rafmacalaba/datause-encoder-data huggingface dataset:
|**Country,**<br>**year**|**Population**|**Exposure/Control**|**Outcome**|**Measure of**<br>**effect**|**Source**|
|---|---|---|---|---|---|
|Mexico,<br>2002|Children<br>under-5<br>(n=732)|Consumption of water<br>from IWS/Consumption of<br>water from CWS|Caretaker<br>reported<br>diarrhea|RR = 1.80<br>(1.14 – 2.85)|(Cifuentes<br>et al., 2002)|
|Gaza,<br>2004|Households<br>(n=1625)|Consumption of water<br>from IWS/Consumption of<br>water from CWS|Self-reported<br>diarrhea|RR = 1.35<br>(1.12 – 1.78)|(Abu<br>Mourad,<br>2004)|
|Gaza,<br>2006|Gaza<br>residents<br>(n=141)|Consumption of water<br>from greater than 1 day<br>intermittency/Consumption<br>of water from 1 day or less<br>intermittency|Self-reported<br>diarrhea|RR = 1.33<br>(0.92 – 1.91)|(Yassin,<br>Amr, & Al-<br>Najar,<br>2006)|
|Norway,<br>2007|Households (n=1200)|Consumption of water from<br>low pressure episode due to<br>mains breaks or maintenance<br>of dist. system / Consumption<br>of water from unaffected dist.<br>system|Self-reported<br>diarrhea|RR = 1.58<br>(1.1 – 2.3)|(Nygård et<br>al., 2007)|
|Gaza,<br>2008|Khan Yunis<br>Governorate<br>residents<br>(n=200)|Consumption of water<br>from greater than 1 day<br>intermittency/Consumption<br>of water from 1 day or less<br>intermittency|Self-reported<br>diarrhea|RR = 1.49<br>(1.06 – 2.09)|(Abu Amr &<br>Yassin,<br>2008)|
|India,<br>2015|(i)Households<br>with children<br>under 5<br>(n=3,922)<br>(ii) Low income<br>households<br>with children<br>under 5<br>(n=1,961)|Consumption of water<br>from IWS/ Consumption of<br>water from CWS|(1) diarrhea<br>(2) typhoid<br>fever|(i.1) RR = 1.08<br>(0.96 – 1.20)<br>(ii.1) RR = 1.59<br>(1.15 – 2.17)<br>(2) CIR = 1.72<br>(1.28 – 2.44)|(Ercumen<br>et al., 2015)|
|Alabama,<br>US, 2015|Households (n=470)|Lost water service for >7 days<br>/ Normal service|Self-reported<br>diarrhea|RR = 2.4<br>(1.1<br>5.2)|(Gargano et<br>al., 2015)|

30
INFRASTRUCTURE AND GROWTH IN AFRICA

# **Figure 4. Road infrastructure, 1991–2005**

<!-- Start of picture text --> a. Length of the road network<br>(in km. per sq. km. of arable land)<br>2.5<br>1991–5 2001–5<br>2.0<br>1.5<br>1.0<br>0.5<br>0.0<br>-0.5<br>Western Europe EAP7 Africa North Africa West Africa East Africa Southern Africa Central Africa<br>b. Length of the road network<br>(in km. per sq. km. of surface area)<br>1.0<br>0.5<br>0.0<br>-0.5<br>-1.0<br>-1.5<br>-2.0<br>-2.5<br>-3.0<br>1991–5 2001–5<br>-3.5<br>-4.0<br>Western Europe EAP7 Africa North Africa West Africa East Africa Southern Africa Central Africa<br>Median (logs)<br>Median (logs)<br><!-- End of picture text --> 30
**Behavioral Framework** We assume that a firm’s current profit depends on its own state variables, including firm productivity ( _φ_ ), exchange rates ( _E_ ), and capital stock ( _K_ ). Profits are also influenced by other common factors, such as factor prices. At the start of each period, an incumbent exporting firm observes the exchange rate of each source country, its productivity and capital stock, and makes two decisions simultaneously: (1) how much to use in variable inputs, including imported materials, _P_<sup>_W_</sup> _M_<sup>_W_</sup> , which is part of imports, _IMP_ , and (2) the optimal level of investment, which includes imported capital goods, _I_<sup>_W_</sup> . We abstract from modeling firm entry and exit decisions in export markets, as the customs transaction data cannot reliably distinguish true exits from missing values. Following OP, we assume that productivity ( _φ_ ) evolves according to a Markov process, leading to a Markov perfect Nash equilibrium in firms’ decision regarding capital goods imports. With this framework, we can use observed capital goods imports to control for the unobserved productivity, accounting for exchange rates, capital stock, and other common state variables. The imported capital stock of each firm, _Kit_<sup>_W_, is constructed using the perpetual inventory method, with a depreciation rate</sup> of _γ_ :

where (11) describes the law of motion for firm-level imported capital stock, which is determined by adding current-period imported capital good investment to the existing imported capital stock, after accounting for depreciation. In other words, at the start of each period, after observing productivity, exchange rates, and current imported capital stock, firms decide on their investment in imported capital goods. This investment augments the existing capital stock, net of depreciation, to establish the capital stock level for the following period.

**Estimation** The regression specification in (12), which is based on the export revenue identity (8), provides a structural interpretation of the coefficient on imports, _κ_ , which represents the share of imported inputs used in the production of exports:

where the dependent variable, _EXPit_ , represents the value of exports for firm _i_ in year _t_ , with the key variable of interest _IMPit_ , being the value of imports. We decompose the domestic value added of firm _i_ in year _t_ into the following terms: (1) _D_ ( _Eit, Kit_<sup>_W_),which is a function of firm’s exchange</sup> rate, _Eit_ , and imported capital stock, _Kit_<sup>_W_;(2)yearfixed-effects,</sup><sup>_αt_,whichcapturecommonstate</sup> variables across firms, such as wages and rental prices of capital; (3) firm fixed-effects, _αi_ , which account for firm-specific characteristics, such as foreign ownership and processing trade status; and 11
# Latent Trade Diversification and Its Relevance for Macroeconomic Stability

Daniel Lederman, Samuel Pienknagura, and Diego Rojas<sup>1</sup> JEL Codes: F1, O11

## Keywords: Trade Concentration, Diversification, Extensive Margin of Exports, Volatility

1 Daniel Lederman is Lead Economist and Deputy Chief Economist at the Office of the Regional Chief Economist for Latin America and the Caribbean, The World Bank (dlederman@worldbank.org). Samuel Pienknagura is a Research Economist at the Office of the Regional Chief Economist for Latin America and the Caribbean, The World Bank (spienknagura@worldbank.org). Diego Rojas is a Consultant at the Office of the Regional Chief Economist for Latin America and the Caribbean, The World Bank (lrojasalvarado@worldbank.org).

This paper benefitted from useful comments from Augusto de la Torre, Jaime de Pinies, Juan Carlos Hallak, William Maloney, Marcelo Olarreaga, and participants at DEGIT 2014 Conference, LACEA 2014 Annual Meetings, and the Authors’ Workshop for the World Bank’s Regional Study on Growth and Volatility in the Caribbean. The authors acknowledge financial support from the Office of the Regional Chief Economist for Latin America and the Caribbean. Nicolás Kohn provided stellar research assistance.
**Table A.12:** Effects on Self-Reported Bureaucratic Knowledge

||Training|Village Law|Knowledge index|
|---|---|---|---|
||(1)|(2)|(3)|
|New village head|-0.089|0.019|0.088|
||(0.117)|(0.107)|(0.119)|
|Observations|1067|1065|1065|
|Control mean|0.61|0.76|0.12|
|Robust p-value|0.313|0.886|0.391|
|<br>MSE-opt. bandwidth|19.1|17.8|28.4|
|Effective obs.|500|476|662|

_Notes_ : This table reports RD estimates of _γ_ in equation (1) obtained via the non-parametric method from Calonico et al. (2014). Units of observation are bureaucrats in all columns. The dependent variable is: in column 1, a dummy equal to 1 if the bureaucrat received any training in the past 12 months; in column 2, a dummy equal to 1 if the bureaucrat reports being informed about Village Law regulations; in column 3, a standardized index of self-reported knowledge across 5 topics: development management & accountability, financial management, village regulations, drafting development plans, and the Village Law. See Section 4 for details.

* p _<_ 0.1, ** p _<_ 0.05, *** p _<_ 0.01. Robust bias-corrected standard errors clustered by village in parentheses.

**Table A.13:** Effects on Village Transfers and Budgets

|||**Adminis**|**trative data**||**Village head survey**|
|---|---|---|---|---|---|
|Village Funds:|Allocated|Utilized|% Spent|FullySpent|Budget|
||(1)|(2)|(3)|(4)|(5)|
|New village head|-0.367|-0.320|0.025|0.038|-0.347|
||(0.625)|(0.704)|(0.062)|(0.140)|(1.205)|
|Observations|423|422|422|422|418|
|Control mean|2.04|1.91|0.87|0.58|2.47|
|Robust p-value|0.517|0.635|0.699|0.854|0.732|
|MSE-opt. bandwidth|28.0|23.7|31.6|28.6|24.7|
|Effective obs.|259|226|275|262|224|

_Notes_ : This table reports RD estimates of _γ_ in equation (1) obtained via the non-parametric method from Calonico et al. (2014). The sample includes all villages with available data. In column 1, the dependent variable is the 2022 Village Funds Allocation (ADD) in millions of Indonesian Rupiah per capita. The Village Funds Allocation includes funds allocated by district governments to fund village government salaries, benefits, and operations. In column 2, the dependent variable is the 2022 ADD amount utilized by the village for programs and activities in millions of Indonesian Rupiah per capita. In column 3, the dependent variable is the share of ADD utilized. In column 4, the dependent variable is a dummy equal to 1 if the amount allocated is fully utilized. Finally, in column 5, the dependent variable is the 2021 village budget reported by the village head in Indonesian Rupiah per capita. See Section 4 for details. * p _<_ 0.1, ** p _<_ 0.05, *** p _<_ 0.01. Robust bias-corrected standard errors in parentheses.

52
_Education_ : Education plays an important role in household welfare. _Ceteris paribus_ , higher educational attainment is positively correlated to higher welfare. Attainment of at least 11 years of education by the household head tends to increase the welfare ratio by 63% in metro areas, 81% in urban areas, and 60% in rural areas. Having a spouse with education tends to increase household welfare (e.g. by 50% on average with 11 or more years of education), although to a lesser extent than the head. When household members (e.g. children) have more education than either the head or spouse, household welfare also tends to be higher, in particular when the difference is large. For instance, with a difference of 10 years or more, per capita welfare ratio increases by over 50% in metro and urban areas and 38% in rural areas.<sup>7</sup> _Occupation_ : The primary occupation of the household head is classified in one of the following categories: (1) professional or military, (2) technician, (3) administrative services, (4) service workers and vendors (reference case), (5) agriculture, (6) manufacturing and industrial services, and (7) other occupation or missing. There is a large disparity in returns for professional and technical occupations between metro/urban and rural sectors (i.e. 0.548 and 0.477 versus 0.188). And a household head whose primary occupation in agriculture tends to have a lower welfare ratio by about 9% in urban areas and 16% in rural areas (relative to service workers).

Comparing the mean log welfare ratios across the different areas (table 3.2), we find that the mean welfare exceeded the poverty line by 177% in metro areas, 97% for urban areas, and 22% for rural areas. The largest negative factor is the household size and composition, and the largest positive factors include education and other demographics. Also, the constant term accounts for a substantial part of the expected mean welfare for metro and rural areas. However, these factors do not distinguish between the difference in returns and in characteristics.

**Table 3.2: Contributing Factors to Average Levels of Living and Metro-Urban and Urban-Rural Disparities**

|**Mean log welfare ratio**|**Metro**<br>1.02|**Urban**<br>0.68|**Rural**<br>0.20|
|---|---|---|---|
|Constant|0.77|0.01|0.47|
|Geographic dummies|-0.06|-0.07|-0.09|
|HH Composition|-0.77|-0.72|-0.81|
|HH Demographics|0.52|0.85|0.45|
|Education|0.47|0.57|0.31|
|Occupation|0.10|0.04|-0.13|

> 7 Elementary education is compulsory and eight years, divided into two four-year cycles.  Secondary education is another three years.

14
- Golub, A., Hertel, T. and Sohngen, B.: 2009, Land Use Modeling in RecursivelyDynamic GTAP Framework, in T. Hertel, S. Rose and R. Tol (eds), Economic Analysis of Land Use in Global Climate Change Policy, Routledge, pp. 235 278.

- Gouel, C. and Hertel, T.: 2006, Introducing Forest Access Cost Functions into a General Equilibrium Model, GTAP Research Memorandum 8, Center for Global Trade Analysis, Purdue University.

- Guerrini, L.: 2006, The Solow-Swan Model with a Bounded Population Growth Rate, Journal of Mathematical Economics 42(1), 1421.

- Guerrini, L.: 2010, A Closed-form Solution to the Ramsey Model with Logistic Population Growth, Economic Modelling 27(5), 11781182.

- Hartley, P., Medlock III, K. and Rosthal, J.: 2008, The Relationship of Natural Gas to Oil Prices, The Energy Journal 29(3), 4766.

- Hertel, T.: 1997, Global Trade Analysis: Modeling and Applications, Cambridge University Press.

- Hertel, T., Golub, A., Jones, A., O'Hare, M., Plevin, R. and Kammen, D.: 2010, Eects of US Maize Ethanol on Global Land Use and Greenhouse Gas Emissions: Estimating Market-Mediated Responses, BioScience 60(3), 223 231.

- Hertel, T., Rose, S. and Tol, R.: 2009, Economic Analysis of Land Use in Global Climate Change Policy, Routledge.

- Hertel, T., Stiegert, K. and Vroomen, H.: 1996, Nitrogen-land Substitution in Corn Production: a Reconciliation of Aggregate and Firm-level Evidence, American Journal of Agricultural Economics 78(1), 3040.

- Hertel, T., Tyner, W. and Birur, D.: 2010, The Global Impacts of Biofuel Mandates, The Energy Journal 31(1), 75100.

- IPCC: 2000, Summary for Policymakers, in N. Naki¢enovi¢, J. Alcamo, G. Davis, B. De Vries, J. Fenhann, S. Gan, K. Gregory, A. Grübler, T. Jung, T. Kram et al. (eds), Special Report on Emissions Scenarios: A Special Report of Working Group III of the Intergovernmental Panel on Climate Change, Cambridge University Press.

37
**Table 4: Coefficient of Variation of Capital Outflows**

||FDI|Portfolio Equity|Portfolio Debt|Other flows|
|---|---|---|---|---|
|1996-2000|0.285|0.475|-0.666|0.699|
||[0.46]|[0.42]|[0.84]|[1.23]|
|2001-2005|0.931**|0.358|-1.219|1.427*|
||[2.43]|[0.50]|[1.31]|[1.97]|
|2006-2010|0.542*|-0.164|-0.667|1.589**|
||[1.86]|[0.20]|[0.53]|[2.29]|
|2011-2015|**1.49*****<sup>**,**</sup>**^^**|-0.136|-1.054|0.598|
||**[2.96]**|[0.14]|[0.85]|[0.77]|
|Country fixed-effects|Yes|Yes|Yes|Yes|
|Observations|157|132|132|133|
|# of countries|34|32|32|34|
|R-squared|0.072|0.010|0.011|0.089|

_Notes:_ The dependent variable is the coefficient of variation of capital flows of type i in country c in period p. Five separate 5-year periods are considered: 1991-1995, 1996-2000, 2001-2005, 2006-2010 and 2011-2015. The coefficients of variation are regressed on time dummies indicating the different periods, where the first period (1991-1995) is excluded. The interpretation of the coefficient is thus in relation to this first period. We exclude observations where the coefficient of variation exceeds value of + 10 or is below - 10. Robust t-statistics are displayed in brackets. ***, **, * indicate significance at the 1%, 5% and 10% level. Additionally, tests are conducted for whether the coefficients are significantly different from the previous period. ^, ^^, ^^^ indicate significant differences at the 1%, 5% and 10% level.

# **5. Capital Flows in Sudden Stops and Capital Flight Episodes**

Following Eichengreen and Gupta (2016), we classify an episode as a sudden stop when total capital inflows (FDI, portfolio equity and debt, and other inflows by nonresidents) decline below the average in the previous 20 quarters by at least one standard deviation, when the decline lasts for more than one quarter, and when flows are two standard deviations below their prior average in at least one quarter.<sup>6</sup> The sudden-stop episode then ends when flows recover to at least the prior mean minus one standard deviation. Analogously, we define an episode of capital flight as a sharp increase in gross outflows by residents. Specifically, a period qualifies when total capital outflows (FDI, portfolio equity and debt, and other outflows by residents) exceed the average in the previous 20 quarters by at least one standard deviation, when the increase lasts for more than one quarter, and when outflows are two standard deviations above their prior average in at least in one quarter. Capital flight episodes then end when capital outflows decline below the prior mean plus one standard deviation.

> 6 One difference is that here we define sudden stops in terms of the behavior of total capital flows--FDI and non-FDI alike – whereas in Eichengreen and Gupta (2016) we defined sudden stops in terms of the behavior of non-FDI flows only.

11
# **6. Concluding observations**

Country‐level macro SAMs are useful for description and policy analysis and as inputs to the building or updating of more disaggregated SAMs. This paper presents a simple procedure for constructing macro SAMs of three types, accompanied by an electronic appendix with a program that, drawing on cross‐country databases, uses the procedure to build such matrices for 133 countries. If the procedure is implemented manually in a spreadsheet, it is straightforward to draw on additional country‐specific sources, making it possible to construct a SAM for virtually any country; this is demonstrated in the paper appendix in an application to Guinea. A companion paper – Cicowiez and Lofgren (2017) ‐‐ presents and applies a policy‐oriented CGE model that only requires a macro SAM and a handful of elasticities and stock parameters.

We hope that by making the procedure and its data sources transparent, it can be applied by others and improved. The paper suggests that, in order to better facilitate SAM construction and economywide analysis, cross‐country databases need to provide more complete and detailed data for the government budget and the balance of payments, subjecting the information generated to consistency checks.

Before being used in analysis, the SAMs in the electronic appendix should be scrutinized by country experts who can compare the data to what is available in additional country‐specific sources and adjust when needed; given the high level of aggregation, it is straightforward for anyone familiar with the procedure to adjust selected cells while maintaining a balanced SAM.

# **References**

Agénor, Pierre‐Richard. 2004. The Economics of Adjustment and Growth. Second Edition. Cambridge, MA: Harvard University Press.

Aguiar, Angel, Badri Narayanan and Robert McDougall. 2016. An Overview of the GTAP 9 Data Base. Journal of Global Economic Analysis 1 (1): 181‐208.

Cicowiez, Martín, and Hans Lofgren. 2017. A GEM for Streamlined Dynamic CGE Analysis: Structure, Interface, Data, and Macro Application. Unpublished.

‐24‐
limited model for data transfers and processing shows a negative significant coefficient in many cases, indicating a negative association with digital services trade.

Table 5: Robustness check baseline equation (2) using Data Restrictiveness Index as control

||**(1)**<br>**Digital+**|**(2)**<br>**Digital**|**(3)**<br>**Publishing**|**(4)**<br>**Telecom**|**(5)**<br>**IT & Info**|**(6)**<br>**Business**|
|---|---|---|---|---|---|---|
|MODEL**CB**OP|-0.257|0.908*|0.092|-0.342|1.731***|-0.257|
||(0.345)|(0.064)|(0.833)|(0.369)|(0.000)|(0.471)|
|MODEL**CB**CT|0.399*|-0.121|0.451|0.449|-0.775***|0.060|
||(0.089)|(0.765)|(0.243)|(0.113)|(0.010)|(0.811)|
|MODEL**CB**LT|-0.444|-0.213|-1.779***|-0.997**|-0.157|-0.694|
||(0.147)|(0.647)|(0.000)|(0.040)|(0.664)|(0.207)|
|MODEL**DR**OP|-0.124|-1.602***|-0.950**|0.523|-1.627***|0.374|
||(0.702)|(0.001)|(0.020)|(0.246)|(0.000)|(0.361)|
|MODEL**DR**CT|-0.173|1.029**|-0.269|-0.111|1.397***|-0.260|
||(0.513)|(0.014)|(0.430)|(0.674)|(0.000)|(0.347)|
|MODEL**DR**LT|-0.218|-1.213***|-0.021|0.350|-1.380***|0.283|
||(0.520)|(0.009)|(0.954)|(0.392)|(0.000)|(0.479)|
|DTRI CB|-1.969***|0.342|2.962*|-1.738*|0.406|-1.083|
||(0.006)|(0.675)|(0.088)|(0.064)|(0.679)|(0.319)|
|DTRI DR|1.003*|-0.679|1.829|0.352|-2.031**|-2.320***|
||(0.090)|(0.318)|(0.116)|(0.656)|(0.016)|(0.009)|
|WEF NRI|1.114***|1.355***|1.897***|1.212***|1.186***|1.536***|
||(0.000)|(0.000)|(0.000)|(0.000)|(0.000)|(0.000)|
|Control GRAV|Yes|Yes|Yes|Yes|Yes|Yes|
|Control C|Yes|Yes|Yes|Yes|Yes|Yes|
|FE exp + imp|Yes|Yes|Yes|Yes|Yes|Yes|
|Obs|3717|3717|3717|3717|3717|3717|
|P-R2|0.986|0.967|0.962|0.963|0.934|0.973|

Note: * p<0.10; ** p<0.05; *** p<0.01. The dependent variable is bilateral digital services exports (DSX) using the underlying gross trade data from the TiVA database. Sector groupings in each column can be found in Annex Table A2. Robust standard errors are clustered at the country-pair level. Fixed effects for exporter and importer are applied. CB refers to the cross-border transfer of data, whereas DR refers to the domestic data processing. Both ECIPE CB and ECIPE DR variables are taken for the importer’s side and are interacted with intl as part of the C ∗intlo term. Coefficient results for the control vectors of GRAV and C can be found in Annex Table A4.

Regarding the two control variables, they show an expected outcome, which however is not always significant and at times positive. The coefficient for the DTRI CB is negative and significant only for the Digital+ grouping and only weakly significant for Telecom. On the other hand, the coefficient for DTRI DR is negative and significant for IT and information services, but positive for Digital+. In 30
Stock Exchange, we follow Alquist (2010) and Chavaz and Flandreau (2017) and use the spread between the so-called “business done” and the “closing price” as a proxy for bid-ask spreads (71,108 observations). A Using these data, we compute s = 1 <u>,A</u> AA BBiiBB, ~~,~~ which represents the spread of bid and ask prices, 2 ~~�~~ PPii tt −PPii tt � ii,tt AA BBiiBB and , of bond _i_ in month rrs ss _t_ . We then compute an aggregate proxy for market liquidity by PPii,tt +PPii,tt BBiiBB AAssAA averaging the time series of bond-level, bid-ask spreads in our global portfolio on a monthly level as ii,tt ii,tt NN NN 1 follows: = ∑ s , where is the number of bonds _i_ trading in month _t_ .<sup>44</sup> NNtt MMssrrMMssttMMiiM iissiittMMtt NNtt ii=1 rrs ssii,tt NNtt Figure 10: Bid-ask spreads and total returns, 1870-2016 <!-- Start of picture text --> Panel A: 1870-1970<br><!-- End of picture text --> <!-- Start of picture text --> 10<br>Viennacrash of 1873Panic ArgentinadefaultBaringcrisisand of 1907Panic WorldWar I break-upsRussiandefault,Empire DrepressionGreat War IIWorld<br>5<br>0<br>Egypt<br>and<br>-5 Ottoman Empiredefault<br>1870 1875 1880 1885 1890 1895 1900 1905 1910 1915 1920 1925 1930 1935 1940 1945 1950 1955 1960 1965 1970<br>Total real returns (monthly moving average, in %) Bid-ask spreads (monthly moving average, in %)<br><!-- End of picture text --> <!-- Start of picture text --> Panel B: 1995-2016<br>5 crisisAsian defaultRussiaand Argentina defaults,Brazil crisis bankruptcyLehmann announcementrestructuringGreek<br>LTCM crisis<br>0<br>-5<br>1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016<br>Total real returns (monthly moving average, in %) Bid-ask spreads (monthly moving average, in %)<br><!-- End of picture text --> _Note:_ This graph shows monthly bid-ask spreads (blue line) and total real returns (red shaded) for the global external bond portfolio. Both series are computed as 12-month moving-average (-6/+6). The shaded grey bars represent major crises, wars, and global shocks.

Figure 10 shows a 200-year time series of monthly bid-ask spreads and total returns for our global bond portfolio, with two key insights. First, the figure suggests that the liquidity of this asset class may importantly depend on global events, often originating or affecting the financial centers. Average bond liquidity declines markedly during global wars and financial crises, with bid-ask spreads spiking in the

> 44 The graph looks very similar when using weighted average bid-ask spreads (e.g. weighted by issuance amounts).

34