rafmacalaba/data-use-mentions-extended huggingface dataset:
governments in the 1980s and 1990s were not particularly transparent and neither are other non-Paris Club creditor governments today. This lack of data on defaults and debt forgiveness is problematic, especially in a world battered by the Covid-19 crisis. Any assessment of distressed developing countries today requires knowledge on what happens to outstanding Chinese loans, which can be very large in volume and difficult to repay due to their comparatively short maturities and high interest rates (see Horn et al. 2021 for details).

This paper draws on a broad range of sources to create an encompassing data set of credit events on Chinese overseas loans to developing and emerging market sovereigns over 20002021, including many ”hidden defaults.” We present information, where available, on the timing and basic terms of debt restructuring operations and provide context on how defaults and debt restructurings on debts owed to China compare with other creditor groups. The analysis incorporates updated existing data sets of defaults and restructurings involving private external creditors (in particular, international loans and bonds) and of debt defaults toward mostly high-income governments organized in the so-called Paris Club. Three findings stand out.

First, we find that restructurings on Chinese debts are more frequent than restructurings involving the private sector or Paris Club official creditors. Since 2008, Chinese creditors arranged at least 71 distressed debt restructurings, more than three times the number of restructurings of private external debts (we record 21 bond and bank debt restructurings) and higher than the total number of Paris Club restructurings with distressed debtors (68 cases) in the same period. The increase of restructurings with China in the past 7 years largely reflects deteriorating economic fundamentals in many EMDEs and the fact that China became the largest creditor for many high-risk countries.

Second, many restructurings with Chinese state-owned creditors are preceded by a lengthy spell of default involving payment arrears, according to data from the World Bank’s Debtor Reporting System (DRS). Yet, the observed scope of arrears in this database is likely the tip of the iceberg because missed payments can go unreported,
* p|dard errors in pare<br>_<_0.01, ** p_<_0.05,|ntheses<br>* p_<_0.1|||

# **Financial aid applications**

This subsection explores financial aid applications as a major roadblock to access to higher education. The descriptive statistics and OLS results illustrate that students who apply for financial aid are more likely to enroll in higher education. Information about access to this aid has been found to correlate with higher enrollment, especially when this information is provided at a younger age (Hastings et al., 2015; Dinkelman and Mart ́ınez A, 2014). Students who apply for financial aid could be self-selecting into treatment, which would bias our regression analysis. Alternatively, there could be 18
the program opens calls for inscription and enrollment into the _FA_ program only on specific dates for each municipality, making it impossible for people to join the program at other times.

Problems of misspecification of the underlying regression models may introduce bias in treatment effects, particularly in the RD design. In order to avoid such problems, we checked the sensitivity of program impacts using various alternative specifications. Overall, the existence or lack of program effects on school completion and test scores,

> 34 Camacho and Conover (2011) find evidence of manipulation for the _Sisben_ proxy-means test constructed with the same census of the poor. However, this problem occurs only for people around the level 2 threshold which determines eligibility for other social programs such the Subsidized Health Insurance ( _Régimen Subsidiado_ ).

29
sup>3</sup> Since not all food items were available in all district markets at all times of the year, we imputed the missing elements to obtain a complete price matrix, which provides prices for those items that households may have produced at home or those that households may have obtained from more distant markets.<sup>4</sup> The survey includes questions on the percentages of imported wheat and rice the household consumes;

> 3 Households are asked for the quantity of foods consumed over the past seven days; these quantities are multiplied by 4.2 to get monthly values.

> 4 The imputation process filled in missing values using the first feasible methodology according to the following order: 1) median of that month of the 20 nearest neighbors (weighted by inverse distance); 2) province median of that month; 3) national median of that month; 4) median price of 20 neighboring districts of the quarter (weighted by inverse distance); 5) province median of that quarter; and 6) national median of that quarter.

6
*1990s**|**2000s**|
|---|---|---|---|
|Liberia<br>Senegal<br>Mauritius|Djibouti<br>Togo|Burundi<br>Cameroon<br>Cape Verde<br>Equatorial Guinea<br>Ghana<br>Kenya<br>Madagascar<br>Malawi<br>Mozambique<br>Namibia<br>Nigeria<br>Rwanda<br>Seychelles<br>Sudan<br>Uganda<br>Zimbabwe|Gabon<br>Gambia<br>Mali<br>South Africa<br>Zambia<br>Eritrea<br>Mauritania<br>Tanzania|

Source: FIAS (2008) with author’s additions / amendments According to FIAS (2008), a total of 114 zones exist in Sub‐Saharan Africa – this represents somewhere between 3% (based in ILO data) and 4.5% (based on FIAS data) of the total number of global zones. This

> 4 Both the Liberia and Senegal programs became dormant and are in the process, as of 2010, of being overhauled and relaunched.

4
br>Profit per ha (US$)|753|316|390|-0.58|-0.48|
|<br>Number of farms|817|817|750|||
|_Farms >120 ha_||||||
|Area cultivated (ha)|527.5|531.6|495.1|0.01|-0.06|
|<br>Any sales|0.87|0.80|0.87|-0.08|0.00|
|<br>Share of output sold|0.80|0.55|0.60|-0.31|-0.25|
|<br>Revenue per ha|1,215|773|662|-0.36|-0.46|
|<br>Total input use per ha|<br>377|453|198|0.20|-0.48|
|Profit per ha (US$)|839|306|463|-0.64|-0.45|
|Numberof farms|389|389|456|||

_Source:_ UCAB price data for panel A and household survey panel data for panel B, as described in the text. _Note:_ Prices in panel A are in US$ and exclude value-added tax. Output prices are at the elevator in central Ukraine. Profit is the value of output net of purchased inputs, that is, it includes remuneration for fixed factors and family labor. The six main crops are wheat, barley, rapeseed, soybean, maize, and sunflower. ha = hectares; km = kilometer; t = ton.

23
Measuring Poverty Dynamics and Inequality in Transition Economies Disentangling Real Events from Noisy Data Erzo F. P. Luttmer* _The World Bank, 1818 H Street NW, Washington, DC 20433_ I am grateful to Wlodzimierz Okrasa for sharing and explaining the Polish Household Budget<sup>Survey data. I would like to thanik</sup> Branko Jovanovic for performing calculations with the Russian Longitudinal Monitoring Survey.<sup>I would like to thank Carlos</sup> Cavalcanti, Christine Jones, Michael Lokshin and Ana Revenga and semninar participants<sup>at the World Bank for discussions and</sup> helpful suggestions. This paper was prepared as a background paper for the report "Making Transition<sup>Work for Everyone: Povcrty</sup> and Inequality in Europe and Central Asia". Views in this paper are those of the author and<sup>should not be attributed to the World</sup> Bank. E-mail: erzo_fp_uttmer@post.harvard.edu
outcomes, such as macroeconomic data, the desired frequency may be monthly or quarterly. In each case, the desired frequency of data capture depends ultimately on the temporal dynamics of the outcome of interest, and this frequency may change as other conditions change (e.g. ideal frequency of economic data is most likely high during volatile economic times and might be low during stagnant periods).

- _Timely_ : Timely data means that the time from data collection to data release is short, or that the time between an event of interest occurring and data collection on the event is short. This is particularly relevant for emergencies where policy response does not have the luxury to wait for data to arrive, such as environmental disasters, health crises, conflicts and more. The timeliness of data depends on the mode of data collection. Some machine-generated data can be made available nearly instantaneously, while other data, such as survey data, often takes place over a longer period of time and needs careful analysis and cleaning before it can be released, leaving a time lag of several months. A related name for this feature is that the data need to be punctual.

# **High quality**

- _Accurate_ : For data to be valuable, they need to measure their concept of interest with minimal error. This means that data collection and processing should use methods and tools that are designed to capture the signal of some phenomena while filtering out noise or measurement error that can bias conclusions and evidence-based policies.

- _Comparable_ : For data to be used for cross-country analysis or for tracking patterns over time, they need to be comparable across space and time. This means, for example, that the data should conform to certain standards and data collection instruments should be relatively stable over time. Examples of conforming to standards include the near universal acceptance of governments in using the System of National Accounts for measuring the size of their economies, and similarly the widely accepted standards for measuring geolocation and units of time. There are fewer clear examples of
the stratified survey data, we employ weighted least squares (WLS) estimation using the sampling probabilities as weights. WLS estimation ensures that the proportional contribution to the parameter estimates of each stratum in the sample is the same as would have been obtained in a complete census enumeration (cf. DuMouchel and Duncan 1983; Wooldridge 2001).

# **4. Student Background and Educational Achievement**

This section reports the results of regressing students’ educational performance on a host of student characteristics, which mainly measure their family background. Because this section is interested in the total impact of student background on educational performance, including any effect that might work through families’ differential access to schools with different endowments and through their influence on the institutional features of schools, the estimation of the student-background effects does not control for school characteristics, such as resource endowment and institutional characteristics, as in equation (2): While this specification omitting school characteristics should be preferable for the research question considered in this section, we also tested the robustness of our student-background results to estimating equation (2), which includes the school characteristics. None of our qualitative results are sensitive to the alternative specification.

Tables 3a and 3b report the results of estimating equation (5) for each of the considered countries. Fourth-grade reading performance as tested in PIRLS is strongly positively related to reading performance at the start of primary school in each of the countries. The measure of pre-school reading performance has the highest _t_ -statistic of all variables in Argentina and Colombia, and also one of the highest _t_ -statistics in all other countries. This provides some confidence in the value-added specification employed.

Kindergarten attendance is not statistically significantly related to reading performance of Argentine students, and in Colombia, students who attended kindergarten for a considerable time even performed significantly worse. In the other considered countries, there is similarly very little evidence of positive relationships between kindergarten attendance and later student performance. In interpreting this result, it should be borne in mind that children are not randomly assigned
Policy Research Working Paper 8932

# **Abstract**

Recent work on China’s accession to the World Trade Organizations pays little attention to the wave of reforms in China in the 1980s and 1990s. These reforms created the preconditions for accession and strongly influenced its outcomes. The preeminence of processing trade at the time of accession sharply reduced the impact of accession-related tariff reductions on exports and set the stage for China’s increases in domestic value added and reduction in China’s involvement in global production sharing since that time. The assessment in this paper, based on export data and simulation results on the ex ante accession-related effects on export volumes in the literature, finds that the accession must have increased China’s real export growth by at most 6 percentage points between 1997 and 2005. This effect is substantial, but not as large as suggested by the difference between the pre- and post-accession export growth rates in the four years before and after accession. This is because the influence of cyclical fluctuations related to the Asian financial crisis and the U.S. dot-com crash dampened export growth in the period before accession in 2001 and accelerated it afterward.

This paper is a product of the Office of the Chief Economist, Latin America and the Caribbean Region. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at eianchovichina@worldbank.org or w.martin@cgiar.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
above the threshold. This paper finds that a fixed tax rate of 2.5 percent on turnover and exemptions for low revenue firms would be simpler for tax assessment and more progressive.

This paper makes a contribution to the literature in three ways. First, the literature studying the tax burden of the presumptive tax is limited because of the lack of disaggregated data and the low priority given by governments as aforementioned. Among the exceptions are empirical studies by: Waiswa et al. (2021), who find some regressivity in the recently reformed presumptive tax in Uganda; Semboja (2015), who shows there is regressivity within some turnover bands in Tanzania; and Engelschalk and Loeprick (2016), who examine the policy design of simplified tax regimes of micro and small businesses in transition economies and conclude that these regimes often do not adequately consider tax fairness. Using a _de jure_ analysis, Dube and Casale (2019) find the presumptive tax in Zimbabwe to be regressive with a particularly high tax burden for hairdressers, who are predominantly women. This paper builds on these studies by examining the equity and gender implications of the tax in Ethiopia. The explicit consideration of the different tax burdens on women and men in the Ethiopian context is important given that the activityspecific tax rates in the tax code and the gendered sectoral distribution of activities are likely to interact.

> 2 The literature that estimates the effective corporate tax rates uses turnover, gross profits, net profits, or pre-tax income in the denominator (see Bachas et al. 2023 for a review). This paper uses turnover because it is the tax base used to calculate the presumptive tax.

3
.html</u> (accessed March 9, 2018) International Journal of Epidemiology, 2019. “Instructions to Authors.” Available at: <u>https://academic.oup.com/ije/pages/Instructions_To_Authors (accessed March 9, 2018, and February 11, 2019)</u> Ioannidis, J.P.A., Stanley, T.D., and Doucouliagos, H., 2017. “The power of bias in economics research.” _The Economic Journal_ , 127 (October): F236‐F265.

ISRCTN, 2018. “ISRCTN ‐ About.” Available at: https://www.isrctn.com/page/about (accessed March 14, 2018) J‐PAL, 2012. “Deworming: A Best Buy for Development.” _J‐PAL Policy Bulletin_ . March. Available at: ‐ <u>https://www.povertyactionlab.org/sites/default/files/publications/2012.3.22 Deworming.pdf</u> (accessed March 9, 2018) Kremer, M., and Glennerster, R., 2011. Improving health in developing countries: evidence from randomized evaluations. In Handbook of Health Economics (Vol. 2, pp. 201‐315). Elsevier.

LSHTM, 2015. “Educational benefits of deworming children questioned by re‐analysis of flagship study.” Available at: <u>https://www.lshtm.ac.uk/newsevents/news/2015/educational_benefits_of_deworming_children_questioned.htm l</u> (accessed March 9, 2018) Maniadis, Z., and Tufano, F., 2017. “The research reproducibility crisis and economics of science.” _The Economic Journal_ , 127 (October): F200‐F208.

McCullough, B.D., McGeary, K.A., and Harrison, T.D., 2006. “Lessons from the JMCB Archive,” _Journal
|**Table 5A: Im**|**pact of SOF**<br>|**ES on jobs on**<br>|**the side, Bache**<br>|**lor students, SO**|**FES data**<br>||
|---|---|---|---|---|---|---|
||N|<RD|>RD||PROBIT||
|DS=0.08|(1)|(2)|(3)|(4)|(5)|(6)|
|±0.005|253|0.237|0.226|-0.010<br>(0.056)|-0.011<br>(0.057)|-0.030<br>(0.054)|
|±0.010|733|0.238|0.252|0.015<br>(0.032)|0.047<br>(0.034)|0.046<br>(0.034)|
|±0.015|1,051|0.249|0.235|-0.014|0.018|0.013|
|||||(0.026)|(0.028)|(0.028)|
|±0.020|1,292|0.245|0.246|0.001<br>(0.024)|0.026<br>(0.025)|0.023<br>(0.025)|
|DS=0.15|||||||
|±0.005|125|0.221|0.196|-
range of additional training services offered by SHG hosts. One of, if not the, most technically sophisticated studies to date, Datta’s (2015) assessment of the JEEVikA program in Bihar, finds that economically and socially marginalized groups can benefit significantly from SHG membership through a reduction in reliance on high cost sources of borrowing, as well as increased participation of women in household decision-making.<sup>1</sup> In addition, there is also evidence of SHG impacts on other aspects of human development. Saha et al. (2013), for instance, use national data, from a district level household survey, to show that the uptake of maternal health services is greater in villages where an SHG is present. Furthermore, a study by Deininger and Liu (2013) that characterizes the State of Andra Pradesh _Indhira Kranthi Patham (IKP)_ as focusing on twin goals of financial improvement and empowerment, finds that social capital was enhanced, that program members had higher savings and were more able to move freely within their village and interact within their caste. Protein and energy intake, and consumption also increased though the authors importantly noted that this might well reflect agricultural aspects of the program as much as income or asset changes.<sup>2,3</sup> A more recent experimental paper study finds little impact on empowerment of a microborrowing scheme although as its authors accept, Banerjee et al (2015 p27), the

> 1 Datta’s study is perhaps the closest to ours though there are two significant differences. JEEVikA is funded by the World Bank and operates by saturation and so offers a best-case but difficult to replicate study of self-help. From a methodological perspective, his paper depends on recall: while he argues plausibly for the approach, no such requirements were made of subjects in this study.

> 2 For relevant background research on female empowerment, see Brody et al (2015), Doepke (2014), Ganle (2015), Prennushi and Gupta (2014)
with cheap production factors and low regulatory costs. This idea is formalized within the literature on the Pollution Haven Hypothesis (PHH) and Environmental Dumping (ED). In a nutshell, the PHH states that a country's comparative advantage in hosting pollution intensive industries is decreasing in stringency of its respective regulatory framework. Assuming that an increase in environmental regulation makes a state's pollution intensive production more expensive, imports of such "dirty" goods will increase while their home production will decrease. Then, the lower are trade barriers between states, the more clean and dirty industries will sort across different (stringent=developed country and lax=developing country) regulatory environments (see Copeland and Taylor (1994) and Taylor (2004) for a detailed discussion). Such a pure effect of environmental regulation (stringency) may trade off with a Heckscher-Ohlin factor endowment effect<sup>5</sup> that assumes a positive correlation between a sector's capital and pollution intensity. Then, trade liberalization would induce specialization of capital abundant, developed countries into pollution intensive production. Furthermore, if the regulatory effect was true and states started to interact strategically by competing for production, international capital and/or firm location, equilibrium levels of environmental (or social) regulation could end up well below optimal levels and give rise to ED or, in the extreme case, lead to a "race to the

> 4 Source: OECD FDI Statistics (Directorate for Financial and Enterprise Affairs, www.oecd.org/investment/statistics)

> 5 also referred to as the capital labor effect by Cole and Elliott (2003)

Page **3** of **27**
_._ 043, p-value = 0 _._ 861) or motivation (RD estimate = _−_ 18 _._ 217, p-value = 0 _._ 257) for the job.

While our data shows no measurable differences between elected challengers and reelected incumbents at the RD threshold, it remains possible that challengers who won the most recent election are, on average, unobservably more competent than incumbents who secured reelection. Furthermore, if the gap in unobservable quality between elected challengers and elected incumbents is larger in close village elections than in typical elections outside our sample, then the findings we obtain in our sample of close elections may not generalize to other, less competitive elections.

To probe these concerns, we implement an empirical exercise designed to distinguish elections featuring less competent incumbents from those involving “unlucky incumbents” who faced adverse external circumstances as they competed for another term. Building on Marx et al. (2024), this approach identifies incumbents who faced a close reelection battle not mainly as a result of their poor past performance, but because of unfavorable external factors that created an electoral playing field hostile to incumbents, such as a natural disaster affecting the local community. Intuitively, natural disasters provide exogenous variation that reduces an incumbent’s reelection chances for reasons beyond their control. Using the administrative _Podes_ data, we identify a subsample of 242 villages that experienced a natural disaster—a major landslide, flood event, ocean tide, hurricane, or drought—during an incumbent’s term prior to the election observed in our data. In these elections, incumbents earned a vote share 6.5 p.p. lower on average, and they were 14 p.p. less likely to be reelected overall (Appendix Table A.25).<sup>14</sup>

> 14A stricter test would involve only considering natural disasters occurring the year of, or the year prior to an election. This would help rule out that an incumbent’s disaster response could also shift