rafmacalaba/data-use-mentions-extended huggingface dataset:
composition of the board of directors? What percentage of board members represents management? Major owners (more than 5% shares)? Small shareholders?

3. Theory suggests that debt and equity contracts give shareholders and especially creditors control rights to intervene when a contract is violated. Bank surveys, however, in general do not provide information on the financial structure and the rights of stakeholders. _Suggested questions: (1)_ What is the share of outside equity? Share of inside equity? Share of debt financing? Debt-equity ratio? What type of institutions or individuals are the main shareholders (bank, pension funds, a holding company, a foreign investor, or a domestic investor)? The main creditors? (2) What are the control rights of the equity-holder, of the debt-holder? Would a firm become bankrupt if it defaults interest payment for debts? Does the firm post collateral when borrowing from banks? What is the share of tangible assets? What are the shares of long- and short-term debts in total debts respectively''

# **Internal Incentives**

Internal incentives help align managerial and employee objectives (see Holmstrom and Tirole, 1989, for a survey). There are many ways to motivate workers:

- Promotion, In theory, a large wage differential between workers at different levels improves performance by spurring employees to work hard in order to advance to the next layer, but this benefit has to be balanced against the loss of control and of information. The tournament literature suggests measuring the number of layers within the firm and wage differential between production workers and managers (Lazear and Rosen, 1981; Nalebuff and Stiglitz, 1983; Rosen, 1986).

- The threat of firing. Presumably, internal incentives are stronger when involuntary turnover is a real and credible threat. This may be achieved by allowing a certain percentage of contract workers. A high share of temporary workers, however, may have unintended consequences: The threat cf firing as a disciplinary option spurs worker incentives, but temporary workers
_Herfindahl index_ is the sum of the squared market share of each bank.<sup>12</sup> In both cases, higher values indicate more concentration.

Our final dataset is limited to countries which have both firm-level data on access to finance from the Enterprise Surveys and bank-level data from Bankscope to calculate the Lerner index and the concentration measures. This dataset contains information on 53 countries for the period 2002-2010.<sup>13</sup> Table A1 gives a list of the countries and years included in the final dataset. Some countries have had only two surveys in our time frame, while others have had three or more, with a maximum of five surveys for Bulgaria. The coverage of firms varies by country. For example, India has more than 4,000 firms covered in two surveys, while Malawi has fewer than 300 firms covered in two surveys. Because of this variation, we test the robustness of our results to weighting our regressions by the inverse of the square root of the number of firms in the survey, so that each country carries the same importance in our estimations. Our bank-level sample contains data on 3,409 banks and over 16,000 bank-year observations. Table A2 gives a list of countries in our sample along with the value of the Lerner index for each country over time.<sup>14</sup> Finally, we supplement our dataset with annual country-level data from several sources. We obtain data on private credit to GDP and inflation from the World Bank _World Development Indicators_ database. Data on the quality of credit information come from the World Bank _Doing Business_ dataset<sup>15</sup> and information on the share of assets held by government-owned banks comes from the World Bank Survey of Bank Regulation and Supervision.<sup>16</sup> Table 1, Panel B reports basic summary statistics for the firm survey variables and country-level variables. More than half of the firms in our sample have access to finance; in other words
CAGR) of 15.91 percent, while its trade with the world has grown at a CAGR of 10.17 percent. In addition, the intra-South Asia textile trade has experienced much faster growth than the region’s trade with the world. The inter-industry trade in the T&C sector offers crucial insights on specialization in different product lines that could play a pivotal role in fostering backward and forward linkages. Such value chains could be powerful drivers for developing regional production networks.

This study analyzes the bilateral T&C value chains between India and Bangladesh with a focus on policy related issues. The issues include tariff, non-tariff barriers (NTBs), industrial policies, trade facilitation, and rules of origin (RoO). Based on the comprehensive analysis of both primary and secondary data, it provides a set of recommendations which include common inextricably linked areas of reforms where India and Bangladesh can work together to strengthen bilateral T&C value chains. It also delves into areas of domestic reforms to enhance the competitiveness of their domestic T&C value chains and their subsequent integration into Global T&C value chains.

4
Arai et al. (2020) : The Hidden Potential of Call Detail Records in The Gambia.

A workshop in February 2020 was instrumental to initiate a partnership on “Big Data for Development” that evolved around ownership and sustainability. A joint vision and a clearly specified use case to focus the analysis on internal migration helped to coordinate expectations, and build capacity using a practical example. Moreover, a broader audience during the initial workshop - involving stakeholders from the private sector, ministries and agencies, academia, civil society, and development partners - confirmed the demand and interest for access and analysis of CDR data. During this discussion, it was instrumental to convince the MNO to join this initiative as they are collecting and providing the CDR data. Their agreement was based on the idea that training would also enhance their inhouse capacity to analyze CDR data to improve their business operations and enhance customer relations. The workshop also offered a forum to discuss any institutional, organizational, or technical challenges, and showcase how other countries have dealt with these concerns.

Once COVID-19 hit The Gambia in March 2020, all parties agreed to revisit the focus of the collaboration and explore the use of CDR data to respond to the health and economic crisis. In light of limited testing and health facilities, the government announced a national health emergency with profound restrictions on human mobility (Hale et al, 2020). As part of this dialog it became clear that prolonged social distancing would bear a high cost for households and firms (Gottlieb, Grobovsek, Poschke, & Saltiel, 2020), and there was interest to create an evidence base for smart containment measures. Based on successful applications in other countries, the analysis then focused on the use of CDR data to understand patterns of human mobility during COVID-19.

# **B. Strengthening technical capacity**

In addition to building a consensus about the analysis of CDR data, the partners agreed to strengthen technical capacity and ensure knowledge transfer. Crucially, rather than building a system from scratch, efforts
. (2020). “Career trajectories and cumulative wages: The case of temporary employment.”</u> _Research in Social Stratification and Mobility_ , _69_ , 100529.

- <u>Fouarge, D., de Grip, A., Smits, W., and R. de Vries (2012). “Flexible Contracts and Human Capital</u> Investments.” _De Economist_ , 160(2): 177–195.

- <u>Gagliardi, N., Grinza, E. and F. Rycx (2022). “Workers’ tenure and firm productivity: New</u> evidence from matched employer-employee panel data.” _Industrial Relations_ : early view online at https://doi.org/10.1111/irel.12309.

<u>Garcia-Cabo, J. and R. Madera (2019). “The Self-Employment Option in Rigid Labor Markets: An</u> Empirical Investigation.” _International Finance Discussion Papers_ 1264.

- <u>Graetz, G., Restrepo, P., and O. Skans (2022). “Technology and the Labor Market.”</u> _Labour Economics_ 76: 102177.

27
3 capture the sentiment in genuinely local news. Second, we augment our specifications with a daily “global news sentiment index” extracted from the original cross-country sentiment indices, allowing us to control for the tone of news published in the world every day. We find that the effect of local sentiment shocks is still significant and transitory. However, its magnitude is roughly cut in half, peaking around 5 basis points before vanishing after a week. By contrast, global news sentiment shocks have a stronger impact on returns around the world and do not reverse in the short run (i.e. at least 3 weeks). In our sample, sudden changes in optimism (or pessimism) in global news generate a permanent increase (decrease) of 25 basis points - roughly five times the size of the local sentiment shock – that reaches its peak slowly after ten to fifteen days, both in Advanced Economies (AEs) and in Emerging Markets (EMs).

Finally, we extend our analysis to international equity flows. Using daily equity flows from a very large number of international equity mutual funds investing in sixteen EMs between 2007 and 2015, we explore the response of capital flows to both local and global news sentiment shocks. Overall, we find a very similar response to that of stock returns: although local news optimism attracts equity fund flows for a few days only, global sentiment optimism generates a permanent equity inflow peaking after two weeks. Using the official domicile of each fund as a proxy for the location of investors, we then contrast the response of local and foreign equity funds. Although funds located outside the country respond strongly to news sentiment, the response from local funds is muted, suggesting that news tone affects prices mostly through its impact on foreign investors’ sentiment.

We close the paper by investigating further the properties of global news sentiment. We report three interesting findings. First, the effect of global news sentiment is state-dependent: the impact of global news sentiment is four times stronger in global “bear”
they faced no frictions. We turn to the National Study of the Changing Workforce which, in addition to data on incomes and hours worked, contains data on preferred hours of work. We use this measure of preferred hours worked to recover productivities and preferences ( _n, α_ ) for each individual as discussed in Section 3.4. While labor market frictions are modest (optimal hours worked differ from observed hours worked by about 10% on average), they do not appear to be an overly large driver of income inequality relative to productivity and preference differences. We discuss our findings with frictions in more detail in Appendix C.2.

# **6 Application: Optimal Income Taxation**

In this section we analyze how labor supply elasticities impact the optimal extent of redistribution via the implied degree of income inequality due to heterogeneity in productivities vs. heterogeneity in preferences. We calculate optimal income tax schedules using the distribution of productivities and preferences recovered under the various assumptions on the magnitudes of labor supply elasticities in Section 5. We contrast these optimal schedules to the optimal schedules calculated assuming that all inequality is driven by productivity heterogeneity (as in Mirrlees, 1971 or Saez, 2001).<sup>33</sup> We find that (1) optimal tax rates are slightly higher than Mirrleesian optimal rates under our baseline elasticity estimates and (2) larger effort elasticities and larger income effects lead to lower optimal rates relative to the Mirrleesian case.

## **6.1 Optimal Tax Problem**

The optimal tax problem is to maximize social welfare, subject to a budget constraint and incentive compatibility constraints that individuals maximize utility conditional on the given tax schedule. Let us denote _c_<sup>_∗_</sup> ( _n, α_ ), _z_<sup>_∗_</sup> ( _n, α_ ) and _u_<sup>_∗_</sup> ( _n, α_ ) as the optimal consumption, income, and utility levels for individual
**Table 1: Treatment arms and treatment assignments**

|**Treatment**|**Type**|**Message**|**N**|
|---|---|---|---|
|Control|No message||47,555|
|T1|Reminder|“Dear taxpayer, TRA reminds you to pay your property tax.<br>Pay before 30th June. For more information:<br>dial * 152 * 00 #, visit your nearest TRA ofce<br>or call 0800780078. Thank you.”|47,502|
|T2|Reciprocity|[Reminder] + “Your tax facilitates access to social<br>services and infrastructure. Together we build our nation.”|47,547|
|||[Reminder] + “Non-taxpayers are not contributing||
|T3|Social Pressure|to national development and thus hindering<br>development of their communities.”|47,538|
|T4|Enforcement*|[Reminder] + “Pay your tax early to avoid penalties.”|47,553|

***Note:** Subjects randomly allocated to T4 were mistakenly sent T1. See sub-section 3.3 for details. Original messages were sent in Swahili.

4 (Enforcement), essentially doubling the size of the first treatment arm. Thus no enforcement messages were sent as a part of the experiment (although a few were sent independently by the TRA). Second, instead of using the list of cleaned and prepared phone numbers they were provided, the firm chose an unformatted list which contained the same numbers, but in some cases were not usable due to missing pre-fixes or mistakenly included county codes. As a result, between 22-33 percent of each treatment arm was not sent the intended message. Using data from the text message delivery, we can account for which taxpayers were or were not sent a message due to
survey and administrative data it would be useful to better understand why the distributions differ when they do, especially because the few answers that have been given are for rich countries and are not consistent, as we now describe.

A priori, unit non-response of high income or wealth individuals would seem to be the most important factor explaining why surveys under-capture the top of these distributions. Evidence for this proposition comes from J **ohansson-Tormod and Klevmarken (2022), who use administrative data and two wealth surveys from Sweden in -2002 and 2003. The data is unusual because the authors could match both survey responders and non-responders to the admin data they accessed. The authors find that the survey underestimates mean wealth within the top 1 percent by 40 percent relative to the admin data. For the top percentile,** J **ohanssonTormod and Klevmarken (2022)** show that the mean of the top 1% using the survey response values is very similar to the mean when using the same survey respondents (i.e. excluding nonrespondents) but using the wealth values from the admin data for those survey respondents (which they can do because the surveys and admin data are linked). But the means for the top 1% are very different when comparing the survey respondents using their wealth information from the admin data and the full set of respondents plus non-respondents again using their wealth information from the admin data. This implies that under-reporting is not a concern, but that unit non-response is.

J **ohansson-Tormod and Klevmarken (2022) use two surveys, finding similar results in both. One of these is Share. The documentation for the survey indicates that for the Swedish Share survey the only adjustment of the design weights is calibration to eight age and sex totals from external demographic data. It is then perhaps unsurprising that unit nonresponse is found to be the cause of the differences in the top 1%.** Burkhauser et al. (2017) compare UK household surveys and admin data based on tax records
9 The procedure for aggregating consumption in each country was that followed to examine the poverty rate. Per capita expenditures were computed by dividing household expenditures by household size. Population weights were used to classify the entire population into five expenditure quintiles with the same number of people in each quintile. The bottom quintile, or quintile 1, had the lowest expenditure per capita and the top quintile, or quintile 5, had the highest expenditure per capita. Because the household size on average decreases with increasing wealth, the _number of households_ increased with increasing quintile. The quintiles were further segregated into urban and rural. The lower quintiles had many more rural households while urban households tended to be in upper quintiles. In Mauritius, the publicly available survey data sets did not contain information on the split between urban and rural, and hence only national results are presented. Universal access is generally measured by population and not households, including for the purpose of tracking SDG 7, and hence the results in this paper related to access are expressed as percentages of people rather than households. Because household representatives answer questions about household use of energy, the phrase used is “people living in households citing [or using]” a specific form of energy.

For the purpose of computing the share of total household expenditures spent on different forms of energy, outliers—defined as those spending more than 30 percent of total expenditures on any form of energy—were excluded to avoid having them dominate the results. All surveys enabled calculations of total household expenditures, taken as a proxy for income to leading order, with the exception of the South African survey, which asked households to choose one of the 10 income brackets. As a result, expenditure quintiles could not be computed for South Africa. Outlier households were not excluded in all other calculations, such as the number of people citing different forms of primary energy for cooking or those who had abandoned the use of polluting fuels.

One challenge in interpreting the expenditures is that freely acquired goods—the largest proportion
. Nearly all methods trying to predict poverty rates directly give a higher error than the worst method trying to predict from the last survey.

The poor performance of models trying to predict poverty rates directly is probably not due the fact that they have poverty rates on the left-hand side, but more likely that the models we have used with levels of poverty, mean and the Gini on the left-hand side do not use country fixed effects or lagged values. Under one interpretation, this treats these models equally to the models that predict changes from the last distributions, which likewise do not have country-fixed effects or lagged values. Such factors are less important when we have already taken first differences, though. Given that surveys within countries often are incomparable and often have many years between them, lagged information may not provide much additional information. Yet, to the extent that there are country specific effects in levels that are hard to predict, then any model that does not use 15
|Urban|Coefficient<br>Standard<br>error|14.21***<br>(1.731)|10.70***<br>(1.329)|8.787***<br>(0.740)|-0.00615*<br>(0.00345)|-0.00209<br>(0.00236)|0.00148<br>(0.00155)|
|---|---|---|---|---|---|---|---|
||N|119|149|150|119|149|150|

Note: Robust standard errors are in parentheses. *,**,*** significance level at 10%, 5% and 1%.

Source: Author's estimates using data from Bangladesh LFS and UN Comtrade. This table shows the first and second stage of the 2SLS regression. In the first stage, we estimate the contribution of US import demand to the increase in Bangladesh exports, and in the second stage we estimate the effect of an increase in exports on informality rates. This relationship is estimated for different worker types (male, female, rural, skilled, unskilled, young, and old).

# **Annex B**

**Figure B1 . Kdensity plots for the apparel sector in Bangladesh,**

<!-- Start of picture text --> 2010 2013<br>0 10000 20000 30000<br>2016<br>0 10000 20000 30000<br>x<br>Graphs by year<br>.0003<br>.0002<br>.0001<br>0<br>.0003<br>kdensity monthly_wages .0002<br>.0001<br>0<br><!-- End of picture text -->
# **Figure 10**

Monetary Poverty in Argentina and Chile: Total, Permanent Income, and Redistribution <!-- Start of picture text --> Panel A. Argentina<br>40<br>35<br>30<br>25<br>20<br>15<br>10<br>5<br>0<br>Poverty Poverty (Permanent Income) Poverty (Permanent Income and Redistribution)<br>Panel B. Chile<br>30<br>25<br>20<br>15<br>10<br>5<br>0<br>Poverty Poverty (Permanent Income) Poverty (Permanent Income and Redistribution)<br>Notes: Poverty line of 5.50 dollars per person a day (2011 PPP). See Appendix B for details.<br>Sources: Authors’ estimations based on SEDLAC (CEDLAS and World Bank) and WEO (October 2018).<br>Headcount Ratio<br>2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017<br>Headcount Ratio<br>2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017<br><!-- End of picture text --> 26
inequality between 2001 and 2009 using different indicators. Using the difference in education attainment between the highest and lowest groups (the 75th and 25th percentiles in terms of years of education), none of the countries in the region registered a decline in this measure of inequality. The standard deviation in years of education, another measure of inequality, declined only in one country, Honduras. Based on these two indicators, therefore, the range in education attainment within

> 7 Gini coefficients were approximated using detailed information on levels (years) of education achieved using the Barro-Lee (2010) dataset. “Advanced economies” represents countries classified as such in the dataset.

7
._011)|(0_._011)|(0_._011)|
|Country-pair FE|✓|✓|✓|✓|
|Industry-pair FE|✓|✓|✓|✓|
|Shareholder-country-industry-year FE|✓|✓|✓|✓|
|Subsidiary-country-industry-year FE|✓|✓|✓|✓|
|Domestic-year FE|✓|✓|✓|✓|
|Obs.|990,033|990,033|990,033|990,033|
|R<sup>2</sup>|0.565|0.565|0.565|0.565|

Standard errors are clustered at country-industry pairs level and reported in parentheses. * _p <_ 0 _._ 1, ** _p <_ 0 _._ 05, *** _p <_ 0 _._ 01 Table 9 indicates that a higher input specificity raises the marginal effect of any PTA membership and of PTA depth for the propensity of there being any ownership links. The effect tends to be generally bigger in the forward than in the backward integration direction.

– – Hence, overall, PTAs and particularly deep ones raise the propensity of any firm integration, specifically in the forward integration direction, and even more so when the inputs supplied by the parent to the affiliate are more specific and customized than otherwise.

# **4 Conclusion**

In this paper, we investigate the effects of PTAs (and their depth) on firm ownership. Thanks to a unique and novel data set that measures counts of ownership links at a country and sector pair level, we can uncover interesting heterogeneities arising when a PTA comes into force. In particular, given the structure of our data, we are the first to look at sector-specific characteristics when it comes to ownership along 15