rafmacalaba/data-use-mentions-extended huggingface dataset:
or fixed contract and are often connected to larger firms.

The earnings differentials generated are more precise and reliable than those previously generated, but are shown.*t6 be fundamentally unable to prove or disprove segmentation in this context. As an alternative, the paper examines the patterns of worker mobility predicted by the dualism hypothesis, generating both a dynamic overview of movemen **t** hrough the labor market, and characterizations of the interactions among the four classes of work. While unable to provide conclusive evidence, this approach offers substantial reason to question the dualistic view as the primary explanation for the existence and dynamics ofthe sector. The labor market appears relatively fluid with large and symmetric flows of work:ers among all sectors. The data is consistent with selfemployment being a desirable destination for many workers and with contract work being closely related. It also suggests that informal salaried work is the entry point and perhaps training area for young workers who, even if queuing, very quickly leave to take both formal and informal jobs.

# rr. Data:

The study employs two sources of data on men aged 16-65 with a high school education or less in 16 major metropolitan areas. The National Urban Employment Survey (NUES) conducts extensive quarterly interviews and is structureid so as to generate panels that allow tracking a fifth of the samnple across five quarters. Workers are matched by household, role in household, sex, level of education, and age, to ensure against generating spurious transitions. Though five quarters does not permit a full description of the life cycle of an individual, it is nonetheless possible to sketch patterns of mobility among sectors and to identify worker characteristics that correspond to them. To generate a sufficiently large sample of roughly 15,000 observations, three contiguous NUES cohorts were 3
tence of bribery distorts the allocation of resources and the entrepreneurial choices, without financial frictions in a closed, one-sector, neoclassical growth model.

My calibration strategy is similar to the strategy in Leal (2014), which investigates the relationship between the informal sector and misallocation. In his model, firms choose to be small for tax evasion purposes, because big firms cannot escape tax enforcement. Leal (2014) calibrates the model for the Mexican economy with distortions. However, the distortion level is defined as the total tax revenue over the value added associated with the formal sector, instead of calibrating the distortion level itself. My calibration strategy differs from Leal (2014) in terms of estimating the distortion levels. I calibrate the distortion level by using firm-level bribery data and plant-size distribution in Türkiye. Since bribery is illegal and secret, I cannot use the total tax revenue-value added ratio as the distortion rate. In addition, I focus on ‘solving problems’ or ‘getting things done’, instead of only focusing on tax evasion.

# **3 Bribery and Plant Size Data**

## **3.1 Bribery**

There are several corruption and governance indices created by the World Bank and independent institutions, such as the World Governance Indicators and Corruption Perception Index of Transparency International. These indices primarily depend on people’s perceptions rather than direct measures of bribery. Those indices can generally be regarded as indicative of underlying corruption, however, they do not provide information on the general rate of bribery, relative to size, including the level of sales. I use WBES data to analyze bribery, because it consists of data on whether a firm was asked to pay a bribe by government officials as well as the amount of bribes paid by a typical firm. The WBES has been conducted by the World Bank throughout the world, and it provides detailed enterprise-level data from interviews with top managers/owners. Questions vary from government-firm relationships to employee-employer relationships. The WBES has interviewed more than 177,000 firms
respondents) – (20212023)**

<!-- Start of picture text --> a) By area – 2021 b) By area – 2022 c) By area – 2023<br><!-- End of picture text --> <!-- Start of picture text --> 64.8 73.6 68.2 66.8 72.1 68.6 74.6 78.5 75.9<br>25.2 19 22.8 26.8 22.2 25.3 20.8 19.6 20.4<br>10 7.4 9 6.3 5.7 6.1 4.6 1.9 3.7<br>Urban Rural Total Urban Rural Total Urban Rural Total<br>0 hazards 1-2 hazards 3 or more hazards 0 hazards 1-2 hazards 3 or more hazards 0 hazards 1-2 hazards 3 or more hazards<br><!-- End of picture text --> Note: Results are from entire sample of wave 2, wave 3 and wave 4 households.

3
**Figure 3. Political inclusion and accountability and economic development in Sub-Saharan Africa (2021)**

<!-- Start of picture text --> Seychelles<br>4.5<br>Equatorial Guinea Gabon Botswana South AfricaMauritius<br>Eswatini Namibia<br>AngolaMauritaniaNigeriaCote d'IvoireKenya Cabo Verde 4<br>Sudan Cameroon Benin Senegal<br>Guinea Ghana<br>Comoros<br>Eritrea Chad Congo, Rep. Ethiopia Uganda Tanzania ZambiaGuinea-BissauBurkina FasoLesothoSao Tome and Principe 3.5<br>Somalia Zimbabwe Rwanda TogoMaliMozambique Niger Gambia, TheSierra Leone<br>Burundi Central African Republic Madagascar Liberia<br>South Sudan Malawi 3<br>Congo, Dem. Rep.<br>2.5<br>-2.00 -1.50 -1.00 Voice and accountability-0.50 0.00 0.50 1.00<br>log GDP pc PPP<br><!-- End of picture text --> <mark>Note: The x-axis represents the World Governance Indicator on voice and accountability. Oil rich economies are represented in yellow; non-oil but resource-rich economies in green; non-resource rich countries in blue. The data is the latest available (2021). Source: WGI database for voice and accountability indicator and WDI database for database for GDP per capita.</mark> Governance breakdowns underlie security and service delivery challenges and weaken the social contract.<sup>3</sup> Despite increased military spending, national security forces tend to be in decay (Dwyer 2017 and Bagayoko 2022), and African states struggle to respond to the security demands of their citizens. Inefficient public spending and chronic poor or under-administration due to low revenue collection prevent improvements in quality service delivery. More broadly, a global backlash against liberal institutions coupled with the rise of populism, facilitated by the rise of social media and pressure on traditional media, has also contributed to the weakness of social contracts on what a social contract is)
An evaluation of government as trustworthy reflects the judgment that authorities are motivated to fulfill their promises and do what is right for the societies they serve. When citizens conflate liking a leader with assessments of trustworthiness, their trust of leaders will be driven, in part, by leaders’ popularity. Survey evidence from the U.S. suggests trust in national government often reflects shifts in presidential popularity (Levi and Stoker, 2000). Charisma has often proved a basis for citizens’ evaluations of the trustworthiness of leaders, some of whom are trustworthy and others of whom are not. Identifying what drives a leader’s decisions or when or how her personal motives have changed is extremely difficult. Thus, the best objective measure of the trustworthiness of the motivations of government leaders is citizens’ assessment of the quality of institutions in which political authorities are embedded. In countries where citizens assess institutions as effectively selecting and constraining government personnel and as delivering the services they need, citizens’ evaluations of personal motivations are less important than their judgment that the institutions work (Hardin, 2002; Levi, 1988).

When citizens are confident that government has the competence to produce promised services, they are more likely to see government agents as trustworthy. A government’s administrative competence has two attributes: honesty, and the capacity to implement rules and regulations. Citizens are more likely to view government agents as trustworthy when they perceive that government can ensure that revenues will produce the services they expect and government promises. High levels of corruption can undermine this perception, particularly when the corruption takes the form of elected officials and government agents pocketing the revenues rather than spending them on public goods. Corruption reduces the resources government can spend on the poor through social programs and transfers resources generally from the poor to the wealthy (Rothstein and Uslaner, 2005; Tanzi, 1998). Corrupt governments are also left with less money to spend on public officials’ salaries creating incentives for government agents to accept bribes and prey upon citizens rather than serve them (
**Figure 14. Poverty reduction in simulated scenarios by 2030.**

<!-- Start of picture text --> 14a. EU-IDN, FTAAP and TPP-15 scenarios 14b. RCEP and TPP-11 scenarios<br><!-- End of picture text --> <!-- Start of picture text --> Poverty reduction, change wrt baseline<br>Poverty reduction, change wrt baseline<br>3.3<br>2.5<br>2.1<br>1.8<br>1.3<br>1.0<br>0.8<br>0.3<br>0.1<br>$3.20 $5.50<br>-0.3<br>EU-IDN FTAAP TPP-15 $3.20 $5.50<br>RCEP TPP-11<br>4<br>4<br>3<br>3<br>2<br>millions millions 2<br>1 1<br>0<br>0<br><!-- End of picture text --> Incidence curves in Figure 15 below depict deviations from baseline conditions that result from each additional simulation step, by scenario. The larger net effects are caused by growth. Indeed, the EU-IDN CEPA yields the highest expected growth of income relative to the other PTAs at every point of the income distribution. The distributional effects are relatively smaller, considering that at most they account for around 1.75 percent deviations in per capita household consumption from baseline conditions, after growth. In all cases, sectoral reallocation of labor contributes only marginally to changes in the income distribution. This follows from the assumption of fixed labor, for instance PTAs are assumed not to change the willingness of people to join the labor force.

The size of the gains grows monotonically with income for the EU-IDN CEPA while the growth incidence curve has a U-shape in the case of RCEP,
.3**|1.5|1.9|36%|
|<br>Share of EU-15 in exports of networks (%)|36.6|27.5|42.7|55.1|48.7|48.3|39.9|58.9|21%|
|Share of CEEC-8 in Bulgaria's exports of networks (%)|14.4|17.1|14.7|9.9|9.6|8.5|6.5|7.5|-22%|
|<br>Share in EU15 external imports of network (%)|0.03|0.02|0.02|0.02|0.02|0.02|0.02|0.03|50%|
|of which: parts|0.01|0.01|0.01|0.01|0.01|0.01|0.02|0.02|100%|
|final products|0.02|0.01|0.01|0.01|0.01|0.01|0.01|0.02|100%|

Source: Computations based on UN COMTRADE Statistics.

Third, in line with trends observed in other CEEC-10 economies (Kaminski 2004), there has been a shift towards specialization in automotive parts and ICT final products. Exports of the latter practically disappeared. While exports of motor vehicles accounted for 15 percent of automotive network’s EU-destined sales in 1996, this share fell to 2 percent in 2003. On the other hand, the share of other parts and accessories (SITC 7849) increased from 5 percent to 15 percent over this period. Exports of parts to EU-15 and CEEC-8 markets have driven the growth in automotive network exports in 2002-03. The share of parts increased from 69 percent in 2002 to 78 percent in 2003 and
There is a long tradition in development economics of “village studies,” where economists spend months, if not years, studying, engaging with, and collecting data from a small sample of rural villages. T. Scarlett Epstein (1962) spent several months in the late 1950s studying two villages in South India where she examined the links between the local economy and social structure, comparing a “dry” (arid), and a “wet” (irrigated) village. Her data included a mix of field observations, interviews, and survey data. Her approach to research integrated training in economics and anthropology; as a student of William Arthur Lewis she was deeply interested in patterns of employment, agrarian relations and migration, but as an anthropologist trained in the Manchester School she wanted to probe into the link between caste and social class and how these were affected by agrarian change. Her work on South India is therefore suffused by an interesting mix of qualitative and quantitative data and was widely influential.

Two economic theorists, Christopher Bliss and Nicholas Stern, inspired by Epstein, spent eight months between 1974-75 in the North Indian village of Palanpur to study a series of important questions: the nature of land tenure arrangements, labor and credit markers, risk and uncertainty. They were steeped in theory, but they let their conversations, experiences and observations inform the development of new theoretical models and condition the kinds of survey data they collected. Their book (Bliss and Stern, 1982) and the papers they wrote based on their first round of fieldwork, were followed by a series of repeated visits (primarily by Stern), their students and research collaborators that continues to this day (e.g: Himanshu, Lanjouw and Stern, 2018). Interestingly, while all the researchers involved in the Palanpur project are immersed in the conversations they had in the village, rarely are those conversations reported in their published research which is theoretical and econometric. Their work clearly displays a great deal of cognitive empathy, but it is almost
Table 13: Medium-term impacts of _Mais Educação_ on educational outcomes for schools opting for the mathematics activity

|Dependent variable|Dropout rates|Test score<br>mathematics|Test score<br>Portuguese|
|---|---|---|---|
||(1)|(2)|(3)|
|Panel A: Imp|acts on fifth-grade va|riables||
|Dummy_Mais Educação_* year 2011|0.864|–1.395|–0.770|
||(1.355)|(1.914)|(1.783)|
|_R_ <sup>2</sup>|0.701|0.808|0.807|
|Observations|1,162|1,162|1,162|
|Dummy year 2011|Yes|Yes|Yes|
|School fixed effects|Yes|Yes|Yes|
|Panel B: Imp|acts on ninth-grade v|ariables||
|Dummy_Mais Educação_* year 2011|0.342|-3.386|0.111|
||(1.375)|(2.117)|(2.199)|
|_R_ <sup>2</sup>|1,108|1,108|1,108|
|Observations|0.709|0.787|0.775|
|Dummy year 2011|Yes|Yes|Yes|
|School fixed effects|Yes|Yes|Yes|

_Sources:_ Authors’ calculations using MEC/FNDE administrative data, _Prova Brasil,_ School Census, and INEP.

_Note:_ Treatment group are schools that joined mathematics activity in 2008 and remained in the activity until 2011. Pedagogical monitoring must be chosen by schools as one of the fields of knowledge, but schools can choose for which subjects they offer pedagogical support (Portuguese, mathematics, sciences, history, etc.) This table reports
# 1. **Global trends in interest rate levels anfd spreads (annual data)**

# 1.1 Global trends since 1960

Broad trends in global interest rates since 1960 are summarized by the world medians shown in Table 1 .2 There appears to have been a general upward trend in the level of world median real interest rates, but the most striking feature is a pronounced secular swing in real rates over the past forty years, with a sharp dip into negative rates in the 1970s followed by a recovery to higher than previous levels in the 1980s and 1990s, and the beginnings of a reduction again more recently. The swing is evident in both money market and deposit rates.

From a theoretical point of view, variations over time in the general level of unregulated wholesale _ex post_ real interest rates can be explained by deviations of actual from expected inflation, and because of cyclical or trend changes in the productivity of capital and the propensity to save and perceptions of risk. Changes in the degree to which these interest rates are administratively controlled will also be a factor.

The causes of the secular swing in world interest rates since the 1960s, a we][l known feature of industrial country data, have been debated in the literature at length.<sup>3</sup> Was there a downturn in the marginal efficiency of capital (possibly associated with the surge in petroleum and other primary product prices); or was there a transitory an increase in the propensity to save? These are probably the leading explanations. In a fully integrated world capital market, these real factors would be fully transmitted across all markets, and would not retain any national features. Nominal, currency-specific factors such as shifts in the relation between actual and expected inflation are of greater interest in the present context, where we are looking at differences in the behavior of interest rates from country to country. Thus, a fairly plausible and

> **2** In this section, unless otherwise stated, "real"
||2017|24.53 %<br>[ 21.54 % ; 27.53 % ]|_missing_|
|Niger|2011|56.14 %<br>[ 48.77 % ; 63.50 % ]|17.03 %|
||2014|20.78 %<br>[ 15.78 % ; 25.78 % ]|_missing_|
|Nigeria|2019|2.39 %<br>[1.49 % ; 3.29 %]|_missing_|
||2008|12.38 %<br>[10.55 % ; 14.21 %]|_missing_|
||2010|18.98 %<br>[ 16.54 % ; 21.42 % ]|_missing_|
|Tanzania|2012|15.38 %<br>[ 13.08 % ; 17.68 % ]|2.09 %|
||2014|10.27 %<br>[ 4.08 % ; 16.45 % ]|_missing_|
||2019|16.50 %<br>[ 10.07 % ; 22.93 % ]|_missing_|

_Note: missing entries correspond to cases where either no event was reported or information on the population affected was missing in the EM-DAT. 95% confidence intervals for estimated totals are calculated with LSMS-ISA microdata_ 35
Policy Research Working Paper 5460

# **Abstract**

Soaring commodity prices in 2007 and 2008 raised concerns that volatility was also rising, which would have implications for welfare and therefore for the design of public policy interventions. The literature focuses on trends in commodity prices rather than their volatility characteristics. This paper contributes by examining commodity price volatility with a newly compiled monthly panel dataset on 45 individual commodity prices from the end of the 18th century until today. The main conclusions are: the timing and number of breaks in volatility vary considerably across individual commodities, cautioning against generalizations based on the use of commodity price indices; the three most significant breaks common to most commodities are the two world wars and the collapse of the Bretton-Woods system; and structural breaks marking increased price volatility are followed by breaks marking declines in volatility so that there is no upward or downward trend in volatility over time.

This paper—a product of the Economic Policy Unit, Poverty Reduction and Economic Management Department, Latin America and the Caribbean Region—is part of a larger effort in the department to provide policy relevant research on topics of interest to the region. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The authors may be contacted at rshankar@worldbank.org and ocalvogonzalez@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
≤_ 1. We thus view Ψ( _._ ) as an “inconvenience” yield, that is, a premium demanded by investors as compensation for holding the bond, in excess of its default risk.<sup>27</sup> Table 4 lists the calibrated parameters. For the subset of fixed parameters (Panel a), we set _γ_ = 2, which is a standard value for risk aversion in the literature. We also set a quarterly risk-free rate of _rf_ = 0 _._ 01, in line with the average real risk-free rate observed in the United States. The probability of re-entering international markets is set to _θ_ = 0 _._ 0385, implying an average exclusion duration of 6.5 years. We set _λ_ = 0 _._ 05 to target a debt maturity of 5 years and _ν_ = 0 _._ 03 to match Argentina’s average debt services. The parameters for the endowment process, _ρy_ and _σy_ , are based on log-linearly detrended quarterly real GDP data of Argentina. All these parameters are taken from Morelli and Moretti (2023). Last, we set _τ_<sup>_⋆_</sup> to match the average share of Argentina’s external debt tracked by passive investors, and calibrate _ρτ_ and _στ_ to match the persistence and volatility of our FIR measure.

We internally calibrate the remaining parameters (Table 4, Panel b). We jointly calibrate the default cost level and curvature, _{d_ 0 _, d_ 1 _}_ , together with the government’s discount factor _β_ , to target Argentina’s average ratio of (external) debt to GDP, average spread, and volatility of spreads.<sup>28</sup> Additionally, we calibrate _κ_ 0 to match the estimated (inverse) reduced-form ˆ demand elasticity, _η_ = _−_ 0 _._ 33. Last, we set _A_<sup> ̄</sup> to match the average
find new suppliers; and (ii) whether firms maintain links with suppliers. The first measure is summarized, for each firm _j_ and time period _t_ by the which measures the percent of suppliers who are new in the current period. A market is resilient if the entry rate does not decrease after a shock. The second measure is summarized by the which measures the percent of suppliers from the last period that no longer supply in the current period. A market is resilient if the separation rate does not increase after a shock. A net separation rate may also be measured as To evaluate the resilience of U.S. supply chains during COVID, we construct these measures for U.S. imports. The data source is Panjiva, which compiles the manifests for all container shipments coming into U.S. ports. Though the data do not include trade values, firm identifiers allow us to measure the extensive margin of trade. For each shipment, the data identify the U.S. firm receiving the shipment (consignee) and the foreign firm sending the shipment (shipper).

Figure **5** shows the quarterly averages of the entry and separation rates across all importing firms in the U.S. during COVID and a year before. Both measures have an average of about 35 percent, as many firms do not receive shipments from the same firms each quarter, and receive shipments only once or twice a year.

By these measures, supply chains appear resilient in the quarter of March 2020 to May 2020, the first wave of the pandemic. In that quarter the total value of imports dropped 13 percent in real terms relative to same quarter in the previous year, reflecting reduced demand and prices as the country entered lockdown, and potentially supply reductions elsewhere.<sup>18</sup> Despite this, the entry rate spiked to 42 percent. The separation rate also fell, as firms held on to previous

> 18 Total import values reported by the Census Bureau: https: