rafmacalaba/datause-encoder-data huggingface dataset:
# **References**

Amel, Dean, Colleen Barnes, Fabio Panetta, and Carmelo Salleo, 2004, Consolidation and efficiency in the financial sector: A review of the international evidence, _Journal of Banking and Finance_ 28, 2493-2519.

Berger, Allen N. and Loretta J. Mester, 1997, Inside the box: What explains differences in the efficiencies of financial institutions?, _Journal of Banking and Finance_ 21, 895-947.

Billet, Matthew T., Jon A. Garfinkel, and Edward S. O’Neal, 1998, The cost of market versus regulatory discipline in banking, _Journal of Financial Economics_ 48, 333-358.

Demirgüç-Kunt, Asli, and Harry Huizinga, 2004, Market discipline and deposit insurance, _Journal_ _of Monetary Economics_ 51, 375-399.

Demirgüç-Kunt, Asli, and Harry Huizinga, 2010a, Bank activity and funding srrategies: The impact on risk and return, _Journal of Financial Economics_ 98, 626-650.

Demirgüç-Kunt, Asli, and Harry Huizinga, 2010b, Are banks too big to fail or too big to save?

International evidence from equity prices and CDS spreads, CEPR working paper 8097.

Demsets, Rebecca S. and Philip E. Stahan, 1997, Diversification, size, and risk at bank holding companies, _Journal of Money, Credit, and Banking_ 29, 300-313.

DeYoung, R. and K.P. Roland, 2001, Product mix and earnings volatility at commercial banks: evidence from a degree of total lending model, _Journal of Financial Intermediation_ 10, 54-84 Flannery, Mark J. and Sorin M. Sorescu, 1996, Evidence of bank market discipline in subordinated debenture yields: 1983-1991, _Journal of Finance_ 51, 1347-1377.

Goldstein, Morris and Nicolas Véron, 2011, Too big to fail: The transatlantic debate, Peterson Institute for International Economics WP 11-2, Washington, DC.

Kane, Edward J., 2000, Incentives for banking megamergers: What motives might regulators infer from event-study evidence?, _Journal of Money, Credit, and Banking_ 32, 671-701.

Kwast, M.L., 1989, The impact of underwriting and dealing on bank returns and risk, _Journal of Banking and Finance_ 13, 101-125.

Park, Sangyun, and Stavros Peristiani, 1998, Market discipline by thrift depositors, _Journal of Money, Credit, and Banking_ 30, 347-364.

Penas, Maria Fabiana, and Haluk Unal, 2004, Gains in bank mergers: Evidence from bond markets, _Journal of Financial Economics_ 74, 149-179.

Sironi, Andrea, 2003, Testing for market discipline in the European banking industry: evidence from subordinated debt issues, _Journal of Money, Credit, and Banking_ 35, 443-472.

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comparable to those from actual trips in cities, including relatively small cities where one might worry more about poor data quality.

The speed indices in Akbar et al. (2021) are based only on trips within the built-up urban cores, whereas we sample establishments from the broader metropolitan area surrounding it. However, given the monocentric nature of most metropolitan areas in the developing economies, it is safe to assume that the effect of mobility within the urban core on the agglomeration economies of establishments within and outside of it are correlated. Finally, for ease of interpretation, in the regression analysis we use the additive inverses of the mobility indexes.

# _Other agglomeration diseconomies_

We control for several agglomeration diseconomies, including whether a firm experiences crime, theft, and disorder as an obstacle to doing business (measured on a 0-4 scale), whether a firm perceives competition from informal firms as an obstacle to doing business (measured on a 0-4 scale), and whether a firm experienced electricity outages during the previous year (measured with a dummy variable). We also control in our regressions for the average level of air pollution (PM 2.5), obtained from the Urban Centre Database, which is a part of the Global Human Settlement Layer Database.

# _Controlling for physical geography_

Several other metropolitan area-level variables may confound the relationship between urban density and labor productivity, particularly non-human endowments, such as average temperature and precipitation. We obtain information on these variables from the Urban Centre Database, available as part of the Global Human Settlement Layer Database, and control for them in our regression analysis.

# _Controlling for firm sorting and initial sorting of workers_

To control for firm sorting, we include the size of the firm by accounting for (1) the number of full-time employees working in the establishment and (2) a dummy variable indicating whether the establishment is part of a large conglomerate. As noted by Van Oort et al. (2012) the effects of agglomeration that are often found in area-based studies can be compositional in nature because a location may be productive due to the density of large firms and the presence of internal rather than external economies of scale. Along similar lines, the productivity premium may be driven by a higher capital intensity of firms in cities rather than agglomeration economies. Hence, in the absence of a uniform measure of capital for a large set of firms, we control for investments in fixed assets made by firms over the past year. Second, we must account for the fact that cities 12
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younger firms and lower for incorporated or larger firms. The correlation of business formality transitions with the business enforcement variable is somewhat odd. We find no relationship for the formalization indicator, but a positive association with the informalization indicator as the distance from the firm to the nearest SUNAT office increases.

Turning to the labor formality transitions, we find that the probability of moving from not complying with labor formality in 2011 to complying in 2012 (labor formalization) increases with the product per worker and for incorporated businesses. The labor enforcement variable is also positively correlated to labor formalization: when the fraction of firms already registered in the Electronic Payroll Registry increases, so does the probability that a firm formalizes. By contrast, the probability of moving from complying with labor formality in 2011 to not complying in 2012 (labor informalization) decreases with product per worker, for incorporated businesses, and for higher levels of labor enforcement.

<u>Table 3: Transition Probability Regressions</u>

||<br>(1)|<br>(2)|<br>(3)|(4)|
|---|---|---|---|---|
|Variable|OLS<br>License|OLS<br>License|OLS<br>Payroll|OLS<br>Payroll|
|Change t=2011=>t=2012|0=>1|1=>0|0=>1|1=>0|
|Log distance to SUNAT|0.0135|0.0438***|||
||(0.0575)|(0.0162)|||
|Firms in E-Payroll / firms in SUNAT|||0.0156**|-0.0150**|
||||(0.008)|(0.006)|
|Growth in per capita electricity consumption<br>2011/12|-0.191|0.00229|0.102|-0.000937|
||(0.192)|(0.0684)|(0.149)|(0.102)|
|Log product per worker|0.0100|0.00474|0.0658**|-0.0292*|
||(0.0315)|(0.0128)|(0.0310)|(0.0175)|
|Log number of workers|0.0677|-0.0370***|0.0595|-0.0276|
||(0.0497)|(0.0137)|(0.0443)|(0.0200)|
|Incorporated business|0.208**|-0.0875***|0.277***|-0.239***|
||(0.0914)|(0.0255)|(0.0687)|(0.0401)|
|Age 1-4|-0.0267|0.0598**|-0.0912|-0.0437|
||(0.0777)|(0.0265)|(0.0653)|(0.0383)|
|Constant|0.576|0.163|-0.706|1.234***|
||(0.579)|(0.211)|(0.581)|(0.338)|
|Observations|168|720|287|601|
|R-squared|0.086|0.078|0.161|0.105|

_Note:_ Standard errors are in parentheses. Significance: *** p<0.01, ** p<0.05, * p<0.1.

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which gives the probability of livestock ( _j_ ) to be chosen among ( _J_ ) animals (McFadden 1981).

> For each AEZ _w_ , the marginal effect of a change in climate on the probability an animal _j_ is chosen can be obtained by differentiating Equation (6):

The coefficients of the choice model γ and α are not dependent on the AEZ. However, the marginal impact of climate on the probability of selecting an animal depends on the climate conditions in each AEZ and so will vary by AEZ.

# **3. Description of the Data**

The FAO has developed a typology of Agro-Ecological Zones as a mechanism to classify the growing potential of land (FAO 1978). The AEZs are defined using the length of the growing season. The growing season, in turn, is defined as the period where precipitation and stored soil moisture is greater than half of the evapotranspiration. The longer the growing season, the more crops can be planted (or in multiple seasons) and the higher are the yields (Fischer and van Velthuizen 1996; Vortman et al. 1999). FAO has classified land throughout Africa using this AEZ concept. Our study will use these FAO defined AEZ classifications.

The economic data for this study were collected by national teams as part of the GEF/World Bank project on climate change in Africa (Dinar et al 2008). The survey asked detailed questions on crops and livestock operation during the agricultural period of July 2002 to June 2003. The data were collected for each plot within a household and household level data were constructed from plot level data. In each country, districts were chosen to get a wide representation of farms across climate conditions in that country. The districts were not representative of the distribution of farms in each country as there are more farms in more productive locations. In each chosen district, a survey was 6
poorest quintile of households in the country covered (World Bank, 2018), the program is a core pillar of the government’s social protection strategy.

The 4P program provides three types of transfers: education grant, health grant and rice-subsidy.<sup>2</sup> The education grant is provided to every child who complies with the education conditions of the program: is enrolled in school and attends 85 percent of the school days every month. Children enrolled in daycare/kindergarten or elementary schools receive 300 pesos per month while children enrolled in high school receive 500 pesos per month. These grants are provided for a period of 10 months per year. The program maintains a limit of three child beneficiaries for the education grants.

The health grant amounts to 500 pesos per month and is given to households subject to compliance of all health conditionalities. These conditionalities include, (i) all children under the age of five must regularly visit a health center or rural health unit for growth monitoring, vaccines and preventive health check-ups; (ii) pregnant women must visit their health center monthly for antenatal and postnatal care, and must deliver in a health facility attended by a trained health professional; (iii) all school-aged children (6-14 years old) are to comply with de-worming protocol at schools; and (iv) among households with children 0-14 years old, the household grantee (mother) and/or spouse must attend Family Development Sessions (FDS) at least once a month.

To be eligible for the program, households must be identified as poor by the _Listahanan_ , formerly known as National Household Targeting System for Poverty Reduction Program (NHTS-PR). They also must have a pregnant member or at least one child aged 0-18 years old at the time of selection and must be willing to commit to meeting the program conditionalities. The _Listahanan_ targets beneficiaries through a household assessment and application of a Proxy Means Test (PMT) methodology to predict income of households based on characteristics such as household composition, education, housing conditions, access to basic services, ownership of assets, etc. Predicted incomes are then compared with poverty thresholds at the provincial level to identify households below (poor) or above (non-poor) these thresholds.

The program has undergone two key changes since its inception in 2008. First, during initial rollout of the 4P program, few randomly selected barangays were given treatment. However, 1.5 years after the program was rolled out, the control group was also exposed to the program. Second, initially the program was designed to provide benefits for children 0 – 14 years old. However, this age limit was extended to

> 2 We do not provide a detailed description of the rice subsidy as it was introduced in 2017.

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changes for some exogenous reason, such as a sudden global tightness in oil or electricity and an unexpected shutdown of domestic power plants, the equilibrium would behave differently depending on price elasticity of demand. If the demand is elastic, a small change on the supply side would result in large adjustments in energy consumption. By contrast, if demand is relatively price-inelastic, there is little room for consumption to accommodate a given supply-side change. Instead, prices must be of necessity adjusted to a large extent. This is the basic reason why the Ramsey pricing calls for lower margins (or prices) for more elastic demanders in the price discrimination context.

According to the traditional literature review, the price elasticity of “electricity” demand is estimated from –1.02 to –2.00 for residential users and from –1.25 to –1.94 for industrial consumers (Taylor, 1975). More recently, a meta-analysis by Espey and Espey (2004) shows that the average residential electricity price elasticity among earlier studies published between 1971 and 2000 is –0.35 in the short run and –0.85 over the long run. Bernstein and Griffin (2005), using U.S. state-level data, find that the residential electricity elasticities are – 0.24 and –0.32 in the short and long run, respectively. For commercial users, the short- and long-run price elasticities are estimated at –0.21 and –0.97, respectively. Noticeably, another recent work, which relies on the same U.S. data for a similar period of time but at the national aggregate level, indicates that the industrial demand elasticity may be rather smaller in absolute terms than other previous works (Kamerschen and Porter, 2004). It is shown that the residential electricity elasticity ranges between –0.85 and –0.94, while the industrial one varies from –0.34 to –0.55.

As to “energy” demand in general, the price elasticity of manufacturing energy demand is estimated at –0.28 to –0.49 in the United States (Anderson, 1981). As per Pindyck and Rotemberg (1983), the elasticity can be different depending on firms’ dynamic investment behavior; the estimated elasticity of U.S. manufacturing is –0.36 in the short run, –0.58 in the medium run, and –0.99 in the long run. It is also shown that energy demand elasticities are different across industries (Denny _et al_ ., 1981). The short-run elasticity varies from –0.61 in the paper industry to nearly zero in the tobacco, metal fabricating, and machinery electrical
exclusive concern with flows limits its ability to assess the viability and adequacy of external indebtedness **--** a stock problem **by** nature.

In this paper we propose a third approach, according to which external sustainability is driven **by** portfolio equilibrium conditions in the long run and **by** the dynamics **of** asset reallocation in the short run. Long-run external equilibrium occurs when international and domestic investors achieve their desired portfolio allocation across countries. Thecorresponding short-run external equilibrium is given **by** the current-account balance consistent with acquiring the desired international portfolio, given any existing constraints to immediate portfolio adjustment.

The paper outlines a simple model of international portfolio diversification in which riskaverse investors allocate their wealth optimally across countries according to their relative returns and risks. Using a new cross-country time-series dataset on net foreign asset positions, we implement this approach empirically and assess quantitatively the level of external imbalances consistent with equilibrium asset allocation across countries

> The paper's plan is as follows. Section 2 describes the methodological approach. It presents a Markowitz-Tobin model of portfolio diversification, according to which the ratio of a country's net foreign assets to its total wealth is a negative function **of** investment returns in the country relative to the rest of the world, a positive function of investment risk also relative to the world, and an inverse function of the ratio of foreign-owned to domestic-owned wealth. This defines the stock equilibrium towards which the economy converges gradually over time. The second part of section 2 presents the paper's econometric strategy to estimate this long-run

> relationship, allowing for short-run dynamic effects which can be heterogeneous across

> countries. _Our_ empirical _approach_ is based on the Pooled-Mean Group dynamic panel data estimator recently proposed **by** Pesaran, Shin, and Smith **(1999),** which combines the efficiency

> gains of restricting long-run parameters to be the same across units (countries in our case) with the flexibility of allowing short-run parameters to differ across coutries.

> We do recognize, however, that this approach might be of limited applicability in countries whose external indebtedness is partly driven **by** non-market forces, such as political interests or humanitarian reasons **-** e.g., low income countries (who are the main recipients of large aid flows), countries located in regions of geopolitical interest, and **/** or countries imposing stringent barriers to portfolio diversification. For this reason, in our empirical analysis we perform robustness checks **by** examining the validity of our basic model in various samples of countries

> defined according to income level and extent of barriers to portfolio diversification.

_4_
non-host components in column (3). All, moreover, are highly significant. In the FDI data, in contrast, while the coefficients on the statutory rate terms take the expected sign they are in all cases not only much lower but also insignificant. In all columns the host _KF_ - _METR_ enters with the expected negative sign, and is again insignificant in the FAI data. It proves significant, however, in the FDI data—though only moderately so.

While this result—that tax effects are stronger using FAI data than FDI data—is not wholly unexpected given some previous work, it may nonetheless seem counter-intuitive: Might one not, to the contrary, expect the tax-motivated use of conduit arrangements, picked up directly in FDI statistics but not in FAI, to generate tax effects that are more marked, not less, in FDI data?

Some explanation can be found, however, in the narrative around Table 1 in Section 3 and Figure 1. Suppose, for instance, that the statutory rate in host country _H_ is reduced, so that real investment there by the parent in _UP_ increases; and suppose moreover that this is financed by increasing both direct investment from _UP_ to _H_ and conduit investment from _UP_ through _IP_ to _H_ . In the FAI data the aggregate of these two flows, FAI from _UP_ to _H_ , is indeed identified as responding to the tilting of the tax differential in favor of investment in _H_ . In the FDI data, the increased FDI flows from both _UP_ and _IP_ to _H_ are also in line with the shift in tax differentials. But the FDI data also picks up the increased flow from _UP_ to _IP_ : that is, the conduit country _IP_ experiences an increase in inward FDI even though in tax terms it has become less attractive. In this way, FDI flows through conduit countries can dilute the apparent impact of taxation on investment. And, given the significance of conduit activities, the downward bias this implies in measured tax effects may be sizable.

# **6 The Global Minimum Tax and Tangible Investment**

A centerpiece of the G20/OECD-led agreement of October 2021, and now in the process of implementation (‘Pillar Two’), is the proposed adoption of a minimum effective corporate tax rate of 15 percent. The primary motivation behind this is to limit the shifting of paper profits and tax competition for both investment and shifted profits. The impact on productive flows remain as real investment.

35
Tariff exemptions or duty drawbacks for exporters are widely used by many countries as an instrument for providing export incentives. Duty exemptions for exporters are intended to- eate a 'free trade regime' for exporters thereby reducing the degree of antiexport bias that would otherwise exist in an economy characterized by import protection. The practice is not defined as an export subsidy and is therefore compatible with the GATT rules. Other than to aid exports, a number of developing countries grant exemption from duty to purchases of certain inputs by certain industries located in specific regions in order to promote those industries and/or regions. The present paper argues that when tariff exemptions are granted as a means of industrial/regional promotion to an industry independently of its export performance, the tariff exemptions lose their potential as an export promotion instrument. To show this point, a simple modell/ is used in the first part of the paper. In the second part, the results of the model are applied to the case of Argentina were duty exemptions are widely used for purposes other than export promotion. The case of Argentina is of interest because it exemplifies the practice of many developing countries where duty and other tax exemptions are used as a means of industrial/regional promotion. Summary and conclusions are contained in the last section.

1/ This model is based on Richardson (1987) and Herander et al. (1986).
sources of FDI, which are nearly evenly divided between mostly OECD countries and Hong Kong (SAR of China), Taiwan (China) and Macau (SAR of China) (henceforth, Hong Kong-Taiwan-Macau for short); China‘s extraordinarily heterogeneous ownership structure, involving state, foreign, and domestic ownership, and tax incentives such as income tax holidays and tariffs. Many foreign investors in China over the last ten years have faced much lower corporate tax rates; before 2008, foreign investors received a 15 percent corporate tax rate while domestic enterprises faced a regular 33 percent corporate tax rate<sup>4</sup> . This policy of promoting foreign investors and other favored firms in China was only discontinued in 2008.

In addition to exploring the differential effects of foreign investment linkages across special characteristics in explaining productivity performance, we also examine how globalization has affected Chinese firm performance. Until 1990, average tariffs on manufacturing in China were as high as 50 percent. There is a rich literature which examines the impact of trade liberalization on productivity, although there are fewer studies that disentangle the effects of input and output tariffs. One example is Amiti and Konings (2007), who use Indonesian manufacturing census data to show that the effect of reducing input tariffs significantly increases productivity, and that this effect is much higher than reducing output tariffs. For China, Brandt, Biesebroeck and Zhang (2008) focus specifically on the impact of trade liberalization on productivity. Using Chinese firm-level data (1998-2005), they suggest that a ten percentage point reduction in final good output tariffs results in an increase in TFP of 0.42 percent.

Our results suggest varied outcomes from promoting domestic productivity growth through these different instruments. The benefits via vertical linkages from foreign investment have been significant and positive, but the impact of tariffs on total factor productivity growth has been negative. We find some horizontal 4 However, the government adjusted this preferential policy in 2008. Starting from Jan 1, 2008, the new corporate tax policy for foreign-invested firms is the following: foreign-invested firms that previously receive preferential corporate tax rates will return to the regular tax rate within 5 years. In 2008, the tax rate increases from 15% to 18%; in 2009, the rate keeps increasing to 20%; in 2010, the corporate tax rate is 22% and will finally reach 25% in 2012.

4