rafmacalaba/datause-encoder-data huggingface dataset:
<mark>We label these developed market institutional investors and emerging market sovereign investors the “cross-over investors,” because they are crossing over from other asset classes into the emerging market corporate debt asset class. In the cross-over corollary in Section 2, we hypothesize that these cross-over investors will tend to invest more in indexeligible bonds relative to specialists.</mark> <mark>In this section, we explicitly test the cross-over corollary, using data on different funds’ holdings of emerging market corporate bonds. Figure 7 presents two pieces of evidence that connect investor interest with changes in the composition of emerging market corporate bond issuance. Panel A plots the cumulative flows into mutual funds that invest in emerging market sovereign and corporate debt from 2003 to 2016. It also plots the number of $500 million bonds issued by emerging market firms, as a fraction of all bonds issued by these firms. The correlation between the two is very high (0.93), showing a clear connection between the growing investor interest in emerging market debt and the growing relative importance of issuances that just meet the threshold of $500 million. Panel B plots the percentage of the portfolio invested in emerging markets by developed market debt mutual funds from 2005 to 2016. We observe a sharp increase in the weight dedicated to emerging market securities within these funds, which is consistent with a crossing-over of developed market debt funds into emerging market securities. Together, these figures show the increase of investor interest in emerging market debt securities, from ultimate investors and from asset managers from developed countries.</mark> <mark>We complement the evidence in Figure 7 with additional evidence showing that investors less familiar with the emerging market corporate asset class tend to hold a greater proportion of index-eligible emerging market corporate debt. We assemble data from Morningstar Direct on debt funds that we categorize into emerging market corporate specialists and cross-overs, using fund categories provided by Morningstar. Within the cross-over category, we also classify funds into emerging market mixed (those that invest</mark> 25
Palestinian Ministry of National Economy and ARIJ (2011). The economic costs of the Israeli occupation for the occupied Palestinian territory, Ramallah and Bethlehem.

Patacchini, E. and Y. Zenou (2005). Spatial mismatch, transport mode and search decisions in England, _Journal of Urban Economics_ , 58(1): 62–90.

Rodriguez, C. and Sanchez, F. (2012). Armed conflict exposure, human capital investments nd child Labor: Evidence from Colombia. _Defence and Peace Economics_ , Vol.23 (2): 161-184.

Ruppert Bulmer, E., (2003), The Impact of Israeli Border Policy on the Palestinian Labor Market, _Economic Development and Cultural Change_ , v. 51: p. 657-676.

Sanchis-Guarner, R. (2012). Driving up Wages: The Effects of Road Improvements in Great Britain, mimeo, London School of Economics.

Shemyakina, O. (2011). The Effect of Armed Conflict on Accumulation of Schooling: Results from Tajikistan. _Journal of Development Economics_ , vol. 95: 186-2000.

UNCTAD (2011). Developments in the economy of the occupied Palestinian territory, Geneva: UNCTAD Secretariat.

UN OCHA (2010). West Bank: Closure and Access, June, Jerusalem.

World Bank (2004). Four Years – Intifada, Closures and Palestinian Economic Crisis: An Assessment, The World Bank.

World Bank (2007a). West Bank and Gaza investment climate assessment, The World Bank.

World Bank (2007b). Movement and access restrictions in the West Bank: Uncertainty and inefficiency in the Palestinian economy, The World Bank.

World Bank (2007c). _A decade of action in transport: An evaluation of the World Bank assistance to the transport sector, 1995-2005_ . Washington DC: The World Bank.

World Bank (2010). The Underpinnings of the Future Palestinian State: Sustainable Growth and Institutions, Economic Monitoring Report to the Ad Hoc Liaison Committee, September, The World Bank.

World Bank (2011a). Building the Palestinian State: Sustaining Growth, Institutions and Service Delivery, Economic Monitoring Report to the Ad Hoc Liaison Committee, April, The World Bank.

World Bank (2011b). West Bank and Gaza coping with conflict? Poverty and inclusion in the West Bank and Gaza, Report No. 61293, The World Bank.

33
ln _TFP_ = ln _Y_ − α ln _L_ − 1( − α ) ln _K_ , where α is the mean labor share over the available time period. The labor share is derived as the ratio of the wage bill over value added.

Fixed capital formation used to construct capital stocks, value added, labor and wages, is from the World Bank data set described in Nicita and Olarreaga (2006), all reported in current US dollars at the 3-digit ISIC codes (Revision 2). Value-added is deflated by the US GDP deflator (1991=100). Fixed capital formation is also deflated by the US GDP deflator (1991=100), and capital stocks are derived from the deflated fixed capital formation series using the perpetual inventory method with a 5% depreciation rate.<sup>4</sup> The _TFP_ index is constructed using the deflated value added, capital stocks, labor and its average income share with the formula provided.

R&D expenditure for the 15 OECD countries is taken from OECD ANBERD with ISIC Revision 2 (2002) covering data from 1973 to 1998, and ANBERD with ISIC Revision 3 (2006) covering data from 1987 on. Since ANBERD ISIC 2 and ISIC 3 have 12 years of data overlapping, we are able match the different specifications. The R&D stock in each country is constructed from R&D expenditures using the perpetual inventory method with a 10% depreciation rate.

Bilateral trade data of the 50 developing countries with the 15 industrialized OECD countries at the 4-digit ISIC 2 level are taken from Nicita and Olarreaga (2006). We construct bilateral trade shares for each of the 50 developing countries with respect to each of the 15 OECD countries, as defined in equation (2).

Average years of education, tertiary education completion ratio, and secondary school completion ratio for the population aged 25 and above are obtained by annualizing

> 4 Given that the data reported in Nicita and Olarreaga (2006) are in current US dollars, we use the US GDP deflator. In the empirical analysis, country-specific as well as year dummies are used in order to control for some of the distortions that might be present because of the conversion.

9
|Quality of<br>Electricity Supply<br>Score|World Economic Forum –<br>Executive Opinion Survey,<br>Global Competitiveness<br>Indicators|2010 and<br>2011<br>weighted<br>average|How would you assess the quality of the electricity<br>supply in your country (lack of interruptions and lack of<br>voltage fluctuations)? [1 = insufficient and suffers<br>frequent interruptions; 7 = sufficient and reliable]|
|---|---|---|---|
|Frequency of<br>Bribes to Public<br>Utilities Score|World Economic Forum –<br>Executive Opinion Survey,<br>Global Competitiveness<br>Indicators|2011|In your country, how common is it for firms to make<br>undocumented extra payments or bribes connected with<br>public utilities? Answers ranges from 1 (very common)<br>to 7 (never occurs).|
|Firms’ current<br>sales|World Bank, Enterprise<br>Surveys|2006-<br>2010|Firm total annual sales in local currency in 2000<br>constant US dollars.|
|Firms‘ labor input|World Bank, Enterprise<br>Surveys|2006-<br>2010|Number of firms’ full time workers.|
|Firms’<br>productivity|World Bank, Enterprise<br>Surveys|2006-<br>2010|Total annual sales in 2000 constant US dollars per full<br>time worker.|
|Firms’ capital<br>input (investment)|World Bank, Enterprise<br>Surveys|2006-<br>2010|Total annual expenditure in 2000 constant US dollars<br>for purchases of machines, equipment and buildings.|
|Direct Export<br>10%|World Bank, Enterprise<br>Surveys|2006-<br>2010|Measure of firms’ international trade engagement:<br>Dummy variable equal to 1 if the firm exports directly<br>at least 10% of its sales.|
|Direct Import|World Bank, Enterprise<br>Surveys|2006-<br>2010|Measure of firms’ international trade engagement:<br>Dummy variable equal to 1 if the firm imports at least a<br>part of its inputs.|
|Two-way Trader<br>10%|World Bank, Enterprise<br>Surveys|2006-<br>2010|Measure of firms’ international trade engagement:<br>Dummy variable equal to 1 if the firm exported at least<br>10% of its inputs AND exports at least 10% of its sales.|
|Firms’ usage of<br>bank finance|World Bank, Enterprise<br>Surveys|2006-<br>2010|Dummy variable equal to one if the firm uses banks or<br>other financial institutions to finance its investments or<br>working capital.|
|Number of power<br>outages per month<br>experienced by<br>firms|World Bank, Enterprise<br>Surveys|2006-<br>2010|Average number of power outages observed by the firm<br>in a typical month.|

26
whether any section of the community found it hard to cultivate their land in 2004 because of insecurity. The predicted value represents the probability that the community would report being insecure in 2004 and therefore represents perceived risk.

The vector of independent variables, _distance_ , represents the distance of community _i_ from the closest LRA attacks in previous years. Separate distance measures are constructed from the NUS and ACLED data. The NUS data only contain information for attacks in 1992 and 1999. The ACLED data contain yearly data for 1997 until 2003. There are therefore nine independent variables (two from the NUS and seven from the ACLED data). The same independent variables are used for both the estimation of statistical and perceived risk. _E_ is an error term.

The results from the estimation of equation (2) to construct the measures of statistical and perceived risk are presented in table 1.<sup>25</sup> The errors in the logistic regression are clustered at the community level. In the first column, the dependent variable is a binary variable for whether or not a community was attacked by the LRA in 2004. Consequently, as previously discussed, the predicted values represent the probability of the community being attacked in 2004, that is the statistical risk. Similarly, the second column uses a binary variable for the perceived insecurity within the community. As previously noted, the fitted values from column (2) are estimates of the likelihood that the community feels insecure and therefore of the perceived risk.

Overall, while the variables are strongly jointly statistically significant, only several of the variables are individually significant, likely reflecting the multicollinearity among measures of distance to the closest attacks in different years. This is not a problem as we are only interested in how fell the model “predicts” attacks as opposed to the relevant contribution of any of the

> 25. This estimation only captures the part of insecurity which is correlated with the proximity of communities to historical violence. While this likely determines the statistical risk of violence, perceived insecurity could arise for a variety of reasons which are unrelated to proximity of violence. Consequently, the estimates only capture part of the overall effects of perceived insecurity and should be considered as conservative estimates.

18
Furlanetto, F., and Lepetit., A., (2024), “The Slope of the Phillips Curve", Finance and Economics Discussion Series No. 43. Federal Reserve Board.

Gagliardone, L., and Gertler, M., (2023), “Oil prices, monetary policy and inflation surges”, NBER Working Paper 31263, National Bureau of Economic Research, Cambridge, MA.

Gambetti, L., Pappa, E., and Canova, F., (2008), “The Structural Dynamics of U.S. Output and Inflation: What Explains the Changes?”, Journal of Money, Credit and ‐ Banking, Vol. 40, No. 2 3, pp. 369-88.

Garratt, A., Lee, K., Mise, E., and Shields, K., (2008), “Real-time representations of the output gap”, Review of Economics and Statistics, 90 (4), pp. 792-804.

Gerlach, S., and Smets, F., (1995), “The Monetary Transmission Mechanism: Evidence from the G-7 countries”, BIS Working Papers 26, Bank for International Settlements.

Giannone, D., and Primiceri, G., (2024), “The drivers of post-pandemic inflation”, Paper prepared for the 2024 ECB conference in Sintra, Portugal.

Goldberg, L., and Krogstrup, S., (2019), “International capital flow pressures”, NBER Working Paper 24286, National Bureau of Economic Research, Cambridge, MA.

Ha, J., Kose, M. A., and Ohnsorge, F., eds., (2019), Inflation in Emerging and Developing Economies: Evolution, Drivers, and Policies, Washington, DC: World Bank.

Ha, J., Kose, M. A., and Ohnsorge, F., (2021), “Inflation during the Pandemic: What Happened? What Is Next?”, CEPR Discussion Paper 16328.

Ha, J., Kose, M. A., and Ohnsorge, F., (2022), “Global Stagflation”, CEPR Discussion Paper 17381.

Ha, J., Kose, M. A., and Ohnsorge, F., (2023), “One-stop Source: A Global Database of Inflation”, Journal of International Money and Finance, Vol. 137.

Ha, J., Kose, M. A., Ohnsorge, F., and Yilmazkuday, H. (2023), “Understanding the Global Drivers of Inflation: How Important Are Oil Prices?”, Energy Economics, Vol. 127.

Ha, J., Kose, M. A., Ohnsorge. F., and Yilmazkuday, H. (2024), “What Explains Global Inflation”, IMF Economic Review (forthcoming).

Ha, J., Kose, M. A., Otrok, C., and Prasad, E. S., (2020), “Global Macro-financial Cycles and Spillovers”, NBER Working Paper 26798, National Bureau of Economic Research, Cambridge, MA.

Harding, D., and Pagan, A., (2002), “Dissecting the Cycle: A Methodological Investigation”, Journal of Monetary Economics, Vol. 49, pp. 365–81.

73
Our paper is technically similar to Pindyck (2000, 2002); and most directly builds on Pindyck (2000).<sup>3</sup> Our solution for optimal timing of a technology retrofit, for given initially invested infrastructure (questions (I) and (II) above), reproduces Pindyck’s result (albeit with a modification; see Framstad (2011)). Our more important contribution is to extend Pindyck’s analysis by deriving the initial infrastructure investment decision (question (IV)), simultaneously with the future retrofit decision. We are also the first to directly answer questions (III) and (V), on effects for accumulated carbon emissions. While we have less formally to say about point (VI), we discuss this issue and its relation to our model in the final section 6.

The combined infrastructure establishment and retrofit issue have been studied by Strand (2011a), Strand and Miller (2010) and Strand, Miller and Siddiqui (2011), albeit in simpler discrete (two-period) models. Shalizi and Lecocq (2009) provide a more descriptive (largely non-technical) framing. Anas and Timilsina (2009), simulating infrastructure investments in roads for Beijing, find that a higher level of road investments makes the chosen residential pattern more dispersed, and also makes later investments in mass transport (“retrofits” in this case) less valuable or more expensive. Vogt-Schilb, Meunier and Hallegatte (2012) discuss timing of sectoral abatement policies within a model of overall optimal climate policy, given that long-lasting effects of particular abatement investments can vary between sectors. They show that marginal sectoral abatement costs should differ by sector, with more “rigid” sectors investing relatively more in early abatement.

# **2. Overview of the paper**

Some of our analysis is analytically complex, but the main results are simple. Given socially optimal pricing, increased volatility of climate damages from given emissions, when future retrofit is an option, implies that a) retrofits will be executed later, and when marginal climate damage has reached a higher level; and b) the initial infrastructure will be chosen with a higher energy intensity. These two factors together imply that both current emissions while

> 3 See also a follow-up paper by Pindyck (2002), which generalizes the analysis in some other directions than those pursued here (to simultaneously uncertain climate impact, and uncertain damage due to given climate impact. See also the review by Pindyck (2007). Balikcioglu, Fackler and Pindyck (2011) and Framstad (2011) rectify errors in the original Pindyck (2000, 2002) presentations.

4
**Figure 16: Sectoral employment shares: Benchmark versus crisis scenario (percentage change)**

<!-- Start of picture text --> 6<br>4<br>2<br>0<br>-2<br>-4<br>Primary Industry Commerce Services<br>year 1 year 2<br><!-- End of picture text --> Source: WB staff calculations based on data from BCU and INE A crisis would significantly reduce the income growth of the bottom 40 percent with respect to both the benchmark and the growth rate of the mean income. In particular, the indicator of shared prosperity in Uruguay would decline from 13.3 percent between 2010-11 to 0.8 percent between 2011-12 and become negative between the baseline year and year 1 at -5.6 percent and showing a slightly recovery for year 1-year 2 with a growth rate of 0.7 percent (Figure 17). For the baseline year and year 1, the growth of the bottom 40 percent will be lower than the growth of the mean, which may translate into an increase in inequality, while for year 1-year 2, these two growth rates will be just above zero.

Similar to the poverty rates, the impact of a crisis on shared prosperity would be less severe than the 2001/02 economic shock. Back in the years 2001 to 2003, the Argentine crisis produced income growth rates of the bottom 40 percent of -5.2 and -16.9 percent, respectively; while the growth rate of the mean income was -3.3 and -19.0 percent. A simulated crisis for the baseline year to year 2 would also have negative consequences on shared prosperity but not as severe in magnitude as of those of the 2001/2 Argentine crisis.

**Figure 17: Impact of the crisis on Shared Prosperity (growth of the incomes of the bottom 40 percent)**

<!-- Start of picture text --> 15.0%<br>12.5%<br>10.0%<br>7.5%<br>5.0%<br>2.5%<br>0.0%<br>-2.5% 2011 baseline year year 1 year 2<br>(2012)<br>-5.0%<br>-7.5%<br>Overall No crisis Overall Crisis<br>Bottom 40 No crisis Bottom 40 Crisis<br><!-- End of picture text --> Source: WB staff calculations based on data from BCU and ECH 2011 16
Source: Delhi Metro Rail Corporation, DMRC

1. This refers to the average daily ridership for the month preceding the one in which the extension was introduced. However, for the two extensions carried out on the last day of the month (Red Line second extension and Blue Line introduction), it refers to the average daily ridership for the month in which the extension was introduced. For example, average daily ridership was 80.1 and 130.8 thousand per day in March 2004 and November 2004, respectively.

2. This refers to the month-to-month percentage change in average daily ridership for the month in which the extension was carried out. However for the two extensions carried out on the last day of the month, the change is for the month following the one in which the extenison was carried out. For example, average percentage daily ridership in April 2004 was 46 percent higher than that in March 2004, while for December 2004 it was 3 percent higher than that in November 2004.

**Table 2. Descriptive Statistics for Pollution and Weather, 2004 to 2006**

||Mean<br>ITO|Std. dev.<br>(in microgram|Min.<br>s per cubic|Max.<br>meter)|Missing<br>Obs.<sup>1</sup><br>(%)|
|---|---|---|---|---|---|
|NO2|145.5|109.3|0.226|1,140|18|
|CO|2,389.5|2,328.5|20.6|25,000|14|
|PM2.5<sup>2</sup>|144.6|131.8|1.69|1,020|42|
||Siri Fort|(in microgram|s per cubic|meter)||
|NO2|45.0|52.5|0.771|805|27|
|CO|1,632.0|1,818.7|2.98|20,400|52|
|||<br>Safdarj|ung|||
|Temperature|25.3|7.9|3.1|43.7|0.01|

42
<!-- Start of picture text --> 2000<br>0 1500<br>2<br>ror<br>Qa<br>S<br>”<br>S 1000<br>3}<br>a<br>£<br>p=|<br>Zz<br>500<br>0<br>0 2 4 6 8 1<br>Average CRI per supplier<br>—--— MeanCRI -—~~ Median CRI<br><!-- End of picture text -->
- [50] Henderson, J.V. and H. Wang (2007) Urbanization and City Growth: The Role if Institutions, _Regional Science and Urban Economics_ , 37, 283-313.

- [51] Hoy, M. and E. Jimenez (1991) Squatters Rights and Urban Development: an Economic Perspective, _Economica_ , 58, 79-92.

- [52] Izraeli, O. and T. McCarthy (1985) Variations in Travel Distance, Travel Time and Modal Choice among SMAs, _Journal of Transportation Economics and Policy_ , 139-160.

- [53] Jayachandran, S. (2006) Selling Labor Low: Wage Responses to Productivity Shocks in Developing countries, _Journal of Political Economy_ , 114, 538-575.

- [54] Jedwab, R. (2015) Urbanization without Structural Transformation: Evidence from Consumption Cities in Africa, mimeo.

- [55] Jedwab, R., L. Christiaensen and M. Gindelsky (2015), Demography, Urbanization and Development: Rural Push, Urban Pull and. . . Urban Push?, _Journal of Urban Economics_ , forthcoming.

- [56] Jimenez, E. (1982) The Value of Squatter Dwellings in Developing Countries, _Economic Development and Cultural Change_ , 30 (4), 739–752.

- [57] Jimenez, E. (1984) Tenure Security and Urban Squatting, _Review of Economics and Statistics_ , 66 (4), 556–567.

- [58] Jimenez, E. (1985) Urban Squatting and Community Organization in Developing Countries, _Journal of Public Economics_ , 27 (1), 69–92.

- [59] Judd, K.L. (1998) _Numerical Methods in Economics_ . The MIT Press.

- [60] Judd, K.L., P. Renner, and K. Schmedders (2012) Finding All Pure-Strategy Equilibria in Games with Continuous Strategies. _Quantitative Economics_ 3, 289-331.

- [61] Kahn, M. (2010) _New Evidence on Trends in the Cost of Urban Agglomeration_ , in Agglomeration Economics, Glaeser ed., National Bureau of Economic Research, 339-354.

- [62] Kim, A. (2004) A Market without the “Right” Property Rights, _Economics of Transition_ , 12 (2), 275–305.

- [63] Knack, S. and P. Keefer (1995) Institutions and Economic Performance: Cross-Country Tests Using Alternative Institutional Measures, _Economics and Politics_ , 1, 207-28.

- [64] Kerekes, C. and C. Williamson (2010) Propertyless in Peru, Even with a Government Land Title, _American Journal of Economics and Sociology_ , 69, 3, 1011-33.

- [65] Konig, D. (1974) Community Custom and the Common Law: Social Change and the Development of Land Law in Seventeenth-Century Massachussetts, _American Journal of Legal History_ , 18, 137-77.

36