rafmacalaba/datause-encoder-data huggingface dataset:
- Hallegatte, S., A. Vogt-Schilb, M. Bangalore, J. Rozenberg. (2017). Unbreakable: Building the Resilience of the Poor in the Face of Natural Disasters. Climate Change and Development; Washington, DC: World Bank

- Hallegatte, S., J. Rentschler, J. Rozenberg. (2019). Lifelines: The Resilient Infrastructure Opportunity. Washington, DC: World Bank.

- Hirabayashi, Y., R. Mahendran, S. Koirala, L. Konoshima, D. Yamazaki, S. Watanabe, H. Kim & S. Kanae (2013) Global flood risk under climate change. Nature Climate Change 3, 816–821 (2013).

- Husby, T., H. L. de Groot, M. W. Hofkes, and T. Filatova. 2015. “Flood Protection and Endogenous Sorting of Households: The Role of Credit Constraints.” Mitigation and Adaption Strategies for Global Change. doi:10.1007/s11027-015-9667-7

- Jongman, B., P. Ward, J. Aerts. (2012) Global exposure to river and coastal flooding: Long term trends and changes, Global Environmental Change, Vol. 22(4), p. 823-835

- Kulp, S.A., B.H. Strauss (2019) New elevation data triple estimates of global vulnerability to sea-level rise and coastal flooding. Nature Communications Vol. 10, 4844

- Lall, S. V., and U. Deichmann. 2012. “Density and Disasters: Economics of Urban Hazard Risk.” World Bank Research Observer 27: 74–105.

- Lall, S. V., M. K. A. Lundberg, and Z. Shalizi. 2008. “Implications of Alternate Policies on Welfare of Slum Dwellers: Evidence from Pune, India.” Journal of Urban Economics 63: 56–73.

- Muis S., M. Verlaan, H. Winsemius, J. Aerts, P. Ward (2016). A global reanalysis of storm surges and extreme sea levels. Nature Communications. Vol. 7

- Narloch, U., & Bangalore, M. (2018). The multifaceted relationship between environmental risks and poverty: New insights from Vietnam. Environment and Development Economics, 23(3), 298-327

- Patankar, A. 2015. “The Exposure, Vulnerability and Adaptive Capacity of Households to Floods in Mumbai.” Policy Research Working Paper 7481, World Bank, Washington, DC.

- Peduzzi, P., H. Dao, C. Herold, F. Mouton (2009) Natural Hazards and Earth System Sciences Assessing global exposure and vulnerability towards natural hazards: the Disaster Risk Index. Natural Hazards and Earth System Science. Vol. 9, pp. 1149–1159,

- Rentschler, J, de Vries Robbé, S, Braese, J, Huy Nguyen, D, van Ledden, M, and Pozueta Mayo, B. 2020. Resilient Shores: Safeguarding Vietnam’s Coastal Development in the Face of Disaster Risks. Washington, DC: The World Bank.

- Smith, A., C. Sampson, P. Bates (2015) Regional flood frequency analysis at the global scale. Water Resources Research. Vol.51, 539–553

- Smith, A., P. Bates, O. Wing, C. Sampson, N. Quinn, J. Neal (2019) New estimates of flood exposure in developing countries using high-resolution population data. Nature Communications Vol.10, pp. 1814

- UNDRR (2017) Global Assessment Report 2017. Geneva: United Nations Office for Disaster Risk Reduction

- World Bank and Australian AID, 2014. “’Where Are We During Flooding?’ A Qualitative Assessment of Poverty and Social Impacts of Flooding in Selected Neighborhoods of HCMC.” Washington, DC.

26
# **1 INTRODUCTION**

What factors predict the performance of public sector management (PSM) projects? This paper explores this question through an econometric analysis of the World Bank’s investment lending project portfolios approved between 1990 and 2013. It identifies observable data on country contexts, reform content and processes, and project management variables that predict the performance of PSM projects, as rated by the World Bank’s Independent Evaluation Group (IEG).

The paper has two main objectives: first, to inform the broader debate on what works and why in PSM reforms; second, to manage uncertainty in PSM projects. For example, do tough PSM reforms fare better under autocratic rule—as in China, Rwanda, and Singapore—or under the incentives provided by inclusive political institutions? These and other questions are investigated. The resulting analysis may help manage uncertainty in PSM projects, by guiding the allocation of managerial attention and Bank budgets. It may also help to adjust risk in the Bank’s PSM lending portfolio to its risk appetite.

The paper uses ordered probit regression estimates to identify performance predictors, employing a project sample of 1,097 PSM projects. It then compares these against a sample of 2,105 non-PSM projects, to ask if country characteristics affect PSM project performance to a greater or lesser extent than other World Bank projects. This difference is identified, among other ways, based on nearest-neighbor and subclassification matching estimators that match PSM projects with similar non-PSM projects in the same country.

The paper contributes to a small but growing set of studies that employ cross-country regression analysis to better understand PSM reform patterns. Such analysis has been constrained by lack of reliable comparative data on public management system properties. Among the few cross-country regression studies on PSM reform, that of Evans and Rauch (1999) on Weberianism and growth is a famous early example. More recently, a number of researchers have used the Public Expenditure and Financial Accountability (PEFA) data set (for example, Andrews 2009) or have coded particular finance management system properties, such as medium-term expenditure frameworks (MTEFs) (World Bank 2012a). This paper focuses on the corporate performance of World Bank PSM projects, and thus cannot be used to generalize about PSM reforms overall.

The remainder of this paper is structured as follows. Section 2 situates it within the existing literature on PSM reform. Section 3 sets out the theoretical questions and hypotheses that underlie the choice of variables included in the review. Section 4 presents underlying data; section 5 sets out identification strategies; and section 6 provides descriptive statistics, comparing the nature and targeting of PSM and non-PSM projects. Section 7 summarizes the major estimation results regarding the predicators of PSM 5
and costs of limits on greenhouse gas emissions and evaluations of how the flexibility mechanisms might work.

# **Policy evaluations and predictions**

Most often in economics, evaluations of policy are based on historical assessments. In the case of integrated evaluations of climate change and its economic impact, evaluations are forward looking and rely heavily on models built up from current and historical physical, technical, institutional and economic relationships. For the most part, the model predictions are against hypothetical alternatives, some of which are unlikely to occur. Still, the models used to evaluate climate change policy contain predictions about the scale of carbon markets and how markets they might work. There is a substantial literature on how to model climate change policies. It is well reviewed elsewhere and this section draws on that work. Comprehensive surveys are given in Weyant (1999, 2004), Löschel (2002), Springer (2003), Sands (2004) and most recently in Working Group III contribution to the Fourth IPCC Assessment (IPCC 2007a).

# <u>Model structures and technology</u>

Springer (2003) broadly categorizes the reviewed models into five groups. The first is made up of integrated assessment models, where physical and human activities are jointly modeled. As Springer notes, there is some overlap between this group and the remaining, since the economic components of the integrated models employ CGE or energy system models. Examples of integrated models are discussed in Manne and Richels (1999), Nordhaus and Boyer (2000), Nordhaus (2001), Jacoby et al. (2006). Another common approach relies on marginal abatement cost curves to examine the effects of trade. Examples include Jotzo and Michaelowa (2002), Löschel and Zhang (2002) and Stevens and Rose (2002). A less common approach focuses on macroeconomic tradeoffs between monetary policy and employment. See, for example, McKibbin, Shackelton and Wilcoxen (1999). An alternative approach is to employ technical engineering models of sectors or energy systems. These bottom-up models are sometimes integrated with other sectors via a CGE model. IPCC (2007a) contains a review of several bottom-up sector models.

An important distinction among the models is whether greenhouse gas concentration is exogenous to the model.<sup>43</sup> Optimization models let the economic sectors maximize profits while adjusting the level of emissions endogenously. This can be done either by adjusting levels of production and the mix of sectoral output, or by introducing and endogenously selecting production technologies with different greenhouse gas intensities. The other approach is to exogenously impose a level of greenhouse gas concentration on the model and to find the most cost effective way to

> 43 See Manne and Rutherford (1994) for a discussion.

22
restrictions after the sanctions. The raw data confirm that households in these industries experienced the lowest incidence of family income changes after the sanctions. Figure 2 panel (a) displays the real median family income for treated households and their synthetic counterpart in the period 1995-2015. The synthetic control almost exactly reproduces the family income for the treated households during the entire pre-sanctions period (1995-2006). As Figure 2 panle (b) shows, the gap between the actual and counterfactual family income widens from around zero over the pre-sanctions period, to 45% in 2013.

Table 1 reports households’ and children’s characteristics of the synthetic group comparing to treated households in the absence of the sanctions. The variables overall are well balanced between these groups. It is important to emphasize that many economic sectors were affected by the sanction. This paper compare the most affected households with the least affected comparison group. In section 6, I check the validity of the synthetic control for counterfactual by checking the sensitivity of results to the choice of different matching methods (different matching variables, different matching year range, and different methods for selecting weights) and different selected donor industries. Overall, the sensitivity tests verify the robustness on the original results (Table S4).

Table 1: Descriptive Statistics (before the 2006 UN Economic Sanctions)

||Treatment|Synthetic Control|
|---|---|---|
|_Household-level variables_|||
|Ln(Total Family income)|18.59|18.50|
||(0.60)|(0.74)|
|demean|-1.23e-07|-7.66e-07|
||(0.59)|(0.73)|
|Ln(Labor income)|18.15|18.02|
||(0.61)|(0.78)|
|demean|-1.63e-07|4.86e-07|
||(0.61)|(0.76)|
|Ln(Education Expenditure)|12.21|12.19|
||(4.78)|(4.82)|
|Observations|2,282|10,405|
|_Child-level variables_ (6 _≤age ≤_24)|||
|Age (female)|14.47|14.55|
||(4.96)|(5.01)|
|Age (male)|14.41|14.28|
||(4.88)|(4.86)|
|Years of schooling: girls|7.26|7.68|
||(3.84)|(4.10)|
|Years of schooling: boys|7.24|7.38|
||(3.75)|(3.87)|
|% In school: girls|77.51|78.63|
|% In school: boys|79.28|81.30|
|% In school: girls 6-18|93.01|93.36|
|% In school: boys 6-18|92.84|93.60|
|Observations|6,295|25,076|

_Source_ : Iranian Households Income and Expenditures Surveys (HIES). Note: The table reports summary statistics of household and child-level data by treatment status. Standard deviations are in parenthesis. Family incomes and expenditures are deflated by CPI. Treated: Oil and Gas, Energy Supply industries. Synthetic control: Information (0.148), Education (0.169), and Health (0.683) industries.

15
of overall short-run economic activity than official GDP statistics which may be biased due to any number of reasons including under-reporting of output by enterprises (to avoid taxes) and old reporting systems used by statistical offices. We find that the GDP troughs identified in the crisis episodes coincide with troughs in manufacturing value added and in power consumption, though the power consumption data do not exhibit a collapse as large as that of the GDP.

While we find no evidence that the GDP statistics are not representative, several papers have suggested that there may be statistical measurement error in computing macro numbers. For instance, Bartelsman and Wolf (2009) show that productivity forecasts using micro data are more accurate and better than the macro alternative. Miron and Zeldes (1989) examine two measures of monthly manufacturing production – the index of industrial production and a second measure constructed from the accounting identity that output is given by the sum of sales and changes in inventories and find several discrepancies. They conclude substantial measurement error exists in the macro (industry level) price indices as compared to those constructed from micro data. Similar discrepancies between macro-level and household consumption data in national account statistics have been noted by Ravallion (2003) and Young (2009). Hence the need for examining the recovery process from a crisis using detailed firm-level data.

# _3.2.2  Sales versus Credit Recovery_

In this section we examine if we observe the phenomenon of Phoenix Miracles at the micro-level by looking at the recovery in corporate sales and short-term borrowing. In Figure 5A we present averages across the 9 episodes and find that there does not seem to be any evidence of a Phoenix Miracle. Both Sales (bold line) and Short Term Borrowing (dotted line) recover simultaneously albeit one period after GDP recovery.

When we start looking at individual countries, we can split the crisis episodes into two main categories: (a) **_Phoenix Miracles at the Micro Level_** : This is the case of Argentina, Malaysia, Korea and Thailand (see Figure 5B) where we see that corporate sales (in real terms) recovers at t+1 in each case while short term borrowing (also in real terms) has still not recovered. While Argentina, Malaysia, and Thailand were also identified as macro miracles in section 3.1, Korea was not identified as a miracle at the macro level since GDP and Private Credit recovered simultaneously. However the micro evidence in Korea shows that sales recover 13
to have adapted to power outages than water shortages, and thus the latter has a negative impact on their productivity while the former does not.

The findings may differ by the sector of activity of the firm. As indicated in table A2 (in the appendix), manufacturing activities primarily include small-scale manufacturing of clothes, shoes, baked foods, and furniture. Service sector activities mainly include retailing groceries, hair dresser and barber shops, and selling business/ computer /phone services. In columns 3 and 4 of table 4, we split the sample by manufacturing and service firms respectively, and estimate the base specification presented in column 4 of table 2. The coefficient of the total duration of water shortages is negative and statistically significant for manufacturing informal firms, but statistically insignificant for services firms. Thus, the results may be driven by the effect of water shortages on informal firms that use water for processing.

# **4.3 Robustness Check - Heckman Selection Model**

The Informal Enterprise Surveys only ask water infrastructure questions to firms that use water for business activities. Only about 710 firms fall into this category in the sample. Thus, we also attempt to model the selection of firms that use water for business activities using a Heckman selection model. The identifying variable for the selection equation is whether or not a firm is located in a household or a fixed premise with a permanent structure. The argument is that stationary informal firms within a permanent structure are more likely to be connected to the water system. The data confirm this with about 27 percent of firms located in fixed premises using water for business activities, and only 19% of firms not located in fixed premises use water for business activities. Thus, firms located in fixed premises are more likely to use water for business activities and thus face issues related to water shortages. Mobile informal firms cannot be connected to a piped water supply and so are unlikely to use water for business activities. When they do use water, they may need to obtain it from alternative sources such as water vendors. Thus, their business activities are unlikely to be immediately affected by piped water shortages. Therefore in combination with equation (1), we estimate equation (2) below: 15
**Figure 4: Cumulative Percentage Changes in Real Wage of Skilled Workers due to Forecasted Demographic changes**

<!-- Start of picture text --> 350<br>Hong Kong<br>300 Japan<br>Rest E. Asia<br>250<br>Thailand<br>200 Taiwan<br>Vietnam<br>150<br>Rest S. E. Asia<br>100 China<br>Philippines<br>50<br>South Korea<br>0 Singapore<br>Malaysia<br>-50<br>Indonesia<br>Source: Authors’ results<br>Figure 5: Cumulative Percentage Changes in Real Wages of Unskilled Workers due to<br>Forecasted Demographic changes<br>600<br>Hong Kong<br>500 Thailand<br>South Korea<br>400 Japan<br>Philippines<br>300<br>China<br>Vietnam<br>200<br>Taiwan<br>100 Rest S. E. Asia<br>Singapore<br>0<br>Malaysia<br>Rest E. Asia<br>-100<br>Indonesia<br>Cum % changes in 2050<br>2007 2009 2011 2013 2015 2017 2019 2021 2023 2025 2027 2029 2031 2033 2035 2037 2039 2041 2043 2045 2047 2049<br>Cum % changes in 2050<br>2007 2009 2011 2013 2015 2017 2019 2021 2023 2025 2027 2029 2031 2033 2035 2037 2039 2041 2043 2045 2047 2049<br><!-- End of picture text --> Source: Authors’ results

**Figure 5: Cumulative Percentage Changes in Real Wages of Unskilled Workers due to Forecasted Demographic changes**

Source: Authors’ results 19
countries, producing large fluctuations in capital flows.<sup>8</sup> In this paper, we show micro-evidence that suggests that shocks to the supply side of funds seem important in the transmission and amplification of shocks. We are able to measure different effects inside financial intermediaries, which other papers that focus on aggregate capital flows (even by type) cannot do. In particular, we disentangle the actions of investors injecting and withdrawing capital from openend funds, possibly as a way to discipline managers, and the behavior of managers actively allocating country portfolios and reacting to shocks from investors and returns. Our results support the claims that shocks to financial institutions and their inner-workings are important to understand crises.

Fourth, there is an increasing interest in studying how portfolios are managed when investing around the world and how shocks impact them.<sup>9</sup> Important among the shocks are valuation effects. One advantage of using mutual fund data is that we can work with actual portfolios. This is helpful because, while there is much discussion on portfolio reallocations, there is limited information on how portfolios are allocated and managed. There are no data on the portfolios of households and little data on those of other institutions like banks and hedge funds. Moreover, unlike country portfolios, the data we use are not inferred from capital flow data. In our case, we link movements in asset allocations to capital flows by an important group of foreign portfolio investors (international mutual funds). Moreover, we analyze in detail what role valuation effects play in changes in portfolio compositions.

The rest of the paper is organized as follows. Section 2 briefly describes the data and provides some basic statistics on mutual fund holdings. Section 3 discusses the shocks to managers and studies the variation in fund allocations (the manager’s decisions). Section 4 analyzes how managers and investors react to crises. Section 5 studies how the variations in the investor and manager responses affect capital flows to different countries. Section 6 concludes.

# **2. Data and Summary Statistics**

In this paper, we use a micro-level dataset consisting of an unbalanced panel of 1,140 international equity mutual funds and 121 international bond funds, containing the monthly country portfolios of these funds over the period January 1996 to November 2010 for equity funds and July 2002 to November 2010 for bond funds. The dataset comes from EPFR Global

> 8 See, for example, Shiller (2008), Eichengreen et al. (2009), Hellwig (2009), and Mishkin (2011).

> 9 In addition to the papers cited above, see Broner et al. (2006), Gourinchas and Rey (2007a), Hau and Rey (2008), Krugman (2008), Devereux and Yetman (2010), and Gourinchas et al. (2010), among others.

6
# **Table A10: Week‐to‐week variation in wages**

Dependent variable = Mean absolute within‐person deviation in weekly hours worked

|Including we<br>ho|eks with zero<br>urs|Conditional<br>wo|on any hours<br>rked|
|---|---|---|---|
|Males|Females|Males|Females|
|Wage work, irregular payment<br>11.982|9.678|7.207|5.574|
|Wage work, regular payment<br>9.180|7.292|5.152|4.309|
|Self employed<br>10.331|8.870|6.597|6.833|
|P‐value from F‐test of difference in coefficients<br>0.496|0.473|0.321|0.005|
|Work close to home<br>9.792|8.224|5.854|6.249|
|Work far from home<br>7.758|5.756|7.251|6.729|
|P values from T‐test of difference in coefficients<br>0.008|0.001|0.001|0.050|

_Notes: Sample includes respondents employed at baseline. P‐values use standard errors clustered at the household level. Regular wage work is defined as jobs for which the respondent listed frequency of pay as every week, every two weeks, or every month; any other responses are characterized as irregular wage work._
# **References:**

Böhringer, C., Fischer, C. and Rosendahl, K.E. (2010): The Global Effects of Subglobal Climate Policies, _The B.E. Journal of Economic Analysis & Policy_ 10 (2) (Symposium), Article 13.

Böhringer, C. and Lange, A. (2005), On the Design of Optimal Grandfathering Schemes for Emissions Allowances. _European Economic Review_ , 49, 2041-2055.

Brechet, T., Meniere, Y. and Picard, P. M. (2011), The Clean Development Mechanism in a Globalized Carbon Market. Center for Research in Economic Analysis Working Paper, no 201112, University of Luxemburg.

Castro, P. and Michaelowa, A. (2010), The Impact of Discounting Emission Credits on the Competitiveness of Different CDM Host Countries. _Journal of Ecological Economics_ , 70, 34-42.

Convery, F., C. De Perthuis and Ellerman, D. (2008), The European Carbon Market in Action: Lessons from the First Trading Period. Interim Report, CEEPR Working Papers WP-2008-002, March 2008, MIT.

Fischer, C. (2005), Project-based Mechanisms for Emissions Reductions: Balancing Trade-offs with Baselines. _Energy Policy_ , 33, 1807-1823.

Germain, M., Magnus, A. and Steenberghe, V. van (2007), How to Design and Use the Clean Development Mechanism Under the Kyoto Protocol? A Developing Country Perspective. _Environmental and Resource Economics_ , 38, 13-30 Gibbons, Robert (1992): _A Primer on Game Theory_ . New York: Prentice-Hall.

Goulder, L. H., I.W. H. Parry, R. C.Williams III and Burtraw, D. (1999): The cost-effectiveness of alternative instruments for environmental protection in a second-best setting, _Journal of Public Economics,_ 72, 329-360.

Harstad, B. and Eskeland, G. (2010), Trading for the Future: Signaling in Permit Markets. _Journal of Public Economics_ , 94, 749-760.

26
**Table 6. Annual Contribution Rate needed for Multi-Pillar System, Including Transition Costs**

||1995|2000|2010|2020|2030|2050|2100|
|---|---|---|---|---|---|---|---|
|No ExDanded Coverage||||||||
|(1) Total (incl. 8.5% D.C.)|28.5|25.5|20.5|19.5|19.5|17.5|19.5|
|(2) Transition<br>cost|20|15|8|5|3|1|0|
|(3) Basic Benefit|0|2|4|6|8|8|11|
|Exianded Coverage||||||||
|(4) Total (incl. D.C)|28.5|20.5|15.5|15.5|16.5|16.5|19.5|
|(5) Transition<br>cost|20|10|4|3|2|0|0|
|(6) Basic Benefit|0|2|3|4|6|8|11|

This table assumes that the 8.5% DC contribution began in 1995.

**42**