rafmacalaba/data-use-mentions-extended huggingface dataset:
mitigated with respect to the previous scenarios. For _Lombardia_ and _Puglia_ the impact on production has a reversed sign. In general, five regions show negative effects on production. Among these regions, _Lombardia_ and _Trentino Alto Adige_ would experience a positive effect in terms of welfare. _Piemonte_ and _Umbria_ , two regions with boosted production, would be slightly worse off than the others in the same category.

Based on the needs of governments and policy-makers, this model can represent a starting point to evaluate fiscal and economic shocks in Italy, with time specification and regional/environmental data. In future research steps, the authors will work on improving the quality of data used as input by regionalizing environmental data through statistical or heuristic procedures. In addition, future versions of the model will be directed in testing closures other than the classic Walrasian one as sensitivity analysis for the Italian regions study case. Finally, the inclusion of information on the regional tax gap in the Italian regional SAM and in the IRENCGE-DF model would make a great contribution to a better representation of the Italian economic system and more complete policy analyses.

# **References**

- Anderson, K. and Martin, W. (2005). Agricultural trade reform and the doha development agenda. _World Economy_ , 28(9):1301–1327.

- Armington, P. S. (1969). A theory of demand for products distinguished by place of production. _Staff Papers_ , 16(1):159–178.

- Bigano, A., Bosello, F., Roson, R., and Tol, R. S. (2008). Economy-wide impacts of climate change: a joint analysis for sea level rise and tourism. _Mitigation and Adaptation Strategies for Global Change_ , 13(8):765–791.

- Bouët, A., Bureau, J.-C., Decreux, Y., and Jean, S. (2005). Multilateral agricultural trade liberalisation: The contrasting fortunes of developing countries in the
Figure 1: The Standard Deviation of Output Growh (a) Developed markets <!-- Start of picture text --> (b) Emerging markets<br><!-- End of picture text --> <!-- Start of picture text --> 2.5 2.5<br>ty in Developed and Emerging<br>2 2 Economies<br>1.5 1.5<br>1 1<br>0.5 0.5<br>0 0<br>1980-1989 1990-2003 2004-2011 1980-1989 1990-2003 2004-2011<br><!-- End of picture text --> The calculations are based on data from the IMF’s World Economic Outlook, September 2011. The calculation for developed markets are based on the series “Advanced Economies”, and the calculation for emerging markets are based on the series “Emerging and Developing Economies”. Growth rates are calculated based on the cumulative output produced by all countries in the group.

This figure plots the standard deviation of output growth in developed and emerging economies during three periods: the “pre-integration” period (1980-1989), the “integration” period (1990-2003) and the “post-integration” period (2004-2011) (see Chinn and Ito [2008] for a discussion of the timing of financial globalization). Two stylized facts are illustrated: (i) the initial divergence in volatility between emerging and developed economies upon financial integration, as illustrated by the decline in output volatility in developed economies between the periods 1980-1989 and 1990-2003, and the increase in output volatility in emerging economies over the same time; and (ii) the spike in volatility in both regions in the post integration period, that includes the subprime crisis.

3
21 admitted as an inpatient, not just the number of visits and admissions conditional on at least one occurring. It is only the latter we can examine if we link the autonomy status of the hospital being used to the person‘s number of visits and number of admissions.

With repeated cross-sections rather than panel data, we cannot estimate a generalized differences-in-differences model. Instead we estimate a district-level fixed-effects model: where _yijt_ is the outcome for individual _i_ who lives in district _j_ at time period _t_ , _Xijt_ is a set of covariates corresponding to the same individual, _AUTONjt_ is the autonomy status of district _j_ at time _t_ ,  _j_ and  _t_ are district- and period-specific fixed effects, and  _ijt_ is an error term.

# **V. DATA**

We have two main datasets – a hospital inventory, which is a panel dataset, and a household survey, which is a series of cross-sections. Our data on autonomy status of specific hospitals are derived in part from the hospital inventory, and in part from a telephone survey of provincial health bureaus conducted in 2007. Table 1 shows the descriptive statistics for our hospital data. Table 2 shows the descriptive statistics for our individual-level data.

## _Hospital data_

We use data from the hospital inventories implemented in 2004, 2006 and 2008, each round covering two years of data. The hospital inventory is an annual survey of hospitals that was initiated in 1996, repeated in 2000, and implemented regularly since then, although the contents have only stabilized since about 2004. The earlier years of the survey involved some non-random sampling of facilities, while the 2011 survey was close to a census of all
is the destination-year level, _it_ . “CR5” stands for the concentration ratio of the top 5 investing firms; countries with fewer than 5 foreign investors on average are excluded. “HHI” refers to the Herfindahl-Hirschman concentration index, calculated using the “CR5” sample. “Uncertainty” is based on the “T3” variable from the World Uncertainty database (Ahir et al. 2022), which counts the occurrence of the word “uncertainty” from Economist Intelligence Unit (EIU) reports for 143 countries since the 1950s. A higher value indicates greater uncertainty. Uncertainty in year _t_ is approximated as the logarithm of the average number of uncertainty-related words in EIU reports over the three years preceding the investment (standardized to have a mean of zero and a standard deviation of 1). The analysis includes country fixed effects and (world) region by year fixed effects. Countries are classified into 7 regions based on World Bank classifications. Destination-specific control variables include the logarithms of population and GDP (source: Conte et al. (2022)) and the average exchange rate over the three years preceding the investment (national currency/USD, Penn World Tables, mark 10.1, Feenstra et al. (2015)). Standard errors are clustered at the destination country level. Significance levels: *** _p <_ 0 _._ 01, ** _p <_ 0 _._ 05, * _p <_ 0 _._ 1.

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Figure 2: The Importance of GVC Trade in World Trade <!-- Start of picture text --> 55<br>50<br>45<br>40<br>35<br>30<br>Sources: WDR 2020 team, using data from Eora26 database; Borin and<br>Mancini (2019); and Johnson and Noguera (2017). See appendix A for a<br>description of the databases used in this Report.<br>1970 1975 1980 1985 1990 1995 2000 2005 2010 2015<br>GVC share of global trade (%)<br><!-- End of picture text --> these exports encompass two broad types of GVC trade. On the one hand, GVC trade includes transactions in which a country’s exports embody value added that it has previously imported from abroad. This type of GVC participation is often referred to as backward GVC participation. On the other hand, GVC trade also comprises transactions in which a country’s exports are not fully absorbed in the importing country, and instead are embodied in the importing country’s exports to third countries. The latter form of GVC participation is often dubbed forward GVC participation. As Figure 2 indicates, the overall share of GVC trade in total world trade grew very significantly in the 1990s and early 2000s, but it appears to have stagnated or even declined in the last 10 years. Still, about one-half of world trade appears to be related to global value chains.

The two components of GVC participation, backward and forward, can also easily be computed at the country and even country-sectoral level. When doing so, it becomes clear that the expansion of GVC activity has occurred in an uneven way in the world. On the one hand, there are regions in the world (such as Europe and East Asia) that are deeply involved in GVCs, while GVC participation is much smaller in other parts of the world, most notably Latin America and Africa
represent the increase in the probability of the dependent variable being 1 as result of changing the gender dummy from 0 (male) to 1 (female). The results for log expenditure on electricity represent the percent increase in expenditure on electricity (not logarithm) as a result of changing the gender dummy from 0 to 1. — = statistically insignificant at 5; n.d. = no distinction made for different sources of electricity; n.a. = not available (data not collected); NA = not applicable.

The surveys in Malawi, Niger, and Nigeria enabled isolation of households who lived in communities connected to the grid. The probit regression for grid connection for households that could have connected was performed by taking these households as a single group as well as by separating urban from rural households. Statistically significant results were obtained when urban and rural households were combined only in Niger—female‐headed households were more likely to connect—and separating urban and rural households similarly returned statistically significant results only in urban Niger—again female‐headed households were more likely to connect.

Given the data limitations, these findings should be viewed only as preliminary, calling for more detailed investigation. A recent detailed analysis of the gender of the head of household in Africa since the 1990s suggests that the share of female‐headed households has been rising, while that poverty has been declining faster among female‐headed households than among their male counterparts (Milazzo and van de Walle 2015). These changing dynamics can inform future research and affect policy direction.
averaged 0.81 in 2000-2013. Even by SSA standard (which has an average index of 0.60), DRC’s product concentration is very high.

Source: calculated using data from UNCTAD database. Source: calculated using data from UNCTAD database.

# **IIIExport Concentration, Exogenous Shocks, and Volatility in DRC**

9. **As the 2007–2008 crisis hit the world and global demand and international prices collapsed, export receipts in DRC declined sharply.** In 2009, DRC’s export earnings declined by more than 28 percent, which is equivalent to US$933 million or 6.4 percent of GDP and led to a quick deterioration in the trade-in-goods balance which decreased from a 5.4 percent of GDP surplus in 2007 to a 3.5 percent deficit in 2009. DRC was much more impacted than other SSA countries. Indeed, for SSA as a whole, export receipts fell by 15 percent, but DRC was among the few countries that registered the biggest losses (see Figure 6). Without the diversification away from the EU27 towards China, the impact of the financial crisis on DRC would have been harder. In fact, as a consequence of the global turmoil, DRC’s exports to the EU27 declined from 47 percent of total exports to 29 percent in 2009, while exports to China increased from 22 percent to 42 percent. However, DRC is now much more exposed to the fluctuations of China’s economy.

6
access to inputs. The logistics measure is from the World Bank Logistics Performance Index (LPI), a widely cited international rating system. The measure is influenced by proximity to markets, so there is some overlap between the two indicators. Wages are measured by the World Bank Doing Business indicators’ minimum legal wage in dollar terms. Although unit labor costs were preferred as a measure of productivity‐adjusted wages, there is no database with sufficient coverage of countries to be suitable. As the indicator is in dollar terms, exchange rate dynamics affect this indicator, including changes owing to lower demand for commodities in recent years. In selecting this as a driver of GVC participation, we also acknowledge that higher minimum wages could be a consequence of GVC participation as well as a driver. Finally, market access and access to inputs are estimates generated by Kee, Nicita and Olarreaga (2008, 2009), who calculated two measures of overall trade restrictiveness that account for tariff barriers and the ad‐valorem equivalent of non‐tariff barriers for countries and their trading partners. As these are aggregate measures focused on removal of trade barriers, they do not fully capture the impact of preferential arrangements with certain markets in affecting an industry’s prospects nor do they account for the impacts of deeper integration like investment flows and trade facilitation.

> 5 This measure includes crop, pasture land, timber, non‐timber forest, protected areas, oil, natural gas, coal, and minerals.

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comprised 36%. When an unconditional cash transfer was launched for a third time in 2013, its coverage was about 40 percent, though beneficiary incidence increased to 40%. Similarly, a conditional cash transfer for families launched in 2007 saw an increase in beneficiary incidence in the poorest 20% from 39% to 44% between 2010 and 2018<sup>3</sup> . Its targeting

> 3Based on Holmemo et al. [2020] and World Bank staff calculations from SUSENAS 2010/2018.

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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.

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.4026|0.4027|
|Observations|668639|668285|668285|668639|668285|668285|668638|668284|668284|
||||||Female|||||
|CatchmentArea X Post|0.0837***|0.0973***|0.0801***|0.0604***|0.0662***|0.0513***|0.0929***|0.1108***|0.0932***|
||(0.0257)|(0.0209)|(0.0202)|(0.0222)|(0.0196)|(0.0193)|(0.0273)|(0.0219)|(0.0210)|
||[ 0.002 ]|[ 0.001 ]|[ 0.001 ]|[ 0.009 ]|[ 0.002 ]|[ 0.002 ]|[ 0.002 ]|[ 0.001 ]|[ 0.001 ]|
|R-squared|0.3984|0.4432|0.4433|0.3889|0.4300|0.4300|0.3713|0.4188|0.4189|
|Observations|818714|818396|818396|818717|818399|818399|818714|818396|818396|
|Controls|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|School FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Exam Year FE|Yes|No|No|Yes|No|No|Yes|No|No|
|YOB FE|Yes|No|No|Yes|No|No|Yes|No|No|
|Municipality X Exam Year FE|No|Yes|Yes|No|Yes
# **A1.2 Aggregation and the market portfolio**

# **A1.2.1 The market portfolio**

The cost of equity finance for an individual security is related to the level of its non-diversifiable risk. Thus, the variance of the security's returns must be compared to their covariance with the market portfolio, consisting of the optimal combination of all available assets. This calculation requires some proxy for the market portfolio to be used. Ideally, this should be an index of world assets including not only equities but also bonds and durable goods such as housing.

In practice, however, researchers usually take a national equity market index as a proxy for the world index. The use of national rather than international markets can be justified by the argument that today's capital markets are essentially international and arbitrage ensures that prices and the risk-return trade-off are equalised between markets. Other assets, such as housing and bonds, are generally ignored-again, arbitrage is assumed to ensure that the risk-return profile for these assets mirrors those of the assets included in the index and so their omission does not cause problems.

However, differences in the composition of national stock markets mean that comparisons of beta values calculated using national market returns could be misleading. For example, some small stock markets are dominated by a single large company, in which case market returns are not representative of the world index. A few capital markets, such as that of Korea, are insulated from international influences and are therefore likely to differ considerably from the world index, rendering a meaningful comparison of beta estimates impossible. It may be necessary to construct a portfolio of world assets and use its returns index to estimate beta values for all securities in order to eliminate problems associated with unrepresentative national markets.

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gap widened, from 7 percentage points in 2014 to 9 percentage points in 2017. In the euro area, the gap stayed at about 20 percentage points between 2011 and 2017.

People save for different purposes. Nearly half of adults in high‐income economies report saving for old age. In 2017, 16 percent of adults in developing economies and 38 percent of adults in the euro area reported doing so. In developing ECA, only 15 percent of adults reported saving for old age.

In 2014 only 4 percent of adults in the developing economies of ECA reported saving to start, operate, or expand a farm or business. The share rose to 8 percent in 2017, still lower than the global average of 14 percent.

# _Informal Borrowing_

In 2017 about half of the world’s adults reported borrowing money the previous year. A larger share (64 percent) did so in high‐income economies, where most borrowers rely on formal credit from financial institutions or credit cards. In developing economies, most borrowers rely on family and friends (figure 11).

## **Figure 11. People in high‐income countries rely on formal sources for borrowing, whereas people in developing countries tend to rely on family or friends**

<!-- Start of picture text --> 80%<br>60%<br>40%<br>20%<br>0%<br>High‐income economies Developing economies<br>Borrowed formally Borrowed semi‐formally<br>Borrowed from family or friends Borrowed from other sources only<br>in 2016<br>Percent of adults who reported borrowing<br><!-- End of picture text -->

### _Source_ : Global Findex database.

_Note_ : People may borrow from multiple sources, but the categories here are constructed to be mutually exclusive. “Borrowed formally” includes all adults who borrowed any money from a financial institution or by using a credit card. “Borrowed semi
Thirdly, Figure 9 shows that both treatments had similar effects on improving respondents’ willingness to get the COVID-19 vaccines regardless of their degree of involvement in household decision making. For example, among respondents who are involved in decision making, there was an almost 10 percentage point impact on people’s willingness to get vaccinated, while among respondents not involved in household decision making, there was a slightly larger than 10 percentage point impact on people’s willingness to get vaccinated. As such, the results from the online survey show that not only is involvement in decision making uncorrelated with willingness to get a vaccine, but there is also an absence of a notable difference in elasticity of preferences based on this dimension. In fact, there are only minor differences between the overall share of respondents willing to get vaccinated across this dimension.

# **4.2.4 Second round impact from treatments**

We illustrate that there were potentially “second round” effects from accurate information about the benefits of COVID-19 vaccines as treated respondents were more likely to encourage other household members to get vaccinated. As this is a “downstream” measure of the impact of information we combine treatments to maximize statistical power. We show that the treatments had a non-trivial impact on the likelihood respondents would encourage a household member to get vaccinated.

The overall effect was driven entirely by respondents who were in households where other members were unwilling to get vaccinated (see Figure 10a). Specifically, the share of respondents in the treatment groups that would encourage others to get vaccinated in their households was around 63 percent, while it was 55 percent in the control group (representing a treatment effect of 8 percentage points). The descriptive results of the online survey illustrate that respondents encouraging others in their household to get vaccinated would likely have additional benefits by creating a pro-vaccination social norm in the household. Among the unvaccinated in the control group, around one-third reported that a family member was the most likely person who could change their mind about getting the COVID-19 vaccine
across countries is allowed, we find that trade flows between Russian regions and countries which signed a free trade agreement with Russia remain similar to intra-Russian trade in the reform period, implying that national borders alone have not diverted trade flows in the FSU.

Third, our results also suggest that slow adjustment of production and of infrastructure has limited trade reorientation. Using past trade to proxy for linkages developed when the Soviet Union was integrated, we find that past linkages significantly influence current trade. Since past trade was centrally planned it is unlikely that we are picking up other unobserved persistent variables, such as tastes or comparative advantage. As linkages with other republics continue to atrophy, the domestic trade bias will increase to well over the 60 percent we estimate currently.

Our estimate of the border effect is significantly lower than similar estimates using OECD data, despite greater trade barriers in the FSU. Using inter-provincial and province-state trade data, McCallum (1995) and Helliwell (1996) find that in 1990 Canadian provinces traded about twenty times more among themselves than with U.S. states. More recently, Helliwell (1998) estimates the bias to be twelve in 1996 following the preferential trading agreement (PTA) between the two countries.<sup>1</sup> Wei (1996) and Helliwell (1998) estimate that the domestic trade bias among OECD countries ranges from three to twelve.<sup>2</sup> One explanation that is consistent with both sets of results is that past linkages have led to a continued domestic orientation in trade in the OECD, despite falling trade barriers, and sustained integration in the FSU in spite of rising trade barriers.

**'An** alternative way to evaluate the border effect is with price data. Engel and Rogers (1996) examine the relative price variability of similar goods among cities in Canada and the United States. They find that relative price variability between cities is significantly greater when a border is crossed.

2Wei (1996) develops a technique to estimate the