rafmacalaba/data-use-mentions-extended huggingface dataset:
Policy Research Working Paper 10159

# **Abstract**

This paper studies the relationship between distortions, plant size, and bribery possibilities. In a distorted economy, bribery is a transfer from a private party to government officials to ‘get things done’. Enterprise Surveys data shows that small plants spend a higher fraction of their output on bribery than big plants. In this paper, a one-sector growth model is developed in which size-dependent distortions, bribery opportunities, and different plant sizes coexist. In the model, bribery is endogenous in the sense that managers decide to use it as a way to deal with distortions. Two sets of exercises are conducted to quantify the interplay of size-dependent distortions and bribery. First, the model parameters are calibrated to generate the plant size distribution of the U.S., by assuming the U.S. is free of distortions. Then, size-dependent distortions are introduced to the undistorted economy, and their effects with and without bribery opportunities are compared. Counterfactual exercises show that size-dependent distortions become less distortionary in the presence of bribery opportunities since plants are able to avoid distortions by paying larger bribes. Second, the model is calibrated with distortions and bribery opportunities using Turkish data. The choice of this country for analysis does not imply that bribery or size-dependent distortions are particularly large in Türkiye relative to countries of comparable development. The choice is driven by the availability of data on both the plant size distribution and spending on bribery in the country. The results indicate that the inferred level of distortions is sizable for all plants. The removal of distortions, which would eliminate the incentive for paying bribes, can have a substantial effect on both the output and the mean plant size which could increase by 63.6 and 82.5 percent, respectively.

This paper is a product of the Global Indicators Group, Development Economics. 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
"On Corporate Debt Maturity StrategiLes." _Journal ofFinance._ Myers, **S., 1977,** "Determinants of Corporate Borrowing." _Journal of Financial Economics._ Myers, **S.,** 1984. "The Capital Structure Puzzle." _Journial ofFinance._ Myers, **S.** and **N.** Majluf, 1984. "Corporate Financing and Investment Decisions When Firms Have Information that Investors Do Not Have." _Journal ofFinance._ Neumeyer, P., **1998.** "Inflation-Stabilization Risk in Economies with Incomplete Markets." _Journal of Economic Dynamics and Control._ Samuel, **C., 1996.** "The Stock Market as a Source of Finance: **A** Comparison of **U.S.** and Indian Firms." Policy Research Working Paper **_1592._** The World Bank.

Schiantarelli, F., and V. Srivastava, **1996.** "Debt Maturity and Firm Performance: **A** Panel Study of Indian Public Limited Companies." Manuscript.<sup>TheWorld Bank.</sup>

**33**
# **D.4 Additional Treatment**

Table D.7 shows the public certification effects of our main labor market outcomes without conditioning on the prespecified covariates. The results are very similar with or without the covariates.

Table D.8 shows public and private certification effects at two points in time: in the text message survey conducted 2-3 days after treatment and the endline phone survey conducted 3-4 months after treatment. This table shows four patterns, which expand on the discussion in footnote 19 of the paper. First, both treatments make candidates more likely to report that their assessment result matches their actual assessment result immediately after treatment. Second, both treatment effects decline over the following 3-4 months, although the different survey methods mean the time comparison should be interpreted cautiously. Third, the public treatment effect on self-beliefs is significantly larger than the private effect after 3-4 months but not after 2-3 days. This suggests that the larger public treatment effect at 3-4 months does not occur because the information it conveys is immediately more credible or easier to understand than the private treatment. Instead, it may be larger because the information is more memorable or the public treatment generates other effects, such as more job interviews or employment that provide more opportunities to learn about skills. Fourth, neither treatment affects self-esteem at either point in time.

Table D.9 shows how treatment effects on employment vary by single index summary measures of candidates’ skills (Panel A) and baseline candidate characteristics that might provide alternative measures of candidates’ skills (Panel B). We discuss these treatment effects in Sections 5.2 and 5.3 of the paper.

Table D.10 reports public and private certification effects on all prespecified workseeker-level job search and labor market outcomes. These are organized into families of conceptually similar outcomes, which we use for multiple testing adjustments. First, we report _q_ -values that control the false discovery rate across outcomes within each family (Benjamini et al., 2006). None of the _q_ -values in
Böhringer, Christoph, Sonja Peterson, Thomas F. Rutherford, Jan Schneider, Malte Winkler (2021). Climate policies after Paris: Pledge, Trade and Recycle: Insights from the 36th Energy Modeling Forum Study (EMF36). Energy Economics, Vol. 103: 105471.

Chen, Y.-H., Govinda Timilsina, Florin Landis (2013). Economic Implications of Reducing Carbon Emissions from Energy Use and Industrial Processes in Brazil, Journal of Environmental Management, Vol. 130, pp. 436-446.

Diao, Xinshen, James Thurlow, Samuel Benin, Shenggen Fan (2012). (eds.) Strategies and Priorities for African Agriculture: Economywide Perspectives from Country Studies. International Food Policy Research Institute (IFPRI), Washington, DC.

Dissou, Yazid and Muhammad Shahid Siddiqui (2014). Can Carbon Taxes be Progressive? Energy Economics, Vol. 42(c): 88-100.

Federal Democratic Republic of Ethiopia (FDRC) (2021). Updated National Communication. Climate Change Commission of the FDRC, Addis Ababa.

Garaffa, Rafael, Bruno S.L. Cunha, Talita Cruz, Paula Bezerra, Andr ́e F.P. Lucena and Angelo C. Gurgel (2021). Distributional effects of carbon pricing in Brazil under the Paris Agreement. Energy Economics, Vol. 101: 105396.

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ratings and Eurobond issuances have a high impact on LICDs’ transparency as a result of the data disclosure and investor relations practices required by them. Finally, increasing the numbers of highly skilled staff in debt offices has also been identified as a key part of improving transparency.

In the absence of reliable and comprehensive data disclosed by the authorities, indirect reporting is currently integrating and, in some cases, replacing direct reporting channels. Indirect reporting has been found to expand the coverage of debt statistics in most LIDCs, and country-specific comparisons of debt stocks from direct sources versus those recorded in the WB-IMF’s DSAs show that deviations therein can represent up to 30 percent of national GDP, confirming that differences in coverage are a key driver of this gap. Even when direct and indirect reporting coincide in terms of sectoral coverage, discrepancies can be as high as 7 percent of national GDP. This additional mismatch can be attributed to the use of decentralized and non-automated recording processes, data manipulation, and different debt definitions.

Given these challenges, this paper puts forward a call for action to i) improve debt transparency by focusing on those factors that are most likely to promote transparency; (ii) shift the focus of MDBs’ operations and technical assistance from indirect to direct reporting; (iii) introduce a core of enforceable
control group. Pre-ruling summary statistics for treatment and control are provided in Appendix Table A1.

Figure 3 contains two panels; the top panel shows neonatal mortality averages from the full period, while the bottom panel uses data within a smaller, symmetric 15-year window (1980-1994) surrounding the Supreme Court verdicts. In both panels, the vertical distance between the treatment and control group means is smaller after the verdicts than before them. The smaller scale of Panel B allows for a closer look, which shows parallel decreasing pre-trends, a roughly-constant slope in the control group over the full period, and a drop in treatment-group mortality in the aftermath of the verdicts.

Figure 4 provides analogous results for river pollution, using a dummy for exceedance of the bathing quality standard (BOD>3) as the dependent variable. Since this specification requires pollution data, the full time period available to us is 1986-2004. The top panel utilizes the full period, while the bottom panel restricts the estimation sample to 1986-1994 – a “short-run” post-period. Here, the outcome variable is somewhat noisy in the treatment group. In the short pre-period available to us for pollution, however, treatment- and control-group trends are parallel. Meanwhile, the likelihood of BOD exceeding the 3-mg/l cutoff drops significantly for the treatment group in the post-period, indicating a large DD estimate of the impact of the ruling on water quality.

While in principle it is possible to compare our treatment group to a single nearby (but not downstream) control district, inherent noise in our infant mortality and pollution data makes such a comparison imprecise. We illustrate this in Appendix Figure 1 by plotting average mortality (Panel A) and pollution (Panel B) in the treatment group and in a single district (Kannauj) just upstream of Kanpur Nagar. 10These graphs document a large time-series variance in outcomes of interest. Using the entirety of the Ganga Basin in our control group allows us to smooth our counterfactual significantly.<sup>11</sup>

# **4
and ALMP indicators across regions and income groups; Section 4 presents and discusses the findings of the regression analyses, and Section 5 concludes the paper.

# **2. Review of the Literature on the Linkages of UB and ALMP with Labor Market Outcomes**

This section provides an overview of theoretical and empirical literature investigating the effects of UB and ALMP on labor market outcomes in developing and developed countries.

## **2.1. UB and labor market outcomes**

Conceptually, UB can affect unemployment and reemployment wages through two channels: the substitution effect and the liquidity effect. The substitution effect of UB arises from the distortion it creates in the prices of leisure and consumption, reducing the marginal incentive to search for a job and potentially leading to moral hazard that in turn promotes unemployment and reduces reemployment wage. The potential for moral hazard is expected to be higher in less developed economies due to a larger informal sector and limited institutional capacity to monitor the job search activities of UB recipients. The liquidity effect stems from the liquidity provided by UB, allowing unemployed individuals to effectively manage and sustain their consumption over an extended period. This prompts UB recipients to devote more time to seeking employment opportunities that align with their skills and offer higher wages. Unlike moral hazard, the liquidity effect proves socially advantageous, fostering better employment opportunities and productivity, even though it may temporarily elevate the unemployment rate in the short term.

Empirical studies of UB typically concentrate on Europe and North America and show that longer UB periods or higher amounts of UB result in prolonged unemployment and reduced formal employment, suggesting, to some degree, the presence of moral hazard (Hagedorn et al. 2015; Tatsiramos and Van Ours 2014; Caliendo et al. 2013; Farber and Valletta 2013; Rothstein 2011; Tatsiramos 2009; Van Ours and Vodopivec 2006). Using macro level panel data, Pignatti and Van Belle (2018) showed that in developed countries, when coupled with sufficiently high spending on ALMP, higher public spending on PLMP can
Policy Research Working Paper 9467

# **Abstract**

Poor households often rely on transfers from their social networks for consumption smoothing, yet there is limited evidence on how antipoverty programs affect informal transfers. This paper exploits the randomized roll-out of BRAC’s ultra-poor graduation program in Bangladesh and panel data covering over 21,000 households over seven years to study the program’s effects on interhousehold transfers. The program crowds out informal transfers received by the program’s beneficiaries, but this is driven mainly by outside-village transfers. Treated ultra-poor households become more likely to both give and receive transfers to/from wealthier households within their communities; and less likely to receive transfers from their employers. As a result, the reciprocity of their within-village transfers increases. The findings imply that, within rural communities, there is positive assortative matching by socio-economic status. A reduction in poverty enables households to engage more in reciprocal transfer arrangements and lowers the interlinkage of their labor with informal insurance.

This paper is a product of the Knowledge and Strategy Team, Development Economics. 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 author may be contacted at gulescis@tcd.ie.

_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
by around 0.5 percentage points between 2005 and 2015 (Figure 10). In India and Sri Lanka, on the other hand, the share of public sector employment increased during this period. The change was a modest 0.25 percentage points in India, but a more substantial 1.5 percentage points in Sri Lanka, making it the only country with a substantial increase in the share of public sector employment.

**Figure 9.** Change in employment rates by type of job from 2005 to 2015.

<!-- Start of picture text --> Percentage points<br>10<br>5<br>0<br>-5<br>-10<br>-15<br>-20<br>-25<br>-30<br>Bangladesh Bhutan India Pakistan Sri Lanka<br>Regular Casual Self-employed Unpaid<br><!-- End of picture text -->

**Source:** South Asia Employment Dataset. **Note:** For Bhutan the comparison of unpaid family work between the two surveys is not comparable due to a reframing of the questionnaire.

**Figure 10:** Change in the employment share of the public sector from 2005 to 2015.

<!-- Start of picture text --> Percentage points<br>2.0<br>1.5<br>1.0<br>0.5<br>0.0<br>-0.5<br>-1.0<br>Bangladesh Pakistan India Sri Lanka<br><!-- End of picture text -->

**Source:** South Asia Employment Dataset.

14
financial dependence, asset tangibility, employment turnover, and the natural rate of establishment entry obtained from secondary sources (e.g. Braun, 2003, and Micco and Pages, 2004), and information on provincial level minimum wages (World Bank, forthcoming).

In addition, we complemented the data with two measures of political connectedness constructed by Mobaraq and Purbasari (2008). The first builds on an insight by Fisman (2001) and identifies firms traded on the Jakarta Stock Exchange (henceforth JSX) whose stock returns responded negatively to news reports about Suharto‟s health. Mobaraq and Purbasari (2008) identify the major shareholders on the boards of these firms, and all conglomerates run by these entrepreneurs, as well as firms owned by these conglomerates and classify those as connected. This measure, however, may only identify those firms for whom connections mattered or spuriously include firms for whom an adverse stock market valuation spuriously coincided with news reports about Suharto‟s health. The second proxy, which is an indicator of whether a firm has a relative of Suharto on its board, overcomes these limitations. For more information see Mobaraq and Purbasari (2008).

The survey design impacts the definitions of key explanatory variables. Entry is defined as entry into the survey; it is when establishments cross the 20 employee threshold, not necessarily when they began operations. Conversely, exit is defined as exit from the survey; we cannot distinguish whether firms go out of business or continue operating with fewer than 20 employees.<sup>7</sup> Information on the capital stock was not collected in 1996. We use data from 1991-95 to predict the capital stock based on output, investment, material inputs, labor usage, ownership characteristics, whether or not the firm exports, province and lagged capital.<sup>8</sup> We also confirmed the robustness of our results by omitting 1996 from the regressions.

> 7 As establishments are not required to report their closure, exit is inferred from establishments
# 8 Conclusion

The model outputs shed a better light on the way classical economic assumptions allow for understanding and reproducing the reinsurance market dynamics. Compared with previous analyses, our approach investigates the issues of reinsurance capacity to withstand important losses in a dynamic framework. Our model relies on a capacity constraint analysis, with a threshold probability of default for reinsurance rms. This factor may enhance the role of regulatory measures on the dynamics and the resilience of the reinsurance market either through the resistance to severe losses or through the time needed for capacity replenishment. Furthermore, we take into account the impacts of the rm choice on the market equilibrium to model potential strategic behaviors of large reinsurers. Our approach is innovative as we compute a dynamics of the market, based on these clear and tractable assumptions.

This analysis is only the rst step of a broader research agenda. Incorporating several lines of risk will rene the reinsurance demand model. A better calibration would allow a better understanding of the market features that inuence the market resilience. Recent data on reinsurance contracts would be needed to do so, and they are dicult to access. The long-term objectives of this model are to develop insights on the needed market capacity and to understand the insurance industry limits in providing disaster coverage. This question is critical when considering the current trends in insured and reinsured exposure. In addition to the classical economic trend issues (Hallegatte, 2011), the IPCC (2007) suggests a probable rise in the frequency and intensity of some natural disasters (e.g., storm surge and coastal oods). Several studies have proposed projections of future exposure due to these trends (see for instance Hanson et al. (2011) on the exposure in coastal cities) and the consideration of dierent scenarios will be necessary. How could the reinsurance industry be able to cover increasing risks, and how could specic policies and regulations enhance its ability to do so?

# 9 Acknowledgements

The authors wish to thank Patrice Dumas, Antoine Leblois and Valentin
movement by about 900,000. But if we discount the 1 million refugees born in displacement/exile supposedly included in the UNCHR data, then the under‐representation of population “loss” in the UN population statistics due to the use of outdated UNHCR data disappears.<sup>28</sup> Perhaps of greater concern then is that the UN population statistics likely do not include non‐registered Syrians in neighboring countries, for which estimates vary but tend to go up to more than a million (see for instance, Vignal (2018) and World Bank (2019)). UNDP and UNHCR (2019) put the difference between estimated total Syrians<sup>29</sup> and registered Syrian refugees at about 1.6 million in December 2018. The difference is wholly accounted for by Egypt, Jordan and Lebanon.

Consequently, as a possible lower estimate to population growth estimates, we calculate an added decline in the Syrian population of 1.8 million, building in additional conflict deaths of 200,000, and unregistered Syrian migrants of 1.6 million. This would reduce the 2018 population from 18.3 million to 16.5 million giving a negative average growth of ‐3.0% over 2011‐2018.<sup>30</sup>

> 25 The UNHCR data available up to 2016 indicate a change of 5,688,897 in the number of refugees, asylum‐seekers (pending cases) and others of concern based on the 2010 and 2016 stock positions. UN data on the international migrant stock for 2017, show net migration for Syria of 5,784,696 over 2011‐2017 (2017 stock compared against 2010 stock).

> 26 Source: http://www.syriahr.com/en/?p=108723. Civilian casualties totaled 111,330 (22.5% of the 494,892 total Syrian casualties), and were mostly adult males (70.1%), as 20,
_Sources:_ Attainment data were calculated from the _European_ <mark>Union Statistics on Income and Living Conditions</mark> and household surveys. Learning-adjusted years of basic education (LAYS) were obtained from the HCI database. _Note:_ For the average standardized quality score for higher education, the quality scores from each of the six university rankings (the Times Higher Education, the Quacquarelly Symonds, Academic Ranking of World Universities, the Center for World University Rankings, the U.S. News Global Universities Ranking, and U-Multirank) are first standardized to a global mean of 0 and a standard deviation of 1 and then averaged for every country. For presentational purposes, this value is then rescaled to range from 0 to 100. A value of 0 for the quality measure implies that no university in that country appears in any of the six university rankings. The education component of the original HCI was updated with PISA 2018 results or the latest available data. HCI = Human Capital Index; ECA-HCI = Europe and Central Asia extension of the HCI.

— Not available.

a. Based on population age 25 and older.

b. Based on population 25–34.

# **_4. Health Component_**

The health component of the HCI seeks to measure the productivity losses associated with poor health that a child born today will face later in life as an adult. The original HCI calculates this component based on two variables: the child stunting rate and the adult survival rate (the chance that a 15-year-old survives to age 60). These variables are understood to be good proxies for unobserved latent health status in a global context. Their effects on productivity are measured by the returns to adult height.

The ECA-HCI takes a different approach. It starts by assuming that good health means the absence of disease and bad health means the presence of disease. To measure latent health status, the ECA-HCI focuses on the factors that may cause disease. A low prevalence of these risk factors implies a lower disease burden; a