rafmacalaba/data-use-mentions-extended huggingface dataset:
the distribution. As a consequence, the economic progress of a country is simply assessed by measuring the growth of gross domestic product per capita.

The recent growth in income inequality at the global and national levels has forced governments around the world to reconsider some of those theories and look at new policies to tackle the increased poverty within their societies (ILO 2011a). Interestingly, in countries where inequality has declined, the expansion of education and public transfers to the poor seem to have played a more important role than free-market allocation (Dabla-Norris and others 2015). In Latin America and the Caribbean, for instance, an increase in public expenditure on education since the early 1990s led to rising secondary school enrollment and completion rates and became a major determinant of the fall in wage inequality (Ferreira and others 2013; López-Calva and Lustig 2010).

The rise in income inequality has generated a rich literature on the causal links between inequality and development, with many researchers suggesting that inequality has a negative effect on economic growth.<sup>11</sup> Stiglitz (2012) argues that income inequality hinders economic growth because it skews the economy toward rich households, which have a lower propensity to consume and spur aggregate demand, rather than to middle- and low-income households. Herzer and Vollmer (2012, 489) also found in their study on per capita income in 46 countries over the period 1970–95 that “inequality has a negative long-run effect on income.”<sup>12</sup> The World Bank (2013a, 54) highlights that “social inclusion matters because exclusion is too costly. These costs are social, economic, and political and are often interrelated.” Also, recent International Monetary Fund studies provide statistical evidence that income inequality (as measured by the Gini coefficient) negatively affects growth and its sustainability (Berg and Ostry 2011; Ostry, Berg, and Tsangarides 2014).

> 8 High-income countries are those with a gross national income per capita of US$12,476 or
risk, versus a 0.86 reduction in risk when concentrating on the areas where the top 25% of sewer bursts are concentrated. The efficiency and cost effectiveness of reducing cholera risk is therefore much greater when concentrating areas that fall within the top 25th percentile of sewer bursts. It is also more efficient to provide piped water to within 500 meters of the existing network (7.55), rather than to extend access to the whole city (8.2) when considering their relative impact on reducing the number of cholera cases.

Finally, we also see how extending access to the sewer network in its state at the time of the data collection in fact seems to increase cholera risk. This finding may seem perverse given that according to the Joint Monitoring Program of the WHO, which set the standards for sanitation globally, sewered sanitation is at the top of the improved sanitation ladder. However, it serves to highlight the importance of proper maintenance of existing sanitation networks rather than necessarily emphasizing new construction. This is particularly important in a context where the institutional capacity and financial resources to maintain large-scale sanitation systems may be lacking.

An important limitation of the study is that the analysis presented relies on observational data and thus we draw correlations and associations, rather than drawing causal inferences. This clearly has important implications when interpreting the scenario analysis. Additionally, there are likely to be some errors in case data such as the locations and times of case identification. Similarly, a number of layers used in the analysis, such as proportion of households with access to piped water, were generated using DHS data, which have been spatially obfuscated. As a result, this may have introduced error into the layer, which could obscure, or falsely lead to, associations with cases. In addition, sewer and water burst density was based on spatiotemporally referenced complaints data, which is obviously a proxy measure with a number of reported and recorded issues. Data from direct inspections of sewer and water networks would have been a more reliable measure
replacement of identity rules by a system of impersonal rule. Societies based on identity rules place few constraints on the power of the elite vis-`a-vis the rest of society but grant some rule of law within elites. Most importantly, unconstrained power makes it difficult for rulers to credibly commit to rule-based dealings with less powerful groups. This is precisely how we think ethnic heterogeneity enters the picture. It reflects the presence of other identity groups, such as the Bemba miners in Zambia, with whom the ruling elite needs to coordinate (Bluhm and Thomsson, 2015).

# **Robustness**

Our main conclusions are unaffected by a variety of robustness checks, such as dropping influential observations, varying the national accounts data, and the inclusion of policy variables. We only present a selection of the most insightful perturbations here. A comprehensive battery of robustness checks can be found in Supplementary Appendix S7.

Table 4 takes the estimated breaks and data as given and focuses on unobserved heterogeneity and influential observations. Column (1) adds country-level random effects (frailties) and income-level fixed effects on top of region fixed effects. Column (2) drops former Soviet countries. Both perturbations do not affect our findings. Column (3) takes a more radical step and drops all of Africa. The estimated effect sizes are smaller and we lose precision on the interaction term. This loss of significance is hardly surprising. The African continent is home to some of the most ethnically diverse countries with the weakest institutions. Africa drives our results but also provides much of the variation needed to estimate the overall effect.<sup>19</sup> Column (4) deletes the censored spells. Now the estimated effect of political institutions is even smaller, and the interaction term becomes insignificant. A look into the underlying data reveals what is going on. Countries with completed spells

> 19We have also taken a less radical approach and interacted our three variables of interest with an Africa dummy. In every case, the added interactions are individually
hand and the limited resources available from industrial countries on the other, aid effectiveness has become a critical issue. In this context official donors in recent years have stepped up their aid efforts in two ways: by providing more grants (with no repayment obligation) and by providing official debt forgiveness, bilaterally and through the Paris Club (World Bank, 1994c, 1995, 1996).

This paper evaluates these efforts based on limited data accumulated over 198993. We provide a descriptive analysis of the impact on the recipient countries of the different aid efforts and channels used in the recent past. Our analysis is limited in that

> I Sub-Saharan Africa is the only region that saw debt indicators worsen during 1995 (World Bank, 1996).

> 2 The SPA is a forum that was formed in 1988 with the international donor community and under the **chairmanship** of the World Bank to coordinate external support for reform programs undertaken by low-income, debt-distressed countries in Africa. The objectives of the SPA are to ensure that adequate balance of payments finance is available for reform programs and to provide a forum for improving the effectiveness and efficiency of donor assistance for the program. Currently, 32 African countries participate in these activities along with bilateral and multilateral donors.

> 3 The share of debt owed to official creditors increases from 69 to 72 percent when Nigeria is excluded from the sample, and the share of concessional debt rises from 59 to 71 percent (51 percent of the total). ODA from OECD countries was equivalent to 0.29 percent of donors' GNP in 1994, its lowest level since 1973 (World Bank, 1996). The ODA figures calculated by the OECD differ somewhat from those calculated by the World Bank. A major difference is that the OECD data include Technical Cooperation grants; World Bank data do not.

I
Bloom, N., B. Eifert, A. Mahajan, D. McKenzie and J. Roberts. (2013). “Does management matter? Evidence from India." _Quarterly Journal of Economics_ , 128(1): 1-51.

Bloom, N., R. Lemos, R. Sadun, D. Scur, and J. Van Reenen. (2014). The New Empirical Economics of Management”, _Journal of the European Economic Association,_ 12(4):835–876 Bloom, N., R. Sadun, and J. Van Reenen. (2016a). “Management as a Technology.” Harvard Business School Working paper # 16-133 Bloom, N., R. Lemos, R. Sadun, D. Scur, and J. Van Reenen. (2016b). “International Data on Measuring Management Practices”, _American Economic Review: Papers & Proceedings,_ 106(5): 152–156 Bloom, N., E. Brynjolfsson, L. Foster, R. Jarmin, M. Patnaik, I. Saporta-Eksten. and J. Van Reenen. (2019a) “What Drives Differences in Management Practices?”, _American Economic Review_ , 109(5): 1648-1683 Bloom, N., L. Iacovone, M. Pereira-López and J. Van Reenen. (2019b) “Spillovers, Market Size and Misallocation: Management in Mexico”, mimeo Foster, L., J. Haltiwanger, and C. Syverson. (2008). “Reallocation, Firm Turnover, and Efficiency: Selection on Productivity or Profitability?” _American Economic Review_ , 98 (1): 394–425.

Grover, A. and I. Torre. (2019). “Management Capabilities and Performance of Firms in the Russian Federation”, World Bank policy research working paper, _forthcoming_

- Grover, A., D. Medvedev. and E. Olafsen. (2019) High <u>Growth Firms: Facts, Fiction, and
dispersion of the returns from their mean, then the higher deviation signals that the returns are becoming more dispersed. In this sense, the returns to schooling are becoming more volatile. This sense of volatility or uncertainty means that people about to invest in their schooling are less sure about the expected returns to that investment.

<!-- Start of picture text --> Figure 3: Standard Deviation of Rate of Return to Year of<br>Schooling<br>8<br>6<br>4<br>2<br>1970 1980 1990 2000 2010<br><!-- End of picture text --> Source: Calculated from Montenegro and Patrinos 2014 The empirical evidence from the 1960s to the 1990s suggested that in most countries the relationship between education and earnings was concave (Psacharopoulos 1972, 1981, 1985, 1994; Psacharopoulos and Patrinos 2004). In the 1970s the private rate of return was higher at the primary school level than at the secondary level or higher (Psacharopoulos 1981). The same pattern was evident by the early 1980s (Psacharopoulos 1985). Similar evidence was produced for the 1980s and 1990s, but already the pattern was shifting (Colclough et al. 2010). In fact, by the early 1990s, the returns to primary schooling were decreasing, while the returns to higher education were rising (Psacharopoulos 1994).

More recent evidence suggests that the rate of return to primary education is lower than that to higher levels of education. Many studies using cross-section data from the 1990s and early 2000s find that the return to primary education in wage employment is significantly lower than that to higher education (Psacharopoulos and Patrinos 2018). But analyses of current rates and levels 9
take the simulated total benefits generated by Fonden and compute the mean and the standard deviation.

The second row of panel C reports the average total benefit from the simulations divided by the total cost. Rows three and four report the total benefit one standard deviation below and above the mean divided by the total cost. All in all, while our ratios vary too broadly to clearly pin down the effect of Fonden, they are of a reasonable magnitude. The average benefit cost ratio, across specifications, lies in the 1.52 (with a probability of 0.66 of being greater than one) to 2.89 (with a probability of 0.8 of being greater than one) range, and is therefore consistent with previous estimates of fiscal multipliers. These cost-benefit ratios might, however, be too conservative as they account for the overall expenditures of Fonden while assuming that the benefit of the program is only accrued in the year following the disaster.

# **5.2 The dynamic impact of Fonden on local economic activity**

As previously mentioned, we turn to month frequency night lights data to document the dynamic impact of Fonden on local economic activity. Figures 3a to 3d plot the mean change in log night lights relative to rainfall millimeters (mm) to the threshold, at four key points in the post disaster period. Specifically, the four outcome variables are the night lights log difference between the average 12 months before the disaster and the average at 3, 5, 15, and 20 months after the disaster. The figures suggest that the impact of Fonden can be broadly characterized in three phases. In the very short run, while funds and reconstruction efforts are being put into place, we find no difference between those municipalities just above and below the threshold. Between 5 and 15 months after the disaster, we see a clear jump in brightness at the threshold. At its peak, 15 months, moving from just below to just above the threshold increases brightness by roughly 0.1 log
is quantitatively the most important destination for 20 regions. However, regions on the Eastern coast and the South-East of Russia are oriented toward Asia orientation, the latter mainly to China and the former to the rest of Asia. Data on the export orientation of Russian regions are from GTIS, which does not report Russian exports to Kazakhstan and Belarus, the other members of the EACU.

No matter what the export category, the EU-28 and the nonCU-CIS always rank either first or second in the count of products for which they are the main destination, with the third most frequent top destination being Rest of Asia. The EU-28 is the main destination for exports of oil, minerals,<sup>11</sup> and metals by most regions as well as for all non-oil and all non-mineral products when grouped together. Meanwhile, the nonCU-CIS is the main destination for exports from most regions of machinery & transport, food and agriculture, chemicals, and wood, though for the last two product categories the EU-28 is the main export destination for nearly as many regions. Rest of Asia is the third most frequently reported destination for exports of oil, metals, food and agriculture, and aggregate non-mineral exports.

When relating the analysis of export intensities and geographic orientation by product categories to that of leading and lagging regions of Russia, the EU-28 is the main destination for total exports of leading (55 percent) and middle-range (58 percent) federal regions. The nonCU-CIS is the main destination (55 percent) for the exports of most lagging regions. The same pattern emerges in analysis of the main markets for exports of wood, chemicals, non-oil products, and non-mineral products; a close but not identical pattern is observed in exports of metals, minerals, and oil.<sup>12</sup> This suggests that while pursuing export opportunities within the former Soviet Union may be of some benefit to lagging regions, high standards of living require the ability to compete globally.

Wood exports have a strong geographical orientation (see
and 2000 IFLS rounds for the following reasons. The survey was not designed to focus on international migrants and remittances, and only limited information were collected about migrant characteristics and remittances sent by these migrants. The earlier years of the IFLS have a very small sample size of migrants, unlikely to provide sufficient statistical power to

> 5 Community data is available for slightly fewer households than the full sample since only original IFLS communities were surveyed.  As households moved to new communities, data is not available for the new ones.

6
The population of refugees reflected in the global statistics does not necessarily coincide precisely with the population of particular focus for development actors. Of the 16.1 million refugees and people in refugeelike situations under UNHCR’s mandate at the end of 2015, 2.4 million were living in high-income (OECD and non-OECD) countries that do not qualify for assistance from development organizations.<sup>21</sup> Additionally, even within low and middle-income asylum countries, there may be pockets of refugees in protracted displacement who may not have achieved a legal solution to their displacement (i.e. naturalization in the host country, voluntary repatriation to their home country or resettlement in a third country) but who no longer have specific economic and social vulnerabilities linked to their having been displaced. They may no longer have protection or assistance needs, they may have achieved a high degree of economic and social inclusion, they may enjoy most of the privileges of citizenship, and they may have achieved a standard of living equal to that of nationals, e.g. over 300,000 ethnically Chinese refugees who fled from Vietnam to China between 1979 and 1982 (UNHCR 2007). On the other hand, there may be refugees who fall out of the global statistics when they are naturalized but who continue to be of interest to development actors because they still face specific vulnerabilities linked to their displacement, e.g. Burundian refugees newly naturalized in Tanzania.

# **The special case of Palestinian refugees**

The legal definition of Palestinian refugees is unique and distinct from the definition of refugees in the 1951 Convention. Palestinian refugees fall within the mandate of UNRWA,<sup>22</sup> which operates in Jordan, Lebanon, the Syrian Arab Republic, Gaza and the West Bank. UNRWA defines “Palestine refugees” as individuals and their patrilineal descendants whose normal place of residence was the British Mandate for Palestine during the period 1 June 1946 to 15 May 1948, and who lost their homes and livelihoods as
The remainder of this paper is organized as follows. The second section discusses the methodology and data used to measure and labor and jobs content of exports to construct the LACEX database. The third section discusses how the labor content of exports has evolved globally, and the fourth section turns attention to the jobs content. These developments are explored across income groups and regions, sectors, as well as at the country level. The concluding remarks are in the fifth section of the paper.

# **2. Data and methodology**

We compute the labor value added and jobs content of exports on the basis of a panel of global IO and other aggregate data from the Global Trade Analysis Project (GTAP). GTAP represents a massive combined effort of international institutions and universities. Over time, the data set has grown to include more countries and more sectors. The latest version, GTAP 9, has data on 129 countries/regions and 57 sectors (Narayanan et al., 2012). Table A1 in Appendix 1 provides the description of the 57 GTAP sectors.

## _<u>Why GTAP</u>_

Although recent years have seen the emergence of different databases of global IO tables, we believe the GTAP data offer a balance between quality and country coverage, making it more suitable than other alternatives for our purposes. There are at least three other databases that could be used to compute the labor contained in exports: the World Input-Output Database (WIOD), the OECD-WTO Trade in Value Added (TiVA) Database and the UNCTAD-Eora Database. These data differ in their coverage of countries, sectors, and years as well as in overall accuracy.<sup>5</sup> WIOD and TiVA include a limited number of developing countries, although they account for most of world trade in value added, which is generated by high income countries. Because they comprise only high income or large middle income countries, these databases tend to have great levels of accuracy. On the other hand,
areas. So even if raising income or its monthly share is not the most direct route, there must be other routes through which SP1 might have helped Thai households smooth their consumption by reducing monthly variations in per capita income. In order to test other possible routes other than income through which government programs help, we also examine at the coping strategies households adopt during an economic crisis other than participation in a government welfare or transfer program. As described earlier, the NSO in Thailand collected information about the strategies households adopted during an economic crisis in its 2008, 2009 and 2010 SES surveys. The household responses are categorized in two ways: What would they do in an economic crisis, and what do they expect the government to do during the crisis? The responses are presented in Table 2. We find that the most frequent coping strategy households say they would adopt during an economic crisis was reducing household spending (more than 85 percent of Thai households report they would reduce spending during a crisis). Households might also adopt some other strategies: 9 percent of households say they would tap into savings, and 8 percent report that they would seek part-time employment if hit by a crisis. Interestingly, when borrowing is a way to meet the gap between expected income and consumption, about half of Thai people do not like to be in debt when a crisis hits. On the other hand, households are found to repay debt at a higher percentage rate: For example, in urban Thailand, some 41 percent repaid debt in 2008 and 46 percent in 2009, and 44 percent in 2010 (Table 3). The loan repayment rate is higher in rural areas than urban areas.

We therefore examine the effects of the stimulus on savings, borrowing, and debt repayment by households in the community as possible coping strategies. Did households draw down on their savings, or change their borrowing patterns to raise or maintain consumption? Table 6 presents the results, again in a dynamic panel framework estimated by GMM. We find a negative
point of each administrative area to the coordinates of the capital (from the Global Insights (v 6.1) data), which we call _dist2cap_ . Likewise, we calculate distance from the center point of each administrative area to the border ( _dist2border_ ) using the boundary file provided by the NSO.

In consideration of the initial conditions of administrative areas for regional trade, we construct distance to major rivers ( _dist2major_ _~~r~~ iver_ ) and distance to coast ( _dist2coast_ ). Also here we use the center point of the administrative areas as the points of origin and destination. Major rivers and coastline data are from Natural Earth<sup>12</sup> .

In addition, we consider the terrain with regards to the elevation, slope and ruggedness. The elevation data are from the Shuttle Radar Topography Mission (SRTM) Digital Elevation dataset (version 4) and is measured in meters at 3 arc-seconds and 30 arc-seconds (approximately 1km<sup>2</sup> ) (Jarvis et al. 2008). We summarize mean and max: _elev_ and _elev_ _~~m~~ ax30_ . We derive elevation in populated places using a 1% threshold level, which is approximately 10 people per km2 from the Landscan 2012 dataset (described in the population count and density section). We take advantage of the slope function on Google Earth to process the 3 arc-seconds elevation data into a slope variable: _slope_ .<sup>13</sup> Figure A2.1 (left panel) illustrates the variation of slope, where the Great Rift Valley is seen to run from North to South. The Ruggedness Index we use is developed by Nunn and Puga (2012) which measures the level of ruggedness in hundreds of meters of elevation difference for each grid cell. We summarize the pixel mean ( _rugged_ ) and maximum ( _rugged_ _~~m~~ ax_ ) of this ruggedness index evaluated over the entire administrative areas as well as over populated areas ( _rugged_ _