rafmacalaba/data-use-mentions-extended huggingface dataset:
Chaurey, R., 2017. Location-based tax incentives: Evidence from India. Journal of Public Economics, 156, pp.101-120.

- Chirinko, R.S. and Wilson, D.J., 2008. State investment tax incentives: A zero-sum game?. Journal of Public Economics, 92(12), pp.2362-2384.

- Diodato, D., Neffke, F. and O’Clery, N., 2018. Why do industries coagglomerate? How Marshallian externalities differ by industry and have evolved over time. Journal of Urban Economics, 106, pp.1-26.

- Duranton, G., Gobillon, L. and Overman, H.G., 2011. Assessing the effects of local taxation using microgeographic data. The economic journal, 121(555), pp.1017-1046.

- Duranton, G., and A. J. Venables. 2018. “Place-Based Policies for Development.” Policy Research Working Paper 8410, World Bank, Washington, DC.

- Ellison, G., Glaeser, E.L. and Kerr, W.R., 2010. What causes industry agglomeration? Evidence from coagglomeration patterns. American Economic Review, 100(3), pp.1195-1213.

- Faggio, G., Silva, O. and Strange, W.C., 2017. Heterogeneous agglomeration. Review of Economics and Statistics, 99(1), pp.80-94.

- Fox, W.F., 1986. Tax structure and the location of economic activity along state borders. National Tax Journal (1986-1998), 39(4), p.387.

- Giroud, X. and Rauh, J., 2019. State taxation and the reallocation of business activity: Evidence from establishment-level data. Journal of Political Economy, 127(3), pp.000-000.

- Glaeser, E. L., and J. D. Gottlieb. 2008. “The Economics of Place-Making Policies.” NBER Working Paper w14373
2

# **I. Introduction**

Have countries reformed bank regulatory and supervisory policies over the last twelve years, and which reforms have enhanced banking system performance? The Bank for International Settlements (BIS), the International Monetary Fund (IMF), and the World Bank (Bank) work on designing effective financial sector reform strategies, communicating those recommendations to countries, and helping countries implement changes to improve the efficiency and stability of their financial systems. Indeed, in 1999 the Bank and the IMF started the Financial Sector Assessment Program (FSAP) to assess systematically the status of banking systems in countries and to make recommendations for reform. But have these recommendations been adopted and have they been successful at boosting the efficiency and stability of financial systems?

Motivated by these very basic questions, the World Bank asked us more than twelve years ago to start assembling the first cross-country database on bank regulation and supervision. Based on guidance from bank supervisors, financial economists, and our own experiences, the Bank began putting together an extensive survey of bank regulation and supervision.<sup>1</sup> The original survey, Survey I, had 117 country respondents in 1999.<sup>2</sup> The first update in 2003, Survey II, characterized the regulatory situation at the end of 2002, and had 152 respondents. Survey III, the last one taken while one of us was still with the Bank, has responses from 142 countries characterizing regulation and supervision as of 2005-2006. The World Bank completed Survey IV in 2011 (with some corrections in 2012) with responses from over 125 countries.

In this paper, we reassess what works in banking regulation based on the new World Bank survey (Survey IV) of bank regulation and supervision around world. This paper briefly

> 1 As in Barth, Caprio, and Levine (2006), we sometimes use the term regulation generically to apply to banking-sector policies and compliance mechanisms, while at other times to discuss particular, specific regulations or special aspects
“noisy,” filled with identity claims, emotional rhetoric, and demands for individual entitlements. Second, discourse often reflects the existing patterns of social standing and hierarchy, with elites dominating discussion in less fractionalized villages, and women remaining relatively silent even when they attend _en masse_ . While these inequalities are undoubtedly concerning, three sets of empirical findings reveal the ways in which deliberation in India still thrives. First, these inequalities have been challenged from _within_ the deliberative forums, with lower castes using the open discursive space to make claims of dignity that cannot be ignored by those in power. The use of such dignity claims, while not traditionally considered “rational deliberation,” still plays a vital role in enabling citizens to generate credibility, create empathy, and trigger a sense of injustice (Mansbridge 2015). Second, state policies and intervention have been shown to help lessen the extent of inequality within the deliberative sphere. Programs to train and empower citizens, like the People’s Campaign in Kerala or the women-centered Pudhu Vaazvhu Project in Tamil Nadu, suggest that facilitated deliberation can usher the _sabhas_ closer to the deliberative ideal – in which citizens of equal standing arrive at consensual decisions on matters of public interest. And third, literacy in India is on the rise; over the past decade alone, adult literacy has grown by roughly 10 percent to 74 percent, with the strongest gains in rural regions and among women (Census of India 2011).<sup>4</sup> Given these gains, one could speculate that the quality of deliberation should improve. The Kerala’s People’s Campaign has not translated well in other, less literate, parts of India (Ananthpur et al. 2014); thus, secular improvements in literacy levels should improve the efficacy of attempts to make deliberative democracy a more practical tool for collective action.

The deepening of democracy via deliberation, however, has also faced meaningful limitations. Perhaps most obviously, the 73<sup>rd</sup> Amendment only applies
Times change. Newspapers are full of recent examples when tariffs have been used to achieve macroeconomic gains. For instance, tariffs are claimed to raise GDP and/or employment by improving the trade balance. So it seems an opportune moment to see what the historical record has actually been in terms of the macroeconomic effects of tariff changes. Taking a fresh look at the macroeconomic evidence is also important because previous studies are dated.

Our strategy is to use a straightforward and established methodology that strikes us as fit for the purpose at hand. We use Jorda’s (2005) local projection method to estimate impulse response functions, which allows us as to account flexibly for non-linearities without imposing potentially inappropriate dynamic restrictions. We also try to account for potential endogeneity by allowing changes in tariffs to react to changes in economic activity within the same year—that is, by controlling for contemporaneous changes in economic activity – and via an instrumental variable approach in the spirit of Acemoglu et al. (2019) and Furceri and Loungani (2018), where changes in tariffs in major large trading partners are our instrument for national tariff rate changes. Our panel of annual data is long if unbalanced, covering 1963 through 2014; more recent data is of greater relevance, but older data contains more protectionism. We use a broad span of 151 countries, including 34 advanced and 117 developing countries.

We focus on the key macroeconomic variables: output; productivity; unemployment; inequality; the real exchange rate; and the trade balance. Our chief data set is aggregate in nature, but we also use sectoral data, both to control for macroeconomic shocks that may drive tariff changes and to identify some of the channels through which tariffs affect 3
**_5_**

Researchers typically define TFP growth as a residual: real per capita **GDP** growth minus real per capita capital growth times capital share in the national income accounts, which is commonly taken to be between **0.3** and 0.4. Thus, simply **by** using better capital data, we obtain more accurate measures of TFP growth. Moreover, aggregate studies of TFP growth frequently ignore human capital accumulation. In contrast, we use both the Mankiw **(1995)** and the Bils and Klenow **(1996)** specifications to control for human capital accumulation. Thus, we obtain three improved measures of TFP to examine the impact of banking sector development on productivity growth. Finally, this paper also uses an improved measure of banking sector development. We measure banking sector credits to the private sector relative to **GDP.** This measure more carefully distinguishes who is conducting the intermediation, to where the funds are flowing, and we more accurately deflate financial stocks than past studies [e.g., King and Levine 1993a,b]. Finally, we check our results using the King and Levine **(1993** a,b) and Levine and Zervos **(1998)** measures of financial intermediation after extending their sample periods and deflating correctly.

We find that banks exert a strong, causal impact on real per capita **GDP** growth and per capita productivity growth. Using both the pure cross-sectional instrumental variable estimator and system dynamic-panel indicator, we find that higher levels of banking sector development produce faster rates of economic growth and total factor productivity growth. These results are robust to alterations in the conditioning information set and to changes in the measure of banking sector development. Thus, the data are consistent with the Schumpeterian view that the level of banking sector development importantly determines the rate of economic growth **by** affecting the pace of productivity growth and
# **Introduction**

A great deal of attention has been paid to two trends in the US economy: (1) the emergence of star firms that have pulled away from the rest of the economy (Furman and Orszag [2015], Koller, Goedhart, and Wessels [2017], Autor, Dorn, Katz, Patterson, and Van Reenen [2017]) and (2) the introduction of new technologies together with a fundamental structural change towards a more intangible intensive economy (Corrado and Hulten [2010]) with corresponding implications for corporate investment and the overall economy.<sup>1</sup> However, the two trends are typically analyzed separately, and the rise of star firms has been linked to a decline in market competition. In fact, we have little systematic evidence on the characteristics of these star firms. Is the rise of star firms the result of increasing market power and increasing concentration in the U.S.? What is the role of intangible capital? What is the relation between productivity and returns on invested capital and market value?

In this paper, we use a dataset of publicly listed firms in the United States from the Compustat database to identify star firms<sup>2</sup> and their industries. We examine the role of market power, intangible capital, technological change at the industry level (dependence on routine manual tasks vs high cognitive skills), and productivity in influencing star firm status. We use three measures of market power - a firm-level measure of operating markups, firm-level market share, and a raw measure of firm size.<sup>3</sup> Finally, we examine the persistence of the star firms’ performance and whether they differ in their investment and output per unit of capital compared to other firms in the economy.

Determining whether the performance gap between star firms and other firms is the result of luck, market imperfections, measurement of intangible capital, or a reflection of successful idiosyn-

> 1Several papers
the precise way in which management practices are measured also di↵ers across settings, although each approach is anchored in the BSVR methods. The Appendix details these key data features from Nigeria.

Table 12 repeats our core results for Ghana and Nigeria. Columns 1 to 3 refer to Ghana; Columns 4 to 6 refer to Nigeria. A common set of results emerge across contexts: (i) CS-autonomy is robustly positively correlated with project initiation, full completion and completion rates; and (ii) CS-incentives/monitoring is robustly negatively correlated with project initiation, full completion and completion rates in both settings. Moreover, the estimates show similar e↵ect sizes of both dimensions of management practice on the initiation and full completion margins, in which the two settings are most comparable.

We have to be cautious in further comparing project types across contexts. As described earlier, the bulk of tasks conducted by the Ghanaian bureaucracy relate to non-physical projects whereas the bulk of projects RR have data on from Nigeria relate to physical infrastructure (boreholes, roads etc.). However, overall the results suggest the impacts of management practices do seem 23
>|0.83|0.86|-0.03<sup>***</sup>|
|Early marriage|0.24|0.55|-0.31<sup>***</sup>|0.17|0.53|-0.35<sup>***</sup>|
|_MPI poor households_|||||||
|Years of schooling|1.00|0.99|0.01|0.83|0.87|-0.04|
|School attendance|0.72|0.75|-0.03|0.81|0.84|-0.03<sup>*</sup>|
|Unemployment|0.11|0.13|-0.01|0.10|0.17|-0.07<sup>***</sup>|
|Legal identification|0.90|0.92|-0.02|0.97|0.97|-0.01|
|Early marriage|0.26|0.59|-0.33<sup>***</sup>|0.21|0.67|-0.46<sup>***</sup>|

Source: Authors’ calculations using the Somalia HFS (2017). Significance at 1% ***, 5% **, and 10% * levels.

Early marriage rates are significantly higher among women than men in both displaced and non-displaced communities, although the gender gaps are larger among the nondisplaced. Among the MPI poor, non-displaced women also see higher levels of deprivation than their displaced peers.

# _5.2.4 South Sudan_

In South Sudan (Table 10), the gender gaps in education are significant only for the host community, when comparing men and women
13

**<u>Table 4:  Indicators for Analysis</u>**

|Postal index|Index of postal success calculated as (log) actual minus (log) predicted<br>letters/capita from regression of log letters/capita against log GDP/capita<br>(PPP). Letters/Capita from UPU, GDP/Capita from WDI.|
|---|---|
|GIT Postal trust|Score on GIT index of ‘trust on postal services’ (survey response to<br>question: ‘Do you trust your country’s postal system sufficiently to have a<br>friend mail a small package worth $100 to you (1=not at all 7= yes)’).<br>Source Kirkman et. al. (2002)|
|Illiteracy|Illiteracy rate, adult total (percentage of people ages 15 and above). Source:<br>World Bank, 2002.|
|Urban population|Urban population (percentage of total). Source: WDI.|
|Savings and giro|Number of post office savings and giro accounts per 1,000 people. Source:<br>constructed from data in UPU.|
|Money orders|Number of money orders, domestic service per 1000 people. Source:<br>constructed from data in UPU.|
|Letters/staff|Letters/staff. Source: constructed from data in UPU.|
|Receipts/Expenditure|Postal receipts/Postal operating expenditure. Source: constructed from data<br>in UPU.|
|KM/Office|Average area covered by a permanent post office (km2). Source: UPU.|
|% of Offices staffed private|Secondary offices staffed by nonadministration as a percentage of total<br>offices. Source: constructed from data in UPU.|
|Expenses/Letter|Postal operating expenses/letter (SDRs). Source: constructed from data in<br>
records at the individuallevel to find that proxy reporting leads to significant underreporting of earnings for single female workers (also see Hill, 1987). Cristia and Schwabish (2009) demonstrate that other covariates (in particular, earning higher incomes) lead to underreporting in the SIPP, and that individual characteristics that are associated with higher earnings (including age and marital status) are systematically associated with underreporting, leading to bias in estimates of these commonlyused predictors of earnings. Abowd and Stinson (2013) do not treat either approach as a “gold standard,” showing that both sources of data have measurement errors and argue for constructing a hybrid measure of earnings from survey and administrative data. Quality issues with earnings data across survey and administrative sources have also been raised. In the U.K., Britton et al. (2019) find systematically lower reporting of earnings in a dataset linking administrative tax records with student loan data, as compared to the concurrent Labor Force Survey — likely due to a combination of low survey response in the LFS from low/irregularly-paid earners, as well as a

> 2 Some studies on health outcomes in high-income countries, for example, have shown that proxy reporting can provide more accurate and detailed information on other household members’ health, including fewer issues with social desirability bias (see Mathiowetz and Groves, 1985, for a discussion using data from the U.S. National Health Interview Survey).

3
Mongolia, Iraq, Chad and Algeria are also poised to be significantly impacted as their fossil fuel exports range between one-fifth and one-third of their GDPs (Figure 3).

**Figure 3 Top 10 developing countries according to economic exposure of fossil fuels to the EU, US, and China policies**

<!-- Start of picture text --> Economic exposure (LHS) Sector exposure (RHS)<br>60 100<br>50 80<br>40<br>60<br>30<br>40<br>20<br>10 20<br>0 0<br>Libya Azerbaijan Angola Congo, Rep. Equatorial Mongolia Iraq Chad Algeria Gabon<br>Guinea<br>Percent<br><!-- End of picture text --> _Source:_ Authors’ calculation based on 2022 data from the World Bank’s WITS.

_Note:_ Fossil fuels (coal, oil, and gas) data are collected based on four HS digit codes (2701, 2709, 2710, and 2711). Sector and economic exposure are export of fossil fuels to three economies divided by exports of fossil fuels to the world and by GDP, respectively.

**The EU CS3D regulation will increase costs for exporters in fossil fuel sectors.** Countries involved in fossil fuel sectors, even if not exporting directly to the EU, will face higher costs when participating in oil and chemical supply chains for the EU market, due to the new requirements related to information gathering and reporting. Figure 4 shows the GVC exposure to the EU for petroleum, chemical, and non-metallic mineral products,<sup>18</sup> considering both direct and indirect exports based on the GVC exposure index developed by Arvis et al. (forthcoming). Niger has the most exposed sector, with about one-third of its direct and indirect exports of petroleum, chemical and non-metallic mineral products focused on the EU, followed by Bosnia and Herzegovina, and Tunisia
find that self employment allows a better balancing of home and work responsibility. For some workers, informality offers more flexibility and mobility opportunities than the patronage-tenuredbased formal sector, for instance, Bolivian indigenous workers do not face earnings gaps with respect to non-indigenous in informal jobs contrary to the formal sector.<sup>9</sup> A sizable group of informal salaried workers would value at least partially the benefits that usually are tied with a formal labor contract, but they cannot afford these benefits due to their lower productivity and/or due to the fact that benefits are usually bundled with other undesired but costly programs. In this light, the equation of “decent work” to formal work is probably not helpful. The informal sector is very heterogeneous and maybe best approximated by an upward tier of largely voluntarily informal workers (including most selfemployed and some informal salaried) and a smaller lower tier for which the sector functions as a safety net. The relative size of these groups may vary across countries depending on overall productivity levels and labor institutions.

**_The relative size of the informal sector is, in the long run, related to demographic and productivity related factors rather than to distortions._** Crude regressions using pooled OECD/LAC data sets show that the relationship of the share of self employment and formal sector productivity is log linear and most of the variance is driven by demographic variables, suggesting the that decision to go into self employment is partly driven by the opportunity cost measured in terms of formal employment foregone.  Firing cost measures and social security taxes appears significant but of second order.<sup>10</sup> Wage gaps between workers in small and large firms in the region are similar to those in the U.S (IDB 2004).

**_In the short to medium term, sector size does reflect the macro economic situation and how much adjustment occurs through quantities v. real wages and the incentives of firms to become formal._** The large expansion of the sector
<br>40<br>30 Exports of<br>commercial services<br>20<br>excluding travel and<br>10<br>transport<br>0<br>1998 1999 2000 2001 2002 2003 2004<br><!-- End of picture text --> Source: Balance of payments statistics.

Because of the strong performance of tourism, the decline in trade turnover of other services and the collapse in exports of other commercial services (excluding travel and transport) had no impact on the ratio of exports to imports of commercial services. Annual changes in the value of exports varied in the range between –29 percent and +29 percent and in the value of imports between –24 percent and +33 percent. Moreover, they coincided in time—the value of the correlation coefficient for annual changes in 1999-2004 is high at 81 percent. In consequence, exports in terms of imports remained stable—the average ratio for the 1998-2004 period was 2.32, with a low value of the coefficient of variation of 0.09 and standard deviation of 0.20.

While undoubtedly earthquakes, war in Iraq and perceptions of instabilities in the Middle East might have suppressed tourism in the region, tourism (travel services) recorded hefty growth in current US dollars of 15.5 percent per year in 1998-2004. It was the only item that did not experience contraction in exports in terms of value over this period. But its performance was not sufficiently robust to offset the decline in other exports, as total receipts had a negative growth rate in 1998-2004. Among other categories of services, the strongest performer was transport, with the average annual, negative growth rate of (-) 1.5 percent in 1998-2004.

Stagnation in exports of services over 1999-2004 was the result of the collapse of receipts from sales of all services sectors identified in balance of payments statistics except for tourism and transport (Table 4). The aggregate receipts from other sectors stood in 2004 at
, and vulnerability were translated into numerical indices; what variables were used; how variables were aggregated into sub-indices and sub-indices into a composite vulnerability index; and how sub-national geographical areas were determined.</mark>

# **<mark>Conceptual approach</mark>**

<mark>Our measure of vulnerability to climate change takes as its starting point the IPCC working definition of vulnerability as a function of exposure, sensitivity, and adaptive capacity (IPCC, 2001) and incorporates social, economic, and natural science indicators. We construct the index of vulnerability as the simple average of three sub-indices: exposure, sensitivity, and adaptive capacity. In line with previous literature (e.g., Polsky et al., 2007), we include a range of climatic, economic, social, and institutional variables as the drivers of vulnerability and focus on vulnerability to current climate variability. One advantage of this approach is the reduction in dependence on climate models and projections which despite recent advances are still presented at too coarse a scale with too high degrees of uncertainty to be useful for regional analysis (Hahn, Riederer and Foster 2009).</mark> <mark>We improve upon the approaches used by previous studies by covering all areas of the country, both rural and urban, and by carefully exploiting a host of available survey, census, and meteorological data. We use indicators of past climate variability to assess</mark> <mark>5</mark>