rafmacalaba/data-use-mentions-extended huggingface dataset:
credit-constrained firms than for firms that are not credit constrained.

We therefore also estimate the model with instrumental variables in order to control for possible endogeneity bias. As instruments for the demand shock, we use the information on exporting behavior from the pre-crisis ICS 2007 survey as (pre-crisis) international exposure will most likely have affected the extent to which a firm has been affected by the global financial crisis. We include three proxies for pre-crisis international exposure, namely the percentage of sales that the firm exported in 2007, the destination of these exports in 2007 (USA, EU or other international destination) and the first year of exporting (before 2000, between 2000 and 2003, and after 2003). Because we use these instruments for both the sales shock and its interaction with the dummy variable for temporary demand shock, these instruments are also interacted with this dummy variable.

23
have reported a high impact of education on growth, others have argued that there is little or nothing to be learned from the empirical growth literature (for example, Topel 1999; Pritchett 2006; Banerjee and Dufflo 2005; Durlauf, Johnson and Temple 2005).

The inconsistency between micro and newer macro evidence on the returns to education has occupied the literature for a long time (for example, Krueger and Lindahl 2001; Sianesi and van Reenen 2003). The main reason for such inconsistency is that countries differ in many other respects that cannot easily be measured and controlled for in cross-country regressions, whereas within-country micro data automatically control for such factors.
mainstreaming of data on forced displacement in household surveys; and

- (c) Support to test and mainstream the use of more innovative mechanisms for data collection, analysis and dissemination.

For such efforts to be successful and sustainable, it is important to ensure they are appropriately institutionalized and that they reflect the expertise and comparative advantages of the organizations involved. This can be achieved within the broader context of ongoing initiatives aimed at strengthening statistical capacity at the country level, and enhancing cooperation across countries to ensure comparability of data. Considering the number of actors involved in data collection and compilation, each with their own objectives and agendas, coordination is crucial. This would also require engaging with host governments (at national and local levels), international organizations (e.g. UNHCR, IDMC, IOM), international and local NGOs, and research organizations, through awareness raising, capacity building and the development/improvement of effective and timely data collection systems (Brookings 2013). Institutional arrangements would need to delineate data collector and data compiler roles, as well as reflect the following principles to ensure sustainability:

- (a) Pursue additional activities within the overall framework of existing initiatives to ensure coherence with the activities of other actors.

- (b) Primary responsibility for data collection rests with national statistical agencies (with adequate arrangements in the case of IDPs to mitigate political risks).

- (c) Definitions and methodologies should be harmonized across countries, through a process managed under the auspices of the UN Statistical Commission.

- (d) Agencies such as UNHCR and IDMC can play a leading role in ensuring quality, providing technical assistance as may be needed, and aggregating data for global analyses.

102 See http://www.internal-displacement.org/database.

> 103 See http://unstats.un.org/unsd/statcom/doc15/2015-9-RefugeeStats-E.pdf.

> 104 See conference documentation at http://www.efta.int/seminars/refugee.

> 105 See http:/
# **End Notes**

1. Concerns about the distributional impacts of the growth process have led to a debate. For a recent review, see Chaudhuri and Ravallion (2007).

2. This section draws on Kuijs (2005) and Kuijs and Wang (2006).

3. See Kuijs (2005) and (2009) for details.

4. The exact breakdown of the increase in China’s domestic savings in recent years is not yet fully clear, with the headline national accounts data in the flow of funds difficult to line up with data from the household survey, the industrial survey, and fiscal information. Large discrepancies have also appeared in the flow‐of‐funds data themselves, suggesting that the asset data imply higher enter‐ prise saving and lower household and government saving in recent years than the headline flow‐of‐ funds data shown in figure 2.

5. A 2003 World Bank report concluded that “service sector development suffers from restrictions and regulation and a lingering bias against private ownership.” The OECD (2005) saw similar room for improvement by removing entry and other barriers to the development of services industries.

6. This implication is from Aziz and Cui (2007).

7. The ratio of loans to GDP decreased from a peak of 130 percent in early 2004 to 92 percent in mid‐2008.

8. At the end of 2007, gross external reserves were reportedly almost 700 percent of short‐term external debt by remaining maturity.

9. Until the fourth quarter of 2008, the central bank had been pursuing a tight monetary policy to prevent a spiral of inflation expectations. These inflationary pressures were arising from food and commodity price increases and rising asset prices, and the tight monetary policy aimed to stop overheating in high‐growth sectors. Controls on new lending and credit ceilings (partially voluntary) were put in place.

10. The quick response of government spending partially reflected the fact that a large share of the program had already been drafted for
the role of hypotheically reducing biofuel mandates on prices. Mallory et al (2012) use this approach to link ethanol and corn markets, ...nding that under certain conditions, future price expectations can in‡uence nearby futures across each other. Tegene et al (1988) make use of this method to estimate long-run price elasticity of corn acreage, ...nding it to be 0.2.

More generally, the literature has also examined whether commodity markets have been altered for non fundamental factors in the past decades, such as ...nancial speculation. For instance, in energy markets results tend to indicate small or null e¤ects of speculation on oil prices. In their canonical paper, Kilian and Murphy (2014) develop a VAR model with speculative demand shocks and contrast it with recent oil inventory data and ...nd no basis for speculation’s being blamed for 6
On the basis of 50 subsamples of 1012 observations, we get the results for _g_ , numbers of models visited and model size presented in Figure 10. Results are now a bit different from those in the growth applications, as a result of the much larger value of _n_ . In particular, the Maruyama-George, Forte _et al._ and Zellner-Siow priors as well as the case with fixed _g_ = _n_ lead to much higher posterior medians of _g_ , a smaller number of models visited in the chain and smaller mean model size than in the previous applications. Of course, all of these priors depend on _n_ , but it is interesting that the F-Z prior with _a_ = 2 + 2 _/n_ and the hyper- _g/n_ and horseshoe _/n_ priors are less affected. Of course, the benchmark priors and the Bottolo-Richardson prior depend on _n_ through max _{n, k_<sup>2</sup> _}_ , which is here of the same order of magnitude as for the FLS data.

<!-- Start of picture text --> log ( g ) : Random θ , m = 7 Number of Models: Random θ , m = 7 Model Size: Random θ , m = 7<br>G<br>G G<br>G<br>G G G G<br>G G<br>G G G G G G<br>G G<br>1 2 3 4 5 6 7 8 9 11 13 15 1 2 3 4 5 6 7 8 9 11 13 15 1 2 3 4 5 6 7 8 9 11 13 15<br>7<br>4e+05<br>6 3e+05 14<br>5 12<br>2e+05<br>4 10<br>3 1e+05 8<br>0e+00<br><!-- End of picture text -->

**Fig. 10
<br>3 3 3 3<br>2 2 2 2<br>1 1 1 1<br>0 0 0 0<br>0 .2 .4 .6 .8 1 0 .2 .4 .6 .8 1 0 .2 .4 .6 .8 1 0 .2 .4 .6 .8 1<br>Specialization Index Specialization Index Specialization Index Specialization Index<br>Rural Urban Male Female<br>Fractional Index 4<br>Ethiopia Malawi Ethiopia Malawi<br>5 5 5 5<br>4 4 4 4<br>3 3 3 3<br>2 2 2 2<br>1 1 1 1<br>0 0 0 0<br>0 .2 .4 .6 .8 1 0 .2 .4 .6 .8 1 0 .2 .4 .6 .8 1 0 .2 .4 .6 .8 1<br>Nigeria Uganda Nigeria Uganda<br>5 5 5 5<br>4 4 4 4<br>3 3 3 3<br>2 2 2 2<br>1 1 1 1<br>0 0 0 0<br>0 .2 .4 .6 .8 1 0 .2 .4 .6 .8 1 0 .2 .4 .6 .8 1 0 .2 .4 .6 .8 1<br>Specialization Index Specialization Index Specialization Index Specialization Index<br>Rural Urban Male Female<br>Density Density<br>Density Density<br>Density Density<br>Density Density<br>Density Density<br>Density Density<br><!-- End of picture text --> <!-- Start of picture text --> HHI 2<br><!-- End of picture text --> _Note:_ The table displays kernel density graphs for the pre-COVID-19 round for each of
A second and different methodology is based on the Mendelsohn and Schlesinger (1999) reduced form Agricultural Response Functions in the formulation proposed by Cline (2007), where the variation ( _DY_ ) in output per hectare is expressed as a function of temperature _T_ , precipitation _P_ and CO2 concentration _K_ : We need to link changes in yield to variations in average temperature only. To this purpose, we rely on temperature and precipitation data from the USGS Coupled Model Intercomparison Project Phase 5 (CMIP5) Global Climate Change Viewer (GCCV), averaging results from many Global Circulation Models<sup>1</sup> . We collected information on baseline levels and variation in average annual temperature and annual precipitation, by country, comparing the period 1980-2004 (central year 1992) with the period 2050-2074 (central year 2062) under the RCP 8.5 scenario. We also assume that from 1992 to 2062 (70 years) the concentration of CO2 rises (from a baseline level of 365 ppm) at an annual rate of 2.11 ppm.

We use the variation in temperature as an indicator, expressing how much the climate has changed. By dividing the country-specific variation in precipitation with the one of temperature we get a precipitation to temperature coefficient _p_ . In the same way, we get a CO2 concentration to temperature coefficient _k_ , so that we can write: Finally, we need to transform _DY_ to percentage changes _DY/Y_ , which can be done by dividing _DY_ by the output per hectare _Y_ , in millions of dollars. Cline (2007) uses estimated values for the year 2003 which, unfortunately, vary widely (for example, from 29 in Australia to 8707 in the Republic of Korea), ultimately producing unrealistically volatile percentage changes for agricultural productivity.

Here we follow a different strategy, which is based on the “calibration” of the output per hectare _Y._ The latter is chosen so that the percentage change for +3°C is “in line” with a simple mathematical average of estimated
7

# **II.   DATA AND EMPIRICAL STRATEGY**

# **A.   Data**

The sample includes 19 emerging market economies and 21 advanced economies.<sup>12</sup> Our classification is based on the core lists of countries that the International Monetary Fund and other organizations such as the World Bank define as AEs and EMs. The main results presented in this paper are robust to using different classifications.<sup>13</sup> The sample period runs from 1991Q1 to 2015Q4. The data are at the quarterly frequency. The economic growth series for the two economic blocks namely EM and AE blocks are based on purchasing power parity (PPP) weighted average of local currency real GDP growth from IMF’s World Economic Outlook live database. For the purpose of checking whether our main results are robust to different country grouping we use smaller country groupings including the Group of Seven represent the world’s largest industrialized economies (G-7), a group composed of Brazil, Russia, India, China (BRIC), and China alone.

The data also include control variables that capture the main channels of transmission (trade, finance, and commodities). To control for trade, we use imports over total trade constructed using bilateral data from IMF’s Direction of Trade Statistics. To control for the financial channel, we construct a spread measure based on the MSCI Emerging Index over the MSCI

> 12 The EM block is comprised of: Argentina, Brazil, Chile, China, Czech, Hungary, India, Indonesia, Korea, Malaysia, Mexico, Peru, the Philippines, Poland, Russia, Taiwan Province of China, Turkey, Thailand and South Africa. The AE block is comprised of: Australia, Austria, Belgium, Canada, Denmark, Finland, France, Germany, Ireland, Italy, Japan, Luxembosurg, Netherland, New Zealand, Norway, Portugal, Spain, Sweden, Switzerland, United Kingdom and United States.

> 13 Due to the lack of quarterly
and employed in Russian WTO accession analysis by Rutherford and Tarr (2008 and 2010). The tariff rates for the analysis in this section were based on trade flow data at a slightly aggregated level and were therefore not precise. Shepotylo and Tarr have shown, however, that the tariff calculations used in this paper are not subject to significant errors.

Based on a mapping (of the Russian Statistical Office) from the tariff line data of the Customs Committee to the sectors in our input output table, we calculated a weighted average tariff rate for the sectors of our model. We calculated these rates two ways: based on all imports (where the collected tariff rate as a percent of all imports is 8.1%) and on non-CIS imports (where the collected tariffs as a percent of non-CIS imports is 11.1%). The rates we employ in the model are the rates based on all imports. The rates based on all imports are lower since the base on the calculation includes CIS imports on which no tariffs are imposed. We believe collected tariff rates more closely approximate the protection a sector receives and the incentives it faces. Similar procedures are applied for export taxes. The results at the sector level are in table 3.2.

Applying these tariff rates across all sectors implies that tariff revenue in our model is about 1.6 percent of GDP in the initial equilibrium. Collected tariffs in Russia are closer to 1.1 percent of GDP.<sup>29</sup> There are several reasons that the collected tariffs in Russia are less than the legal rates on most favored nation (MFN) imports. Most notably, exemptions to the Russian tariff are available for regional agreements (most notably the CIS), personal imports and shuttle trade. We adjust for the CIS trade, so we are applying the MFN rates on all imports from the non-CIS. This slightly, but not significantly biases upward the rates we employ relative to collected rates.

**Export tax data.*
and 0.584 log points larger on average than places with routes less paved, in terms of population and lights, respectively, after controlling for distance to the primate. In column 5, after controlling for distance to the primate, more paving is correlated with a larger fraction of adults working in the manufacturing sector, in a sample of districts in 4 countries (Ghana, Guinea, Senegal, and Tanzania) for which census data are available from IPUMS.<sup>48</sup> These results are all consistent with the idea that cities connected by more paved roads could be more hurt by higher oil prices because they are more economically connected to the primate, whereas cities that are connected by mostly unpaved roads are smaller and closer to autarky.

The regressions in Table 8 exploit the paving information by including two terms of the main effect _βptxi_ in Equation (11), one for cities with routes to the primate more paved than the median and one for cities with less paved routes. Column 1 demonstrates that the transport cost effect is similar in the two categories of cities. While paving status was likely determined before the study period in most cases, it is potentially endogenous to local economic activity, limiting the scope for causal interpretation. It is likely that transport costs affect the two sets of cities in slightly different ways. Routes to some cities were paved for any number of reasons (early manufacturing promise, political or military importance, corruption), and then this paving helped these cities to grow more, at least in part because of transport-sensitive firms that were then penalized by increases in oil prices. On the other hand, unpaved roads require slower and more fuel intensive travel, so given the same demand for transport services, cities along them are penalized more per mile.

However, without a mostly paved road to the primate, firms in a city may seek alternate trading connections, relying on intermediate cities instead. Column 2 adds the distance to the nearest city in the top population quintile if that
government and business may affect a government’s approach to regulation and policy, or its susceptibility to bribes. To account for this possibility, we include an indicator of whether the head of the executive is from a party to the left of the ideological spectrum. These measures, too, are from the _Database of Political Institutions_ .

Finally, we also account for the broader political and economic context of a country. Democracies have checks and balances that can help to limit corruption and expose the selection of leaders to electoral competition, so we control for it alternatively with variables from the _Polity_ and _V-Dem_ data sets. Moreover, as prior research finds that economic development is associated with better government performance (La Porta et al. 1999), we include GDP per capita (from the _World Development Indicators_ data set).

# **4. Results**

For a first look at the bivariate relationship of cabinet size with governance we average the available data for the 10 most recent years included in our data set and focus on cross-national patterns. The results are displayed in Figure 2. All measures of governance have a negative relationship with cabinet size, which in all six panels (a to f) implies worse governance. All correlations are significant at the 1% level with coefficients ranging from -.49 (for Control of Corruption) to -.13 (Regulatory Quality). Yet there is a substantial amount of variation that is not accounted for by cabinet size, which is expected. These cross-national patterns are affected by a potentially large number of observable and unobservable characteristics.

8
# **4 Methods**

## **_4.1 Data_**

In order to test our hypotheses, we examine the investment history of 1,001 private electricity projects in 75 countries during the period 1989 – 2001. These projects constitute the population of global private investment in electricity generation over this time period, as compiled by the Hagler-Bailly consultancy. In a small subset of countries (e.g., Germany, the United Kingdom and the United States), a substantial number of private investment projects predate our sampling period and are omitted from our analysis.

We augment the Hagler-Bailly dataset’s information gathered from international, industry and local news reports available through various online databases. A large team of research assistants compiled this information and used it to code several of the variables described below. Validation of the coding of the dependent variable by the authors and a PhD student indicated a reliability of greater than 0.98.

## **_4.2 Unit of Analysis_**

Our hypotheses implicate independent variables at the country level (H2 and H4), the reform level (H1 and H3), and the investor level (H5). We address these multiple levels of analysis with a panel data set whose cross-sectional unit is an investment in a electricity generation facility by (a coalition of) investors, which is the most disaggregated of the three levels. Our unit of analysis is thus the investor coalition-country-year.

## **_4.3 Dependent Variables_**

As noted in the Introduction, resistance may manifest either at the level of the investor or across the board. Our primary dependent variable is a dichotomous measure equal to one if, in a given investing coalition-country-year, there occurs either the former type of event, which we 19
children (cf. World Bank et al. 2014). Moreover, cooking with inadequate cooking fuels such as wood, charcoal, crop waste, dung, coal, or kerosene, also has a wide range of negative environmental and climate change effects.

Durable material is the second most deprived dimension in emerging economies. Only about 66 percent of housing units are built with durable material for roof, wall, or floor. The lowest are Botswana and the Democratic Republic of Congo, where mostly rudimentary or natural material are used.

For tenure security, about three-quarters of HHs feel insecure with the lowest perception of tenure security reported in Mali with only 28 percent of HHs feeling secure. It is key to point out that our operationalization of tenure security (by relying on HCES and Prindex data sources) measure perceptions of tenure security which might differ from _de facto_ tenure security. Yet, perceptions influence behavior and investment decisions and bear key insights into the rather elusive concept of tenure security. Further, this standardized way of measurement allows for comparison across different tenure systems including those where customary titles or traditional forms of ownership may be more meaningful to HHs than legal documentation of rights. Further, this is hitherto the best possible data source on tenure security for a large set of countries (Prindex 2020).
This paper differs from the existing literature in three respects. First, our empirical analyses cast a wider net than the existing literature which focuses only in a particular region or a country and covers a shorter time period. Second we examine time series trends in codependence and test for structural changes over time. Finally, we examine cross-country differences in co-dependence and link the differences to measures of financial and economic openness and regulatory frameworks in different countries.

Policymakers may be able to draw important implications from our analysis. Codependence in bank default risk has important consequences for systemic stability. We find increasing co-dependence in banks located in different national jurisdictions. Although we do find that strong banking supervision tends to reduce co-dependence in a given country, our results call for banking supervisory co-operation at a global level. This is especially true for larger banks which have grown more interconnected over the past decade.

The rest of the paper is organized as follows. Section 2 describes the data sources and describes the construction of the Merton (1974) default risk measure. Section 3 presents the empirical results, and finally Section 4 concludes.

# **2. Data Sources and Credit Risk Measure**

The key variables for our analysis come from _BANKSCOPE_ which provides bank-level balance sheet information, and _DATASTREAM_ which provides information on stock prices, market capitalization and stock volume. We use weekly market data and annual accounting information in creating our credit risk measure. All data items are in US dollars to make comparisons across countries possible. We compute default probabilities implied from the structural credit risk model of Merton (1974). This approach treats the equity value of a company as a call option on the company‟s assets. The probability of default is computed using the “distance-to-default” measure, which is the difference between the asset value of the firm and the face value of its debt, scaled by the standard deviation of the firm‟s asset value. The Merton (1974) distance-todefault measure has been shown to be good predictor of defaults outperforming accountingbased models (