rafmacalaba/data-use-mentions-extended huggingface dataset:
Composition of Official Foreign Exchange Reserves (COFER) database, both compiled by the IMF.<sup>1</sup> Our data sets on bilateral investment positions capture most of the aggregate IIP at the country level as commonly used in the literature (e.g., Lane and Milesi-Ferretti 2018). In particular, aggregating our bilateral positions across all country pairs yields values close to those obtained by aggregating countrylevel data across all countries.

The main findings of the paper document the rise of the South in global finance. First, the South has increased its participation in global finance across all investment types, as a share of world gross domestic product (GDP) and as a share of global investment. The sum of North-to-South, South-to-North, and South-to-South investments as a share of the global total increased by roughly 10 percentage points (p.p.) between 2001 and 2018 for international loans and deposits, portfolio investment, and FDI. The pace of financial integration within the South was particularly fast. Although South-to-South investment remained the smallest block in terms of value throughout, it increased the fastest over this period. For FDI and loans and deposits, the share of South-to-South investment in the global total doubled between 2001 and 2018, from about 6 to 13 percent. For portfolio investment and international reserves, growth within the South was even more marked, albeit from lower levels: each type of investment increased from roughly 0.9 to 3.7 percent of the global total. The expansion of the South in global aggregates is also observed at the country-to-country level, indicating that these trends are not driven by a few large countries in the South.

Second, the rise of the South has proceeded along the intensive and extensive margins. South countries invested increasing amounts in countries with whom they were already connected at the beginning of the sample. The value of investment in the North-to-South, South-to-North, and Southto-South blocks combined represented about 4 percent of world GDP for portfolio investment and

> 1 The data on international reserves
/u> of Finance 36:949953.

- Froot, K. A. 2001. The market for catastrophe risk: a clinical examination. <u>Journal</u> of Financial Economics 60:529571.

- Froot, K. A. and O'Connell, P. G. J. 2008. On the pricing of intermediated risks: Theory and application to catastrophe reinsurance. <u>Journal</u> of Banking <u>&</u> Finance 32:6985.

- Froot, K. A. and Stein, J. C. 1998. Risk management, capital budgeting, and capital structure policy for nancial institutions: An integrated approach. <u>Journal</u> of Financial Economics 47:5582.

- Garven, J. R. and Tennant, J. L. 2003. The demand for reinsurance: Theory and empirical tests. Insurance and <u>Risk</u> Management 7:217237.

- Gron, A. 1994. Capacity constraints and cycles in property-casualty insurance markets. The <u>RAND Journal</u> of Economics 25:110127.

Group of Thirty 2005. Reinsurance.

Guy Carpenter 2009. World catastrophe reinsurance market. Technical report, Guy Carpenter.

- Hallegatte, S. 2011. How economic growth and rational decisions can make disaster losses grow faster than wealth. Technical report, World Bank Policy Research Working Paper 5617.

- Hanson, S., Nicholls, R., Ranger, N., Hallegatte, S., Corfee-Morlot, J., Herweijer, C., and Chateau, J. 2011. A global ranking of port cities with high exposure to climate extremes. <u>Climatic Change</u> 104:89111. 10.1007/s10584-010-9977-4.

23
index

> project - but found that in some countries, there was a significant disconnect between restrictions imposed and actual movement of people as indicated by the mobility data (in some countries, measures were stringent but mobility during the same period varied significantly), particularly as the pandemic progressed. The variation in work stoppages and earnings was more closely related to mobility than stringency.

> 13 In Mongolia’s round 4 survey, the reference period for employment was not the week preceding the survey but April 5-11, 2021, before the lockdown occurred.

9
, Paraguay, Peru, Suriname, Uruguay and Venezuela.

6. _Local Europe._ Includes routes between or among the States of geographical Europe, Algeria, Azores, Canary Islands, Greenland, Iceland, Madeira, Malta, Morocco, Tunisia and Turkey.

7. _Local Middle East._ Includes routes between or among the following States: Bahrain, Cyprus, Egypt, Islamic Republic of Iran, Iraq, Israel, Jordan, Kuwait, Lebanon, Oman, Qatar, Saudi Arabia, Sudan, Syrian Arab Republic, United Arab Emirates and Yemen.

8. _Local Africa._ Includes routes between or among the States of continental Africa and offshore islands, but excluding Algeria, Azores, Canary Islands, Egypt, Madeira, Malta, Morocco, Sudan, and Tunisia.

9. _Between Europe and the Middle East._ Includes routes between the two geographical areas defined by route group 6 (“local Europe”) and route group 7 (“local Middle East”) respectively.

> 41 The August 2010 version of the ICAO Carbon Emissions Calculator, in Appendix A on page 12, Bermuda is included in this group. Bermuda has thus also been included in the calculations presented here.

37
(rather than general aggregate variation), and _statesharei,t−_ 4 is based on the eligibility rules for each specic policy change (rather than average transfer shares). Combined, this means that ∆ _tri,t_ reects actual changes in transfers to dierent states very accurately for example, in an IV specication rststage coecients are close to one and F-stats are above 500 rather than being an instrument correlated with transfer changes.

For the 2001 transfers and the 2008 middle-income tax refund the aggregate size of the payments ∆ _trUS,t_ in Equation 1 are taken from JPS and Parker et al. (2013) respectively (subtracting the size of the low income rebate from BEA (2009) in the latter case). Each state's share ( _statesharei,t−_ 4) is calculated using IRS state-level data on individual tax returns for the previous tax year (2000 and 2007 respectively), using eligibility rules for each specic payment. Eligibility for the 2001 and 2008 stimulus payments largely depended on income in 2000 and 2007, as full-year income was not known in 2001Q3 or 2008Q2 when the rst payments were made. The cross-state distribution of one-o transfers is plotted below in Figure 1.

For permanent Social Security benet increases, the aggregate transfer change ∆ _trUS,t_ is taken from Table 1 of Romer and Romer (2016) over 19521974.<sup>6</sup> I allocate the exogenous increase in aggregate Social Security payments across states in proportion to that state's share of Social Security payments a year before, _statesharei,t−_ 4 as in Equation 1 (see Appendix 1 for details). As the policy change essentially involves scaling up existing Social Security transfers, Equation 1 is exact using a contemporaneous _stateshare_ . In practice _statesharei_ is almost unchanged year-to-year, so Equation 1 is likely to be very accurate (see Appendix Figures 1.1 and 3.1B).<sup>7</sup>

>
countries. Total accumulative FDI inflows rose rapidly from $1.8 billion in 1983 to $446.3 billion in 2002. 3 All these achievements so far have been unmatchable in the rest of the world.

# **II. Daunting Challenges Ahead**

At the same time, China is undertaking a transformation toward knowledge and servicebased economy and undergoing wrenching structural changes. In the context of knowledge revolution, as China is further opening up to international competition after its WTO accession, it faces enhanced pressures to keep up with new knowledge and restructuring economic activities to take advantage of new and better ways of producing goods and services.

Internally, it faces many daunting challenges ahead. By 2002, China’s labor force accounts for 25.4% of the world’s total, apparently higher (due to age structure) than the proportion of its population in the world’s total (20.7%). 4 The biggest challenge is: how to productively employ its labor force of over 744 million as it shifts from an agricultural to an industrial and soon a service-based economy, especially how to absorb or reemploy the millions of laid-offs and rural emigrants. It is estimated that China will have to create around 100–300 million new jobs in the coming decade. So it must maintain a high rate of GDP growth, implement an effective employment strategy, and deal with income and

> 1 World Bank SIMA database, 2003.

> 2 The World Bank, _China: Promoting Growth with Equity_ , Country Economic Memorandum, Washington, DC, September 2003.

> 3 National Bureau of Statistics of China, _China Statistical Abstract 2003_ , China Statistics Press, Beijing.

> 4 Author’s calculation based on data from the World Bank SIMA database 2003.

3
62 firm size distributions by country, totaling 434 distributions from which we can extract size shares and information on the shape of the distribution.

We compute the four size proportions from each firm size distribution and the mean, p25, p50, p75, and p90. We have 434 observations to elucidate their relationship to the firms’ size shape accounted for by these four moments. In practice, this implies estimating<sup>9</sup> the following regression for each of the four size proportions: where _zki_ is the proportion _k_ = _{micro_ , _small_ , _medium_ , _large}_ coming from the firm size distribution of all firms aged _i_ = _{_ 0, ..., 61 _}_ . The algorithm uses the estimated _β_ s to predict all remaining countries based on the four moments we reconstruct in the following step.

It is important to estimate equation 1 using the information of several distributions of different shapes so the _β_ s carry the precise connection between the moments and the size shares. Figure 3 shows the correlation between the actual size shares and the predicted ones in every age group. The model produces accurate estimates of the size shares based on these five moments, mean, p25, p50, p75, and p90.

The next step consists of obtaining the five moments for every country so we can use the _β_ s from equation 1 to predict the size shares. For this step, we rely on ILO and WBES data. With ILO data, it is possible to recover at least the nationwide average firm size for every country

> 8Results of this section, and the overall estimations are robust to excluding Kenya survey data and using only census data. Results available upon request.

> 9STATA implements the maximum likelihood estimator developed by Papke and Wooldridge (1996).

11
>8</sup> Moreover, we review the commercial registries as well as the companies’ websites to identify firm characteristics, notably their age, size (in number of employees), and paid-in capital. As these directories fail to report some of the characteristics for some firms (Table 3), we use multiple imputations to estimate the missing values for these observations. The goal of using multiple imputations is to maximize the use of available information, minimize estimation bias, and obtain appropriate standard errors (Enders, 2010). We use multiple imputation, rather than other available techniques such as stochastic or deterministic imputation, to minimize the bias of standard errors in our regression analyses. We leverage the _mi estimate_ command in Stata using a multivariate normal distribution with 10 imputations and take the contract value as an auxiliary variable.<sup>9</sup> Table 3: Number of incomplete observations of supervision and design consultants

|**Supervisors**|**Complete**|**Incomplete**|**Total**|**Percent missing**|
|---|---|---|---|---|
|Age|340|21|361|5.8%|
|Size|332|29|361|8.0%|
|Paid-in Capital|260|101|361|28.0%|
|**Designers**|||||
|Age|207|26|233|11.2%|
|Size|203|30|233|12.9%|
|Paid-in Capital|159|74|233|31.8%|

# **Descriptive statistics**

> 7 The widely-cited work of Faccio (2006), for example, uses a data set of firms worldwide and finds no politically connected firms in Zimbabwe and Venezuela—two countries with an arguably weak record for the control of corruption. Even for the United States
# **1. Introduction**

Episodes of fast growth accompanied by large increases in within-country income inequality were experienced across developing countries during the 1980s and 1990s. Chile was no exception: its economy experienced a sharp recovery after the 1980s‟ debt crisis and sustained growth throughout most of the 1990s and 2000s but growth was uneven across regions.<sup>1</sup> Between 1998 and 2000, Antofogasta, one of the richest regions, experienced growth in GDP per capita three times faster than that of La Araucaina, the poorest region (Duncan and Fuentes, 2006). This uneven growth was accompanied by the agglomeration of economic activity in a few industrial clusters. These clusters are groups of firms and related actors and institutions located near one another that draw productive advantage from their mutual proximity and connections (Cortright, 2006). The importance of firm agglomeration in industrial clusters for long-run growth has been emphasized by Porter (1990).

This paper investigates which type of agglomeration externalities was most conducive of the regional long-run growth pattern in Chilean manufacturing productivity between 1992 and 2004. We ask whether more specialized locations experienced faster long-run productivity growth relative to locations where economic activity was more diversified. We use a rich panel of firm-level data to obtain consistent estimates of total factor productivity (TFP) at the sector-location level. Our findings are supportive of the idea that locations with a more diverse set of activities exhibit higher long-run manufacturing TFP growth. We conjecture that geographical proximity led firms to share the access to specialized inputs provided by multiple sectors ranging from the availability

> 1 See Bergoeing et al. (2002) and Gallego and Loayza (2002).

1
More specifically, wind data is obtained at a six-hour frequency since 2000 for all 52 locations in the US, thus yielding 1.55 million data points. To obtain a measure of storm frequency for a given country, we count all observations that exceed the 99.5<sup>th</sup> percentile from all wind speed observations across all states and years. In the US, this threshold corresponds to about 40 km/h, which corresponds to the lower bound of a level 6 storm on the Beaufort scale. This approach implies that storms are defined at the national level, and certain states with consistently high wind speeds are strongly represented in the analysis. As a robustness check **,** Appendix 4 offers results for a state-specific storm specification that uses a state level wind speed distribution, instead of the national level wind speed distribution. It should be noted that reanalysis datasets like ERA5 tend to underrepresent high-intensity tail events, such as tropical cyclones and severe local convective storms (Hodges et al., 2017).

Similarly, temperature data is obtained at midday with a 24-hour frequency since 2000 for all subnational locations. In line with the approach taken for wind speeds, a heat hazard is defined by counting days on which the midday temperature simultaneously exceeds 20 degrees Celsius and the 99.5<sup>th</sup> percentile of the state-specific temperature distribution since 2000.

# 2.2.2. Climate data for Bangladesh

Climatic hazards in Bangladesh are governed by the monsoon season from July to September. Tropical cyclones occur at the tail ends of the monsoon season. In addition, Severe Local Convective Storms (SLCS) occur in the pre-monsoon season in April and May, and are characterized by localized short and intense thunderstorms – known in Bangladesh as Nor’wester or Kalbaisakhi. We combine two sources of data to capture these distinct events: (i) ERA5 climate data, and (ii) lightning data based on NASA’s Tropical Rainfall Measuring Mission Lightning Imaging Sensor (
0.09)|
||Constant|0.22|1.48***|2.03***|1.17***|0.15|0.20*|1.57***|2.03***|1.17***|0.12|
|||(0.13)|(0.11)|(0.09)|(0.14)|(0.14)|(0.08)|(0.11)|(0.06)|(0.16)|(0.10)|
||N|79|76|48|54|51|79|76|48|54|51|
||R-sq|0.45|0.47|0.18|0.23|0.52|0.66|0.48|0.19|0.28|0.65|

## _Source_ : Authors’ calculations based SEAD data.

_Note_ : Independent variables are dummies taking the value of one if the skills count of a skills category of an occupation is above the meian and thus can be interpreted as percentage point increase (or decrease) towards the probability of being a given occupation digital level. Malaysia does not have one of the 127 occupations of the occupational skills profile (paramedical practitioners, ISCO code 224) according its 2017 labor force survey, so its total number of occupations is 126.

_*p < 0.1 **p < 0.05 ***p < 0.01_ 49
Shapiro and Felter (2009) showing that improved service delivery in Iraq reduced insurgent violence, but in the absence of data on population attitudes the authors do not look at trust outcomes as a consequence of increased aid. In the context of Pakistan, Andrew Wilder reports results from focus groups with NGOs, aid workers and aid recipients on the after effects of the Pakistani earthquake; Wilder believes that the immediate aid following the earthquake may have had an impact but that these attitudinal effects were unlikely to persist over time. Also on Pakistan, several recent surveys measure attitudes towards Americans. As discussed by Fair and others (2009) the quality and coverage of these surveys is hard to gauge—in some cases, respondent-level data are not available (for instance, Gallup), in others non-response rates are high. In general, the coverage of these surveys (primarily urban areas) and social-desirability bias in the questions cloud interpretation of the data. The one exception is a recent large survey that combines a representative sample with an endorsement game to examine attitudes towards militancy and its correlates with education and income; for our purposes, an important finding is the lack of a relationship between support for militant groups and anti-Americanism (Fair and others, 2009).

Finally, a growing literature examines the impact of disasters on economic outcomes. Portner (2009) discusses the effects of natural disasters on child health. Baez and Santos (2007) examine the effect of hurricane Mitch in Nicaragua on child outcomes using a difference in difference strategy based on the LSMS panel data with municipalities that are affected by the hurricane versus those that were not. It is worth emphasizing that causality is harder to establish in this case because the path of hurricanes is known and anticipated 2-3 days before the event— the people who stay behind could well be different from those who leave the area. Similarly, in 9
Policy Research Working Paper 8725

# **Abstract**

Expectations of future socioeconomic mobility are an important determinant of cur- rent policy preferences. But how may these expectations be formed? Using Life in Transition survey data for a large set of transition economies and several Western European countries, this paper examines the link between beliefs about the importance of personal connections for getting access to opportunities, such as a good job or university education, and expectations of future socioeconomic mobility. The analysis of survey data finds evidence that: (i) lack of connections is associated with expectations of a lower position on the future social ladder; and (ii) when informal connections are unavailable, it matters for your aspirations whether you perceive connections to be vital or not. There is also some evidence that in the European Union, where formal institutions are stronger, individuals are less likely to resort to informal institutions such as personal connections, even when these are available. Perceptions of unequal access to opportunities are also linked with stronger redistributive preferences. Finally, there is some evidence that unequal access to opportunities is associated not only with lower intragenerational mobility, but also with lower intergenerational mobility.

This paper is a product of the Poverty and Equity Global Practice. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/research. The author may be contacted at acojocaru@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views
share of inequality of learning achievement. Mother’s and father’s education combined account for a mean of 3.7 and a maximum of 9.2 (in Hungary) percentage points of the overall shares of explained inequality in the set of 57 countries, which take the mean of 24.7. The number of books at home accounts for a mean of 7.2 and a maximum of 14.4 percentage points (in Austria). Add parental education, language at home, numbers of books, and cultural possessions, and this set of “educational and cultural variables” add up to a mean of 15.0 points. Family economic resources also appear as an important source of learning inequalities. Father’s occupation and the “durable assets” indicator account for means of 3.6 and 3.8, respectively. With immigration status, the set of “economic variables” explains a mean of 7.8 points. Finally, the type of area where schools are located accounts for a mean of 1.6 and a maximum of 10.7 (in Kyrgyzstan) points of the overall shares, whereas the student’s gender accounts for a rather limited mean of 0.6 and a maximum of 2.1 (in Chile) points of the overall shares. There are also interesting regional variations in these partial shares of learning inequality. For instance, the partial share associated with educational and cultural resources has a higher mean in Western and Eastern European countries than in other regions, whereas the share associated with economic resources has a higher mean in Latin America.

> 24 GDP per capita is measured at purchasing power parity exchange rates, in 2006 US prices; the data are from the World Development Indicators (WDI) database.

21
Finally, in the absence of credit markets, trade is balanced location by location, so that local nominal income equals total expenditure from all locations _s_ on goods produced in a location _r_ . Formally, that implies: where wt(r) and wt(r)Ht(r)L<sup>�</sup> t(r) stand for the nominal wages and income in location _r_ , respectively. Desmet et al. (2018) show that a dynamic equilibrium of the economy exists and is a sequence of static equilibria where, in each time period _t_ , markets for goods and labor clear. That requires equations (1) and (2) to simultaneously hold in each period _t_ .

# _2.2 Data and parameter calibration: Quantifying spatial frictions_

A path-breaking contribution of the presented framework is its ability to quantify spatial frictions in the world economy using real world data. In this application, the Earth’s surface is divided into about 17,000 1<sup>∘</sup> × 1<sup>∘</sup> grid cells. By using global data at this unit of disaggregation for several characteristics, one can use the model’s structure to back out several parameters of the model, including the two mobility parameters of key importance to our investigation – the trade frictions, ζ(s, r), and the migration entry costs, m2(r). In what follows, we explain the conceptual idea behind the estimation of the mobility frictions in Desmet et al. (2018).

Trade costs, ζ(s, r), are estimated using a rich transportation infrastructure data covering the entire Earth’s surface and capturing different modal types of transportation as well as geographical characteristics. Combined with the Fast-Marching algorithm in Allen and Arkolakis (2014), these data allow us to estimate a bilateral matrix, ζ(s, r), containing about 300 million entries for _s_ - _r_ location pairs (17,000x17,000