rafmacalaba/data-use-mentions-extended huggingface dataset:
(2022), and it is implementable in an open source Python package.<sup>1</sup> The crucial intuition though is simply that we use a subset of high quality expert annotations to train a small bespoke model for each code. These models rely only on the annotated training data, unlike the pre-trained LLMs which are trained on huge quantities of text from a huge range of contexts.

Rather than asking LLMs to directly annotate text, another potential use for them is for data augmentation in combination with a supervised model, such as those described above. Data augmentation is a common technique in machine learning to generate more variation in a training set while preserving the important signals. For example, when training a model on a labelled dataset of images of animals, one might generate extra variation in the training data by rotating the images by 90 degrees or transforming them into a mirror image of themselves. The idea is to generate more training observations where the noise in the data is different but the signals are the same. A good example of this from the natural language processing literature is back-translation, where text is translated into a different language and then back into the original, so that the exact phrasing and style of the text is different but the meaning is the same (Edunov et al., 2018). Using LLMs for data augmentation has been found to increasing prediction performance in some contexts, so we follow the approach set out in Dai et al. (2023) as an additional experiment here. The example prompts and further details on the augmentation are shown in Appendix A.1.

We thus test two different versions of iQual: first, training supervised models on the human annotations without the use of LLMs, and second, training the model on data augmented by the LLMs to generate more variation in the text while preserving the meaning.

# **4 Results**

We assess the performance of LLMs in our annotation tasks along two dimensions. Firstly, we assess how accurate of the
Table 3 The Gains From Agricultural Tariff Liberalization Aggregated Data

|Country|Aggregate<br>Gains|St.Dev.<br>Gains|Consumption<br>Efects|Income<br>Efects|Pro-Poor<br>Bias|Gains<br>Poor|Gains<br>Rich|
|---|---|---|---|---|---|---|---|
|Developing world|2.39|0.85|4.33|-1.94|-0.33|2.16|2.48|
|Armenia|3.52|0.32|4.12|-0.60|0.07|3.70|3.62|
|Azerbaijan|1.32|0.56|2.96|-1.64|1.25|2.02|0.77|
|Bangladesh|1.17|0.27|3.55|-2.38|0.20|1.29|1.09|
|Benin|1.28|1.87|4.29|-3.01|-4.86|-1.13|3.72|
|Bhutan|6.47|1.57|10.45|-3.98|1.77|6.38|4.62|
|Bolivia|3.73|1.17|5.21|-1.48|0.03|2.84|2.82|
|Burkina Faso|1.70|0.57|4.43|-2.73|0.27|1.94|1.67|
|Burundi|-3.12|2.98|9.70|-12.82|-5.70|-5.20|0
25 and above) gender (identified as male or female), income (identified as poor for adults living in the poorest 40 percent of households and rich for adults living in the richest 60 percent of households), employment (identified as wage employed, self-employed, unemployed, or out of the workforce), and ethno-religious groups. To explore the religious and geographic complexities in our data, we assign adults to one of four categories, based on self-reported religious identification and place of residence: Shia, Sunni, Kurds, and Baghdad. See Appendix A for additional information.

The Gallup World Poll conducts annual surveys covering about 150,000 people in more than 140 economies around the world. The data for Iraq is available for years 2010-2019 for 1,000 adults annually (age 15 and above). The surveys were conducted over the phone in Arabic and Kurdish for all years, except 2010, 2011, 2012, 2018 and 2019 where they were conducted face-to-face. We also bring complementary data from the Arab Barometer, which has conducted public opinion surveys in the Middle East and North Africa (MENA) since 2006. The most recent Arab Barometer survey of Iraq was carried out in 2018 and surveyed 2,461 adults (age 18 and above). The surveys were conducted face-to-face.

This paper only makes use of quantitative data sets, which are limited in their ability to provide additional insight into the complex relationships highlighted in many ethnographic and qualitative studies done on the shifting roles and power dynamics over the last couple of decades in Iraq. Its main goal is to support additional research by providing a quantitative background on vertical and horizontal inequalities.

# **3. Quality of Life**

Basic needs such as food and shelter, happiness, and belief that the future can be better than the present, affect the degree of social cohesion between different groups—and a lack of social cohesion and trust can trigger conflict.

# **_3.1 Food and shelter_**

Food and shelter are proxies for
A single standard set of countries is specified for the entire timeframe of the database, for both origin and destination locations, so that migration numbers for pairs of countries can be compared over time. Since many new origin and destination countries emerged during the study period, the most current set of countries and regions was chosen.

A region is defined as any geographic entity that conducts its own census and that commonly features as an origin in the others‘ censuses. For example, Western Sahara is omitted because it does not conduct a census although it is a commonly designated origin region. In all, 226 countries, territories, and regions are included in this list in each of the five migration matrices (see appendix 1). One implication of these inclusion decisions is that migration from Croatia to Germany, for example, is reported in every matrix, even though Croatia did not exist in the early time periods. Researchers interested in migration from Yugoslavia to Germany in 1960 would simply total the individual migration levels from the successor states of Yugoslavia. Performing the analysis according to historical boundaries, though easier, would have masked many recent international movements. Moreover, drawing conclusions about destination countries that no longer exist would offer policymakers less useful information for drawing inferences.

Another complication is the 11 additional destinations with census data that do not map perfectly to the master list. Five of these were aggregated into other countries in the master list: Christmas Islands (to Australia), Cocos Islands (to Australia), Kosovo (to Serbia and Montenegro), South Yemen (to Yemen), and West Germany (to Germany). Six additional countries or territories no longer exist, but they map to two or more of the 226 locations on the master list. These are the Gilbert and Ellice Islands, the former Yugoslavia, Czechoslovakia, Ruanda-Urundi, the Trust Territory of the Pacific Islands, and the Soviet Union. Except for the Soviet Union, the census data for these countries or territories are disaggregated and distributed among the destination countries currently
subject to NTM went from 60 to almost 80 percent. The increasing incidence of NTMs for Indonesian plants is also reflected in our measure of input NTM. In Figure 1, the dashed line represents the average share of intermediate inputs subject to at least one NTM. The share of inputs used by plants subject to NTMs went from less than 30 percent in 2008 to 60 percent in 2015. The higher incidence of output NTM presented in Figure 1 suggests that final consumption goods are more likely to be subject to NTMs than intermediate goods, although the gap shrinks considerably over time.

> 12The input data at the plant level appears to be quite noisy with several examples of plants changing their input mix frequently over the years. The aggregation across plants at the five digit industry allows to smooth this variability.

10
# r= δ+ η∙g

for pure rate of time preference δ, elasticity of the marginal utility of consumption η, and consumption growth rate g.

Strictly speaking, the discount rate used in the World Bank’s work (i.e. the social rate of return to investment) differs from that in the OECD project. The former is a kind of social discount rate from a social planner’s point of view, while the latter is a long-term private return from an individual perspective (Fraumeni, 2011). The difference can be illustrated by the following example.

Consider an individual deciding to undertake formal education. Since the risk that he or she faces cannot be easily diversified, a relatively high rate of return may be needed to induce this person to remain in school. On the other hand, from the perspective of the society as a whole, investment in formal education is diversified across individuals and thus considerably less risky, meaning that future returns to this investment should be discounted at a lower rate (Abraham, 2010). The question of whether there are positive externalities from human capital accumulation (so that social returns exceed private returns) is contested in the recent literature<sup>10</sup> and is not further considered in what follows.

More discussion on the choice of real rates in general, and the income growth rates and the discount rate in particular, are beyond the scope of this paper. For our purpose, we take the total wealth estimates from the World Bank wealth accounts as given, and run a sensitivity analysis with respect to the discount rates applied for calculating both human and natural capital. Our central discount rate is 4.58%, while variants of 4% and 5% are reported in Annex III (note that discount rates of 4.58% and 5% result in slightly different values of natural capital compared with the figures published in World Bank (2011)).

# **_4.3. The choice of time
2004-2017 covering the universe of formal workers linked with longitudinal firm census and customs data. We also link for the first time the employer-employee data with the national registry of lowincome households, the worker registry of unemployment insurance and benefits disbursed, and beneficiary records of worker training from Brazil’s main training provider.

We exploit quasi-experimental variation in firms’ foreign demand resulting from the GFC – lower growth in destination markets’ GDP – to measure firms’ foreign shocks. To study the unequal distribution of effects across firms and pass-through to workers, our specifications examine how the evolution of incumbent worker or firm outcomes over the post-GFC period varies with the magnitude of the firm’s foreign shock, while controlling for observable worker and firm characteristics and fixed effects that capture time-varying domestic shocks at the sector and location levels.<sup>1</sup> Foreign shocks can affect economic outcomes differentially in the presence of informality and for highly concentrated sectors and state-owned firms (SOEs).<sup>2</sup> By leveraging spatial and

> 1For identification purposes, our analysis focuses on a relatively homogeneous universe of firms, those that export, but they exhibit important variation in the intensity of the GFC-related foreign shock they experience.

> 2See Dix-Carneiro et al. (2021) and Ulyssea & Ponczek (2022) on trade and informality and Brugu ́es et al. (2022) and Colonnelli et al. (2020) on how political connections of state-owned firms foster patronage and

2
**Figure 11.  Cash and Financial Savings in Egyptian Listed Firms over Time**

<!-- Start of picture text --> Cash to Assets Financial Savings to Assets<br>0.2 0.1<br>0.15<br>0.1 0.05<br>0.05<br>0 0<br>2001 2002 2003 2004 2005 2006 2007 2008 2002 2003 2004 2005 2006 2007 2008<br>Cash to assets (Mean) Savings to assets (Mean)<br>Cash to assets (Median) Savings to assets (Median)<br><!-- End of picture text --> Source: EGX data, staff calculations.

The second variable of interest is investment in property, plant and equipment (PPE), which represents Physical savings. Investment is an important ingredient of company’s savings policy as it measures the amount invested in additional physical assets, i.e. firm expansion.

Figure 12 presents evolution of our investment measure over time in Egypt. There is some fluctuation in this measure year to year in the mean but less fluctuation in the median. The average IK ratio is around 15%, while the median is lower, indicating that many firms chose not to make any investment in some years.

**Figure 12.  Investment to Capital**

<!-- Start of picture text --> IK<br>0.2<br>0.15<br>0.1<br>0.05<br>0<br>2002 2003 2004 2005 2006 2007 2008<br>IK (Mean) IK (Median)<br><!-- End of picture text --> Source: EGX data, staff calculations.

19
only example of a significant and growing positive marginal impact in both periods. However, this regional picture of East Africa is made up of interestingly different country pictures: Kenya alone stands out as a country whose exports responded positively in both periods; Ethiopia, Rwanda and Tanzania saw little impact in the first period and a strong positive impact in the second period.

Firm-level customs data helped us take a closer look at the exporter dynamics underlying Kenya and Ethiopia’s export growth. It turned out that Kenya saw significant exit of exporters that began when the MFA quotas were phased out and continued during the Great Recession. The revival in Kenya’s exports after 2010 was mostly driven by new entrants rather than firms that had benefited from large preference margins during the early AGOA period. Ethiopian firms did not take any significant advantage of AGOA in the early years and much of the apparel export growth after 2010 also came from new exporters. Thus, in neither of these countries do we see evidence of significant growth along the intensive margin by firms whose international competitiveness might have been nurtured by earlier preferential access to the US market.

What does this evidence reveal about the impact of AGOA, and whether preferential access led to sustained improvements in performance? There is compelling evidence that AGOA had a positive impact on African exports to the US beyond any impetus from supply-side improvements over the period. However, the poor performance of Central and Western Africa, and the delayed spurt in most East African countries, demonstrates that preferential access per se did not lead to and was therefore not sufficient for sustained export growth. The role of new firms in Ethiopia and Kenya’s recent export dynamism suggests that if preferential access created longer-term comparative advantage, it was by inducing improvements at the country-level rather than at the firm-level. One promising development is the recent performance of Ethiopia, Rwanda, and 37
<mark>individual’s inclination to take up the survey, as illustrated in section 3.2 (also observed in Dever (2011), and Valliant and Dever (2011)). Specifically, those who take up the Internet surveys are more likely to be younger individuals, more educated individuals, and males. Additionally, economic variables are important because higher income often correlates with whether an individual has the luxury of time to participate in the survey. Due to the unavailability of some economic variables in specific survey waves, models between GMD and wave-2 data do not include the asset variables, and models between GMD and wave-1 data do not include variables on assets and the pre-COVID sector of work. Moreover, in the education variable, the “no education” category was not collected in wave 1, and hence we use fewer categories on education in wave-1 regressions.</mark> <mark>Asset data in the online surveys varies across countries. In Brazil, the online survey collected information about the availability of land phones, cell phones, computer, and piped water. In Egypt, the online survey collected data on the availability of land phone, computer, and piped water. In Indonesia, the asset variables include the availability of land phone, computer, car, air conditioner, and fridge. In Türkiye, the data on assets include the availability of computer, car, air conditioner, cable connection, and stove. In Kenya, the data on assets include the availability of computer, piped water, modernized floor, and modernized wall. In Sri Lanka, the assets include toilet and piped water.</mark> <mark>In all regressions, standard errors were clustered at the region level, except in Türkiye where the regional indicators were not available.</mark><sup>22</sup> <mark>From the model in equation (1), we obtained the predicted probability of participation in the online survey (</mark> _<mark
were not statistically significant).

## **5.3 Estimate of revenue loss due to tax evasion by registered firms**

We produce a back-of-the-envelope estimate of the revenue lost due to tax evasion by registered firms in Indonesia by combining survey results about the share of firms evading tax (extensive margin) and the extent to which these firms avoid paying tax (intensive margin). The results of the double list experiment estimate the former. The latter is captured in a separate question where respondents are asked what percentage of their tax obligations noncompliant firms pay. The extensive margin results of 25-27 percent are outlined throughout this paper, while the intensive margin results are presented in the Appendix. On average, respondents estimate that non-compliant firms only pay around 23 percent of the taxes they owe. These figures are applied to the existing share of GDP currently collected by the central government in business income taxes and VAT from firms in Indonesia, which is around 8 percent. This results in an estimate of around two percentage points of GDP lost in revenue due to tax evasion by registered firms (the complete working for this estimate is presented in the Appendix).

# **6 Discussion and Conclusion**

This study shows that a sizable share of registered firms in Indonesia admit to not paying all the tax they owe, and this is likely to substantially reduce the total amount of business income tax and VAT collected. The double list experiment provides rigorous, internally consistent results illustrating that around one-quarter of firms report evading taxes. A backof-the-envelope calculation based on this result suggests that revenue lost from tax evasion by registered firms is around two percent of GDP. This is the most common among firms that do not export, face substantial competition from the informal sector, and believe tax administration is a major obstacle to their business activities. This is based on self-reported 15
an incorrect data format was used, the user would receive an error message with an explanation of the correct format or type of information to enter. In addition, experience suggested that pull‐down menus with fixed choices simplified data entry and minimized likelihood of errors.

In the initial phase, tests were run on the database to ensure data quality. The database could be exported to Excel and then used with any statistical software. This was helpful for detecting errors in the system and refining data entry procedures. Moreover, WEDP M&E staff used exported data regularly to conduct advanced analysis. Although the built‐in reporting functions shown in the previous section worked well for basic monitoring and provided some standard evaluation tools, the richness of the database could be used in a range of ways when not restricted to prespecified parameters.

The WEDP also had a database expert conduct in‐depth advanced data quality assessments and check for errors,<sup>8</sup> which provided useful technical inputs for refining the system.

> 8 It was necessary in this case to inquire into errors leading to incorrect identifications when uploading data from the first‐phase Excel‐based system into the final version of the MIS. It is difficult to guard against human errors, but the MIS minimizes the likelihood of such errors.

21
Oxford: Clarendon Press, 1969.

- Holmstrom, Bengt, and Tirole, Jean. "Market Liquidity and Performance Monitoring," Joumal of Political Economy, August 1993, 101(4), pp. 678-709.

- Hoshi, Takeo; Kashyap, Anil; and Sharfstein, David. "Bank Monitoring and<sup>Investment:</sup> Evidence from the Changing Structure of Japanese Corporate Banking Relationships,"<sup>in</sup> Asymmetric Information. Corporate Finance and Investment, ed. R.Glenn Hubbard, Chicago: University of Chicago Press, 1990, pp. 105-26.

- Hubbard, R. Glenn; Kashyap, Anil and Whited, Toni M. "International Finance<sup>and Firm</sup> Investment," Journal of Money Credit and Banking, 1995, 27(3), pp. 683-701.

- International Finance Corporation, Emerging Markets Data Base, Various years,<sup>Washington,</sup> D.C.

- Jacklin, Charles. "Demand Deposits, Trading Restrictions, and Risk Sharing, in<sup>Contractual</sup> Arrangements for Intertemporal Trade, eds. Edward D. Prescott and Neil Wallace,
Policy Research Working Paper 6372

# **Abstract**

This paper uses data from eight different consumption questionnaires randomly assigned to 4,000 households in Tanzania to obtain evidence on the nature of measurement errors in estimates of household consumption. While there are no validation data, the design of one questionnaire and the resources put into its implementation make it likely to be substantially more accurate than the others. Comparing regressions using data from this benchmark design with results from the other questionnaires shows that errors have a negative correlation with the true value of consumption, creating a non-classical measurement error problem for which conventional statistical corrections may be ineffective.

This paper is a product of the Poverty and Inequality Team, Development Research Group. 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://econ.worldbank.org. The author may be contacted at kbeegle@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
5 discusses the key factor market regulations which could be relevant to explaining these results. Section 6 conducts a preliminary exploration of the role of governance, examining how the trends in adjustment costs have varied across high- and lowgovernance states. Section 7 concludes with suggestions for priority areas for future policy analysis, as suggested by the results of this paper.

> 8 The firm-level data set used for the analysis, the Annual Survey of Industries, covers factories with 10 or more workers. We are unable to measure these trends for firms with fewer than 10 workers.

3