rafmacalaba/datause-encoder-data huggingface dataset:
# **1 Identifying Star Firms**

We use data from Compustat that provides detailed financial information on publicly traded firms in the US over an extended period of time. We drop cross listed ADRs and restrict the sample to firms incorporated in the US. We also drop firms in Utilities (SIC 49), Finance, Insurance and Real estate (SIC 60-69) and Public Administration (SIC 90-99), observations with missing SIC codes, negative values for employees, sales, total assets, current assets and current liabilities, fixed assets, cash, and goodwill and missing total assets or sales.

The advantage of using Compustat is that we have detailed balance sheet information that allows us to compute intangible capital. The caveat however, is that there are firm selection issues. First, it may be that listed firms, as a class, might not consistently represent star firms. Doidge, Kahle, Karolyi, and Stulz [2018] and Kahle and Stulz [2017] show that there are fewer US listed corporations today than 40 years ago. However, Grullon et al. [2017] argue that the void left by listed firms has not been filled by an increase in the number of private unlisted businesses. Using US Census data that includes both private and public firms, they show that even though more private firms have entered the economy, their marginal contribution to the aggregate product market activity has been relatively small. Public firms also account for one third of total US employment (Davis, Haltiwanger, Jarmin, Miranda, Foote, and Nagypal [2006]) and about 41% sales (Asker, Farre-Mensa, and Ljungqvist [2014]). Also using U.S. Census data, Maksimovic, Phillips, and Yang [2017] show that high initial firm quality at birth predicts subsequent listing decision. These findings suggest that while our sample will not be picking up small and young potential star firms in their private stages, we are targeting the sample of firms among which economically significant stars are highly likely to arise.

The second, and potentially more important issue, as pointed out by Doidge et al. [2018], is that small, young, high-technology firms may benefit from private status where specific financial institutions, such as venture capital partnerships and private equity firms better meet their financing needs than public capital markets. Thus, such firms may be underrepresented in our sample of star firms. To the extent that this listing gap has emerged only since 1999 (see Doidge, Karolyi, and Stulz [2017]), the early part of our sample period is immune to this.

7
15 Figure 7. Trade Diversification across Partners over Time <!-- Start of picture text --> Emerging Asia Frontier Asia Sub-Saharan Africa Western Europe<br>1965-1970 2006-2010<br>4<br>3<br>2<br>Theil Index on Partners<br>1<br>0<br><!-- End of picture text --> _Sources_ : UN COMTRADE; Author calculations.

Figure 8. Real Sector Share of Frontier Asia, 2000–10 <!-- Start of picture text --> Agriculture Mining Manufacturing<br>Construction Wholesale trade T ransport and communication s<br>2000 2005 2010 2000 2005 2010 2000 2005 2010<br>year<br>Frontier Asia All Developing Countries<br>Graphs by sectorcode<br>.4<br>.2<br>0<br>.4<br>Real Sector Share<br>.2<br>0<br><!-- End of picture text --> _Sources_ : IMF, and author calculations.
American respondents in the WVS favored providing aid to poorer countries. Support in the other 8 donor countries in the WVS ranged from 69.3% (New Zealand) to 88.5% (Japan). Despite its relatively low levels of aid-giving, support for increasing aid, as measured in the GI survey, was also lower in the U.S. (tied with Japan at 45%) than in any of the 17 donor countries included in that survey. Our results can partially explain low support for aid in the U.S., but it remains somewhat of a mystery.

The United States is an influential case for some of our level-2 variables; it is responsible for the negative coefficients for military spending and population size in the World Values Survey analysis. Military spending as a share of national income for the U.S. is about three times as high as for the other DAC donors collectively. Many Americans may view their international policeman’s role as a substitute for development aid. The large size of the United States, both in population and in area, may reduce its citizens’ awareness of events and conditions in less-developed countries, and a reduced sense of responsibility for the welfare of poor people abroad.

Another clue to U.S. exceptionalism in aid-giving may lie in the second question asked about aid in the GI survey. The United States reports the highest percent of respondents who think no aid should be provided to countries with corruption problems. Indeed, the U.S. population is unique among the donor countries in its attention to corruption, being the only country where a higher percentage of respondents favor a more restrictive policy of giving only to non-corrupt countries. Concerns about government corruption, along with stronger traditions of (and tax incentives for) private philanthropy, likely explain Americans’ relatively high levels of private giving to charitable organizations providing non-official aid.<sup>12</sup> Numerous respondent-level attitudes and behaviors measured in the WVS were found to be associated with support for aid. On several of those variables, Americans on the whole tend toward the extremes among the 9 donor countries represented in the survey. However, some of these differences should increase support for aid among

> 12 Americans’ aid giving remains low by DAC standards even when private giving is included.  Americans give about 2% of their incomes to charities, and only about 2% of that amount goes to international charities, or .04% of income.  Adding this amount to U.S. ODA would not close most of the gap between U.S. and other donors’ aid/GNI levels, even if private giving in other donors is ignored.  Moreover, while private charitable giving is lower in other donor countries, a higher percentage of their charitable giving goes to international charities (Roodman and Standley, 2006).

19
Figure 1: Loans Outstanding for Each Scheme in Billions of US dollars <!-- Start of picture text --> 4.50<br>3.96<br>4.00 3.90<br>3.56<br>3.50<br>3.00<br>2.50<br>2.00<br>1.50<br>1.00<br>0.50 0.45<br>0.24<br>0.14<br>0.00<br>2015 2016 2017<br>EFS (Billions of US dollars) LTFF (Billions of US dollars)<br>Billions of US dollars<br><!-- End of picture text --> Figure 2 looks at the concentration of exports in Pakistan (measured as the percentage of exports accounted for by the largest exporting firms) and the concentration of the loans provided under the EFS and LTFF. The message provided by the picture is clear—the recipients of export finance support are among the largest exporters in Pakistan. While the largest 10 exporters in the country account for 10 percent of the country’s exports, they receive more than 20 percent of the loans provided. The largest 100 exporters account for 40 percent of exports but receive two-thirds of the export finance funds. Figure 3 plots the distribution of (log) export sales according to firms’ participation in the EFS and LTFF schemes and confirms the fact that firms that utilize the schemes are substantially larger than those that do not.

11
**Table** **<u>5: Percentage of businesses in non-exempt sectors, by turnover quartile</u>**

|Quartile (turnover)|**Pe**<br>**Male-owned**|**riod 1**<br>**Female-owned**|**Pe**<br>**Male-owned**|**riod 2**<br>**Female-owned**|
|---|---|---|---|---|
|1|26.0|21.4|**37.8**|**2.4**|
|2|19.3|18.3|**28.5**|**4.2**|
|3|24.1|35.1|**33.6**|**12.0**|
|4|46.5|35.5|40.3|27.0|
|_Number of enterprises_|_476_|_452_|_496_|_445_|

Source: Author’s calculations using ESS 2018/2019 and 2021/2022.

Notes: Significant differences at p _<_ 0.05 are shown in bold. Non-exempt sectors are defined as sectors without a tax-free threshold, for example, bars, cafes, hotels, construction, transit services, and trading in specific products, such as flour, chat, and metals (Council of Ministers Regulation No. 410/2017). Businesses in some activities, particularly in the retail sector, that lack sufficient detail to impute presumptive tax are excluded. Businesses are grouped into turnover quartiles.

# **4. Empirical methodology**

The effective tax rate of the presumptive tax is estimated by dividing the tax liability by the turnover, as shown in equation 1. In the literature on corporate tax burdens, this indicator takes a backward-looking approach, which measures the tax burden the enterprises face after all investment and planning decisions have taken place (Nicodème 2002).<sup>14</sup> This is a first-order approximation of the tax burden, which does not account for behavioral responses to taxes. The disadvantage of using turnover (rather than profits) is that it assumes that the profit margins are constant across firms (Nicodème 2002). Nevertheless, turnover is used in this paper because it is the basis for assessing the presumptive tax. Since businesses cannot deduct costs to reduce taxable income in the presumptive tax regime, turnover is equivalent to taxable income. It is also difficult to get reliable estimates of profits in the data because household business expenses cannot be easily separated from the overall household expenses. For example, respondents can misclassify goods and materials for household use as business expenses; owners’ payment to themselves as employee wage costs; and household rent as business rent (see de Mel, McKenzie, and Woodruff, 2009 for a discussion).

Two measures of tax liabilities are used to estimate the tax burden of the presumptive tax. The first measure is the self-reported tax payments made in the 12 months prior in the ESS data, which is what the businesses claimed to have paid in taxes on income and profits. The second measure is imputed tax

> 14 In contrast, forward-looking tax rates are calculated to estimate firms’ decisions about investments, financing, or locations (Lazar 2014).

13
Trade and financial sector reforms: interactions and spillovers

# Ashley Taylor<sup>_∗_</sup> World Bank / LSE

30 September 2009

**Abstract**

The allocation of production across firms is a potentially important explanation of the productivity gap between rich and poor economies. Reforms to trade policy and the domestic financial sector are often both key elements of policy packages aimed at reducing productive distortions. However, the impact of each reform in reallocating production within an economy is usually analyzed independently. This paper asks how do such general equilibrium effects of trade and domestic financial sector reforms interact in terms of their effects on productivity, wages and utility. Motivated by recent firm-level studies, this paper add two-way linkages between firms’ production and exporting decisions and their financial constraints to a general equilibrium heterogeneous firm trade model. The interaction effects between reforms appear qualitatively important. Trade and domestic financial sector reforms have complementary effects on the average productivity and size of domestic producers. However, if much reallocative work has already been done through a well-functioning financial sector, the marginal benefits of trade liberalization for wages and household utility are reduced. Improvements in the ability to use exports as pledgeable collateral enhance both the wage and productivity effects of trade reforms. The model also highlights the potential for financial sector reforms in one economy to be exported via the trade channel, affecting decisions to produce or export in the foreign economy and putting downward pressure on foreign real wages.

> _∗_ This paper was drafted during my PhD studies at LSE. I am particularly grateful for discussions with Gianluca Benigno, Kosuke Aoki and Danny Quah and for comments from Enrica Detragiache and Hubert Kempf (discussants) and from participants at the BETA / Universit ́e Louis Pasteur Workshop on “Cycles and Crises”, May 2008, the IMF Conference On the Causes and Consequences of Structural Reforms, February 2008, the 2008 Royal Economic Society Annual Conference, the 2007 Money Macro and Finance Meeting, the LSE Money-Macro work-in-progress workshop and at seminars in the IMF Research Department and the Graduate Institute of International and Development Studies. I thankfully acknowledge research funding from ESRC Research Studentship PTA-030-2004-00632 and support under “Stability of the Global Financial System: Regulation and Policy Response”, World Economy and Finance, ESRC Research Programme Phase I, Grant Ref. No.: RES-156-25-0026. Email: ataylor2@worldbank.org.

1
Table 2 List of surveyed studies: territorial coverage and performance indicators

|**Author(s)**|**TFP**|**Profitability**|**Sales&Revenues**|**D-M**|
|---|---|---|---|---|
|Andreyeva (2003)|×||×||
|Angelucci, Estrin, Konings, Zolkiewski (2002)|**O**||**O**||
|Brown and Earle (2001a)|×|||YES|
|Brown and Earle (2001b)|×|||YES|
|Brown, Earle and Telegdy(2006)|⊗||||
|Carlin, Fries, Schaffer, Seabright (2001)|||⊗|YES|
|Claessens and Djankov (1998)|**O**||||
|Claessens and Djankov (1999)||**O**||YES|
|Claessens and Djankov (2002)|||**O**||
|Claessens, Djankov and Pohl (1997)||**O**||YES|
|Commander and Svejnar (2007)|⊗||||
|Djankov and Hoekman (2000)|||**O**||
|Frydman, Gray, Hessel, and Rapaczynski (1999)|||**O**|YES|
|Frydman, Hessel, and Rapaczynski (2000)|||**O**|YES|
|Grigorian (2000)|||**O**|YES|
|Grosfeld and Tressel (2002)|**O**||||
|Hanousek, Kocenda, and Svejnar (2007)||**O**|**O**||
|Hanousek and Kocenda (2003)||**O**|||
|Jones and Mygind (2002)|||**O**|YES|
|Jones, Klinedinst and Rock (1998)|**O**|||YES|
|Maurel (2001)|||**O**||
|Miller (2006)||**O**|||
|Orazem and Vodopivec (2004)|**O**||||
|Perevalov, Gimadii, and Dobrodey(2000)||×|×|YES|
|Pivovarsky(2001)|||×||
|Pivovarsky(2003)|×||||
|Sabirianova, Svejnar, and Terrell (2005)|⊗||||
|Salis (2006)|**O**||**O**||
|Simoneti, Damijan, Rojec, and Majcen (2005)|**O**||||
|Smith, Cin, and Vodopivec (1997)|**O**|||YES|
|Simoneti and Gregoric (2004)|**O**|**O**|||
|Weiss and Nikitin (2002)|**O**|**O**||YES|
|Warzynski (2003)||×||YES|
|Zalduendo (2003)||**O**|||

Note: **O** denotes coverage of the CEE countries; × denotes coverage of the Russia and CIS region; ⊗ denotes combination of the coverage for CEE, Russia and CIS. Yes in the D-M column indicates the study is covered by Djankov and Murell (2002).
<!-- Start of picture text --> ' Country 1 contributes 50% of |<br>. | services X's regional web traffic |<br>Applying Web ce al<br>Traffic Shares to F aN = =a]<br>ServicesChainalysis— Source: _| RegionOfservice A contributesX's web traffic 75% | \ receivedCountry§37.5M1 |<br>Region A received 375M | [ Country 2 contributes 50% of \<br>\ } | services X's regional web traffic |<br>a. Country 2 \<br>| Service X receives $100M | \ received $37.5M |<br>| in-economic transfer value | $$$<br>| Country 1 contributes 100%<br>| of services X's regional web traffic |<br>Region B contributes 25% | a<br>| of service X's web traffic | | Country 1 \<br>— \ received $25M ]<br>| Region B received $25M | a hs,<br>\,= a_i / || of servicesCountryX's2 contributesregional web0%traffic ||<br>i Country 2 \<br>\ received $0M |<br><!-- End of picture text -->
# OX M1.2: Tbe debt **serice squeeze** in Tanzania

Tanzania (an intensely adjusting country) reduce;d the public budget allocation to education and to other public sectors between 1984 and 1988, while increasing the debt service allocation continuously to over one third of the public budget. Wher.ever the budget expanded, debt service constituted most of the increase. In real terms, education expenditures decreased, other public expenditures increased slightly, but debt service more than doubled. Betveen 1986 and 1987, although education's share of the total public budget fell from 16 to 14 percent, its share of the discretionary public budget (excluding debt service) rose from 21 to 24 percent. The debt burden crowded out education expenditures and overwhelmed the government's efforts to favor education in the budget allocation process, and real education expenditures declined.

# Index of real public expenditures by sector (No of public budget)

||1984|1985|1986|1987|<br>1988|
|---|---|---|---|---|---|
|Debt Service<br>|100 (189o)<br>|114 (20%)<br>|126 (24%)<br>|176 (28%o<br>|)256(34%o)<br>|
|Education<br>|100 (16%)<br>|92_(14%)_<br>|96 (16%)<br>|95 (14%)<br>|98 (12%o)<br>|
|Other<br>|1UO (66%)<br>|102 (66%o|) 87 (60%)|99 (58%)|111_(54%o)_|
|Total|100|103|95|112|135|

Sourc: Berstecher and Carr-Hill (1990).

3.06 Figures that describe changes in education's share of the public budget or GNP alone do not indicate whether education was protected or vulnerable relative to other sectors in the allocation of public resources. Comparing intersectoral allocations over time reveals government priorities during periods of recession and adjustment. One method is to calculate the coefficient of vulnerability (Hicks and Kubish, 1984): the ratio of the percentage change in education spending to the percentage change in total public spending during the adjustment period, defined only for countries where government spending fell. The coefficient is greater than one if the sector was vulnerable, less than one if the sector was protected, and negative if the sector was highly protected. For example, the coefficient for Guinea during the adjustment period 1986-89 is 0.64. Although real education spending fell by 25 percent over the period, education was "protected" relative to the rest of the government budget, which fell by almost 40 percent in real terms. In Malawi, the coefficient of vulnerability is 5.5, indicating a high degree of vulnerability (all government spending fell 3.2 percent in real terms over the adjustment period 1982-88, but education spending fell by 17.4 percent).

3.07 Cornia, et.al. (1987) compare government expenditure cuts across regions from 1979-83 (table HI1.4). They define the education sector as: "vulnerable" if expenditure cuts in education exceed aggregate percentage cuts; "protected" if expenditures in education 24
<!-- Start of picture text --> Total Study Area<br>No. of Plots Area (Ha) | % of Public % of % of Study<br>Land Developable Area<br>Land<br>PUBLIC LAND<br>MunicipalStateCentralGovernmentGovernmentCorporation dehemmedmmm tS8025 conanfet1,001.59dt tO2PAO 4B)OOnnneonrnannreo,Zh9%Me, WS)13%)5% nounmente,tO:3.91%d1%72%<br>SubTotalROADS.Sabarmaticcnnnnmninmnnnnnnnnineersnnnnnnennnsnapenannnind| C240] 850.16]tee: 80] nnn61%PO rounnmnennnnninnisersnnenan32% 26.74%de, 5%<br>OtherRailwaysWaterRIVETBodies ammf suppunmmmnmninnnnmnnefnenmnmmnnmnenenenefeannnmnneanna452.02SEOto OADOA)A.smneanennnnnnd cuneneuninenn 4%teOd wmmmnmnmnnennnnfimnenennnenesdtunminmnmnnnnnenninefnnmnnennenates1.76%Sose 2|<br>SubTotalTotal CT t—“‘CSC*rLSCOCOC(*édG,3390.93[ 39%] | 17.14%<br>Public Land PC‘ C11,2.41.09 100%| 43.88%<br>esCO CO<br>Walle”GamtalsCity.(VillagemmmmnnmnnenefnnnnmnennnninnninSites enmmnnanninn481.19rg gS) annnenonennan pounmnennnnn1.88%eer ee<br>Total Private Pp | 14,376.21] | BM 56.12%<br><!-- End of picture text -->

<!-- Start of picture text --> Ahmedabad - Pubic and Private Lands in Study Area<br>Total Private Land 56.12%<br>Ganitals (Village Sites) | 1.48%<br>Walled City yy 2.2555<br>Private land parcles | 51.90%<br>Total Public Lane 43.88%<br>Sub Total - Non Marketable Public 17.10%<br>Railways J L76%<br>Other Water Bodies | 1.45%<br>Sabarmati River | 1.88<br>Roads 17.15%<br>Sub Total Public - ANIC, State and Central Gavi. 26.4<br>Central Government | 4.1%<br>State Goverment | 10.72%<br>Municipal Corporation | 12.11%<br>Oe 10% 20 305 a0% SO BO<br><!-- End of picture text -->
Policy Research Working Paper 5472

# **Abstract**

Capital is a fundamental component of agricultural production, and the accumulation of capital is key to growth in agriculture and the process of development. Unfortunately, cross-country data sets on agricultural fixed capital are rare. Using a common methodology that allows comparisons across countries, as well as over time, this paper introduces a data series on fixed capital in agriculture, based on national accounts data. The fixed capital measure differs remarkably from the Food and Agriculture Organization’s data series on tractors, which has been widely utilized as a proxy for agricultural fixed capital. The authors construct comparable measures of capital in livestock and tree stock. They examine the evolution of the capital stocks from 1970 to 2000, paying particular attention to the changing composition of agricultural capital, as well as differences in the accumulation of capital for high-income and middle and lower-income countries. Using the capital measures in agricultural productivity analyses, the data yield estimated input elasticities substantially different from those found previously in the literature. The authors show explicitly that this is due to the improved data set on agricultural capital stocks, as well as the methodology used in the study.

This paper—a product of the Agriculture and Rural Development Team, Development Research Group—is part of a larger effort in the department to examine the determinants of agricultural growth. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The author may be contacted at DLarson@worldbank.org. This study was supported by the Bank’s Research Support Budget under the research project, “The Contributions of Governance to Growth in Agriculture”.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team