rafmacalaba/datause-encoder-data huggingface dataset:
notably on the structure of the tax cuts and as well as the way in which they are financed (Gale and Samwick 2016; Zidar 2015).

Public infrastructure spending is generally estimated to have larger short-term effects on U.S. activity, with fiscal multiplier comprised between 0.4 and 2.2 (Auerbach and Gorodnichenko 2012; Bivens 2014; Whalen and Reichling 2015). This reflects the direct impact of public investment on aggregate demand, a relatively low import content of infrastructure spending and positive effects on private investment and productivity.

Fiscal loosening in the United States could have positive cross-border spillover effects, raising U.S. demand for trading partners’ exports and hence leading to faster global growth in the near-term. However, some factors could mitigate these positive effects, including offsetting cuts in government spending and fluctuations in exchange rate and financing conditions.

Further dollar appreciation associated with fiscal stimulus measures in the United States could trigger financial stability concerns in economies with elevated U.S.-dollar denominated liabilities. Empirical evidence of the impact of U.S. fiscal policy on the strength of the U.S. dollar is mixed, however.<sup>14</sup> If U.S. fiscal stimulus leads to a higher level of U.S. public debt in the long-term, this could also cause an increase in global interest rates and be a source of adverse cross-border spillovers through tightening financial conditions (Cardarelli and Kose 2004).

# **4.5 Uncertainty spillovers**

Increased uncertainty driven by financial market volatility or ambiguity about the direction and scope of policies could discourage investors in the United States and elsewhere that base their decisions about long-term investments on stable financing conditions and predictable policies. Sustained increases in financial market uncertainty, e.g., as captured in the implied volatility of the U.S. stock market (VIX), could set back output and investment growth in the United States, other advanced economies and EMDEs (Carriére-Swallow and Céspedes 2013; Bloom 2009). In particular, a 10 percent increase in the VIX could reduce average EMDE output growth by about 0.2 percentage point and EMDE investment growth by about 0.6 percentage point after one year (Figure 10). These estimates are based on a vector autoregression model including the VIX index, emerging market equity prices, emerging market bond spreads, and GDP and investment growth in 18 EMDEs (World Bank 2017). The impact on other advanced economies would be broadly comparable.

> 14See Enders, Müller, and Scholl (2011); Ravn, Schmitt-Grohé, and Uribe (2012); and Corsetti, Meier, and Müller (2012); Forni and Gambetti (2016); and Auerbach and Gorodnichenko (2016).

19
- 42 -

than was found for central budget infrastructure expenditures. A one percentage point increase in the urbanization rate reduces per capita consolidated budget expenditures on infrastructure by approximately (1980) US $ 0.55, also marginally more than was found for central budget expenditures. Although there is virtually no variation in the size of the estimated coefficient across base runs, there is minor variation in the estimated size of the coefficient in the sensitivity analyses.

# _4. Sectoral Balance_

Consolidated budget infrastructure expenditures increase with the extent of the imbalance between rural and urban sectors, as was hypothesized. While a strictly linear and positive relationship was found for the central budget, here the effect is nonlinear. However, the peak of the inverted-U occurs when MIGZ is 5.52 (as calculated from Table 9, M2), more than two standard deviations above its mean value, so that except when the imbalance is exceptionally pronounced, the positive relationship holds. The coefficient for the ruralurban migration rate, MIGZ, is significant at the 1 percent level in most regressions and that for its square at the 5 percent level or better as can be seen from Tables 9 and 10 and Appendix C. There are a few cases when these variables are not highly significant; when DOLLAR4 is used to measure the external balance, the quadratic term is not significant, and when WBPOOR is substituted for PCOM, nether term is significant.

The magnitude of the estimated coefficients across sensitivity analyses is generally between 6.0 and 7.5 for the linear term and .45 to .65 for the quadratic term. The magnitude of the estimated coefficients is somewhat smaller in the alternative bases, and when PCOM is omitted from the regression or several of the poverty measures are substituted for PCOM. It is somewhat greater when GDPCAP2 replaces GDPCAPK in the regressions and when DOLLAR4 is used to measure the external balance, and it is considerably greater when HDRPOOR replaces PCOM. Focusing on the FCINFPA base regression in Table 9 including foreign flows, but not the mix of foreign funding, one finds that when the migration rate increases by one percentage point from the mean, per capita consolidated budget expenditures on infrastructure increase by about (1980) US $ 4.50. This is the same rate of increase found for central budget infrastructure expenditures. The estimated elasticity at the mean is 0.37; the comparable figure for central budget infrastructure expenditures was 0.41.
Policy Research Working Paper 7096

# **Abstract**

A new database on the barriers faced by foreign suppliers of services has been produced by the World Bank. Data for 103 countries are available on 11 of the most important services sectors in international trade. Based on these data and building on the methodology and publications supported by the Australian Productivity Commission, this paper produces estimates of the ad valorem equivalents of the barriers for all these sectors and countries. Compared with estimates available in the literature that are based on assessments of country- and sector-specific barriers to services providers, the estimates expand the set of sectors and more than triple the set of countries for which estimates of the ad valorem equivalents of the services barriers are available.

This paper is a product of the Trade and International Integration Team, Development Research Group. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The authors may be contacted at dtarr@worldbank.org.

_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
I POLIcY RESEARCH WORKING PAPER 2103

# Summary findings

Kim investigates whether the credit channel is a key monetary transmission mechanism in the Republic of Korea, especially after its recent financial crisis.

To identify the existence of a distinctive credit channel (especially the bank lending channel), he applies two empirical tests to both aggregate financial data and disaggregated bank balance sheet data.

As a more definitive analysis of the role of the credit channel, he estimates a disequilibrium model of the bank loan market, specifying separate loan demand and supply equations to characterize the credit crunch and identify its intensity in the wake of the crisis.

He finds convincing evidence of the importance of the credit channel in the aftermath of the crisis. Bank lending plays a significant independent role in amplifying the real effects of the tightened monetary policy implemented in response to the crisis.

There is strong evidence to suggest a substantial excess demand for bank loans following the crisis. This excess demand was caused by a sharp decline in loan supply largely attributable to pervasive and stringent bank capital regulation (a capital-induced bank credit crunch), rather than by weak demand for loans.

- This paper -- a product of the Financial Sector Development Unit, East Asia and Pacific Sector Units was presented at the international conference on Exchange Rate Stability and Currency Board Economics on November 18-29, 1998, in Hong Kong. Copies of this paper are available free from the World Bank, 1818 H Street NW, Washington, DC 20433. Please contact Debbie Peterson, room MC3-844, telephone 202-473-2692, fax 202-522-3454, Internet address dpeterson2@)worldbank.org. Policy Research Working Papers are also posted on the Web at http://xvxw.worldbank.org/ html/dec/Publications/Workpapers/home.html. The author may be contacted at hkim8@cworldbank.org. April 1999. (32 pages)

_The Policy Research Working Paper Series disseminates the findings of wvork in progress to encourage the exchange of ideas abont| 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 he cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the aothors. 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
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The modeling strategy is inspired from Levine and Romanoff (1989) and Romanoff and Levine (1977, 1986, 1993), who introduced the Sequential Interindustry Model (SIM) framework to evolve from the classic static IO model to a dynamic model. Doing so requires the introduction of inventories and specific demand dynamics, and has been used already in disaster assessment, for instance in Okuyama (2004) and Okuyama et al. (2004).

# _3.1 Model principles and equations_

Each economic sector produces according to (i) observed demand, which depends on the orders it receives from its clients (households and other sectors); (ii) input availability, which depends on supplier production and the level of inventories; and (iii) its own internal production constraints (i.e. productive capital). In this version, labor is not considered as a possible constraints. The following description only describes changes with respect to the H08 model.

We assume that _Y_ is the vector of outputs of the different sectors and _A_ is the IO matrix, i.e. the matrix that describes the quantity each sector is providing (or, equivalently, purchasing) to other sectors. The coefficient _A_ ( _i, j_ ) is the consumption of commodity _j_ by the production process of sector _i_ . The production is used to satisfy the demand for intermediate goods and final demand. The equilibrium equation is then: Where _C_ is the vector of final demands and _Y_ the equilibrium production. Classically, the optimal production is: 9
Table 10: Second generation health impact

||(1)<br>Height for<br>age sd<br>(HAZ)|(2)<br>Weight for<br>height sd<br>(WHZ)|(3)<br>Weight for<br>age sd<br>(WAZ)|(4)<br>Body mass<br>index sd<br>(BMIZ)|
|---|---|---|---|---|
|Mother's age 22 to 29 _×_|0.048|-0.032|0.005|-0.045|
|Mother's exposure to conict|(0.046)|(0.032)|(0.040)|(0.028)|
|Mother's age 9 to 15 _×_|-0.010|-0.041**|-0.034*|-0.044***|
|Mother's exposure to conict|(0.033)|(0.017)|(0.020)|(0.015)|
|Mother's age 4 to 8 _×_|-0.005|-0.039**|-0.025|-0.040**|
|Mother's exposure to conict|(0.030)|(0.017)|(0.022)|(0.017)|
|Mother's age 0 to 3 _×_|-0.011|-0.030**|-0.025|-0.031**|
|Mother's exposure to conict|(0.027)|(0.015)|(0.020)|(0.014)|
|Observations|2,165|2,163|2,169|2,164|
|Adjusted R-squared|0.131|0.114|0.132|0.110|
|Mother and other controls|Yes|Yes|Yes|Yes|
|Children contrls|Yes|Yes|Yes|Yes|
|Village xed eects|Yes|Yes|Yes|Yes|
|Number of clusters|373|373|373|373|
|Control mean (Mother's age 16 to 21)|-1.836|-0.580|-1.472|-0.423|

Note: Sample is children aged 0 to 59 months at the time of the survey. *** (**) (*) indicates signicance at the 1% (5%) (10%) level. Treatment on the treated estimates are reported based on a dierence-in-dierence specication. Standard errors are clustered at village level. Mother's age in the table refers to mother's age at the start of the war. Comparison cohort is children born to mothers whose age was 16 to 21 at the start of the war. Children born to mother's cohort 22 to 29 is a second comparison group that serves as a placebo test. Mother's controls are mother's years of birth xed eects, mother's month of birth xed eects, region specic trends. Household controls are indicator for high caste, female headed households, and whether residing in a rural area. Child controls are indicator if child is a girl, a twin, birth order xed eect, and xed eects for child years of birth, month of birth, and month of anthropometric measurements. Reported outcomes are z-scores based on the WHO anthropometric measurement standards.

48
- Collier, Paul (2007) _The bottom billion: why the poorest countries are failing and what can be done about it_ , Oxford: Oxford University Press.

- Conley, Timothy G. (1999) “GMM estimation with cross sectional dependence,” _Journal of Econometrics_ , Vol. 92, No. 1, pp. 1–45.

- Coulibaly, Souleymane and Lionel Fontagn ́e (2006) “South–South Trade: Geography Matters,” _Journal of African Economies_ , Vol. 15, No. 2, pp. 313–341.

- Deutsche Gesellschaft f ̈ur Technische Zusammenarbeit (2009) _International Fuel Prices 2009_ , Eschborn, Germany: Deutsche Gesellschaft f ̈ur Technische Zusammenarbeit, 6th edition.

- Donaldson, Dave (2010) “Railroads of the Raj: Estimating the Impact of Transportation Infrastructure,” NBER Working Papers 16487, National Bureau of Economic Research, Inc.

- Duranton, Gilles and Matthew A. Turner (2011) “Urban growth and transportation,” mimeo, University of Toronto.

- Elvidge, Christopher D., Kimberly E. Baugh, Eric A. Kihn, Herbert W. Kroehl, and Ethan R. Davis (1997) “Mapping of city lights using DMSP Operational Linescan System data,” _Photogrammetric Engineering and Remote Sensing_ , Vol. 63, No. 6, pp. 727–734.

- Elvidge, Christopher D., Jeffrey M. Safran, Ingrid L. Nelson, Benjamin T. Tuttle, V. Ruth Hobson, Kimberly E. Baugh, John B. Dietz, and Edward H. Erwin (2004) “Area and position accuracy of DMSP nighttime lights data,” in Ross S. Lunetta and John G. Lyon eds. _Remote Sensing and GIS Accuracy Assessment_ , Boca Raton, FL: CRC Press, pp. 281–292.

- Elvidge, Christopher D., Daniel Ziskin, Kimberly E. Baugh, Benjamin T. Tuttle, Tilottama Ghosh, Dee W. Pack, Edward H. Erwin, and Mikhail Zhizhin (2009) “A Fifteen Year Record of Global Natural Gas Flaring Derived from Satellite Data,” _Energies_ , Vol. 2, No. 3, pp. 595–622.

- Fay, Marianne and Charlotte Opal (2000) “Urbanization without growth: a not-souncommon phenomenon,” Policy Research Working Paper Series 2412, The World Bank.

25
# **_5.3 Private-participation investments per types of projects_**

The evidence shows that concessions are on average a little more sensitive to country risk than greenfield projects. Table 7 shows the specifications for each type of project. The importance of country risk for both types of projects can also be observed in its relation with the intensity of the investments. In the table we can see the effect of country risk is higher for concession not only on the probability to commit the investment but also higher in the intensity of the commitments.

This finding is consistent with the nature of concessions vis-à-vis greenfield projects. Many greenfield projects—particularly energy generation, and water and wastewater treatment plants—are backstopped by power purchase or off-take agreements. In those cases, the revenue source is a public utility or government agency. Greenfield toll roads generally have some form of minimum traffic or revenue guarantee associated with them. These agreements effectively cover many of the economic risks incorporated in sovereign risk, such as change in demand due to economic growth or contraction as well as currency or inflation risks. Concessions, in contrast, are more frequently used for existing assets such as power and water distribution companies. These investments expose the PPI investor to commercial and economic risks that are not easily ring-fenced or backstopped by government.

**Table 7: Probability of Undertaking an Investment in Greenfield Projects and Concessions**

|**VARIABLES**|**PPI Green F**|**PPI Concess**|
|---|---|---|
||**(1)**|**(2)**|
|Country Risk|0.0192***|0.0224***|
||(0.00599)|(0.00600)|
|Log GDP ppp|0.855***|0.324***|
||(0.0898)|(0.0516)|
|Log GDP Growth|-0.0169|0.0245|
||(0.0547)|(0.0643)|
|Log Inflation|-0.141***|-0.0405|
||(0.0410)|(0.0471)|
|Log Openness|0.712***|-0.258*|
||(0.171)|(0.155)|
|Constant|-23.15***|-8.766***|
||(2.368)|(1.443)|
|Observations|1,753|1,753|
|Number of countries|121|121|

*** p<0.01, ** p<0.05, * p<0.1 Source: Authors’ calculations

14
Table 2.2a. Macro SAM for Yemen 2020 (% of GDP)*

||act<br>com|f-lab<br>f-capprv|f-oilgas|tax-imp<br>tax-com<br>tax-dir|hhd|gov|row|cap-ngov|cap-gov|cap-row<br>drf|inv-prv<br>inv-gov|total|
|---|---|---|---|---|---|---|---|---|---|---|---|---|
|act<br>com|147.6<br>49.3||||110.2|19.4|6.3||||8.0 0.7|147.6<br> 194.0|
|f-lab|59.7|||||||||||59.7|
|f-capprv|36.2|||||||||||36.2|
|f-oilgas|2.4|||||||||||2.4|
|tax-imp|0.8|||||||||||0.8|
|tax-com<br>tax-dir|0.9||||1.2|||||||0.9<br>1.2|
|hhd||59.7 36.1||||4.5|19.7|||||120.0|
|gov|||2.4|0.8 0.9 1.2|1.1||12.9|||||19.3|
|row|44.6|0.1||||||||||44.7|
|cap-ngov|||||7.4|||||8.4||15.8|
|cap-gov||||||-4.5||5.3||-0.1||0.7|
|cap-row|||||||5.9|||2.5||8.3|
|drf||||||||2.5||||2.5|
|inv-prv||||||||8.0||||8.0|
|inv-gov|||||||||0.7|||0.7|
|total|147.6 194.0|59.7 36.2|2.4|0.8 0.9 1.2|120.0|19.3|44.7|15.8|0.7|8.3 2.5|8.0 0.7||

*For notation, see Table 2.2b.

Source: Authors' calculations based on World Bank, IMF, UN, and WIDER.

Table 2.2b. Accounts in macro SAM for Yemen 2020

|**Account **|**Description**|**Account **|**Description**|
|---|---|---|---|
|act|activity|gov|government|
|com|commodity|row|rest of the world|
|f-lab|factor - labor|cap-ngov|capital acc - domestic non-gov|
|f-capprv|factor - private capital|cap-gov|capital acc - government|
|f-oilgas|factor - oil and gas|cap-row|capital acc - rest of the world|
|tax-imp|tax - imports|drf|change in international reserves|
|tax-com|tax - commodities|inv-prv|investment - private|
|tax-dir|tax - income|inv-gov|investment - government|
|hhd|households|||

Drawing on the disaggregated SAM used with the model, Figures 2.3-2.6 provide a summary of the structure of Yemen’s economy in 2020 from an aggregate sector perspective, showing (a) sectoral shares in value added, employment, exports, and imports (Figure 2.3); (b) export and import intensities (i.e., the shares of sector output that is exported and the share of domestic demand met by imports; Figure 2.4); (c) sectoral factor intensities (i.e., factor shares in the value added of each sector; Figure 2.5); and (d) the demand composition for each sector (i.e., the shares 6
material and political incentives, struggle to account for the relative effectiveness of such officials; a different lens, one which pays closer attention to idiosyncratic contextual realities, shows that a salient variable shaping performance can be the means by which honor is produced and upheld in interactions between villagers and officials, a process that requires an expanded understanding of what “incentives” mean to participants, and thus to analysts. For Tsai, it is the everyday practice of social interaction between officials and villagers that consolidate such common understandings and (thus) nurture shared interests and accountability; importantly, she also argues that distinctively Chinese social institutions which are open to all (as opposed to church groups, which are restrictive by way of membership and, by definition, place officials in an overtly religious space they are otherwise formally barred, qua an official, from occupying) are more likely to facilitate and sustain such understandings.

As we show below, these types of examples are instructive for policy, especially as it pertains to the formulation of strategies linking development outcomes and rule of law reform in contexts of state fragility and societal conflict and violence.

# **(c) Intersubjectivity and Donor Theory and Practice of Rule of Law Reform**

(Re-)establishing the rule of law in situations of fragility and conflict is a pervasive concern for donors of all stripes, including major multilateral institutions (World Bank 2011a; UNDP 2000; African Development Fund 2008; European Commission 2007) and bilateral actors (DFID 2010; Dutch Ministry of Foreign Affairs 2007; France Coopération 2007; German Federal Ministry for Economic Cooperation and Development 2007). These actors ascribe a range of definitional characteristics to the rule of law, have a range of concerns – from developmental impacts, to security risks, to political or military interests – and a concomitant range of policy approaches: some emphasize the importance of establishing basic security, others viable mechanisms for dispute resolution, still others building the presence of the state. As a result, it is difficult to ascribe a single unifying feature or approach to donors; nevertheless, we make some general claims here (albeit understandably broad-brush), based on a critical understanding of the _World Development Report 2011_ (World Bank 2011a), which through research and an extensive consultation process does attempt to draw out commonalities and shared understandings in donor approaches.

Rule of law reformers share a fundamental axiom: that establishing and upholding the rule of law – be it to establish law and order, enforce contracts or hold political power-holders to account - is a 23