rafmacalaba/datause-encoder-data huggingface dataset:
Note: Data represent unweighted averages across countries in our sample. Capital-intensive green investments are investment in more climate-friendly energy generation on site, machinery and equipment upgrades, vehicle upgrades and investment in waste minimization, recycling and waste management. ECA – Europe and Central Asia; MENA – Middle East and North Africa.

Source: Enterprise Surveys and authors’ calculations.

Based on the percentage of firms making a certain number of different green investments in Figure 7, we consider two categorical groupings: one with four categories and one with six categories. Four category version of the number of different green investments variable has the following categories: no green investments; one green investment; two to four different green investments; and five or more different green investments. Six-category version of the same variable has the following categories: no green investment; one green investment; two different green investments; three different green investments; four different green investments; and five or more different green investments.

# _Independent variables: Green management practices_

The Green Economy Module asked firms about their green management practices in four areas. The first area concerns whether firms have strategic objectives related to the environment and climate change. The second area looks at whether firms employ a manager with an explicit mandate to deal with green issues. Conditional on the existence of such an environmental manager, additional information was collected on whom they report to, and whether their performance is evaluated against how well the establishment performs on energy consumption, CO2 emissions or other pollution or environmental targets. The third area covered by the Green Economy Module asks whether firms have clear and attainable environmental targets. Lastly, the fourth area looks at whether firms actively and frequently monitor their energy and water usage, CO2 emissions and other pollutants in order to reduce their environmental footprint.

We normalize the scores for each question such that they have a mean of 0 and a standard deviation of 1 in the sample. We then aggregate them to average z-scores for each of the four areas of green management. Lastly, we create an overall green management z-score as a normalized unweighted average of the four categories. A z-score above zero indicates that a firm’s green management practices are better than the sample average.

# _Independent variables: Credit constraints_

To identify credit-constrained enterprises, we combine answers to various survey questions and first distinguish between firms with and without a demand for credit. Among the former, we then identify those that were credit constrained as those that were either discouraged from applying for a loan or were 12
Those assigned the Proempleo voucher also received an instruction lasting 2-3 hours which explained the program and how to use the voucher. The voucher had the participant's name on it, and was non-transferable.

The training had two components. The first was a three-day "labor market orientation" workshop which included presentations on labor demand in the area, how to look for work, and how to become self-employed. This component was mandatory. Once this workshop was completed, training coupons were issued for the second component, which provided training in a specific skill and required 200-300 hours of attendance. In this second part, the participants were given working materials and had to fulfill labour practices. They also received economic support benefits, paid at a 10% lower rate than for the Trabajar program.1<sup>2</sup> According to their personal interest and the available quota, participants proceeded to select from a list of 12 subjects, chosen in the light of local labor demand and the profiles of participants. Two of the courses were on the management of small-scale enterprises, two on industrial welding, two on home-building, one on professional cooking, one on raising pigs, one on greenhouse cultivation and one on skills needed for an electrician.

The data collection began in December 1998 with a baseline survey. The invitation to join the treatment groups for those selected was made in January and February of 1999, either by a house visit or at the place of work. The sample was re-interviewed for three waves at intervals of 5-6 months (June 1999, December 1999, May 2000).

The last six months or so of the study period saw a sizable retrenchment of national ALMP's, including the Trabajar program, to help keep within macroeconomic targets. Aggregate spending on the Trabajar program in the first five months of 2000 was only 29% of its level in 12 The courses were part of a larger training program of the Ministry of Labor, called "Project Joven." This was financed by the Inter-American Development Bank.

10
Low-income countries to increase their fiscal deficit during the fiscal spending growth period up to 4% of GDP (considering that for low income countries, external borrowing is likely to be on highly concessional terms). Results are reported in the next section.

Fourth, with better indicators of performance of government expenditures than GDP per capita growth, this “event-type” analysis could be extended directly to the indicators of fiscal expenditure that concern the debate on fiscal space, e.g. health and/or education expenditures and expenditures on transport & communication (capturing then, for instance, event in budget reallocation between government functions for a given amount of total outlays).<sup>12</sup>

# **4. PATTERNS OF FISCAL AND GROWTH EVENTS**

Keeping in mind the shortcomings of this methodology, we take an exhaustive approach by constructing fiscal events for as many countries as possible. Since we are interested in the various components of fiscal expenditures, the best database is the IMF Government Financial Statistics (GFS). GFS statistics are available for a large number of countries since 1972 and up to 2005.<sup>13</sup> Following most previous studies on fiscal expenditures we use data on fiscal expenditures by function (instead of expenditures by economic classification--i.e. by current vs. capital expenditures). As described in annex A.1, after reconciling the fiscal data, our sample includes 140 countries, of which 118 are developing (i.e. non High Income OECD countries).

Data are described in table 1, which gives decadal averages for the fiscal variables used in the study. A comparison of actual vs. potential observations indicates a large number of missing observations, even for OECD countries, especially over the period 1991-2000 where the coverage is the weakest. For developing countries, coverage gets better through time with data for half of the potential observations during the nineties. One of the reasons for the small sample (relative to the potential sample) is that we require that data be available for all the components of fiscal expenditures by function, else the year observation is entered as missing and hence excluded from the sample. This lack of data raises the question of biases for the subsequent analysis. In view of the relatively evenly spread out pattern of missing observations across the sample, and in the absence of any other information, we proceed as if there were no selection effects operating via missing data.

> 12 However, as already mention, given the low quality of fiscal data, volatility in narrower series may be more difficult to interpret.

> 13 There was a major change in the GFS in 1989 causing concern about the comparability of data before and after that date (see details on the data reconciliation in annex A.1.2). Using box-plots, we explored the possibility of lack of comparability for the series of interest. Fortunately, as discussed in the annex A.1.2 (see figure A1), this is not the case.

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- Cross-sectional, also known as survey or prevalence studies:

- Serological survey

- Knowledge, attitudes and practices survey

- Demographic and health surveys

- Cross-sectional studies of other topics

- Case series studies

- Others

# **4. Priority population or universe**

This item takes into account the population engaged by each study:

- People living with HIV

- Men who have sex with men

- Female/male sex workers

- Injection drug users

- Prisoners

- Migrant population

- Pregnant women

- General population

- Youth

- Other populations as defined by the country

# **Evaluation of methodological content**

ASAP defined eight items or characteristics linked to “best practices”. Analysis of these methodological characteristics yields important information, such as the existence of bias, statistical significance, or the feasibility of generalizations based on the findings. For each criterion, a question with yes/no answer has been defined. If the answer is positive, one point is awarded; if it is negative, no points are awarded. Three of these items are doubleweighted because of their critical importance. If the answer to a double-weighted criterion is positive, it is multiplied by a factor of two. These double-weighted criteria are: (i) sample size; (ii) sampling technique; and (iii) techniques to reduce data collection errors. Finally, the results are added up, to obtain a total score that is regarded as the quality index of the methodological content. The score scale has a range of 0 to 11. Two cut-off points have been defined in this scale, establishing that a score of 8 or more represents high methodological quality; a score of 5 to 7 represents intermediate quality; and a score of 4 or less represents limited quality.

# **1. Objective of the study**

Is/are the objective(s) of the study clearly defined? Do the development and results of the study respond to the objective(s)? The answer to both questions should be yes.

**Yes= 1**

44
# **1 Introduction**

Tax evasion by firms in middle-income countries is often thought to be one of the key reasons why revenue levels as a share of GDP remain well below those in high-income countries (Slemrod, 2019; World Bank, 2023). However, it can be challenging to measure how pervasive tax evasion is by firms by drawing on tax administrative and survey data (Slemrod and Weber, 2012; Timofte (Coca) et al., 2019). Estimates based on administrative data typically rely on tax declarations being checked for accuracy with the limited third-party information that is available in these settings, and it can be unclear whether lower-than-expected tax declarations are due to changes in economic activity, misreporting, or genuine evasion (Pomeranz, 2015; Carrillo et al., 2017). On the other hand, relying on surveys of taxpayers also has its challenges as respondents may be hesitant to directly admit to evading tax, which leaves many studies using questions about ”tax morale” as a proxy for compliance (e.g., see discussion in Luttmer and Singhal, 2014; Ali et al., 2014; and Hoy, 2022). The absence of reliable information about tax evasion levels can undermine revenue authorities’ ability to improve firms’ compliance and identify the types of firms they should focus their enforcement efforts on (Slemrod, 2008; Mascagni et al., 2023).

To overcome these challenges, we embedded an experiment that indirectly captures levels of tax evasion as part of a nationally representative survey of 2,955 firms in Indonesia. Specifically, we use a list experiment (also known as the “item count technique”) whereby respondents only state the number of statements on a list that apply to them but do not reveal directly which of the statements are true (see detailed explanation of this approach in Ahlquist, 2018, Blair and Imai, 2012, Imai, 2011 and Rosenfeld et al., 2016). We draw on best practices in measuring sensitive topics, such as drug use and risky sexual behavior, by fielding a ”double” list experiment that overcomes some of the concerns with traditional ”single” list experiments<sup>1</sup> (e.g., see Chuang et al., 2021). Namely, all respondents are randomly

> 1For example, one of the most common criticisms of list experiments is that respondents’ willingness to answer honestly may be driven by the non-sensitive statements on the list. A double list experiment allows us to check whether, across the two lists with different non-sensitive statements, there are similar levels of

1
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# Budget Deficit(-)/GDP Low Indebted Countries

<!-- Start of picture text --> &jgg g.NGOp<br>*°2<br>.-4<br>is 71 I 9.0 0I Da<br>Your<br>-Pteleeton +A.tuale<br><!-- End of picture text -->

# Current Account/GDP Low Indebted Countries

<!-- Start of picture text --> Cunint A..mODP<br>0<br>-4<br>*10. *t *t @ * 9§§0 1 (.<br>Yew<br>IPte,tb*n +fAtael *M tWTO? rdjuuia<br><!-- End of picture text --> <!-- Start of picture text --> TOT a Twrm Gd Trade<br><!-- End of picture text -->

# Gross Domestic Investment/GDP Low Indebted Countries

# Domestic Savings/GDP Low Indebted Countries

<!-- Start of picture text --> X2 , _ = -~ _ _ _ ,<br>N @7 @6 60 . 00 01 02<br>Year<br>-Pvjteean +Ametui<br><!-- End of picture text --> <!-- Start of picture text --> __ _ - - '<br>A<br>@7 60 @0 se 00 of 92<br>Yewr<br>-P1ojetb.e +A.tsMIe<br><!-- End of picture text --> o~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~loo

# Real Export od GNFS Growth Rate Low indebted Countries

# Real GDP Growth Rates Low Indebted Countries

<!-- Start of picture text --> I0<br>C<br>I<br>Year<br>~-ftJ.Uw 1_ htsiI<br><!-- End of picture text --> <!-- Start of picture text --> 7-.<br>- : C" -o A<br>I<br>You<br>_PrjlUese +FWgj<br><!-- End of picture text -->
convenience, we label dams in the former group as “small” and in the latter group as “large” dams. Note that our categorization of dams rests implicitly on the DWA’s assessment of potential economic losses in case of failure and thus is based on an evaluation of the local South African context rather than on another relatively arbitrary classification adopted elsewhere. We restrict our analysis to dams whose main purpose is irrigation, constituting 55 percent and 79 percent of all large and small dams in the data, respectively. Thus, we have a total of 114 large and 3,257 small dams (as of the year 2000). Summary statistics provided in table 1 show that large dams are, on average, nearly twice as old as smaller ones. Unsurprisingly, their mean reservoir capacity is substantially larger (over 360 times), constituting 93 percent of the total storage capacity in South Africa. Similarly, the surface area is substantially larger (1,310 Ha for large dams compared to 103 Ha for small dams). Finally, as of the year 2000, most large dams were located in the former nonhomeland territories, whereas most small ones were in the former homelands. We depict the location of large and small irrigation dams in figure 2.

Table 1. Dam Characteristics Means

||**Nb**|**Age**<br>(inyear)|**Capacity**<br>(in ‘000s of m<sup>3</sup>)|**Surface**<br>(in Ha)|**Height**<br>(in meters)|**% Former**<br>**Homeland**|
|---|---|---|---|---|---|---|
|**LARGE**|114|42<br>(25)|128,124<br>(579,173)|1,310<br>(4,167)|47<br>(20)|38.6|
|**SMALL**|3257|23|353|103|10|625|
||,|(19)|(727)|(3,035)|(4)|.|

_Source:_ DWA (2011) _Note:_ Statistics for dams existent in the year 2000. Standard deviation is presented in parentheses.

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<!-- Start of picture text --> Figure 6. Extent of State Capture in Colombia and Peru'<br>Impact of Bribes to:<br>Parliamentarians to shape laws<br>Public officials to shape ministeriaUpresidential<br>decrees<br>Regulatory agencies to influence regulatory regime<br>Judiciary to influence judicial outcomes<br>Central Bank to influence policies/decisions<br>* | Colombia [i Peru | 40 60 80 100<br>% of firms report high impact of type of corruption/capture on business environment<br>Source. World Bank Institute Governance Diagnostic (2001).<br><!-- End of picture text -->

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A Metadata Schema for Data from Experiments in the Social Sciences<sup>∗</sup> Jack Cavanagh<sup>†</sup> Jasmin Claire Fliegner<sup>‡</sup> Sarah Kopper<sup>†</sup> Anja Sautmann<sup>§</sup> Keywords: Randomized control trials, metadata, data publication, secondary research, trial registration JEL codes: C10, C81, C90

> ∗We would like to thank David Rhys Bernard, Merc`e Crosas, Maya Duru, Benjamin Morse, Julian Gautier, Steven Glazerman, Jakob Hennig, Maria Ruth Jones, Jessaca Spybrook, Wendy Thomas, Gabriel Tourek, James Turitto, Keesler Welch, Lars Vilhuber for providing detailed feedback on the metadata schema, and Caitlin Brown and Rachel Griffith for providing helpful comments on the paper. We would also like to thank Davi Bhering, Simon Cooper, Michael Gibson, Sabhya Gupta, Katharina Kaeppel, Daniela Muhaj, Isabela Salgado, Sheral Shah, and Selva Swetha for helping us test the schema with data from 29 RCTs. We also thank Mehmood Asghar, Barbara Bierer, Olivier Dupriez, Julie Goldman, Rebecca LI, Katherine McNeill, Amy Nurnberger, Limor Peer, Matthew Welch, and Julie Wood, for support and feedback at various stages of the project. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent.

> Supplementary materials, including proposed controlled vocabularies, can be found in the associated GitHub repository https://github.com/sakopper/rct ~~m~~ etadata ~~s~~ chema.

> †J-PAL/MIT, email: jcavanagh@povertyactionlab.org and skopper@povertyactionlab.org

> ‡The University of Manchester, email: jasmin.fliegner@manchester.ac.uk

> §Development Economics Research Group, World Bank, email: asautmann@worldbank.org.
_30 Political Economy of Policy Reform in Turkey_ table 3. They ran as high as 40 percent for industrial exports. Tax rebates were initially designed to compensate exporters for indirect taxes and as a substitute for a VAT rebate, which is allowed under the GAIT. The term rebate is really a misnomer, however. First, the subsidy rate was not related to the total amount of taxes paid by the exporter, and could exceed it. Second, the rebate scheme was introduced prior to the introduction of the VAT, and when the actual VAT rebate was added, the prior rebate scheme remained as a pure subsidy. Over the long term, these subsidies are correctly viewed as undesirable distortions. In the short-run, however, they may have had some rationale as a way to foster infant industries and, perhaps more important, to speed up the expansion of trading and manufacturing firms in the export sector. These firms had a vested interest in supporting the internationalist stance of the Ozal government.

Table 3. Export Incentives

||1980|1981|1982|**1983**|1984|**1985**|1986|<br>**1987**|<br>**1988**|**1989**|
|---|---|---|---|---|---|---|---|---|---|---|
|Total|22%|21%|**22%**|24%|**14%**|**19%**|25%|23%|**15%**|**8%**|
|Export Tax Rate|1|**4**|**10**|12|11|7|6|**_5_**|**4**|**2**|
|Duty Free Imports|6|_5_|**4**|6|3|10|**14**|12|**4**|-|
|Pref. Export Credits|||||||||||
|or**FX**Allocation|16|13|7|**7**|*-|**-**|**-**|**-**|**-**|**-**|
|Cash Grants and|||||||||||
|Corp. Tax Rebates|-|-|-|-||**3**|**6**|7|**6**|**6**|

_Source: Bateinan and Arslan, 1989._ The export subsidy rates for the manufacturing sector from 1980 through 1983 averaged 22 percent. (See table 3 and Milanovic 1986.) In the first two years of the program, this subsidy was largely in the form of export credits. The subsidy varied widely - - some sectors, such as metal products, received subsidies over 100 percent. Milanovic's calculation show that the higher subsidy rates appeared to go to those sectors in which import-substitution had been of long-standing, including ferrous and non-ferrous metals, electrical and non-electrical machinery and transport equipment. These were mostly sectors where public-sector enterprises were predominant, but the private firms tended to be more involved in the export-oriented subsectors, because they could produce the higher quality products demanded in the export markets.

Not all exports were effectively subsidized. Agriculture, traditionally a stronghold for the Justice Party, lost subsidies. The January 1980 measures established a Price Support and Stability Fund at the Central Bank. The Fund, one of the first new extra-budgetary funds, was financed by a levy equal to the difference between export receipts and domestic support prices for agricultural products. In effect, a tax on agricultural exports would be used to subsidize basic agricultural inputs purchased by farmers, but also to finance export-oriented investments and to provide exporters' risk protection.
# Appendix 1: Cross national data availability and reliability

This appendix provides details on four aspects of the cross national regressions: data on health status, data on independent variables, data on health sector strategy, and functional form.

# a) Data on health status

The data used here are as reported by UNICEF (1992) except for the mortality rates for Zaire which are unbelievable: under-5 mortality rate for 1990 is reported as 130 and infant mortality rate as 79. These are replaced with the, more reliable, estimates reported in United Nations (1992) for 1984. These are 200 for under-5 mortality and 126 for infant mortality.

# b) Data on non-health sector variables

|_Income_|Real GDP per capita in 1995 international dollars (i.e. adjusted for<br>Purchasing Power Parity) are from the Penn World Tables 5.6.|
|---|---|
|_Education_|Average education levels for men and women over 15 are from Barro<br>and Lee (1996).|
|_Income inequality_|Gini coefficient as calculated by Deininger and Squire (1996) multiplied<br>by 100.|
|_Percent urban_|Percent of the country's population that lives in urban areas. From the<br>World Bank's Social Indicators of Development database.|
|_Predominantly_<br>_Muslim_|Dummy equal to one if over 90 percent of the country's population is<br>Muslim.|
|_Ethnolinguistic_<br>_fractionalization_|Index of ethnolinguistic fractionalization for 1960. Measures the<br>probability that two randomly selected people from a given country will<br>not belong to the same ethnolinguistic group as reported in Easterly and<br>Levine (1996).|
|_Tropical country_|Dummy equal to one if part of the country's territory lies within 20<br>degrees of the equator.|
|_Access to safe_<br>_water_|Percent of population with access to safe water. From the World<br>Bank's Social Indicators of Development database.|
|_Oil exporter_|Dummy equal to one if the country primary export is fuels (mainly oil)<br>as classified by the World Bank's World Development Indicators<br>(1996)<br>plus Kuwait.|

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