rafmacalaba/data-use-mentions-extended huggingface dataset:
_Source:_ World Bank’s World Development Indicators, July 25, 2023, update.

_Note:_ Unemployment refers to the share of the total labor force that is without work but available for and seeking employment. Youth Unemployment is similarly defined for the share of the total labor force ages 15–24.

Even among those with employment, many find themselves in low paid, informal jobs, and would like to do better. Changes in the structure of the economy and job opportunities due to automation, climate change, and lasting impacts of the COVID-19 pandemic are thought to have made the challenge even harder, resulting in headlines such as “Robots Pose Big Threat to Jobs in Africa, Researchers Warn” (Ridgwell, 2018), “Rising heat stress could destroy 80 million jobs by 2030, UN says” (Taylor, 2019), and “This Chinese jobs crisis could be its worst” (Chen, 2023).

Faced with these current employment challenges and such forecasts, governments face pressure to help job-seekers. One viewpoint is that the problem is a skills mismatch, where many of those seeking jobs do not have the skills sought by employers. This may be due to poor education systems, as well as changes in skills demanded by employers as economic growth, technological change, globalization, and the desire for a green transition change the structure of the economy. A popular solution is then for the government to provide _job training_ so that jobseekers can acquire these skills. An alternative viewpoint is that even when workers have skills that employers want, they have difficulty finding the right job fit due to search and matching frictions. Fragmented and largely informal labor markets may make it difficult to identify job openings, and workers without much work experience may not know how best to look for jobs or signal their skills to potential employers. This results in governments providing a range of _job search assistance_ policies designed to help jobseekers better use the skills they do have.

This paper asks whether, when, and how developing
Preference-Based Measure of<br>Health from the SF-36|John Brazier|Journal of Health Economics|774|31|
|Economic evaluation<br>|1991<br>|Economics of Coronary Artery Bypass Grafting<br>|Alan Williams<br>|The economics of health. Volume 2<br>|587<br>|55<br>|
|Economic evaluation|2004|Worms: Identifying Impacts on Education and<br>Health in the Presence of Treatment Externalities|Edward Miguel|Econometrica|587|55|
|Economic evaluation|1995|The Friction Cost Method for Measuring Indirect<br>Costs of Disease|Marc A. Koopmanschap|Journal of Health Economics|486|84|
|Economic evaluation|1997|Economic Foundations of Cost-Effectiveness<br>Analysis|Alan M. Garber|Journal of Health Economics|422|99|
|Economic evaluation|1991|<br>Economic Evaluation of Neonatal Intensive Care<br>of Very-Low-Birth-Weight Infants|Michael H. Boyle|The economics of health. Volume 2|417|100|
|Economic evaluation|1995|<br>Multi-attribute Preference Functions: Health<br>Utilities Index|George W. Torrance|PharmacoEconomics|386|118|
|Economic evaluation|2006|Willingness to Pay and Quality Adjusted Life<br>Years|James K. Hammitt|Economic Valuation of Environmental Health<br>Risks to Children|359|136|
|Economic evaluation|2000|Handling Uncertainty in Cost-Effectiveness<br>Models|Andrew Briggs|PharmacoEconomics|339|152|
|Economic evaluation|1997|<br>Modeling in Economic Evaluation: An<br>Unavoidable Fact of Life|Martin J. Buxton|Health Economics|330|158|
|Economic evaluation|1997|Pulling Cost-Effectiveness Analysis Up by Its<br>Bootstraps: A Non-parametric Approach to<br>Confidence Interval Estimation|Andrew Briggs|Health Economics|326|163|
|Economic evaluation|2001|Representing Uncertainty: The
security particularly salient.<sup>1</sup> The intervention, carried out in partnership with one of the largest humanitarian aid organizations in Jordan, randomized rental subsidies across 2,870 refugee households residing in Irbid and Mafraq governorates in Jordan. The randomization was implemented over geographic unit clusters and designed with sufficient statistical power to detect moderate impacts on important life outcomes, including living standards measures, labor market performance, and subjective well-being.

The housing subsidy covered, on average, a year of rent, combined with funding for landlords to upgrade the refugees’ housing quality. Subsidies were offered for recipients’ current housing arrangement, and thus their physical location (and by extension, their neighborhood) was typically held constant. To measure effects on refugee recipients as well as Jordanian neighbors, the study collected both in-person and phone surveys over a total of three and a half years since the start of implementation, with refugee households surveyed three times and neighbors once. The data collection succeeded in attaining relatively high sample tracking rates, overcoming some challenges associated with the COVID-19 pandemic. Although 86% of the refugee sample was surveyed at least once, there was some differential attrition between the treatment and control refugee samples, and therefore we estimate bounded effects following Lee (2009) (and the text highlights the results for which both estimated bounds take the same sign unless otherwise noted). These data are analyzed largely following the econometric models and primary outcomes specified in an AEA pre-registration (AEARCT#0006141) and associated pre-analysis plan, while making note of additional and exploratory results.

Another core contribution of this study is to examine the host community reaction to refugee assistance, utilizing a detailed survey of the attitudes and experiences collected among a representative sample of the Jordanian neighbors (N=2,146) of both treatment and control households. To our knowledge, this is among the first studies to experimentally examine how humanitarian assistance to refugees affects the views of the local communities who do not directly benefit. Specifically, we examine whether refugee-targeted
education, and experience. A worker’s entire history of oDesk employment, including wages and hours, is publicly observable. For jobs that have ended, a feedback measure from previous work is publicly displayed. Figure 1 provides an example of a worker profile.

> 6 oDesk’s expansion mainly reflects increasing demand for online labor services over time. Statistics from compete.com, a company that tracks Internet traffic, show that unique visits to oDesk and its four largest competitors (some of which pre-date oDesk) increased simultaneously in recent years. Overall growth of online outsourcing slowed with the financial crisis, but oDesk has continued to grow rapidly.

7
It is important to remember that the 1995 survey was carried out in the early part of 1995, before the economic recovery had taken hold. The 1995 survey thus provides a picture only of the short-run impact of the devaluation and accompanying policy measures. In aggregate, one would expect that poverty would have begun to fall in 1996, given the strong economic turnaround observed in 1995 and 1996. The strong growth in the secondary and tertiary sector of the economy is likely to have a positive impact on reducing urban poverty. While agricultural growth is positive, it would appear to be somewhat selective. To the extent that it bypasses many of the food crop and only partially touches cocoa farmers, its impact will be limited. Since food crop farmers account for roughly a quarter of CMte d'Ivoire's population, and are the poorest socioeconomic group, whether or not they benefit from growth in aggregate demand will have a large bearing on what happens to overall rates of poverty. The rate at which poverty declines among this group of farmers may depend more on the fortunes of the export crop sector and what happens in the rural labor market, assuming that real producer price of the main staple food crops continues to decline. Heavy taxation of export crop farmers has repercussions not only on export crop farm households but also on food crop farm households.

While taxation of export crop farmers (as a share of the world price) has decreased since the devaluation, export crop farmers, especially cocoa farmers, are still being taxed. The tax is often justified on the grounds that Cote d'Ivoire has an important share of the cocoa market, and that restricting supply by taxing cocoa producers raises world prices and increases the gains to Cote d'Ivoire. Analysis suggests that in the past the actual level of taxation exceeded the optimal level (World Bank, 1994; Panagariya and Schiff, 1990) although the gap between the optimal and actual export tax may be narrowing at present. Unfortunately, the burden of taxation falls heavily on the
Policy Research Working Paper 6404

# **Abstract**

This paper addresses whether microcredit participants in Bangladesh are trapped in poverty and debt, as many critics have argued in recent years. Analysis of data from a long panel survey over a 20-year period confirms this is not the case, although numerous participants have been with microcredit programs for many years. The results of the analysis suggest that participants derive a variety of benefits from microcredit: It helps them to earn income and consume more, accumulate assets, invest in children’s schooling, and be lifted out of poverty. This is not to say that non-participants have failed to progress over the same period. Both participants and non-participants have gained as the economy has grown; however, the rates of poverty reduction have been higher for participants. Testing the net effect of microcredit programs requires applying an econometric method that controls for why some households participated and others did not, conditional on their initial characteristics. In addition, the method must control for time-varying, unobserved heterogeneity that affects everyone over time, albeit in possibly different ways. The paper’s econometric estimates show significant welfare gains resulting from microcredit participation, especially for women. They also show that the accrued benefits of borrowing outweigh accumulated debt. As a result, households’ net worth has increased, and both poverty and the debt-asset ratio have declined.

This paper is a product of the Agriculture and Rural Development 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 skhandker@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
pay for housing (Table 1). Nearly all (93.7 percent) of those in the bottom quintile are housing poor, and the housing unaffordability rate decreases as household income rises. Using the conventional EIR approach, and adopting 30 percent as the threshold; however, housing is unaffordable for a larger share of households (43.9 percent), and higher-income groups have more affordability challenges. This highlights one of the main shortcomings of the ratio method: it fails to account for the fact that the well-off can afford to spend a larger proportion of their overall expenditure on housing, even after paying for basic needs. Conversely, people at the bottom end of the expenditure spectrum struggle with meeting their basic needs, even if they spend less than 30 percent of their expenditure on housing.

**Table 1. Share of Urban Households in Unaffordable Housing as Measured by REM and EIR**

|**Urban Welfare Quintiles**|**Housing Poverty - REM (%)**|**Unaffordability - EIR (%)**|
|---|---|---|
|All Pakistan|25.2|43.9|
|Q1 (lowest)|93.7|28.1|
|Q2|29.3|35.6|
|Q3|2.6|44.3|
|Q4|0.6|52.0|
|Q5 (highest)|0|59.6|

Note: Sample restricted to urban areas only. Source: Authors’ calculations based HIES 2018-19 microdata.

The REM applied in Pakistan is adapted from the traditional RIM in two ways: (i) expenditure data is used instead of income, and (ii) a non-housing expenditure threshold was derived from HIES data instead of 16
annotations) are not random. Second, LLMs over-predict many of our codes. We can think of this as the LLM bringing the "pre-conceptions" it has learned from its training data to the annotation task. Consequently, LLMs are probably not suited for coding most qualitative data that requires nuanced and contextual analysis. This sort of analysis has traditionally been the province of anthropology and sociology but is increasingly being used by more quantitative fields such as economics and political science.

To analyze large-N qualitative data, such as those obtained from open-ended in-depth interviews, we show that a coding scheme based on a close-reading of transcripts by experts in qualitative analysis with a sub-sample of the full corpus of data is vital for interpretation and analysis. Firstly, high quality annotations are necessary in order to assess whether the LLM is introducing bias in its annotations. Secondly, these high quality annotations can then be used as a training set for smaller bespoke models. These bespoke models may be able to leverage LLMs through data augmentation, but importantly they are trained on context-specific data so researchers have better control, and an overview, of the information that is used. We suspect that these limitations will continue even as LLMs improve, and we encourage researchers using LLMs for annotation tasks to be aware of and check for bias.

# **References**

- Appadurai, A. (2004), ‘The capacity to aspire: Culture and the terms of recognition’, _Culture and Public Action, ed. Vijayendra Rao and Michael Walton, Stanford, California: Stanford University Press_ pp. 59–84.

- Ashwin, J., Rao, V., Biradavolu, M., Chhabra, A., Haque, A., Krishnan, N. and Khan, A. (2022), ‘A method to scale-up interpretative qualitative analysis, with an application to aspirations in cox’s bazaar, bangladesh’.

- Atari, M., Xue, M. J., Park, P. S., Blasi, D
**with measures**<br>**currently in effect**|
|---|---|---|---|---|---|
|Dumping|240|91.6%|216|74|34.26%|
|Subsidies|19|7.2%|18|7|38.89%|
|Safeguards|3|1.1%|3|1|33.33%|
|Total|262|100.00%|237|82|34.18%|

Source: UPCI and 2003 DOF data.

According to the information available, 73% of the requests for investigation filed are admitted by the competent authority.<sup>46</sup> Eighty-six percent of the investigations initiated lead to the application of provisional duties, while 58% concluded with the imposition of antidumping or countervailing duties. These percentages have apparently increased with the passing of

> 45 The statistical annex includes a detailed list of all the investigations initiated in that period.

> 46 This percentage corresponds to the 1992-2002 period, of which records of rejections are available. Prior to that date, any request rejected by the authority on the grounds of non-compliance with formalities or irrelevance was notified to the interested party only.

16
# **Appendix 1: Comparison with Deaton (2005) and Ravallion (2003)**

In this appendix, we compare our estimates of the gaps between household surveys and national accounts with earlier studies by Deaton (2005) and Ravallion (2003). Before discussing the differences, we discuss several reasons for why the methods and data differ.

The most important difference is that our analysis is based on significantly more countries and years. Since the time of the Deaton (2005) and Ravallion (2003) papers, there have been significantly more household surveys accessible for comparisons. We match 2,020 household survey means to estimates of HFCE from national accounts, and 2,052 surveys to estimates of GDP. Deaton (2005) presents estimates from matching 543 survey means to HFCE (277 consumption and 266 income surveys) and 272 income means surveys to GDP. Ravallion (2003) uses only 88 observations when assessing differences in levels, 52 for consumption surveys and 36 for income, matched to both GDP and HFCE.

Our analysis covers four decades, from 1977 to 2019, a much longer and more recent time period than previous assessments. Ravallion uses surveys from a little more than a decade, spanning from 1985 to 1998, while Deaton uses surveys from 1979 to 2000. Although our time periods overlap, we have data from different years and surveys in the overlapping periods, so reconstructing the exact same samples used by previous studies with our data is not possible. One reason for this is that since the time of the previous studies, data have been revised, removed or added to both national accounts and household survey collections. National accounts series are revised with irregular intervals, and past HFCE and GDP estimates are recomputed using new methods and data. Therefore, we use a vintage of the national accounts data that is different, even for the same years and countries. Similarly, survey data is sometimes revised, or could also be removed. Deflators – both for surveys and national accounts are also often revised
The results for business environment variables are presented in Table 3, Panel A, and the results for governance indicators are presented in Table 3 Panel B. GDP per capita is significantly positive in all specifications, suggesting that the overall level of development is a significant predictor of subsequent business registration. An important caveat is that our dataset only includes new formal, limited liability firms – yet the entry of individuals into informal and formal self-employment is likely higher in lower-income countries with fewer employment opportunities. For instance, according to the World Value Survey<sup>13</sup> , 28.4 percent of individuals report being self-employed in Sub-Saharan Africa and 21.3 percent in South Asia, while only 6.6 percent of individuals in North America and 8.0 percent in Europe report being self-employed. Although we cannot directly compare our flow of new limited-liability firm registration to the stock of informal and formal self-employment, the data suggest that self-employment is higher in lower-income regions. In other words, we find that higher economic development is related to a more dynamic formal private sector, with greater potential for high-growth entrepreneurship; but our results do not suggest that entrepreneurship, per se, leads to greater economic growth.

Next, we find that Starting Costs, Starting Days, and Number of Procedures have a negative and significant relationship with the subsequent growth of new businesses: a faster and simpler registration process results in greater new business creation. Employment Rigidity is negative, but not significant. Financial Development is positive but not significant despite the strong unconditional correlation.<sup>14</sup> Finally, the tax rate has important implications for new business registrations. We find that the higher the tax rate, the less formal business registration. This is expected, since taxes represent the main cost of becoming registered, versus operating in the informal economy. The higher these costs, the less incentive firms have to register.

> 13 Calculations by the authors.  Complete data is available at:  http://www.worldvaluessurvey
investment growth in EMDEs? Third, what are the factors associated with the investment slowdown, including spillovers from weak activity and investment in major economies? Fourth, what are the implications of weak investment growth for long‐term growth prospects? Fifth, which policies can support investment?

The paper informs the debate on the recent slowdown in investment by making the following contributions _._ First, it focuses on EMDEs, whereas the bulk of the existing literature has focused on AEs. The few existing studies that analyze EMDE investment are either based on pre‐crisis data or confine their analysis narrowly to the 2008‐09 crisis or simply focus on specific regions.<sup>4</sup> Second, it estimates the contributions of a comprehensive set of factors associated with weak investment growth. A number of empirical methods are used to zoom in on specific external and domestic factors. Third, it examines the implications of investment weakness in EMDEs for global trade, long‐term prospects for growth and catch‐ up, and it highlights the potential impact on productivity growth. Fourth, in light of its findings and insights from an extensive literature, the paper provides a wide range of macro‐ and microeconomic policy recommendations to revive investment growth.

The rest of the paper is organized as follows. Section 2 describes the main features of the investment slowdown. Section 3 explores the macroeconomic backdrop behind the slowdown. Section 4 examines the factors associated with the slowdown. Section 5 assesses the implications of weak investment for global trade, long‐term growth, and income catch‐up. Section 6 discusses policies to promote investment growth. Section 7 concludes.

# **2. Main features of the investment slowdown**

During 2003‐08, EMDE investment growth reached historic highs averaging 12 percent a year, more than twice the long‐term average growth rate of 5 percent (Figure 2). The investment boom was particularly pronounced in commodity exporters, where soaring commodity prices encouraged investment in resource exploration and development and,
.

Lastly, we estimate a model that measures the number of years with conflict out of the contemporaneous year and the preceding four years,<sup>16</sup> similar to our lagged tertiles model. The years of conflict covariate is categorized into none, one-two years, and three or more years (out of five). This model focuses on the duration of conflict as a form of intensity as an alternative approach to measuring conflict-affected contexts. Both the model with lags and the model with years of conflict necessarily limit our sample, effectively dropping observations for 1989-1993 since we do not observe sufficient preceding years of conflict.

# **_3.7 Controls_**

In all our models we control for calendar year (as described above), current first level administrative geography and current urban/rural residence (DHS surveys generally lack full residential histories so using locations of birth is not possible). The calendar year controls (entered as calendar year fixed effects) are particularly important for accounting for time-varying country characteristics that may be related to both conflict and child marriage, for example, poverty. Additional controls, although desirable, are not possible since most of the factors that influence age at marriage relate to the natal household. Unfortunately, the DHS surveys do not collect natal household data, and while the unmarried are generally still in their natal households, the married are not. Other characteristics, such as a woman’s education level, may also be population in the year, based on UN population estimates (World Bank, 2021). This assumes that the shares of the population remained constant at the second-level administrative geography over time.

> 14 The tertiles are calculated among observations with conflict; the low, medium, and high tertiles are thus equally sized among observations with conflict, across countries, but the category “no conflict” that the tertiles are compared to is much larger.

> 15 We use contemporaneous here to refer to conflict in the same year (e.g. conflict in year