rafmacalaba/datause-encoder-data huggingface dataset:
As an alternative, Table 17 shows the return differentials conditional on periods in which the simulated global funds perform poorly. In these situations, global funds yield slightly higher weekly returns, with differentials between 0.14% and 0.95% p.w., although these return differentials are not always statistically different from zero. Table 18 reports the results of a similar exercise that focuses on periods when global funds do not perform well. In this case, the simulated global funds perform significantly better than global funds. For example, when the return of global funds is less than 10% in one week, the simulated global funds yield an extra 2.65% p.w. when maximizing the expected return with the largest number of funds. If the longest available sample for variance minimization is considered, the difference in returns is 1.88% p.w.

# **6. Conclusions**

Using a novel micro dataset of portfolio holdings of U.S. mutual funds, this paper studies how institutional investors diversify their portfolios across countries. This dataset allows us to analyze important aspects of international asset allocation, documenting new stylized facts and shedding new light on existing explanations of international portfolio allocation. In particular, we take advantage of the fact that mutual funds belong to families, each having several funds with different scopes for international investment. As the investment scope broadens, one would expect funds to hold more securities and to be better diversified internationally, to the extent that asset returns are not perfectly correlated. This feature of the mutual fund industry enables us to study to what extent organizational aspects and other factors are important for international asset allocation and whether the investment practices exploit the potential gains from international diversification.

We find that global funds have expanded substantially, giving investors more options to diversify risk internationally. However, regardless of their investment scope, mutual funds tend to invest in a relatively small number of countries and firms, in about 100 stocks. In fact, as their investment possibilities widen, mutual funds invest in fewer stocks and fewer countries within each region of exposure. Consistently, the number of also suggests that such a portfolio shift is not behind the evidence related to the higher moments of the return distribution.

34
# **<u>Table 1. Number of observations in panels, by country</u>**

|_Country_|_2005–08panel_|_2015–18panel_|
|---|---|---|
|_Eastern Europe_|||
|Bulgaria|735|3,249|
|Czechia|3,052|2,028|
|Estonia|1,226|1,344|
|Hungary|1,844|1,529|
|Lithuania|1,337|1,145|
|Latvia|999|1,268|
|Poland|3,175|2,439|
|Slovenia|1,583|1,645|
|_Continental Europe _|||
|Austria|1,102|1,258|
|Belgium|1,266|2,106|
|Cyprus|761|1,015|
|Denmark|880|990|
|Spain|2,650|2,376|
|Finland|1,461|2,078|
|France|4,989|5,415|
|Greece|1,268|5,006|
|Italy|4,276|5,614|
|Luxembourg|349|823|
|Malta|674|841|
|Netherlands|1,286|2,672|
|Norway|2,616|1,216|
|Portugal|911|3,272|

20
# **Modeling the roles of heterogeneity, substitution, and**

# **inventories in the assessment of natural disaster**

# **economic costs**

## St ́ephane Hallegatte<sup>a</sup><sup>_,_b</sup><sup>_,∗_</sup>

> a _The World Bank, Sustainable Development Network, Washington D.C., USA_

> b _Ecole Nationale de la M ́et ́eorologie, Toulouse, France_

_Key words:_ Natural disasters, Economic impacts, Heterogeneity, Substitution, Inventories JEL: C61, D24, D57, D85, E27, L23, Q54, R15

### **1 Introduction**

Natural disasters have multiples impacts. Beyond their direct impacts on human lives and assets, they also perturb the functioning of the economic system, leading to additional losses, often referred to as indirect losses or higher-order losses (see Pelling et al., 2002, Lindell and Prater, 2003, Cochrane, 2004, Rose, 2004, and a review in Hallegatte and Przyluski, 2010).

Part of these indirect losses arises from output losses, i.e from reductions in economic valueadded production. These reductions can be due to the disaster itself, or to its impact on productive

> _∗_ Corresponding author: St ́ephane Hallegatte.

> _Email address:_ hallegatte@centre-cired.fr (St ́ephane Hallegatte).
parental disability does not reject the exogeneity of parental disability in the child education equation.

Table 3 presents the estimate of ATT of parental disability. The estimates are quite similar in the three models. The effect of parental disability on children‟s education enrollment is negative and statistically significant at the 5% significant level in all the models. The point estimates are also quite similar between different models. According to Model 1 in Probit with IV regressions, parental disability reduces child schooling rate by around 8.1 percentage points from 81.3 percent to 73.2 percent. This means children whose parents have a disability have a school enrollment rate of 73.2 percent, but if their parents had not been disabled, their schooling rate would have been around 81.3 percent.

It should be noted that adding education and income of households (Model 2 and 3, both with and without instruments) leads to a smaller estimate of the effect of parental disability. This implies that education of parents and income can be channels through which disability can affect children‟s education. Put differently, disability can lead to lower education and lower income, thereby lower education for their children.

Table 3: The impact of parental disability on child schooling rate

||P|robit regressio|ns|Probi|t with IV regre|ssions|
|---|---|---|---|---|---|---|
||Y1|Y0|ATT=Y1–Y0|Y1|Y0|ATT=Y1–Y0|
|Model 1|0.732***|0.811***|-0.081***|0.732***|0.813***|-0.081***|
||(0.025)|(0.010)|(0.024)|(0.024)|(0.011)|(0.024)|
|Model 2|0.732***|0.788***|-0.056***|0.732***|0.793***|-0.061***|
||(0.024)|(0.012)|(0.022)|(0.024)|(0.013)|(0.023)|
|Model 3|0.732***|0.786***|-0.054**|0.732***|0.793***|-0.061***|
||(0.024)|(0.012)|(0.021)|(0.024)|(0.013)|(0.022)|

Note: Y1 is the observed schooling rate of children whose parents have disabilities. Y0 is the counterfactual schooling rate that children would have if their parent not been disabled. ATT=Y1–Y0 is the effect of parental disability on children’s schooling rate.

* significant at 10%; ** significant at 5%; *** significant at 1%

Figures in parentheses are standard errors. Standard errors are corrected for sampling weights and estimated using bootstrap (non-parametric) with 500 replications. Source: Estimation from the 2006 VHLSS.

We estimated a probit with instrumental variables regression including interactions between parental disability and different characteristics of children including gender, urbanity, ethnicity and age. The instrumental variables for the interactions are the interactions between these interacted characteristics and the instruments for parental 13
and in the long term, as new products become available through innovation. Third, a period of higher prices with demand outstripping supply leads to induced innovation in the production of commodities. These three trends were observed throughout the events of the past twenty years. The dramatic changes in commodity demand were driven by China, which experienced rapid growth at the start of the 1990s, but has slowed more recently, and also saw a shift from being a lower‐income country to a middle‐income country over this period (World Bank 2018). As such, its income per capita growth, and income elasticity of demand, both slowed, from a high level. The period of strong com‐ modity demand (which was also associated with high prices) induced innovation and substitution in the use and production of commodities—the most notable example of in‐ novation being the development of the U.S. shale oil industry.

The objective of this paper is to shed light on the first two principles by estimating income elasticities of energy and metal commodities at the individual and aggregate level and incorporating cross‐price effects to test for substitution between commodities. Our approach, which takes into consideration both non‐linearities as well as short‐ and long‐ term substitution effects, extends the literature of commodity demand in four ways.<sup>**1**</sup> First, it utilizes a comprehensive data set and applies a common framework to three energy and six metal commodities for up to 63 countries over more than half a cen‐ tury (1965‐2017). The broad and long data coverage, which encompasses the two post‐ WWII price cycles (1970s and 2000s), enables us to obtain a consistent picture across the full spectrum of industrial commodities. This is especially pertinent in the context of EMDEs, for which the literature is not as rich as that of high‐income countries, even though EMDEs account for two‐thirds of world metals demand and nearly half of world energy demand (Focacci 2005; Canas et al. 2003; Roberts 1996; Rogich 1996; Radetzki and Tilton 1990).

Second, the demand equations provide estimates of income elasticities of demand that vary by income level. We find that while income elasticities of demand for energy and metal are close to one at median income levels, they are substantially higher at low income levels (close to 2) but drop rapidly (approaching 0.2) at high income levels. These results complement the existing literature which typically provides single estimates for the income elasticity of demand for commodities.

Third, the paper accounts for substitutability among commodities—in the short term by including cross‐price effects and in the long‐term by estimating commodity

> **1** This paper builds on earlier work which estimated income elasticities of demand for three energy (coal, <mark>natural gas, and oil) and three metal (aluminum, copper, and zinc) commodities (Baffes et al. 2018).</mark>

_— 3 —_
Third, why are the data for _eo_ and _eu_ distributed as if inside two triangles? The data show that overspending and under-spending tend to occur simultaneously most frequently when net resource deviations are small. When resource deviations are very large, there is almost no under-spending. Conversely, when the shock is very negative, there is almost no over-spending.

To probe more deeply into this question, two additional compositional measures are introduced. One is the share of over-spent, under-spent, and accurately-spent budget heads ( _fo_ , _fu_ , and _fb_ ). For example, in Table 2, there were 7 under-spent heads out of a total of 12, so _fu_ = 58 percent. These shares sum to unity: The other measure is the depth expenditure deviations within each share above ( _do_ , _du_ , and _db_ ). Thus one can write: _(8) e_ = _fo∙do + fu∙du + fb∙db_ where _du_ < 0 For the majority of countries in the sample, most ministries were able to obtain a share of the largest windfalls and almost every ministry shared the pain of large unexpected losses. This can be seen in Figures 8a and 8b where the over-spent fraction of the budget _fo_ is positively correlated with net resource shocks while the fraction of the under-spent budget _fu_ is negatively correlated with such shocks. As these fractions approach unity, the depth of expenditure deviations ( _do, du_ ) must necessarily approach the aggregate deviation _e_ .

An unsurprising consequence is that the share of the budget _fb_ that is accurately spent tends to be higher when unanticipated shocks to net resources are small. This is depicted in Figure 8c. The looseness of the relationship, however, suggests that this broad tendency may be shaped in some countries by other considerations beyond the strength of unanticipated shocks. This will be explored more in the sections below.

# **BUDGET SHARES AS A FUNCTION OF RESOURCE DEVIATIONS**

<!-- Start of picture text --> Figure 8a. Heads Over-spent, fo Figure 8b. Heads Under-spent, fu Figure 8c. Heads Within-Budget, fb<br>100 100 90<br>80<br>80 80 70<br>60<br>60 60<br>50<br>40<br>40 40<br>30<br>20 20 20<br>10<br>0 0 0<br>-60 -40 -20 0 20 40 60 -60 -40 -20 0 20 40 60 -60 -40 -20 0 20 40 60<br>Resource Deviation, e Resource Deviation, e Resource Deviation, e<br><!-- End of picture text --> Before concluding this section, it is useful to summarize the three multiple levels of decision making that take place in budget execution: policy makers and stakeholders choose the size of the _aggregate_ deviation, _e,_ which is equivalent to deciding the amount of revenue deviation, _r_ , and within period counter-cyclical financing, _nf_ ; they choose spending _across_ groups of budget heads 14
- 2 -

# **I. Introduction**

Health care access is an important challenge in rural areas, particularly in developing countries. It is generally difficult for people who live in remote areas to access good-quality health services (e.g., Gamm et al. 2003; Institute of Medicine 2005; Bourke et al. 2012). Even in the United States, rural residents of Washington State were less likely to visit the emergency department than their urban counterparts (Lishner et al. 2000). According to Laditka et al. (2009), rural residents are more likely to be hospitalized for ambulatory care sensitive conditions in the United States. This could be prevented if people had better access to primary care, but because they do not, their condition tends to deteriorate.

Among others, transport connectivity is a particularly important constraint in rural areas. Rural dwellers’ travel distance to health facilities is much longer than that of urban dwellers (Gamm et al. 2003). In Zambia, about 75 percent of urban residents, but only 30 percent of rural residents, live within 2 kilometers of the nearest health facility (Zambia Central Statistical Office 2012). Remoteness and unavailability of transport services increase costs further for rural residents, limiting them from accessing health care services (e.g., Klemick et al. 2009).

Limited transport connectivity not only reduces people’s accessibility, but also compromises the quality of health care services provided. Bronstein et al. (1997) show that rural health services are cheaper but that fewer ancillary services are provided. Sufficient doctors and nurses may not be available in rural areas as well. This may be a more generic disadvantage of rural areas or remoteness. For whatever reason, doctors are less available in rural areas. In the United States, only 9 percent of physicians practice in rural areas where about 20 percent of the total population lives (Rosenblatt and Hart 2000). As a result, rural physicians often have to see more patients than urban doctors do, therefore, potentially compromising the level of services they provide. Medical supplies may also be compromised in rural and remote areas. How to ensure supply chain sustainability is an important research issue (e.g., Uysal and Tosun, 2014).
129 countries and 749 goods, after dropping some regions and countries with limited coverage or no mapping with exchange rate data and special SITC codes used by Feenstra et al. (2005) to make 4-digit ows compatible with 3-digit values. In particular, all countries exporting less than 10 4-digit SITC commodities and all commodities exported by less than 10 countries are excluded, as well as the US, which is the country of the anchor currency. Country-commodity pairs where the correlation between international prices and exchange rates was constructed using less than 8 annual observations or those with a correlation larger than 0.9 (in absolute value) were also dropped. Using this dataset the export shares of a country in dierent goods was constructed as described in the previous section. The countries included in our main sample as well as their average exports at SITC level, and number of goods exported are reported in the appendix.

The international price of a good _Pi,t_ is measured by its global unit value, using data on total values and quantities exported across the world, also from the Feenstra et al. (2005) database, where _Vi,t_ is the total value of global gross exports of good _i_ , and _Qi,t_ is the total global quantity exported (in tons). The price index built in this manner corresponds to a quantity weighted average of country-level unit values.

Despite is simplicity and wide availability there are several shortcomings in using unit values as measures of international prices, most importantly that they do not control for changes in quality. Thus, an increase in the quality of goods could result in an increase in unit value that should not be captured by an ideal price index that controls for the characteristics of a good. However, since this paper focuses on global unit values, this would not be a problem if the average quality of traded goods across the world does not change signicantly. Although there is no evidence on this regard, the average quality of exported goods is more likely to be stable than the quality of individual country exports, as during the life-cycle of a good, production of the cheapest varieties shifts to poorer countries. The concerns about quality dierences are also reduced by using a highly disaggregated classication, which is the motivation for working with the lowest level of aggregation reported by Feenstra et al. (2005). Also, problems with the level of unit values should not aect this measure because this paper focuses on the correlation between the growth rate of these global unit value indexes and the growth of the nominal exchange rates of dierent countries. Dierences between the growth of this index and that of the actual price of goods exported by a country within an SITC class may arise from dierences in the growth of the quality of a country's exports relative to global exports. However, these dierences would only introduce measurement error that would make it less likely to nd any signicant result.

A more pragmatic reason to use uctuations in global unit values as proxies for price uctuations 13
mobility. _Annu. Rev. Econ._ , 6(1):689–733.

- Helburn, S. W. and Howes, C. (1996). Child care cost and quality. _The Future of Children_ , pages 62–82.

- Hotz, V. J. (2003). The earned income tax credit. In _Means-tested transfer programs in the United States_ , pages 141–198. University of Chicago press.

- Hotz, V. J. and Xiao, M. (2011). The impact of regulations on the supply and quality of care in child care markets. _The American Economic Review_ , pages 1775–1805.

- Hoynes, H. W. and Schanzenbach, D. W. (2018). Safety net investments in children. _Brookings Papers on Economic Activity_ , (Spring).

- Keane, M. P. (2011). Labor supply and taxes: A survey. _Journal of Economic Literature_ , 49(4):961–1075.

- Kimmel, J. and Connelly, R. (2007). Mothers’ time choices caregiving, leisure, home production, and paid work. _Journal of Human Resources_ , 42(3):643–681.

- Laughlin, L. (2013). _Who’s minding the kids?: Child care arrangements, Spring 2011_ . US Department of Commerce, Bureau of the Census.

- Liu, H., Mroz, T. A., and Van der Klaauw, W. (2010). Maternal employment, migration, and child development. _Journal of Econometrics_ , 156(1):212–228.

- Løken, K. V., Mogstad, M., and Wiswall, M. (2012). What linear estimators miss: The effects of family income on child outcomes. _American Economic Journal: Applied Economics_ , 4(2):1–35.

- Love, J. M., Kisker, E. E., Ross, C., Raikes, H., Constantine, J., Boller, K., Brooks-Gunn, J., Chazan-Cohen, R., Tarullo, L. B., Brady-Smith, C., et al. (2005). The effectiveness of early head start for 3-year-old children and their parents: lessons for policy and programs. _Developmental Psychology_ , 41(6):885.

- Lundstrom, S. (2017). The impact of family income on child achievement: Evidence from the earned income tax credit: Comment. _American Economic Review_ , 107(2):623–28.

- MaCurdy, T. E. (1981). An empirical model of labor supply in a life-cycle setting. _Journal of Political Economy_ , 89(6):1059–1085.

- Mazzeo, M. J. (2002). Product choice and oligopoly market structure. _RAND Journal of Economics_ , 33(2):1–22.

- Milligan, K. and Stabile, M. (2009). Child benefits, maternal employment, and children’s health: Evidence from canadian child benefit expansions. _American Economic Review_ , 99(2):128–32.

- Mitchell, A. and Stoney, L. (2012). _Generic Cost Model: Centers_ . Alliance for Early Childhood Finance.

- OECD (2011). _Doing Better for Families_ .

- Reiss, P. C. and Wolak, F. A. (2007). Structural econometric modeling: Rationales and examples from industrial organization. _Handbook of Econometrics_ , 6:4277–4415.

- Ribar, D. C. (1992). Child care and the labor supply of married women: Reduced form evidence. _Journal of Human Resources_ , pages 134–165.

43
their pension fund management services, as the concept of individual accounts became meaningless. Contribution rates to the second pillar were further reduced, and there have been several political attempts to completely dismantle the second pillar.

The expectation of decreasing replacement rates has helped to create consensus about the need of building savings for retirement. However, the expected gradual fall in replacement rates in the future, as shown in Figure 4, is triggering the need for adopting policy options, with limited fiscal support, that can help to improve retirement income in the future. In the presence of a Social Security scheme based on notional defined contributions, in the case of women replacement rates are expected to fall from almost 45 percent to levels below 25 percent by 2040.

Figure 4. Poland: projected replacement rates<sup>1</sup> (as a percentage of average salary) <!-- Start of picture text --> 65.0%<br>60.0%<br>Avg repl rate males<br>55.0%<br>Avg repl rate females<br>50.0%<br>45.0%<br>40.0%<br>35.0%<br>30.0%<br>25.0%<br>20.0%<br>2011 2021 2031 2041 2051 2061 2071<br><!-- End of picture text --> 1 Projections after the 2011 adjustments in contribution rates. Source: World Bank (2012)

# _Main features (PKK)_

In November 2018, the government enacted a law that creates an AES for all employees or an Employee Capital Plan ( _Pracownicze Plany Kapitałowe_ – PPK). The law includes contributions from the employers, employees and the government. From the tax perspective, contributions are taxed, interest earned exempted and withdrawals exempted (TEE), which is along the best practices for avoiding distortions on savings decisions. Employers are required to enroll all employees with age below 55, but older employees may decide to opt in. The system applies to all companies (at least a single employee), but there is a gradual process for complying with the law, according to the number of employees of the company, as shown in Table 2.

10
Map 1: Major Urban Regions in East and South Asia <!-- Start of picture text --> Beijing-Tianjin<br>Seoul<br>Tokyo<br>Yangtze River Delta Nagoya<br>Osaka<br>PRD<br>Mumbai<br>Bangkok<br>Chennai<br>Singapore<br>Jakarta<br><!-- End of picture text --> Source: World Bank Are similar urban regions likely to emerge in Southeast Asia and how might they achieve comparable levels of economic performance and technological dynamism? During the next fifteen years, mega urban economies could coalesce in three locations: Bangkok, Jakarta and Singapore – South Johor. The Bangkok and Jakarta (Jabotabek)<sup>2</sup> metropolitan regions<sup>3</sup> have passed the threshold at least in terms of population size but they have yet to approach the industrial diversity, dynamism and growth rates of a

> 2 Jabotabek is the metropolitan amalgam of Jakarta, Bagor, Tangerang and Bekasi. Both Bangkok and Jakarta have expanded well beyond the core urban areas into a broad peri-urban fringe to which manufacturing industry has begun to migrate. The Bangkok urban region embraces parts of adjacent provinces. A peri-urban fringe is also expanding around the core Metro Manila region (Jones 2002). 3 The population of the Jakarta metro region alone now exceeds 13 million, that of Bangkok is 6.5 million.

2