rafmacalaba/data-use-mentions-extended huggingface dataset:
first explore the sources of bias introduced at each stage of the phone survey selection and interview process, by comparing the profile of characteristics of the F2F and the phone survey samples. After examining the source and extent of bias, it will then assess the effectiveness of reweighting adjustment methods to correct for these biases. These adjustment methods are especially powerful when using samples drawn from existing representative face-to-face surveys, like the Living Standards Measurement Study (LSMS) surveys, which contain a wealth of detailed information on all sampled units. While similar adjustments can be performed under other sampling approaches, such as sampling from a list of numbers from a telecom provider or random digit dialing, the scope of the adjustments is more limited in those approaches since they often do not have detailed information on sampled units that is available in a representative F2F survey. This could present one advantage of sampling from representative F2F surveys over other sampling approaches.

The remainder of this paper will proceed as follows. In Section 2, the benefits and limitations of phone surveys will be briefly reviewed including the different sources of bias and methods available to reduce the bias. In Section 3, data and methods will be discussed. Section 4 presents the results and Section 5 concludes.

# 2. Phone Surveys

Even though F2F surveys have been the main mode of data collection in developing countries for many years, there is also a growing list of successfully implemented phone surveys (see Tomlinson, 2
country compared to others, half the income level (in US$) is needed for the same consumption welfare. Such comparisons are facilitated through PPP adjustments, as in the PWT series _rgdpo_ or _rgdpe_ (Feenstra et al., 2015), which are also contained in our data set. PPP adjustments, however, can cause short-term country-specific fluctuations in the PPP-income variable (Inklaar et al., 2022), which can distort analysis of country-specific growth contributions.

Our model and results based on _lrgdpna_pc_ still have a proper welfare interpretation because they control for cross-fixed effects and hence differences in price levels across countries. But for cross-country comparison of welfare levels or analyses without country fixed effects, we recommend the variables _lrgdpe_pc_ (or _lrgdpo_pc_ ), which are also contained in our data set.

## **4.2 Correlates of Growth**

In table 1, we provide an overview of variables contained in our dataset and models. Variables are ordered by their availability: variables in the “small model” are widely available, hence allowing for a sample of approximately 1,500 observations (168 countries). The “small model” hence has the broadest coverage and should therefore provide most precise parameter estimates. However, it does not contain some potentially relevant variables that previous research has found to correlate with growth but which are only available for fewer countries. We hence include those additional variables in a separate “medium” and “large” model. For those models, the sample decreases to 967 (149 countries) and 635 observations (128 countries), respectively. The variables, their construction, and rationale for inclusion are discussed below the table. While the table focuses on variables we include in different model variants, the text also highlights alternative variables included in the data set, where available.

> 13 The NAMES_STD is a standardized country identification from STATA’s ‘kountry’ ado command. 14 Some of the mentioned factors may impact country groups differently (e.g., commodity prices for importers
of origin. Section 6 analyzes differences in yield spreads between bonds issues at home and abroad. Section 7 concludes.

# **2. Data and Descriptive Statistics**

To compare the major characteristics of corporate bond issues in international and domestic markets and analyze how firms use these markets, we assemble a comprehensive dataset on firms‘ public debt issues in capital markets around the world from 1991 through 2008.

Our data on firms‘ debt issuance activity come from the SDC Platinum database from Thomson Reuters, which provides transaction-level information on new bonds issued in public capital markets with an original maturity of one year or more.<sup>10</sup> Given that SDC does not collect data on debt issues with a maturity of less than one year, our dataset does not include commercial paper issues with such short-term maturities. Because our analysis focuses on corporate bond issues, we exclude all public sector debt issues, comprising bonds issued by national, local, and regional governments, government agencies, regional agencies, and multilateral organizations. We also exclude debt issues by investment funds, investment companies, and real estate investment trusts (REITs), as well as mortgage-backed securities and other asset-backed securities.

SDC provides data on several major characteristics of corporate bond issues, including the amount raised, issue date, maturity date, currency denomination, credit rating, type of rate, and yield at issue. SDC collects data on security issuances mostly from filings with local regulatory agencies and exchanges. These data are augmented with data from other sources such

> 10 SDC does not provide accurate data on the location of issuance of privately placed bonds. Thus, we cannot classify these issues as domestic or international. We, therefore, exclude private placements from our sample. According to SDC, private placements account for less than 18 percent of the total amount raised through corporate bond issues in capital markets around the world during our sample period.

9
Research, Cambridge, MA.

- De’, Rahul, Neena Pandey, and Abhipsa Pal. 2020. “Impact of Digital Surge during Covid-19 Pandemic: A Viewpoint on Research and Practice.” _International Journal of Information Management_ , 55(C): 102171.

- Deléchat, Corinne, Monique Newiak, Rui Xu, Fan Yang, and Goksu Aslan. 2018. “What is Driving Women’s Financial Inclusion Across Countries?” IMF Working Paper No. 18/38, International Monetary Fund, Washington, DC.

- Demirguc-Kunt, Asli, Leora Klapper, Dorothe Singer, and Saniya Ansar. 2022. _The Global Findex Database 2021: Financial Inclusion, Digital Payments, and Resilience in the Age of COVID-19_ . Washington, DC: World Bank.

- Dupas, Pascaline, Dean Karlan, Jonathan Robinson, and Diego Ubfal. 2018. “Banking the Unbanked? Evidence from Three Countries.” _American Economic Journal: Applied Economics_ 10(2): 257–297.

28
I compare the educational attainment of two groups. The first consists of adults whose mandatory schooling was completed before the war started (born 1966-1973). The second group contains individuals who were of school age or relatively young during the war (“exposed” cohort - born 1976-1985). The latter group is then sub-divided into two subgroups where one of the subgroups lived in the areas highly affected by the conflict (the main group of interest) and the second subgroup lived in the lesser affected areas. The inclusion in the exposed cohort is determined by the age of mandatory school attendance, where children in Tajikistan are required to start attending school at age seven and nine years of education is mandatory and free of charge. I assume that a child was exposed to the conflict if he was between ages 7 and 15 during the 1992-1998, and therefore was eligible to be enrolled in a publicly funded school. The study of educational outcomes in 2007 is a follow-up on Shemyakina (2011) who found using the 1999 and 2003 data that in the short- and medium runs, young girls from the households and regions more affected by the conflict, were less likely to be enrolled in school or less likely to complete nine grades (equivalent to basic education level) of schooling by 2003 as a cohort. This analysis explores the long-run effect of conflict on educational outcomes and uses a pooled data from the 2003 and 2007 surveys.

As explained in more detail in section 5.2, the sample for the analysis of labor market outcomes is limited to those who were 22-49 year old in 2007. This age group is the most economically active. In the analysis of labor market outcomes, the “war-affected cohort” is defined as those born between 1970-1985 and thus also includes individuals whose early labor market experiences may have been disrupted by the war. The comparison group is set to those born in 1958-1969.<sup>13</sup>

> 13 Since exposure to conflict could affect all birth cohorts differentially, my initial model specification
)|(2)|(3)|(4)|
|IDN exports per worker|-0.000377||-0.000376|-0.000293|0.000520||0.000510|0.000402|
||(0.000169)**||(0.000169)**|(0.000179)|(0.000187)***||(0.000186)**|*(0.000197)**|
|Share of hhlds with computer||0.0425|0.0410|0.0507||-0.142|-0.140|-0.153|
|||(0.0702)|(0.0699)|(0.0712)||(0.0737)*|(0.0747)*|(0.0759)**|
|IDN exports per worker *||||-0.00194||||0.00256|
|Share of hhlds with computer||||(0.000927)**||||(0.000939)***|
|Observations|2,734|2,734|2,734|2,734|2,734|2,734|2,734|2,734|
|R-squared|0.963|0.963|0.963|0.963|0.812|0.813|0.813|0.812|

Source: Authors’ calculations based on Sakernas 2005-2010 and 2011-2015, Susenas 2005-2010 and 2011-2015, 2004 U.S. O*Net database, and UN Comtrade database 2005-2010 and 2011-2015.

Notes: IV regressions. IV: IV: Global demand for foreign products excluding Indonesian products. Indonesian exports per worker in thousands of USD at 2010 prices and global demand for foreign products in millions of USD at 2010 prices. ICT variable at
In Figure 3, we begin to show that migration flows between origin and destination countries could be associated with corruption levels. Although most country-pairs bilateral migration is relatively low, high migration outflows are observed for high levels of corruption in sending countries, while much lower outflows are observed from countries that experience low levels of corruption. Preliminary analysis also suggests that higher migration flows are observed from relatively more corrupt to relatively less corrupt countries. Corruption appears to be both a push and a pull factor for migration decisions.

# **2.3 Control variables**

The control variables that we use in the analysis are summarized in Table 1.

First, following the literature on gravity models, we measure the geographical distance between countries by including a dummy variable equal to one when countries share a common border, as well as indicators for the difference in time zones, the distance (weighted by population), and the area of the country. To control for cultural and economic distances, we include variables measuring whether countries share a common language, a common religion, and a common currency. All these variables come from the CEPII Gravity Database.

Second, to control for differences in income levels, labor markets, and openness, we include GDP per capita levels, inflation rates, and the importance of agriculture and services in the local economy. These variables come from the World Bank World Development Indicators.

Third, to measure differences between countries in legal barriers to mobility, employment legislation, and the generosity of the welfare system, we consider OECD measures on: _i_ ) employment protection legislation; _ii_ ) product market regulation; _iii_ ) social expenditure; and _iv_ ) the right to work.<sup>12</sup>

> 12The OECD employment protection legislation is a synthetic indicator of the strictness of regulation on the dismissal of workers on regular contracts and the hiring of workers on temporary contracts. It ranges from 0 to 6, where 6 measures the strictest regulation. Then, a larger number indicates more job security.
are larger than the costs of attending a screening session.

This highlights the role of present bias in the low uptake of screening. People generally tend to be timeinconsistent and delay screenings, and the reduced value that people attribute to screenings reinforces the impact that present bias can have in postponing appropriate preventive care. A theoretical study using US data found that time-inconsistent health investment behaviors -including with regard to screenings- could explain a loss of about four years in life expectancy (Strulik & Wener, 2021). Further studies have also shown that high discount rates are associated with lower screening rates for cancer and measurements of blood cholesterol (Bradford, 2010). It is important to emphasize the relevance of motivation in terms of healthy aging, since preventive screenings are not a one-time process. Usually, screening for NCDs needs to be conducted annually after certain ages. Therefore, these biases can have long-term consequences if motivation for screening uptake remains low.

Loss aversion also influences the motivation to attend health screenings in a subtle way. Even people with accurate perceptions may not take up screening to avoid the realization of a foreseeable bad outcome. A qualitative study in the United States found that fatalism and negative outcome perspectives were important barriers to participation in screening for breast cancer (Peek et al., 2008). Similar findings were obtained across multiple countries, linking fatalism to lower attendance at cancer screening programs (De Los Monteros & Gallo, 2011). Another literature review based on the UK population found fear of a negative outcome as one of the reasons individuals did not attend the regular health checks proposed by the NHS (Harte et al., 2018). While one might think that loss aversion would only affect voluntary attendance at screenings, other studies suggest that this fear also leads people to reject explicit invitations to screening programs (Witte & Allen, 2000). More importantly, loss aversion does not only have a personal impact. A field experiment in Australia showed that loss aversion among relatives can influence an individual’s 16
and inconsistent exchange rate policies (Dollar, 1992; Razin and Collins, 1999). A relatively stronger currency tends to raise the cost of imports (among them, intermediate inputs and capital goods) and has a detrimental effect on investment. Moreover the loss of competitiveness associated with the overvaluation could hamper the country’s ability to adjust internationally and reallocate resources more efficiently across the different sectors of economic activity. However, the literature on the growth effects of RER undervaluation is not abundant. As we mentioned above, Hausmann et al. (2005) and Rodrik (2008) have suggested that RER undervaluation may trigger growth.<sup>1</sup> If it is true that real undervaluation of the currency leads to higher growth, the relevant policy question is what type of policy shocks may cause RER undervaluations and how persistent these are.

To accomplish this task, we use RER misalignments based on Kubota (2009). This measure of RER misalignments is as deviations of the actual from the equilibrium RER. We estimate the fundamental RER equation using the following econometric techniques: (a) Johansen time series cointegration methods, and (b) pooled mean group (PMG) for non-stationary panel data.<sup>2</sup> This equilibrium level is derived from a theoretical model that guarantees intertemporal BOP equilibrium and equilibrium in the tradable and non-tradable goods market by solving for the current account dynamics and Harrod-Balassa-Samuelson (HBS) productivities. We calculate the RER misalignment using two different types of estimates for the coefficients of the long-run RER equation: the time series estimates (Johansen, 1998, 1991) and the PMG panel estimates.

The main goal of our paper is to test whether economic policies and regulations undertaken by the authorities affect the likelihood of keeping the RER undervalued and/or determine the size of the undervaluation. This will allow us to test whether the ― _mercantilist_ ‖ view of the exchange rate policy is empirically valid. To accomplish this task we gather an unbalanced panel dataset of 79 countries, of
# **E. Revenue and expenditure volatility**

# **F. Risk of debt distress**

<!-- Start of picture text --> Standard deviation Percent of countries<br>8 In debt distress High risk<br>60 Moderate risk Low risk<br>6<br>50<br>4 40<br>30<br>2<br>20<br>10<br>0<br>Small EMDEs Small EMDEs 0<br>states excl. small states excl. small 2015 2023 2015 2023<br>states states<br>Small states EMDEs excl. small<br>Expenditure Revenue states<br><!-- End of picture text --> _Sources:_ EM-DAT (database); International Monetary Fund; WDI (database); WEO (database); World Bank.

_Note:_ EAP = East Asia and Pacific; EMDEs = emerging market and developing economies; LAC = Latin America and the Caribbean.

A. GDP weighted average annual GDP per capita growth for groups of countries. Sample includes 34 small states, 111 other EMDEs and 37 advanced economies. Guyana is excluded.

B. Frequency is the average number of natural disasters per year, adjusted by land mass, for the period 2000-22. Unit of frequency is the number of natural disasters per year per one hundred thousand square kilometers. Average cost of natural disasters per year as percent of GDP for 24 small states and 93 other EMDEs. EAP small states include 8 economies, LAC small states include 11 economies, and other small states include 5 economies. Natural disasters include droughts, storms, floods, extreme temperatures, earthquakes, volcanic activity, and wildfires.

C. Average ratio of trade (exports plus imports of goods and services) to GDP, 2000-22, for 27 small states and 114 other EMDEs. Whiskers indicate interquartile range.

D. Standard deviation of annual real GDP growth in 34 small states and 115 other EMDEs for
management score is associated with a 14.5 percent increase in sales per worker (Bloom et al., 2019a). The associated increase in value added per worker is 9 percent in the United States, 12 percent in Pakistan and 15 percent in Mexico.<sup>31</sup> As the evidence presented in this paper comprises of simple association between firm performance and better management practices, it is natural to have concerns regarding endogeneity in adoption of better management practices. For example, it is possible that more productive and efficient firms develop more sophisticated management capabilities as a part of learning by doing, rather than other way round. Due to limitations in our survey data (e.g. information on management practices are based on one year of data collection), we are unable to test for the causality of management practices with superior firm performance in Russia. Nonetheless, the literature on other countries has shown that indeed there is a causal relationship going from improved management practices to increased productivity. For example, in the case of post-war Italy Giorcelli (2018) finds long term effects of better management practices on performance of firms (e.g. productivity, sales, and survival) relative to machinery purchases or technology. The key channel through which skills improved firm performance was by helping managers make better investment decisions— investing in new plants or new machines, for example—which made their production more efficient. The fact that the coefficients obtained from our analysis are similar to those found in the existing literature (see Table 5), reassuring in that better management practices in Russia will likely drive positive effects on firm performance.

Another interesting finding in Table 4 is that the type of management practices affects the productivity indicator in distinct ways. Columns 2, 6, 10 and 14 of Table 4 show the unconditional correlation of the two sub-types of management practices – those related to data driven performance monitoring and those relating to incentives and targets. Data driven performance monitoring has a stronger association with increased sales per
# **1. Introduction**

Business regulations often create opportunities for public officials to collect bribes (De Soto 1989; Shleifer and Vishny 1993; Djankov et al. 2002; Svensson 2005). If true, this simple insight provides a practical and powerful way for deregulation to combat corruption and its many harmful effects on the economy.<sup>1</sup> However, empirical evidence on how regulation affects corruption is limited in several ways. For one, the existing studies use macro-level corruption indices based on subjective opinions of experts and, therefore, are prone to perception bias (Svensson 2003; Fan et al. 2009). Regulation is often measured by laws on the books rather than the actual regulatory burden on the firms even though it is the latter that is the primary determinant of corruption (Kaufmann and Wei 2000; Duvanova 2014). The present paper attempts to fill this gap in the literature by using firm-level survey data on the actual corruption and regulatory burden experienced by the firms. We find a large positive impact of regulation on the level of corruption. The result is robust to alternative corruption indicators, estimation methods and various endogeneity checks.

Theoretical models posit two views of regulation. The first view, the public interest view, goes back to Pigou (1938) who argued that markets are prone to frequent failures and regulation is intended to correct for these market failures. Corruption may still arise as implementing rules requires bureaucrats who may be corrupt (Acemoglu and Verdier 2000). The second view, the public choice theory, stresses that regulation is intended to create rents to be distributed between the industry incumbents and the corrupt public officials. In some cases, the main beneficiary of

> 1 Corruption is found to slow down economic growth (Mauro 1997; Fisman and Svensson 2007), lower productivity and investment (Mauro 1995; 1997; Keefer and Knack 1995) and discourage foreign investment (Alesina and Weder 1999; Wei 2000).

2
|
||Local area human capital|-0.012***|-0.010***||-0.012***|-0.010**|
|Worker Sortin||(0.004)|(0.002)|_[1.00]_|(0.004)|(0.004)|
|g|Individual-level human capital|-0.014**|-0.013***||-0.014**|-0.011*|
|||(0.006)|(0.003)|_[1.00]_|(0.006)|(0.006)|
||Constant|-0.002|-0.005||0.000|-0.004|
|||(0.010)|(0.005)|_[1.00]_|(0.010)|(0.009)|
||Observations|1,242|1,242||1,242|1,242|

Notes: Outcome is the reported agglomeration elasticity. All models are estimated under PET-PEESE, “precision-effect estimates with standard errors” following Stanley and Doucouliagos (2019), which to correct for publication bias includes a squared standard error term, and its interaction with a developing country dummy, and with a post-1990 dummy. The standard error terms are not reported for parsimony. PET-PEESE estimates are precision-weighted, using the inverse of the reported estimate’s variance as the weight. Robust standard errors are presented in parentheses, these are clustered at the study level for all model selection methods except Bayesian Model Averaging (BMA). The square brackets reflect the probability of each variable’s model inclusion under BMA. The General model includes all control variables. BIC and AIC are the specifications (from all 17million combinations of controls) that minimize