rafmacalaba/datause-encoder-data huggingface dataset:
beginnig and end of year outstanding balances.<sup>13</sup> Table I shows that during the sample period of 1983-1988, total domestic and foreign loans account on average for about 35 percent of the book value of firm capital, while debentures account for about 22 percent. The table also illustrates a high degree of variability in the loan to asset ratio across industries. Firms in "other manufacturing" have the lowest average loan to asset ratio (9 percent) while firms in the rubber tire industry have the highest (48.9 percent).

# 3. Measuring Investment in Speculative Assets

Detennining what portion of a finm's assets are being held for speculative as opposed to productive purposes is difficult. Balance sheet information, even if it were available in greater detail, could not fully resolve the issue. Clearly, investments in certain assets, such as machinery and equipment, can be regarded as productive, while investments in marketable securities can be categorized as speculative. The difficulty resides in the treatnent of other balance sheet items, particularly land and buildings, which can satisfy both productive and speculative needs. While finms clearly need land and structures to house their machinery and equipment, these assets are at the same timne known to be the most popular avenues of speculative investnent for Korean corporations.

Thus to define speculative assets, we classify total assets into three broad categories: 14 i) Liquid assets = Cash + Accounts and Notes Receivable + Other Current Assets, ii) Productive Assets = Machinery and Equipment + Inventories + h (Buildings and Land), 13See User's Guide of PACAP Database for more precise definitions of short-term loans (BAL 11) and long-term loans (BAL 14). The data on foreign loans are obtained from National Infomation and Credit Evaluation, Inc. of Korea.

14See User's Guide for the PACAP Database for the definition of cash (BAL 1), Accounts and Notes Receivable (BAL 3), Other Current Assets (BAL 5), Inventories (BAL 4), Marketable Securities (BAL 2), and Investnents and Other Assets (BAL 8). PACAP Database does not separate fied assets into machinery and equipment versus buildings and land. These data are obtained from National Information and Credit Evaluation. Inc. of Korea.
**Table 2: Estimation results -- the MTC equation**

||(1)|(2)|(3)|(4)|(5)|(6)|
|---|---|---|---|---|---|---|
|Distance|1.3046***|1.2811***|1.2765***|0.9912***|1.4514***|1.1193***|
|Transhipment|0.6164**|0.5922**|0.5783**|0.8615**|0.9128***|1.0566***|
|Total Import Vol|-0.5715***|-0.5312***|-0.5823***|-0.6908***|-0.5813***|-0.6651***|
|Abs Import Imbalance|-0.0899|-0.1175|-0.0757|-0.0052|-0.0687|0.0306|
|Cargo Reservations||0.6473*||||-0.0159|
|Liner Conference|||-0.262|||-0.3168|
|Port Terminal Costs||||0.0143**||0.0105|
|STRI 2nd quartile|||||1.3093***|1.1532***|
|STRI 3rd quartile|||||2.0545***|1.5336**|
|STRI 4th quartile|||||2.5594***|2.3215**|
|Observations|9984|9984|9984|8790|9984|8790|
|Right-censored obs|5199|5199|5199|4512|5199|4512|
|Uncensored obs|4785|4785|4785|4278|4785|4278|
|Pseudo R2|0.2053|0.2055|0.2054|0.2032|0.2059|0.2038|

Source: Authors’ calculation. Notes: significance levels: * 10% level, ** 5% level, *** 1% level. The dependant variable is the log of per-unit maritime transport cost expressed in dollars per kilogram. The variables _Distance_ , _Total import volume_ and _Absolute Import Imbalance_ are in logarithms. Tobit coefficients reported for all models, marginal effects discussed in the text. The R-squared is McFadden's pseudo R-squared. Estimations use White heteroskedasticity-consistent standard errors which are clustered at the country-pair level. The number of observations is lower in Models (4) and (6) due to unavailability of information on port terminal costs in Bahrain, Egypt, India, Korea, Lebanon, Madagascar, Qatar, Saudi Arabia and Turkey. Origin, destination and commodity fixed-effects as well as intercepts are included in all models but not reported.

We subject the results from the main specification to three further robustness checks. First, we check whether our results are affected by the potential endogeneity of MTC with respect to bilateral total trade volume. Using applied most-favored-nation (MFN) tariffs and nonseaborne imports as instruments for seaborne import volumes leads only to minor changes in the coefficients (see Annex Table A.6); more importantly, the impact of the STRI liner shipping index remains significant and virtually unchanged in terms of magnitude. Hence, as the degree of endogeneity appears to be small while IV estimation is associated with efficiency losses, we conclude that model 6 is our preferred specification for estimating the MTC equation.

Second, in our sample we observe a number of minuscule trade flows that arise from the shipment of tiny physical quantities. The concern is that the distribution of per-unit transport costs is artificially inflated by these observations (Baldwin and Harrigan, 2007; Harrigan 2010). Thus, we re-estimate model 6 after dropping all observations for which the physical 13
of trade liberalization in services. Because the services sector is so large relative to manufacturing, and many services industries are, according to their analysis, tradable, there is significant potential for liberalization to give rise to trade and employment effects. They also extend their analysis to tariff equivalents for the BRICs and the EU, again drawn from external sources.

The approach we take in this paper is complementary to, but different from, the one adopted by Gervais and Jensen (2013). It is primarily driven by the desire in the policy community for indicators of tradability in services that cover multiple countries, including developing countries. Although the production data approach can in principle be replicated, it is a country by country exercise, and data constraints mean that it is only likely to be practical in developed, transition, and some middle income countries. Service sector technologies are likely significantly different across countries, which means that so too is the degree of tradability. Our point of departure is, therefore, the importance of a crosscountry approach.

As noted above, these considerations necessarily mean that we must rely on international trade statistics to draw inferences as to tradability. Our approach is therefore closer in spirit to an analysis of revealed comparative advantage than to an analysis of sectoral characteristics, as in earlier work. Gervais and Jensen (2013) note the limitations of this approach relative to theirs, and most particularly the limited sectoral scope of trade data relative to national production data of the type they consider. Our results are therefore much less sector-specific than theirs. However, we gain considerably in country coverage, as we are able to provide indices for up to 99 countries, including some developing countries. The two approaches are, therefore, strongly complementary. In qualitative terms, so too is the core result: many services are highly tradable under GATS Mode 1, i.e. through pure cross-border trade.

Against this background, the paper proceeds as follows. The next section defines tradability in the sense in which we use it in this paper, and discusses its main determinants. Section 3 then presents our empirical model and dataset. The following section discusses our econometric results, highlights our key 5
In the initial step of our analysis, we confirm the negative association between governance quality and the share of short-term in total debt. In particular, the marginal effects of governance variables on short-term debt are all negative in our baseline regressions. For instance, a 10percentage point increase in our overall firm governance index, which ranges from zero to one, leads to a 10.7 percentage point decrease in the portion of short-term debt in total debt. The use of alternative governance metrics in regressions leads to similarly large economic effects. In running these regressions, we control for a large set of firm- and country-specific factors that may affect short-term debt.

Next, we investigate whether the relationship between debt maturity and governance depends on the institutional environment that determines the extent of investor protection in a given country. We argue that countries with better legal protection of shareholders would reduce the need for costly external monitoring using short-term debt. If strong shareholder rights reduce discretion of insiders and allow for more efficient internal monitoring, then we would expect governance and short-term debt to act as substitutes. To empirically test this conjecture, we interact each of our governance variables with a variable that is likely to capture the level of investor protection in sample countries and include them as explanatory variables for short-term debt. Our candidate proxy is the minority investor protection index that is constructed in the World Bank’s Doing Business Survey. In our estimations, these interaction terms attain statistically significant and negative coefficients, suggesting that governance and board independence are negatively related to short-term debt in countries with greater investor protection. Thus, good governance and short-term debt become stronger substitutes when the investor rights are protected by law.

To alleviate endogeneity concerns, we instrument investor protection with legal origin. La Porta et al. (1998) suggest that investor rights are largely a function of national legal origin, and that legal systems based on common law offer external investors better protection than those based on civil law. Hence, we use instrumental variable estimation and use legal origin as an instrument for investor protection. The findings are similar to the baseline results; governance and short-term debt act as substitutes in countries with greater investor protection.

The prior literature suggests that a similar trade-off between reduction in agency costs and increase in distress costs applies to the use of leverage. We use leverage as the dependent variable and obtain similar results. We find that the relationship between leverage and governance, similar 4
# **_3.5. Community involvement_**

The typology of community responses can also be categorized according to _the extent of community involvement_ . This is often closely related to whether or not the response is initiated and led by the community or by external actors. Community involvement can be described along a continuum that ranges from collective responses driven and led by communities, and supported with community resources, to “co‐option” of communities by external actors, with various degrees of community consultation, community participation, and community mobilization in between.

A study of PLHIV involvement in community‐based programs (International HIV/AIDS Alliance and Horizons 2002) identified a spectrum of five “areas of involvement”: (1) use of services; (2) support of services; (3) delivery of services; (4) planning and design of services; and (5) management, policy making, and strategic planning. PACT Cambodia involved the national Cambodian PLHIV network (CPN+) and its eight provincial networks in implementing a one‐year program called the Community Response to Reducing HIV/AIDS Stigma and Discrimination. PLHIV were involved in program design and implementation and were empowered to advocate for their rights and to gain the support of their communities through community forums. These groups strengthened the capacity of PLHIV organizations.

Informal responses, initiated and led by communities, are an important manifestation of community involvement. As discussed, however, they are not well documented. Most examples in the literature involve an external agent mobilizing or facilitating the mobilization of communities. In such externally driven responses, a key factor influencing the degree of community involvement is the extent to which external actors work through and strengthen existing community structures rather than displacing or duplicating them.

Community responses, and the extent of community involvement, may also be influenced by the characteristics of communities, such as capacity, skills, and social capital (shared norms and social networks), that enable community members to engage in common action.

# **_3.6. Partnerships and collaboration_**

Finally, the typology of community responses can be categorized according to the extent to which these responses involve different actors working together within and across sectors, and the different ways in which partnerships and collaboration are structured and managed (see boxes 9 and 10). Networking and partnerships are important elements of community responses, and can be structured in a variety of ways: 21
<!-- Start of picture text --> CPTPP, ESTIMATED INCREASES IN EXPORTS CPTPP, ESTIMATED INCREASES IN IMPORTS<br>Viet Nam, 14.2 Canada, 10.3Chile, 1.0 Viet Nam, 14.0 Canada, 10.6<br>a Chile, 1.1<br>Mexico, 8.3 .<br>Mexico, 8.3<br>Peru, 1.5 P 1.7<br>Singapore, 17.2 Singapore,. 15.6 eru, 1.<br>Australia, 20.6 New Zealan<br>New Zealand, 4.5} 4.7 Australia, 21.7<br>Brunei<br>Darussalam, 0.2 Brunei<br>Malaysia, 24.0 Malaysia,alaysia, 24.224. Darussalam, 0.2<br>Japan, 25.8 Japan, 25.1<br><!-- End of picture text --> <!-- Start of picture text --> TPP12, ESTIMATED INCREASES IN EXPORTS TPP12, ESTIMATED INCREASES IN IMPORTS<br>Canada, 24.1 _Mexico, 9.1 Canada, 24.4 Mexico, 9.1<br>Chile, 0.6 7 ‘ Pa i<br>/ Peru, 2.1 “cg a 2s<br>USA, 90.1 Australia, 17.1 Australia, 17.9<br>Brunei Darussalam, USA, 93.9 sa<br>0.3<br>_ Brunei Darussalam,<br>0.3<br>Japan, 57.3 Japan, 55.1<br>Viet Nam, 59.1 Viet Nam, 58.6<br>Malaysia, 41.9 Malaysia, 42.3<br>Singapore, 15.8 — Zealand, 5.3 Singapore, 14.7 \ New Zealand, 5.6<br><!-- End of picture text -->
**Table 2. Zambia Government Revenues, 2015**

||Revenu<br>Kwacha,<br>billions|es,2015<br>% of GDP|Included in<br>Analysis?|
|---|---|---|---|
|**Total Revenue & Grants**|34.4|18.7%||
|Revenue|34.1|18.5%||
|Tax Revenue|26.4|14.4%||
|Direct taxes of which|12.9|7.0%||
|Personal Income Tax|7.4|4.1%|Yes|
|Corporate Income Tax|2.9|1.6%|No|
|Payroll Tax|--|--||
|Taxes on Property|--|--||
|Contributions to Social Insurance of which|--|--||
|From Employees|--|--||
|From Employers<br>From Self-Employed|--<br>--|--<br>--||
|Indirect Taxes of which|22.9|12.4%||
|VAT|8.37|4.6%|Yes|
|Sales Tax|--|--||
|Excise Taxes|3.25|1.8%|Yes|
|Customs Duties|1.90|1.0%|No|
|Taxes on Exports|0.03|0.0%|No|
|Nontax Revenue|7.6|4.1%||
|User Fees, fines, Charges|2.2|1.2%|No|
|Dividends/Interest|0.6|0.3%|No|
|Mineral Royalty|5.9|3.2%|No|
|Farmer Input Recoveries (FISP)|0.4|0.2%|Yes|
|Grants|0.4|0.2%||

_Source_ : Republic of Zambia Annual 2015 Economic Report (2016) and Republic of Zambia 2015 Budget Address, both from the Ministry of Finance. _Note:_ Revenue collections included may not be fully allocated within the LCMS (2015) for various reasons. See sec. 3 for detail on the allocative methods and assumptions.

At approximately 14 percent of GDP, Zambia’s tax ratio is low. The taxable base is eroded by a significant list of exemptions. For example, value-added tax (VAT) exemptions include domestic kerosene, health, education, domestic house rentals, water, transport, financial and life insurance services, and food and agriculture, among others; books are zero-rated. VAT’s efficiency rate is defined as the actual, confirmed VAT revenue raised relative to the value of consumption for each percentage point of the statutory VAT rate, or the effective VAT rate (over all consumption) relative to the statutory rate. Against the background of Zambia’s exemptions, the VAT efficiency rate is quite low, averaging 21 percent during 2008–12. The efficiency rate increased to 36 percent in 2014 but in 2015 fell back to 28 percent.<sup>19</sup> Zambia’s personal income tax, the Pay-As-You-Earn (PAYE) tax, has a high threshold, and several income components (such as capital gains) are excluded from the base. The base for the corporate income tax is limited by widespread exemptions and multiple tax rates. Zambia does not have a property tax.

> 19 The improvement in efficiency to 2014 was artificial, driven by significant delays in the payment of VAT refunds to exporters; these backlogs were partially cleared in 2015.

13
Energy Information Administration (EIA), United States Department of Energy (USDOE) (2007a), International Energy Annual 2007, <u>http://www.eia.doe.gov/iea/</u> Energy Information Administration (EIA), United States Department of Energy (USDOE) (2007b), International Energy Outlook 2007, Washington, DC.

Environment Canada (2007), Clean Air Regulatory Agenda -Regulatory Framework for Industrial Air Emissions: In-depth Technical Briefing, http://www.ec.gc.ca/4F2292E9-3EFF-48D3-A7E4-CEFA05D70C21/techbrief_e.pdf Grubb, Michael, Carlo Carraro and John Schellnhuber (2006), Technological Change for Atmospheric Stabilization: Introductory Overview to the Innovation Modeling Comparison Project, _The Energy Journal_ , A Special Issue, 2006, pp. 1-16.

Intergovernmental Panel on Climate Change (IPCC) (2000), Special Report on Emission Scenario, Cambridge University Press.

Intergovernmental Panel on Climate Change (IPCC) (2007), Climate Change 2007: Mitigation of Climate Change, Working Group III contribution to the Intergovernmental Panel on Climate Change Fourth Assessment Report, Summary for Policy Makers, <u>www.ipcc.ch</u> International Energy Agency (IEA) (2006), World Energy Outlook 2006, IEA, Paris.

Kopp, R. (2004), Near-Term Greenhouse Gas Emissions Targets, Resources for the Future, Discussion Paper (RFF DP 04 – 41), RFF, Washington, DC.

-33-
equations for log real GDP and log inflation have satisfactory normality<sup>and</sup> heteroskedasticity statistics, but there is evidence of (higher-order) residual autocorrelation. Goodness of fit measures are also satisfactory,<sup>indicatingthat this model</sup> is indeed an improvement over random walk with drift models. Nevertheless, the estimated variance of the slope disturbances is non-zero, suggesting that<sup>theestimated</sup> trends are 1(2), instead of I(1), as we expected based on<sup>theresultsobtainedfrom</sup> preliminary conventional unit root tests.

The results of a third model, taking logs of real MI, real GDP and<sup>therateof</sup> nominal currency depreciation as endogenous variables, are presented below. Very strong convergence was achieved in 79 iterations.

**Venezuela: Structural Time Series Model for Logs of Real Money Balances, Real GDP and Currency Depreciation** _Key_ **_Summary Statistics (T=34)_**

|**Statistic**|**LogReal**<br>**Ml**|**LogReal**<br>**GDP**|**Logof**<br>**Depr.**|
|---|---|---|---|
|Normality(Bowman-Shenton):n<br>4|0.36|0.52|0.28|
|Skewness:S__Xl_|0.05|0.26|0.15|
|Kurtosis:k_- x,2_|0.30|0.26|2.36|
|Heteroskedasticity:_H(1O)- Flo.lo_|0.74|0.19|0.13|
|Autocorrelation(upto14korder)|17.10|15.50|17.27|
|(Box-Ljung):Q(14,6)<br>6||||
|Autocorrelation (firstorder)|1.87|2.06|2.62|
|Durbin-Watson:DW_-_ N(2,-)<br>_T_||||
|GoodnessofFit(improvement over random<br>walkplusdriftmodel)<br>_R_2=I_<br>_(T_<br>d)&2|0.84|0.79|0.69|
|GoodnessofFit(ordinary_R_<sup>_2_</sup>_)_|0.84|0.81|0.24|
|AkaikeInformationCriterion|-3.54|-5.37|-1.71|
|BayesInformationCriterion|-1.77|-3.60|0.06|

Money Demand in Venezuela, Mario A. Cuevas 17
# **5.4 Full set of**

The baseline regressions at the product level include three sets of fixed effects, controlling for unobserved heterogeneity at the product-destination-year, firm-product-year, and firm-product-destination level (equation (4)). The high dimensional panel allows us to further include firm-destination-year fixed effects. The additional fixed effects control for any shock that affects firms’ export decision to a given destination in each year, only leaving the variation across products (within firm-destination-year) to exploit. This means that the identification relies on multi-product firms exporting different products to different destinations. Our main findings remain consistent using this specification, implying that third-country effects for multi-product firms are driven by adaptation or learning at the product level, with limited spillovers across products (Table 10).<sup>31</sup> Table 10: Full set of fixed effects

||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
|---|---|---|---|---|---|---|---|---|
|||Export pa|rticipation|||Export|entry||
|Exp Other...(t-1)|||||||||
|... RTA<br>... RTA x STC|0.004***<br>(0.000)<br>0.003***|0.004***<br>(0.001)<br>0.000|0.002***<br>(0.001)<br>0.002**|0.002***<br>(0.001)<br>0.001**|0.002***<br>(0.000)<br>0.001***|0.002***<br>(0.000)<br>0.000|0.002***<br>(0.001)<br>0.001|0.002***<br>(0.000)<br>0.000|
||(0.001)|(0.001)|(0.001)|(0.001)|(0.000)|(0.000)|(0.001)|(0.000)|
|... Depth||-0.001||||0.000|||
|||(0.001)||||(0.001)|||
|... Depth x STC||0.006***<br>(0.001)||||0.003***<br>(0.001)|||
|... Depth MFN|||0.008***<br>(0.002)||||0.006***<br>(0.001)||
|... Depth Pref|||-0.004**<br>(0.002)||||-0.004***<br>(0.001)||
|... Depth MFN x STC|||0.009***<br>(0.002)||||0.004**<br>(0.001)||
|... Depth Pref x STC|||-0.006***<br>(0.002)||||-0.002<br>(0.001)||
|... SPSorTBT||||0.003***<br>(0.001)||||0.001**<br>(0.000)|
|... SPSorTBT x STC||||0.002**||||0.001**|
|||||(0.001)||||(0.001)|
|Observations<br>1|56,595,040|156,595,040|156,595,040|156,595,040|156,302,077|156,302,077|156,302,077|156,302,077|
|R-squared|0.536|0.536|0.536|0.536|0.431|0.431|0.431|0.431|
|Spatial controls|YES|YES|YES|YES|YES|YES|YES|YES|
|Firm-prod-year FE|YES|YES|YES|YES|YES|YES|YES|YES|
|Dest-prod-year FE|YES|YES|YES|YES|YES|YES|YES|YES|
|Firm-prod-dest FE|YES|YES|YES|YES|YES|YES|YES|YES|
|Firm-dest-year FE|YES|YES|YES|YES|YES|YES|YES|YES|

Robust standard errors, clustered at the firm-product level, are in parentheses. The dependent variable _yijp,t_ is a dummy variable equal to 1 if firm _i_ exported (or started exporting) product _p_ to country _j_ in year _t_ . STC=1 if a Specific Trade Concern (STC) on product _p_ has been raised in the WTO. Coefficients on spatial exporting controls are not reported for brevity. *** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1

> 31This is in line with Defever et al. (2015) who find no evidence of across-product learning for multi-product exporters.

20
**5**

**rejression trend** line.6 These indices were supplemented by a **measure of longer-** term change based on the average price in 1965-68 and 1985-87.7

III. Secular Chanqes _in Commodity Trade Table 1 presents sumamary information on changes in the structure of developed countries' commodity imports for selected years from 1965 to 1987. Shown **here** are total imports of the primary stage and processed commodities **as** well as similar data for four major subgroups: foodstuffs, agricultural materials, ores and metals, and petroleum. The table also shows the share of the primary stage (unprocessed) commodity in total developing country exports as well as the share of both primary and processed commoc'_ty imports supplied by developing countries.

Table 1 documents two important secular trends in developing countries commodity exports: a shift toward processed form commodities, and also a bSpecifically, the first i.nstability inacex zc:r,puted from, where Ut is the unit value in years t and 1N is the nu'-jer of years for which these data were available. If the commodity's prices were subject to a significant predictable trend equation (1) would overstate the degree of "unexpected" instability. Fcr this reason a linear trend was fitted to the annual price data ar.d the coefficient of determinaticr (R2) used as a measure of unexplained instability. That **is,** the higher the value of R<sup>2</sup> the lower the level of unexplained insstabil.ty.

> 7 The measure of price level change was defined as,

(2) Pl **=(P 85**<sup>**-**</sup> **87** - P65 -68 ) ' P65-68J **x** 10 where P85.87 and P65 -68 represent the average unit value for Lhe processing **stage** over 1965 to 1968 and 1985 to 1987 respectively.