the period of analysis.<sup>28</sup> Out of these four currencies, only the EUR offers a market with the depth needed to produce externalities to the private sector. When deciding on the currency (ies) to issue, the DMO also looked at the ability of corporates to issue in markets other than the USD. Given rising rates in USD and the relative excess of liquidity in the EUR market, corporates were also interested in exploring funding in EUR. This contrasted with markets such as NZD, AUD or CHF, where there would be little interest and no advantages for the private sector. While EUR did not offer the best correlation with copper, it was always ranked the 5<sup>th</sup> or 6<sup>th</sup> .

The lower coupons in EUR were also a factor of consideration, especially in the current context of rising deficits. While it is true that this is a short-term consideration that leaves out the impact of the exchange rate, a low-interest bill is particularly welcomed in times of tight fiscal constraints.

> 28 The rationale for the currency choice of the liabilities is the mirror image of what was done for the asset allocation, which sought currencies uncorrelated, or, negatively correlated with the copper price and therefore with the CHP. The logic is that in an economic downturn when deficits increase and the peso depreciates, the buffer of the wealth fund rises in value. This is why the JPY is an appropriate choice for the assets and a poor one for the liabilities.

20
**Table A8: Reasons for not applying fertilizer.**

|**Burkina Faso**|**Did**<br>|**not need**|**Too expensive**|**Not available**|**Unable to travel**|
|---|---|---|---|---|---|
|2021/01|Mean|27.8|76.1|0.0|0.0|
||Std. Err.|2.1|2.0|0.0|0.0|
||_N_|472|472|472|472|
|2023/01|Mean|17.0|76.5|6.4||
||Std. Err.|1.8|2.0|1.2||
||_N_|441|441|441||
|2024/01|Mean|13.9|76.3|7.3||
||Std. Err.|1.6|2.0|1.2||
|**Ethiopia**|_N_|448|448|448||
|2023/01|Mean|67.8|19.0|13.2||
||Std. Err.|3.4|2.9|2.5||
||_N_|187|187|187||
|2024/02|Mean|29.6|58.0|6.9|2.3|
||Std. Err.|2.5|2.7|1.4|0.8|
|**Malawi **|_N_|327|327|327|327|
|2021/06|Mean|37.9|63.9|0.0|0.2|
||Std. Err.|3.6|3.6|0.0|0.3|
||_N_|184|184|184|184|
|2023/04|Mean|31.6|59.7|6.4||
||Std. Err.|2.5|2.7|1.3||
||_N_|334|334|334||
|2023/06|Mean|17.4|68.6|11.8||
||Std. Err.|2.2|2.7|1.8||
|**Nigeria**|_N_|307|307|307||
|2023/06|Mean|46.8|47.9|3.4||
||Std. Err.|1.9|1.9|0.7||
|**Tanzania**|_N_|686|686|686||
|2023/11|Mean|45.1|45.5|7.3|0.0|
||Std. Err.|1.6|1.6|0.8|0.0|
|**Uganda**|_N_|951|951|951|951|
|2023/01|Mean|49.3|42.6|6.5||
||Std. Err.|1.5|1.5|0.7||
||_N_|1,144|1,144|1,144||
|2023/10|Mean|51.2|46.1|1.6||
||Std. Err.|1.5|1.5|0.4||
|Note: Reasons|_N_<br>for not applying fer|1,157<br>tilizer among HHs|1,157<br>not applying fertilizer. All m|1,157<br>eans in percent.||

Note: Reasons for not applying fertilizer among HHs not applying fertilizer. All means in percent.

29
their welfare. Leaving aside the distinction between low and high productivity for the moment, Table 1 shows the numbers of people working in agriculture and outside of agriculture in both rural and urban areas in Indonesia.

The aggregate evidence from SUSENAS suggests a steady movement out of agriculture over the last 20 years. The share of workers in agriculture dropped from 54% in 1982 to 45% in 2002. Moreover, the official data suggest an even more marked fall in the share of workers in rural non-agriculture; the major growth has been in the share of workers working outside agriculture in urban areas, which has more than doubled in 20 years.

**Table 1. Employment in Agriculture and Non Agriculture, Indonesia**

||198 <br>|2<br>|199<br>|3<br>|200<br>|2<br>|
|---|---|---|---|---|---|---|
||N (000)|%|N (000)|%|N (000)|%|
|Agriculture|30,487|54.24|39,137|49.88|39,035|44.92|
|Non Agriculture|25,724|45.76|39,329|50.12|47,874|55.08|
|Rural|15,939|28.36|18,992|24.20|16,785|19.31|
|Urban|9,785|17.41|20,337|25.92|31,088|35.77|
|All sector|56,211|100|78,466|100|86,909|100|

Source: Susenas, 1982, 1993, 2002 Note: exclude Aceh, Maluku, North Maluku and Papua. Employment is defined as self employed without help, self employed with help of householders/temporary workers, self employed with help of regular workers, employees and family workers with age 10 years and over (definition until 1997). Definition of employment since 1998 is the same as above, but persons 15 years and above.

However, it is important to recognize that many areas which were rural in 1982 and 1993, are now classified as urban. This is because the definition of “urban” used by the Central Statistic Bureau relates to the number of amenities available in an administrative village, the population density and the share of income coming from agriculture. All of these change over time, with the result that at least 10 percent of rural villages in 1993 had been reclassified as urban by 2002. This is illustrated in Figure 2 which shows the change in the share of employment in rural and urban areas and in agriculture and non-agricultural activities between 1993 and 2002. The rural/urban classification in 1993 is used to determine rural/urban status thus avoiding confusion 11
same connection may prove to be a negative when that President is replaced by another. This isn’t to conflate the order with a particular political administration, this varies widely as some closed power structures may persist across political administrations (when “elites” have a common interest) while others may not. This distinction has been made in various ways, identifying regimes that are “patrimonial” or the focus on the lack of constraints on the executive, which combined with inter-temporal uncertainty about regime duration, can create substantial firm level uncertainty, even for those firms inside “closed” deals.

# **III) How to Interpret Firms’ Aversion to “Policy Uncertainty”**

_Rules? In a knife fight? No rules._ Harvey Logan to Butch Cassidy _Butch Cassidy and the Sundance Kid_ The Enterprise Surveys implemented by the World Bank contain both subjective assessments by firms about the obstacles to their growth, but also quantitative information about how long it takes to get various things done and how much they cost – including actions to influence policy implementation, like bribes or gifts or meeting with government officials. Using these data we show six overlapping pieces of evidence that firms’ complaints of “policy uncertainty” are mainly about policy _implementation_ uncertainty.

First, to show _uncertainty_ we start by showing firm level _variability._ The Enterprise Surveys have been used to document differences in the investment climate across countries or regions within countries (Dollar, Hallward-Driemeier, and Mengistae 2005) by looking at the _averages_ or central tendency across firms and, at times, the 21