rafmacalaba/datause-encoder-data huggingface dataset:
greater cash flow rights of large owners are associated with greater bank risk, consistent with the hypothesis that bank shareholders favor risk-taking relative to managers and creditors. These authors also consider the interaction between bank regulation and ownership, finding that deposit insurance is associated with an increase in risk only when the bank has a large equity holder. More recently, using international data, Erkens, Hung, and Matos (2012) find that financial institutions with more independent boards and higher institutional ownership experienced worse stock returns during the global financial crisis.

Our contribution to this literature is threefold. First, we use international banklevel data for 22 countries to study the association between bank risk and corporate governance, adding to a literature which has mostly relied on US data. Second, using multi-country data allows us to exploit differences across country safety-nets to study how the relationship between bank risk-taking and corporate governance varies with the generosity and credibility of the safety net and banks’ ability to engage in risk-shifting. Third, we are able to study how banks increase their risk-taking with more share-holder friendly corporate governance, identifying increased asset risk, reduced capitalization, and the pursuit of more pro-cyclical lending policies as potential channels.

Overall, our findings on the interaction of bank-level corporate governance variables and the financial safety net has important implications for corporate governance reforms in the banking sector, as policy makers question the extent to which governance failures have contributed to excessive risk taking and financial instability. In particular, our results suggest that one has to be cautious to call for ‘better’ corporate governance at banks as long as generous financial safety nets and too-big-to-fail policies are in place, as 6
# The Distortionary Effects of Tariff Exemptions in Argentina

by Faezeh Foroutan*

# Table of Contents

|I.|The Model|2|
|---|---|---|
|II.|Tariff Exemptions in Argentina|8|
||II.1 Structure of Tariff Exemptions by Sector<br>II.2 Fiscal Impact of Tariff Exemptions<br>II.3 The Impact of Duty Exemptions on Fisms'<br>Cost Structure|10<br>15<br>16|
|III.|Summary and Conclusions|19|
|Refer|ences|22|

* I wish to thank Jim de Melo and Dani Roderik for their helpful comments on an earlier draft of this paper.
odds ratio is 1.500 (p-value = 0.000). This is rather remarkable in that it suggests that female clients continue to transact with female agents, even when they are more difficult to access.

With respect to transaction values, the pattern described in Columns 1 to 6 of Table 6 is also evident when restricting the data set in Columns 7 and 8 to markets with high and low presence of female agents. In both markets with more and less than 25% female agents, females make lower value transactions than male customers overall (coefficients = -0.517 and -0.556 with p-values = 0.000 in Columns 7 and 8, respectively), but transaction amounts are again significantly higher for females transacting at female agents (coefficients = 0.500 and 0.436 with p-values = 0.000 in Columns 7 and 8 of Table 6, respectively).

# **Agent characteristics**

A second threat to validity is unobservable differences in agent and customer characteristics. For example, female clients may transact more with female agents simply because female agents tend to also sell other goods or services more demanded by females. While we lack customer-level data, we were able to obtain rich data from the agent registration records. According to Column 7 of Table 1, there are no differences between male and female agents across almost all dimensions including number of employees, sector, existing client relationship with FINCA, opening days per week, daily revenue, stock value, and minimum POS balance (only opening hours per day are significantly lower for female agents). We conclude that based on the data we have available, male and female agents are largely similar and thus differences in the shops or what they sell cannot mainly account for the assortative matching by gender we observe in our data.

We also run our main specifications on a data set which only includes customers transacting at matched agents (i.e., those for whom registration and transaction data could be matched). We do this to ensure that our results hold when we limit observations to transactions with agents that are largely similar. We create two different data sets: one with only transactions at matched agents and another that includes all transactions of customers who transacted at least once with a matched agent. The results are reported in Columns 9 and 10 of Table 4, respectively. Columns 9 and 10 report odds ratios of 2.098 and 1.825, respectively (both p-values are 0.000) and indicate an even stronger association between agent and customer gender with the restricted data set than in our preferred specification in Column 4 of Table 4 (odds ratio = 1.518, p-value = 0.000). Columns 9 and 10 of Table 6 also confirm the findings of Column 4, Table 6 that females transact larger amounts at female agents. Since matched male and female agents are comparable (Table 1), these robustness checks suggest that our results are not driven by unobservable factors.

12
# **<u>ASSESSING THE IMPACT OF PUBLIC SPENDING ON GROWTH</u>**

# **<u>An Empirical Analysis for Seven Fast Growing Countries</u>**

# **Blanca Moreno-Dodson**<sup>**1**</sup> **World Bank**

_Since 1959, when Richard Musgrave published The Theory of Public Finance, it has been customary for economists to classify governmental objectives in the three categories of stabilization, allocation, and redistribution proposed by Musgrave. In 1959 growth was not recognized as a governmental objective requiring explicit policy action. The pursuit of the other three objectives was assumed to automatically generate a natural long run rate of growth. However, in recent decades growth has acquired great prominence in many countries. As a consequence, various policies that do not easily fit into Musgrave's classification have been introduced. It is high time to recognize growth as an explicit, fourth objective to be added to Musgrave's trio. It is an objective that many countries now try to promote with good and, at times, bad policies._ _VITO TANZI, 2008_

# **<u>INTRODUCTION</u>**

Much of the variation in growth rates among countries and over time remains still not fully understood. Although recent cross-country studies, based on dynamic panel data techniques, have been able to correct some of the methodological problems that plagued the earlier literature (such as measurement and specification errors, simultaneity bias, and the potential for bidirectional causality), few empirical regularities have emerged.

Consider, in particular, the relationship between public spending and growth. Since the seminal contribution by Barro (1990), there have been a number of analytical studies highlighting the various channels through which public expenditure may affect growth, as the next section will discuss in more detail.

However, at the empirical level, robust relationships have been difficult to establish. Much of the evidence on the effects of government expenditure on growth appears to be inconclusive. Although there are strong analytical reasons to believe that public spending is one of the important variables that influence growth, there remains significant uncertainty about its actual degree of influence.

> 1 The author is grateful to Pierre-Richard Agénor, University of Manchester and Centre for Growth and Business Cycles Research, Homi Kharas, Brookings Institution, Danny Leipziger, World Bank, and Vito Tanzi, former IMF Director, for their helpful guidance and feedback.  Comments by Carlos Braga, Brian Pinto, Eduardo Ley, and Vivek Suri, World Bank, are also greatly appreciated.  Last but not least, this analysis would not have been possible without the excellent research assistance of Nihal Bayraktar, Pennsylvania State University, for the econometric work, and Borja de Escalada, World Bank, for the data collection.
- Department of Foreign Employment (2014). Labour migration for employment a status report nepal: 2013/2014. Technical report, Government of Nepal, Ministry of Labour and Employment, Department of Foreign Employment.

- Foged, M. and Peri, G. (2013). Immigrants' and native workers: New analysis on longitudinal data. Working Paper 19315, National Bureau of Economic Research.

- Greenwood, M. J. (1969). An analysis of the determinants of geographic labor mobility in the united states. The review of Economics and Statistics, 51(2):189194.

- Hägerstrand, T. (1957). Migration and area. In Hannerberg, D., Hägerstrand, T., and Odeving, B., editors, Migration in Sweden: A Symposium, pages 27158. Gleerup: Lund Studies in Geography.

- Hugo, G. J. (1981). Village-community ties village norms and ethnic and social networks: a review of evidence from the third world. In De Jong, G. F. and W, G. R., editors, Migration Decision Making, Multidisciplinary Approaches to Microlevel Studies in Developed and Developing Countires, pages 186224. New York NY Pergamon Press 1981.

- Kaphle, A. (March 24, 2014). Nepal, once known for farming, now exports people; migrants earn big but face risks. The Washington Post.

- Lokshin, M., Bontch-Osmolovski, M., and Glinskaya, E. (2010). Work-related migration and poverty reduction in nepal. Review of Development Economics, 14(2):323332.

- Lokshin, M. and Glinskaya, E. (2009). The eect of male migration on employment patterns of women in nepal. The World Bank Economic Review, 23(3):481507.

- Massey, D. S. (1988). Economic development and international migration in comparative perspective. The Population and Development Review, 14(3):383413.

- McKenzie, D. and Rapoport, H. (2007). Network eects and the dynamics of migration and inequality: theory and evidence from mexico. Journal of development Economics, 84(1):124.

- McKenzie, D. and Rapoport, H. (2011). Can migration reduce educational attainment? evidence from mexico. Journal of Population Economics, 24(4):13311358.

20
**Figure 7. Access to Electricity and Income per Capita**

<!-- Start of picture text --> 2008 - 2012 Benchmark (average)<br>GABMUS<br>ZAF<br>SENCMRCIVGHA<br>NGA<br>TGOZWEGMBBEN SDN AGO<br>ETH GIN MLI LSOKEN ZMB<br>MOZ<br>MDGNER RWABFA UGATZA<br>MWI<br>-3 -2 -1 0 1 2<br>GDP per capita<br>90% CI Linear Fit<br>EAP ECA IND LAC MENA SA SSA<br>R-sq: 0.6700; Observations 94; Using PWT 9.0<br>Adjusted by demographic and geographic features.<br>2<br>1<br>0<br>Energy Access<br>-1<br>-2<br><!-- End of picture text --> _Sources: World Bank, World Development Indicators. GDP per capita is from Feenstra, Inklaar, and Timmer 2015. Note: CI = confidence interval; EAP = East Asia and the Pacific; ECA = Europe and Central Asia; GDP = gross domestic product; IND = Industrial Countries; LAC = Latin America and the Caribbean; MENA = Middle East and North Africa; SA = South Asia; SSA = Sub-Saharan Africa._ _Access to improved water sources_ . Figure 8. a, plots the conditional correlation between income per capita and access to safe water in 2008–12. For this period, Gabon, Mauritius, and South Africa remain the best performing countries in Sub-Saharan Africa in access rates to water, and these rates are either similar or slightly above the ones predicted by the international norm. MICs in the region, such as Sudan, Nigeria, and Angola, have access rates that are significantly lower than those predicted by the benchmarking exercise. The same holds for Madagascar and Ethiopia (both countries continue to underperform) among LICs.

_Access to improved sanitation facilities_ . Countries with greater income per capita also tend to have greater rates of access to improved sanitation facilities. For 2008–12, Mauritius and South Africa remain along the international norm, while Gabon appears to have fallen off track (figure 8.b). Over the past decade, Ethiopia moved from being the weakest performer to being close to the access rates predicted by the international norm. Niger, Togo, and Tanzania continue to have the lowest adjusted rates of access in the region.

_Infrastructure in Sub-Saharan Africa - 18_
almost halved even in Rural Eastern Hill between the two survey years, a remarkable accomplishment in and of itself. The uneven progress in poverty reduction, however, can translate into widening inequality and, combined with the regional disparity in the depth of poverty, could possibly become the genesis of yet another social unrest that has plagued the country since the mid-1990s. This is an integral part of Nepal’s vigorously debated agenda on economic and social inclusion.<sup>23</sup> The second key challenge facing Nepal’s future is the extent to which the success in lifting people out of poverty translates into moving them into a secure middle class where the risk of falling back into poverty is minimal. How big is the size of this middle class in Nepal? Is it growing or shrinking? What are the characteristics of individuals that constitute this group? These are important questions that have started to emerge in Nepal. World Bank (2016) examines the dynamics of living standards movements across generations as well as within generations to examine long-run economic and social mobility as well as transitions in and out of poverty, vulnerability and the middle class.

**Figure 13 Average per capita consumption of the poor (Rs. 2010 Prices)**

<!-- Start of picture text --> 15,000<br>14,000<br>12,826<br>13,000<br>12,589<br>12,214 12,205<br>12,000<br>11,474 11,428 11,497<br>11,231<br>10,926<br>11,000 10,765<br>10,289<br>10,174<br>10,000 9,769<br>9,378<br>9,000<br>8,000<br>Kathmandu Oth urban R‐W Hill R‐E Hill R‐W Tera R‐E Tera National<br>NLSS 1995/96 Old Poverty Trend NLSS 2010/11 Old Poverty Line<br><!-- End of picture text --> Source: Nepal Living Standards Surveys 1995 and 2010 A vocal, dynamic and assertive middle class can be the foundation for a social contract that can catalyze reforms to both generate and share growth. Postwar Western Europe, post-revolution China, post land reform Republic of Korea, and the United States under the New Deal all bear

> 23 This is also a topic of an in depth exploration in the forthcoming report, “Moving up the ladder: poverty reduction and social mobility in Nepal” (World Bank, 2016).

22
and is frequently updated.<sup>47</sup> The classification rule adopted by the fDi Markets is different from that by the MRIOT. We first aggregate the FDI data at the project level to the countrysector level, and then we do the concordance manually.

To explore the mechanism through which the GVC participation affects the strength of FDI spillovers, we use the _World Bank Enterprise Survey_ data. This data set provides rich firm- level information with firms classified into the same sector levels as the MRIOT. We leverage the firmlevel data to test if FDI is positively associated with firm R&D propensity and whether a firm with lower GVC participation engages less in the R&D faced with FDI in its sector.

# 4.2 Metrics and variable definitions

The main GVC measures of a country-sectors are constructed using the derivations in Wang, Wei and Zhou (2017). The variables are defined as follows, with the label in parenthesis. We first calculate the forward and backward GVC participation separately.

**_Forward GVC participation (GVC_forward_** ) is defined as the share of the value-added created by a country-sector used for intermediate exports. It reflects the strong links that a sector has with buyers abroad, and generally tends to be in the upstream segment of the production process.

**_Backward GVC participation (GVC_backward)_** is defined as the ratio of the domestic and foreign value-added in intermediate imports over the total output of a country-sector. It reflects strong links as a purchaser of foreign goods and thus is as a sector more likely to be located in the downstream segment of the production process **_._**

**_GVC participation (GVC)_** is defined as the simple average of the **_GVC_forward_** and **_GVC_backward._**

Figure 6 shows the variation of GVC participation across all 61 countries. The mean value has not changed much in the eight years to 2017, it remains around 2.5 though the skewness has led to higher values of more sectors in 2017 (bottom panel of Figure 6). Appendix table 1 lists the classification of sectors.

> 4 There are four major sources most frequently used: The World Input-Output Database (WIOD), the Organization for Economic Co-operation and Development’s (OECD) IOT database and ICIO database, the Eora Multi-Regional Input-Output database, and the ADB’s MRIOT database. The WIOD only covers 40 economies including 6 Asian economies and goes to 2015; the OECD IOT and ICIO data covers 61and 74 economies, respectively, but the latest available data goes to 2015. The Eora database has a wider coverage of countries, but the structure of smaller countries is derived from similar countries, and not from country-specific national accounts. Moreover, sectoral data are highly aggregated.
**Table 6: Community participation, school management and characteristics (2009)**

||WSD|Control|Difference|P-value|
|---|---|---|---|---|
|Received support/aid from the community|0.46|0.35|0.11|0.15|
||(0.05)|(0.05)|(0.07)||
|Does the school have a PTA|1.0|0.99|0.01|0.32|
||(0)|(0.01)|(0.01)||
|PTA fund raisers|0.10<br>|0.11<br>|-0.01<br>|0.83|
||(0.03)|(0.03)|(0.05)||
|PTA member contribution|0.09<br>(0.03)|0.05<br>(0.02)|0.04<br>(0.04)|0.23|
|PTA not funded|0.71|0.75|-0.04|0.57|
||(0.05)|(0.05)|(0.07||
|Number of meetings with the parents or PTA|4.45|3.92|0.53|0.19|
||(0.31)|(0.26)|(0.4)||
|Mentoring system in place for junior teachers|0.47|0.53|-0.06|0.41|
||(0.05)|(0.05)|(0.08)||
|Mentors trained|0.7|0.57|0.14*|0.08|
||(0.05)|(0.05)|(0.08)||
|Leadership and Management committee in place|0.94<br>(0.03)|0.75<br>(0.06)|0.19***<br>(0.06)|0|
|Community Participation committee in place|0.79<br>(0.05)|0.63<br>(0.07)|0.16**<br>(0.08)|0.04|
|Curriculum Management committee in place|0.84<br>(0.04)|0.51<br>(0.07)|0.33***<br>(0.08)|0|
|Teachers’ professional development com. in place|0.8<br>(0.05)|0.61<br>(0.07)|0.19**<br>(0.08)|0.02|
|Teaching and learning resources com. in place|0.81|0.59|0.22**|0.01|
||(0.05)|(0.07)|(0.08)||
|Learners welfare committee in place|0.88|0.71|0.17**|0.01|
||(0.04)|(0.06)|(0.07)||
|School has developed school policy|0.45<br>(0.05)|0.36<br>(0.05)|0.09<br>(0.07)|0.26|
|First grade enrollment|91.82<br>(9.85)|76.29<br>(7.02)|15.53<br>(12.12)|0.2|
|Student-teacher ratio (Lower Basic)|53.18<br>(11.55)|53.18<br>(7)|0<br>(13.11)|1|
|Seen records of the teachers attendance|0.91|0.89|0.02|0.64|
||(0.03)|(0.03)|(0.05)||
|Teacher Absenteeism/ Average 5 random days|0.06<br>(0.01)|0.06<br>(0.01)|0<br>(0.01)|0.94|
|School has a library|0.53|0.6|-0.07|0.43|
||(0.05)|(0.05)|(0.08)||
|Observations|88|89|||

Notes: Standard deviations in parentheses. *** 1% Significance Level, **5% Significance Level, *10% Significance level. The test of comparison of means is between each treatment group and the control group.

29
below the 1980 benchmark.<sup>24</sup> The lost decade adage applied to the majority of emerging and developing economies, many of which were coping with _conglomerate_ crises. During this period, reality consistently disappointed expectations, successive downward revisions to economic growth projections were legion.

The experience of the 1980s suggests caution is warranted in projections of post-COVID-19 economic growth, particularly for countries that are already experiencing some variants of financial stress. The October 2020 WEO projections show that by 2025, 29 percent of EMDEs will have per capita incomes below what they were in 2019 (the comparable share for EMDEs is 94 percent in 2020). This is only slightly higher than the share in any “normal” year and spectacularly better than the track record of the 1980s. It is difficult to reconcile this GDP path with the prevalent expectation that there will be a higher incidence of sovereign debt and financial sector problems in the period ahead.

Inflation in some EMDEs may also surprise on the upside. At present, the substantial contraction in aggregate demand acts to dampen inflationary pressures. However, a frequent component of the _composite_ crises that I have described involves inflation, a point stressed in Reinhart and Rogoff (2009). A country losing access to external capital markets relies more on domestic funding sources to meet the government’s financing needs. For many developing countries, the scope for internal financing is limited and governments turn more to the revenue for money creation and, in many cases, government arrears.

I am not suggesting a full-blown reversion to the 1980s, when more than half of the EMDEs had double-digit inflation (Figure 2) and inflation targeting was still limited to a handful of advanced economies. The share of countries with double-digit inflation rates has dropped precipitously since then, possibly hitting a low point in 2014 (7 percent of EMDEs registered double-digit inflation that year). In 2020 that share is about 14 percent. But not surprisingly, the re-

> 24 In any given random year, anywhere between 16 and 21 percent of the countries will have a lower per capita income than five years prior. So the 1986/1981 comparison at 55 percent is more than double the “normal” rate.

17
|**Table 10. Robustness check: Alternative**<br>**measures of informality**2nd stage:|sel|Share of<br>f-employme|nt|M<br>out|IMIC-base<br>put informa|d<br>lity|DGE-<br>calibrate|output infor<br>d to self-em|mality,<br>ployment|
|---|---|---|---|---|---|---|---|---|---|
||[1]|[2]|[3]|[4]|[5]|[6]|[7]|[8]|[9]|
|Real GDP pc (in logs)|-2.52|-2.70|2.53|-8.86***|-8.81***|-7.91***|2.58|3.06|7.85|
||(3.15)|(2.99)|(4.75)|(1.10)|(1.09)|(1.54)|(4.24)|(4.08)|(6.47)|
|Control of Corruption|-1.23|-1.42|-0.93|0.39|0.40|0.48|-1.35|-1.45|-1.10|
||(1.40)|(1.37)|(1.66)|(0.51)|(0.51)|(0.58)|(1.63)|(1.62)|(1.90)|
|Regulatory Quality|1.25|1.07|1.61|0.03|0.04|0.11|-1.69|-1.95|-1.40|
||(1.56)|(1.54)|(1.69)|(0.53)|(0.53)|(0.53)|(2.55)|(2.47)|(2.52)|
|Rule of Law|-0.15|0.01|0.34|-2.82***|-2.82***|-2.84***|0.91|1.09|1.30|
||(1.67)|(1.68)|(1.88)|(0.57)|(0.57)|(0.61)|(2.11)|(2.13)|(2.32)|
|Commercial bank branches|-0.22*|-0.21**|-0.45**|-0.08**|-0.09**|-0.16**|-0.37**|-0.39***|-0.63**|
||(0.13)|(0.10)|(0.21)|(0.04)|(0.04)|(0.08)|(0.17)|(0.14)|(0.26)|
||1st|stage: Ban|kingsector d|evelopment||||||
|Commercial bank branches in the region|0.82***<br>(0.20)|||0.89***<br>(0.18)|||0.80***<br>(0.19)|||
|Distance-discounted commercial bank||0.80***|||0.83***|||0.78***||
|branches (regional)||(0.17)|||(0.15)|||(0.16)||
|Distance-discounted commercial bank|||2.84***|||2.84***|||2.77***|
|branches(global)|||(0.81)|||(0.70)|||(0.80)|
|Observations|1,419|1,399|1,401|2,051|2,051|2,053|1,530|1,517|1,519|
|R-squared|0.97|0.97|0.97|0.99|0.99|0.98|0.97|0.97|0.96|
|<br>Kleibergen-Paap rk LM stat.|<br>16.98***|<br>20.65***|<br>12.27***|<br>21.87***|<br>23.15***|<br>15.87***|<br>16.83***|<br>20.24***|<br>11.73***|
|Cragg-Donald Wald F statistic|17.44|22.95|12.42|25.44|31.18|16.26|17.67|23.17|11.91|

27