rafmacalaba/datause-encoder-data huggingface dataset:
(Compustat item “dltt”). We discard observations where the ratio is not between zero and one. We define leverage as the ratio of total debt to total assets (Compustat item “at”).

When examining the relationship between short-term debt and governance, we use a set of firm specific control variables that have been previously found to affect leverage and debt maturity (Barclay and Smith 1995; Guedes and Opler, 1996; Stohs and Mauer, 1996; Opler et al., 1999; Barclay et al., 2003; Datta et al., 2005; Harford et al., 2008; Fan et al., 2012; Denis and Sibilkov, 2009). Due to data limitations, we follow Rajan and Zingales (1995) and Fan et al. (2012) to determine the set of control variables. Our first control variable is _size_ , which is the natural logarithm of total assets. As shown in previous literature, size is an important determinant of maturity structure. Larger firms are able to borrow long-term debt because they tend to have lower issuance costs, lower information asymmetry and better credit quality. The second control variable is asset tangibility ( _asset_tangibility_ ). It is measured as the ratio of net property, plant and equipment (Compustat item “ppent”) to total assets. When asset tangibility is higher, creditors are more willing to bear the risk since these assets act as collateral and they are more valuable in the case of liquidation. The third control variable is profitability, which is measured by return on assets ( _roa_ ). We compute return on assets as the ratio of net income (Compustat item “ni”) to total assets. We expect profitable firms to have easier access to long term debt, leading to a negative marginal effect of profitability on short-term debt.

The fourth control variable is asset turnover ( _asset_turnover_ ), which is calculated as net sales (Compustat item “sale”) divided by total assets (Compustat item “at”). As argued in Demirguc-Kunt and Maksimovic (1999), firms with high turnover ratios may need short-term debt to support their sales. Because these firms are likely to generate current assets in the form of cash and account receivables, they may have an incentive to match the maturity of these short-term assets to liabilities by holding more short-term liabilities. Thus, high turnover firms may hold more short-term debt. Our final control variable is market-to-book ratio ( _mb_ ). It proxies for growth and investment opportunities as well as for the costs of financial distress. We measure it as the ratio of market value of equity to book value of equity (Compustat item “ceq”). We calculate the market value of equity by utilizing the “Security Daily” database in Compustat Global. Myers (1977) 13
# Table 1: _<u>Covid Deaths, NPIs and Interaction with Pop Density</u>_

||AL|L|Lower-<br>(Low- and Mi|income<br>ddle-Income)|High-i|ncome|
|---|---|---|---|---|---|---|
||(1)<br>ln_sol|(2)<br>ln_sol|(3)<br>ln_sol|(4)<br>ln_sol|(5)<br>ln_sol|(6)<br>ln_sol|
|(log) Number of Death|-0.00852***|0.0674***|-0.0108***|0.0510**|0.0138***|-0.0114|
||(0.000875)|(0.0184)|(0.000929)|(0.0208)|(0.00193)|(0.0332)|
|...×(log) Population||-0.00727***||-0.00517***||0.00319|
|||(0.00163)||(0.00185)||(0.00373)|
|...×(log) Area||0.00465***||0.00145||-0.00336|
|||(0.00103)||(0.00123)||(0.00368)|
|Stringency Index|-0.000371***|-0.00238|-0.000196*|-0.00145|-0.000670**|0.0107|
||(0.000105)|(0.00202)|(0.000110)|(0.00214)|(0.000312)|(0.00663)|
|...×(log) Population||0.000355*||0.000223||-0.00148*|
|||(0.000196)||(0.000208)||(0.000772)|
|...×(log) Area||-0.000626***||-0.000402**||0.00161*|
|||(0.000172)||(0.000184)||(0.000858)|
|Economic Support Index|0.000230*|-0.00291|0.0000999|-0.00303|-0.00135***|-0.00931|
||(0.000119)|(0.00275)|(0.000128)|(0.00306)|(0.000369)|(0.00846)|
|...×(log) Population||0.000149||0.000188||0.00107|
|||(0.000267)||(0.000298)||(0.000978)|
|...×(log) Area||0.000252||0.000151||-0.00120|
|||(0.000219)||(0.000247)||(0.00102)|
|N|198582|198582|174061|174061|24521|24521|
|Cities|2841|2841|2435|2435|406|406|
|r2|0.962|0.963|0.956|0.956|0.937|0.937|
|r2_within|0.0556|0.0561|0.0593|0.0598|0.0116|0.0118|
|City FE|Y|Y|Y|Y|Y|Y|
|Country x Month-of-year FE|Y|Y|Y|Y|Y|Y|
|<br>Country-specificlineartime trend|Y|Y|Y|Y|Y|Y|
|Note: Controls for city fixed effects, co<br>clustered at the city-level. * p<0.1, ** p<<br>2021.|untry-specific time t<br>0.05, *** p<0.01. Es|rend and count<br>timates obtaine|ry-month-of-ye<br>d using monthly|ar fixed effects.<br>data covering th|Standard errors<br>e period Januar|in parentheses<br>y 2015 – March|

12
POLICY RESEARCH WORKING PAPER 1864

# Summary findings

Filmer and Pritchett use cross-national data to examine the impact on child (under 5) and infant mortality of both nonhealth (economic, cultural, and educational) factors and public spending on health. They come up with two striking findings:

* Roughly 95 percent of cross-national variation in mortality can be explained by a country's per capita income, the distribution of income, the extent of women's education, the level of ethnic fragmentation, and the predominant religion.

* Public spending on health has relatively little impact, with a coefficient that is numerically small and statistically insignificant at conventional levels. Independent variations in public spending explain less

than one-tenth of 1 percent of the observed differences in mortality across countries.

The estimates imply that for a developing country at average income levels, actual public spending per child death averted is $50,000 to $100,000. This contrasts markedly with a typical range of estimates for the costeffectiveness of medical interventions to avert the main causes of child mortality of $10 to $4,000.

They outline three possible explanations for this divergence between the actual and apparent potential of public spending: the allocation of public spending, the net impact of additional public supply, and public sector efficacy.

This paper-a product of the Development Research Group-is part of a larger effort in the group to investigate the impact of health sector policies. The study was funded by the Bank's Research Support Budget under the research Project "Primary Health Care: A Critical Evaluation" (RPO 680-29). Copies of this paper are available free from the World Bank, 1818 H Street NW, Washington, DC 20433. Please contact Sheila Fallon, room MC3-63 8, telephone 202-473-8009, fax 202-5221153, Internet address sfallon@worldbank.org. December 1997. (41 pages) _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_ **_view_** _of the World Bank, its Executive Directors, or the countries they represent._

## Produced by the Policy Research Dissemination Center
# Table 1.5.

-

# Turkey: Seasonal Labor Requirements and Supply

# in Agriculture

# (Adults of both sexes, 000)

||1967|1972|
|---|---|---|
|Peak season<br>(July-August):|||
|Labor supply|10.083|10,1?8|
|o<br>demand|9,17"|10,025|
|to<br>surplus|910|11|
|Net seasonally<br>removable<br>labor<br>surplus:|||
|Dec. - Febr.|619|959|
|March- May|3,824|4,46'4|
|June|4,744|5,016|
|July<br>- August|0|0|
|Sept.- Nov.|4,227|4,521|
|Average, weighted|2562,4||

Source: World Bank, Turkey, Febr. 1975, Tables 50 and 51, pp. 193-95) (refers to Duncan R. Miller, ed-, Essays on Labor Force and Employment in Turkey). Average, weighted, my computation.

-27-
Table 5: EFFECTS ON MATCHING IN TRANSFER NETWORKS

||(1)|(2)|(3)|(4)|
|---|---|---|---|---|
|_Panel A:_|_Transfers r_<br>ultrapoor|_eceived by Ul_<br>otherpoor|_tra-Poor Househ_<br>middle class|_olds from ..._<br>upper class|
|Treatment|0.056***|0.036***|0.018|-0.008|
||(0.007)|(0.012)|(0.012)|(0.008)|
|Mean in control|0.128|0.345|0.701|0.260|
|Adj. R-squared|0.439|0.415|0.327|0.407|
|Observations|13464|13464|13464|13464|
|_Panel B:_|_Transfer_|_s given by Ul_|_tra-Poor Househ_|_olds to ..._|
||ultrapoor|otherpoor|middle class|upper class|
|Treatment|0.052***|0.032**|0.054***|0.016***|
||(0.008)|(0.013)|(0.019)|(0.005)|
|Mean in control|0.108|0.252|0.394|0.078|
|Adj. R-squared|0.368|0.306|0.254|0.145|
|Observations|13464|13464|13464|13464|

**Source:** Author’s analysis based on original survey data.

**Notes:** The table reports ITT estimates based on specification (1) estimated with OLS. The sample includes observations from ultra-poor households. All specifications control for the baseline level of the outcome, survey wave and subdistrict (strata) fixed effects. Standard errors are clustered at the BRAC branch office level (unit of randomization). *** (**) (*) indicates significance at the 1% (5%) (10%) level. In Panel A, the dependent variables in columns 1, 2, 3 and 4 are dummy variables equal to 1 if the respondent reported that her household received any transfers from any ultra-poor, other poor, middle class or upper class households (within her community) respectively. In Panel B, the dependent variables in columns 1, 2, 3 and 4 are dummy variables equal to 1 if the respondent reported that her household gave any transfers to any ultra-poor, other poor, middle class or upper class households (within her community) respectively.

40
Until the mid 1970's, the Internal R'evenue Service did not allow a U.S. tax credit for foreign tax withheld on net loans, as it was argued that the interest tax was paid by the borrower and not by the U.S. bank. Until then U.S. banks had an i-centive to write gross loan contracts, even though such loan carried a risk of higher future withholding taxes. Starting in the mid 1970's, however, the Internal Revenue Serv.ce recognized withholding tax credits for all international loans and the incentive to write gross loans disappeared. Overall the banking convention has been to write net international loan contracts.

Let i be the net interest rate in an international loan contract, and let r be the rate of withholding tax levied by the borrowing country. The gross interest rate is then i/(l-r), and the tax withheld per dollar of debt is i7/(l-r). Withholding tax rates on interest typically range from 0 to 30 per cent, while corporate tax rates in the major creditor countries are at least 30 per cent. This suggests that banks would never be in an excess credit position.<sup>3</sup> However, this is not the case as creditor and borrower country definitions of income typically differ. In particular, the withholding tax is imposed on all interest income, while the creditor country taxes interest income net of interest expense and maybe some other bank - - expenses. As an example, let r 0.20, i 0.10, and let the bank's cost of funds, denoted i*, be 0.08. The gross interest rate is 0.125 (from - 0.10/(1.0 0.2)). Per dollar lent, the tax withheld by the borrowing - country is 0.025 (from (0.10*0.20/(1 0.20)). Income by creditor country definition is 0.045 per dollar lent. Hence, the effective borrowing country tax - by creditor countrv definition - is 56 (from 0.025/0.045) per cent rather than 20 per cent. This means the bank may well be in excess credit 5
<!-- Start of picture text --> 4<br>2<br>=<br>DB<br>aa)<br>£-2<br>LD<br>o<br>a<br>O4<br>2<br>0<br>i<br>2<br>2019-01 2019-07 2020-01 2019-01 2019-07 2020-01 2019-01 2019-07 2020-01<br><!-- End of picture text -->
Table 16: Heterogeneous effects of market access conditions for GVC firms

|**Dependent Variable:**|||||||
|---|---|---|---|---|---|---|
||Expor<br>|ts_f _,_j_,_p_,_t_<br>|Prob (Export Pa<br>|rticipation_f _,_j_,_p_,_t_)<br>|Prob (Expo<br>|rt Exits_f _,_j_,_p_,_t_)<br>|
||(1)|(2)|(3)|(4)|(5)|(6)|
|Log (Tariff_i_,_j_,_p_,_t_)|-0.0351|0.1146|-0.0483|-0.0713|-0.0545|-0.0781|
||(0.036)|(0.130)|(0.011)***|(0.018)***|(0.057)|(0.095)|
|Log (Tariff_i_,_j_,_p_,_t_) x GVC Firm_f _,_t_||-0.1509||0.0258||0.0516|
|||(0.118)||(0.014)*||(0.080)|
|SPS_i_,_j_,_p_,_t_|0.0021|-0.1688|-0.0280|0.0126|0.1098|-0.1405|
||(0.048)|(0.055)***|(0.013)**|(0.022)|(0.087)|(0.134)|
|SPS_i_,_j_,_p_,_t _x GVC Firm_f _,_t_||0.1720||-0.0470||0.3144|
|||(0.062)***||(0.020)**||(0.129)**|
|TBT_i_,_j_,_p_,_t_|-0.0971|-0.2451|-0.0343|-0.0293|0.1939|0.0967|
||(0.044)**|(0.101)**|(0.012)***|(0.027)|(0.072)***|(0.182)|
|TBT_i_,_j_,_p_,_t _x GVC Firm_f _,_t_||0.1463||-0.0046||0.0932|
|||(0.106)||(0.024)||(0.168)|
|Pre-Shipment_i_,_j_,_p_,_t_|0.0388|-0.0033|0.0099|-0.0245|0.0516|0.1074|
|3|(0.053)|(0.103)|(0.009)|(0.013)*|(0.096)|(0.119)|
|Pre-Ship._i_,_j_,_p_,_t _x GVC Firm_f _,_t_<br>||0.0506||0.0391||-0.0740|
|||(0.079)||(0.010)***||(0.074)|
|QuantityControl_i_,_j_,_p_,_t_|-0.5739|-0.7828|-0.0537|-0.0904|-0.2712|-0.5528|
||(0.158)***|(0.185)***|(0.021)***|(0.026)***|(0.114)**|(0.132)***|
|Quantity Control_i_,_j_,_p_,_t _x GVC Firm_f _,_t_||0.2191||0.0364||0.2449|
|||(0.119)*||(0.020)*||(0.081)***|
|PriceControl_i_,_j_,_p_,_t_|-0.0483|-0.1134|-0.0043|-0.0138|-0.0495|-0.0685|
||(0.040)|(0.064)*|(0.014)|(0.021)|(0.087)|(0.126)|
|Price Control_i_,_j_,_p_,_t _x GVC Firm_f _,_t_||0.0655||0.0102||0.0216|
|||(0.061)||(0.016)||(0.094)|
|Log (Import Demand_f _,_p_,_j_,_t_)|0.0324|0.0321|0.0110|0.0110|-0.0211|-0.0212|
||(0.006)***|(0.006)***|(0.001)***|(0.001)***|(0.008)**|(0.008)**|
|Observations<br>|253212|253212|253212|253212|17221|17221|
|Adjusted_R_<sup>2</sup>|0.96|0.96|0.15|0.15|0.18|0.18|
|Fixed Effects|_f _−_p _−_j_,_f _−_p _−_t_,_j _−_t_|_f _−_p _−_j_,_f _−_p _−_t_,_j _−_t_|_f _−_p _−_j_,_f _−_p _−_t_,_j _−_t_|_f _−_p _−_j_,_f _−_p _−_t_,_j _−_t_|_f _−_p _−_j_,_f _−_p _−_t_,_j _−_t_|_f _−_p _−_j_,_f _−_p _−_t_,_j _−_t_|

This table estimates equation 4 for the intensive margin in columns (1) and (2); extensive margin I in column (3) and (4) and extensive margin II in column (5) and (6). GVC firm is an indicator variable equal to 1 if the firm both imports and exports goods in a given year and 0 otherwise. All columns show coefficients standardized with zero mean and unit standard deviation. All columns use firm-HS6 product-destination, firm-HS6 product-year and destination-year fixed effects. All columns also control for the Inverse Mills Ratios for each NTM type following estimation of probit model of NTM selection based on NTM intensity in neighboring countries. Clustered standard errors at the HS6 product-year level are presented in parenthesis. ∈ _p <_ 0.10,∈∈ _p <_ 0.05,∈∈∈ _p <_ 0.01
column lists the number of 4-digit economic sectors that have at least one PC firm with each particular kind of connection. Firms in Egypt operate in 320 non-farm, non-government 4-digit ISIC Rev.4 sectors. Politically connected firms are active in about half of these.

**Table 1: Type of political connection across firms and by 4-digit sectors**

|Type of PC firm|Number of PC<br>firms of that type|Number of 4-digit sectors (out<br>of 320) with at least one PC<br>firm of that type|
|---|---|---|
|Any type of politically connected firm|469|174|
|Politically connected CEO|47|50|
|Politically connected owner|334|148|
|Politically connected private equity<br>ownership|172|108|

Having identified politically connected firms, we linked these to data on firm performance in order to compare their performance with that of unconnected firms within their sector. These data were taken from the Orbis database. It contains employment data for 20,000 establishments between 2003 and 2012; operating revenues for about 700 large establishments; and profits for about 400 large establishments.

To analyze the impact of political connections on aggregate employment growth, we take advantage of establishment census data from the department of statistics in Egypt (CAPMAS). The census includes employment and firm characteristics of over two million (non-farm) economic establishments in 1996 and 2006 (repeated cross-sections). It does not contain firm names and so cannot be used for inter-firm comparisons. However, we know which of the 320 non-farm, nongovernment (ISIC Rev. 4) 4-digit sectors have connected firms.<sup>8</sup> We can therefore use the census data to estimate how total employment growth across detailed 4-digit sectors changes depending on the presence and entry of connected firms.

A similar exercise allows us to see if politically connected firms benefited from state-supported barriers to entry and energy subsidies. World Bank data (WITS) provides sector-specific information on non-tariff barriers to trade (NTMs). The UN reports data on the energy intensities of manufacturing industries – the industries that would benefit most from the massive energy subsidies available in Egypt during this period. We match these data to our data on the sectoral intensity of political connections to see if 4-digit sectors with more politically connected firms benefit from more NTMs and are more energy-intensive (receive more energy subsidies).

Moreover, we test whether connected firms benefit from other types of regulatory capture. We do not know the specific regulatory experiences of connected firms relative to unconnected firms, but through World Bank Enterprise Survey (WBES) data, we can compare the regulatory environment across 4-digit sectors with more and fewer connected firms. We pool all available surveys for Egypt

> 8 We exclude the following sectors as firm dynamics in these sectors are driven by the government and not the private sector in Egypt: public administration, education, health, arts (4-digit ISIC Rev. 4 codes larger than 8400).

7
# **2 Background and Data**

## **2.1 Regional Trade Agreements**

Regional trade agreements, beyond eliminating trade barriers between countries, increasingly address non-trade policy areas in their content such as competition policy, intellectual property, and environmental concerns. Non-trade commitments within RTAs have proliferated in recent decades, both in number and in policy scope—the average RTA in the 1950s covered eight policy areas; in recent years they have averaged 17 (Hofmann _et al._ , 2017). The increasing number of policy commitments has been accompanied by an increase in regulatory and enforcement requirements. The changing nature of RTAs is documented in a new database by the World Bank detailing the content of all RTAs in force and notified to the World Trade Organization (Mattoo _et al._ , 2020). Since the signing of the Montreal Protocol in 1987, many RTAs have included policy commitments tied to the Montreal Protocol and/or ozone-depleting substances in general.

Our data on environmental provisions in RTAs have been collected as part of the broader World Bank project on the content of trade agreements and are described in detail in Monteiro and Trachtman (2020). This is the most extensive effort to date to document environmental provisions in trade agreements. The environmental provisions coded include environmental goals, specific commitments, compliance with multilateral environmental agreements, enforcement mechanisms, and external assistance and collaboration. Within this set of environmental provisions, there are two that address ozone depleting substances:

- Does the agreement require states to control ozone-depleting substances?

- Does the agreement require states to comply with the Montreal Protocol on ozone depleting substances?

Figure 1 displays the frequency of RTAs over time, as well as the inclusion of provisions related to ODS. There are 44 agreements that include at least one of the two provisions related to ODS.<sup>3</sup> The first trade agreements to enter into force that included ODS provisions were the Common Market

> `3` The list of trade agreements containing provisions on ODS use is displayed in Appendix Table A.1.

6
- 34 -

Table 7 Probit: Multi-Dolicy models for all social projects Dependent variable: Dummy for unsatisfactory projects

||Model|1|Model|2|
|---|---|---|---|---|
|Variable|Coefficient|t|Coefficient|t|
|Specification<br>1|||||
|Constant|-0.7514|-1.58|-0.6339|-1.27|
|M2BLACK|0.0044|3.04|0.0036|2.37|
|MSM2BLK|-0.5636|-1.12|-0.6971|-1.29|
|HALEVI4|-0.1960|-0.77|-0.1725|-0.66|
|MSHALEVI|-0.5194|-1.20|-0.6182|-1.37|
|MGDPZ|||-0.0533|-1.72|
|MSGDP|||0.3781|1.45|
|Africa|0.2421|0.97|0.2813|1.04|
|EMENA|0.4207|1.50|0.4747|1.65|
|LAC|0.1394|0.50|0.1094|0.36|
|Log likelihood||-121.56||-117.73|
|Specification<br>2|||||
|Constant|-1.1139|-4.29|-0.8747|-2.76|
|M2BLACK|0.0041|2.73|0.0031|2.03|
|MSM2BLCK|-0.5817|-1.46|-0.0067|-1.22|
|BALANKI|12.8630|1.53|13.5250|1.62|
|BALANK2|-1.1914|-0.46|-0.9132|-0.35|
|MSBALAN|-0.0563|-0.22|-0.2783|-1.02|
|MGDPZ|||-0.0681|-2.11|
|MSGDP|||0.3089|1.20|
|Africa|0.1738|0.67|0.2026|0.72|
|EMENA|0.3168|1.06|0.3895|1.28|
|LAC|0.1090|0.39|0.0486|0.16|
|Log likelihood||-120.77||-116.63|
|Specification<br>3|||||
|Constant|-0.6382|-1.25|-0.4988|-0.92|
|M2BLCK|0.0041|2.73|0.0031|2.03|
|MSM2BLK|-0.5817|-1.46|-0.0067|-1.22|
|HALEVI4|-0.2304|-0.87|-0.1874|-0.69|
|MSHALEVI|-0.5710|-1.22|-0.5391|-1.12|
|BALANK1|12.9670|1.52|13.1710|1.56|
|BALANK2|-1.2941|-0.50|-1.0970|-0.42|
|MSBALAN|0.0533|0.19|-0.1541|-0.51|
|MGDPZ|||-0.0629|-1.93|
|MSGDP|||0.3668|1.38|
|Africa|0.1357|0.52|0.1920|0.68|
|EMENA|0.2893|0.95|0.3809|1.23|
|LAC|0.0761|0.27|0.0494|0.16|
|Log likelihood||-119.97||-115.88|

Note: Number of observations - 259. Asia is the left-out regional dummy.
**Table 6:** Aggregate ICT: Enforcement impacts

||TF|PR|Mar|kup|Profts|Sales|Employees|Wages|Intermediate Cost|Product quality|Intangibles|
|---|---|---|---|---|---|---|---|---|---|---|---|
||QMLE|ACF|QMLE|DLW||||||||
||1|2|3|4|5|6|7|8|9|10|11|
|Any intervention|0.0601|0.0628|-0.0015|0.0286|-0.0238|-0.0332|-0.068|0.0807|0.0468|-0.0756***|-0.0426|
|Obs. total|2014|2953|2014|2903|2953|2014|2953|2951|2953|2951|1734|
|Firm FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Sector*Year FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|State*Year FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|

*** ** * _p <_ 0 _._ 01, _p <_ 0 _._ 05, _p <_ 0 _._ 1.

Note: Each column is numbered and corresponds to the index references in text. FE stands for Fixed Effects.
<!-- Start of picture text --> 100%<br>90%<br>80% = Other Services<br>70% | Transport & Comms<br>60% Trade<br>50% = Construction<br>40% mElectricity & Water<br>30% = Manufacturing<br>20% @ Mining & Quarrying<br>m Agriculture<br>10%<br>0%<br>1995 /96 1999 2005 2010/11<br><!-- End of picture text -->

<!-- Start of picture text --> eae i<br>— ae ome Agriculture<br>ees a \ Sitining® Quarrying<br>fh \ manufacturing<br>| MElectricity & Water<br>\ i Mm Construction<br>,hs /i ETrade<br>aie 4 @Transport & Comms<br>mOther Services<br><!-- End of picture text -->