rafmacalaba/data-use-mentions-extended huggingface dataset:
taxes, depreciation, and amortization. ROAA: Return on average assets.</mark> Standard errors in parentheses. Errors are clustered by strata derived from the coarsened exact matching procedure.

* _p_ < 0.1, ** p < 0.05, *** _p_ < 0.01.

In line with this finding, EBITDA to revenue is 16 percentage points lower and EBITDA to average assets 4.5 percentage points lower at SOEs than at the average private firm. These magnitudes are large considering that the average EBITDA to revenues is 18.8 percent and the average EBITDA to assets 6.5 percent, meaning that being an SOE almost implies a zero margin.

Ownership plays a big role in explaining the weak results of SOEs, as similar private companies perform much better. These abnormal losses may also explain SOEs’ propensity to borrow more and their fiscal dependence.

Column 7 reveals that the share of employee costs to total costs is much larger in SOEs (35 percent) than in private firms (20 percentage points higher), suggesting that governments hire inefficiently or that one of the QFOs these SOEs perform is related to creating employment. Their inability to shed labor in hard times suggests that their financial performance is likely to deteriorate over time and to be highly affected by negative shocks.

Do subsidies distort SOE behavior, by encouraging them to remain or become unprofitable? To investigate the question, we split the SOE firm-year observations into those that receive subsidies (the sum of operation and capital subsidies) and those that do not (table 3).

18
>|0.48|0.49|

_Notes_ : This table presents the heterogeneous effects of the predecessor’s promotion status on GDP manipulation at the turnover year when the incumbent assumes office around the end of the year. The dependent variable is a dummy variable that equals one if the city’s reported real GDP growth is less than the predicted real GDP growth using the nighttime lights data, i.e., a dummy indicating under-reporting GDP growth. City controls include city-level time-varying economic controls (the share of industrial and tertiary value added in GDP, log population, and growth of nighttime lights), and leader controls are the same as the ones displayed in the table of baseline results. Pre/Post dummies indicate one year pre and post the turnover year respectively. In column 2, we further include the Pre/Post dummies and their interactions with _Demoted_ and _Scapegoatable_ dummies to control for the potential differential effects in these years. Standard errors clustered at the city level are reported in parentheses.<sup>***</sup> ,<sup>**</sup> and<sup>*</sup> denote significance at 1%, 5% and 10%, respectively.

49
Policy Research Working Paper 5709

# **Abstract**

Global matrices of bilateral migrant stocks spanning 1960–2000 are presented, disaggregated by gender and based primarily on the foreign-born definition of migrants. More than one thousand census and population register records are combined to construct decennial matrices corresponding to the five census rounds between 1960 and 2000. For the first time, a comprehensive picture of bilateral global migration over the second half of the 20th century emerges. The data reveal that the global migrant stock increased from 92 million in 1960 to 165 million in 2000. Quantitatively, migration between developing countries dominates, constituting half of all international migration in 2000. When the partition of India and the dissolution of the Soviet Union are accounted for, migration between developing countries is remarkably stable over the period. Migration from developing to developed countries is the fastest growing component of international migration in both absolute and relative terms. The United States has remained the most important migrant destination in the world, home to one fifth of the world’s migrants and the top destination for migrants from some 60 sending countries. Migration to Western Europe has come largely from elsewhere in Europe. The oil-rich Persian Gulf countries emerge as important destinations for migrants from the Middle East and North Africa and South and Southeast Asia. Finally, although the global migrant stock is predominantly male, the proportion of female migrants increased noticeably between 1960 and 2000. The number of women rose in every region except South Asia.

This paper is a product of the Trade and Integration Team, Development Research Group. 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://econ.worldbank.org. The author may be contacted at cozden@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
|[0.051]|[0.056]|
|Constant|0.04<br>[0.004]***|0.01<br>[0.001]***|||
|Observations|864|432|857|418|
|R-squared|0.045|0.162|||
|Number of Countries|42|18|42|18|
|Country Fixed Effect|Yes|Yes|Yes|Yes|
|Adjusted R-squared|||-0.200|0.090|
|**First Stage IV Estimates**|||**First Stage **|**First Stage **|
|Trade Weighted Growth * Exp|||1.45<br>[.2]***|1.3<br>[.08]***|
|Trade Weighted Growth * Rec|||1.68<br>[.24]***|1.39<br>[.15]***|
|**Underidentification - Weak Identification - W**|**eak Instrume**|**nts Tests**|||
|LM test statistic for underidentification|||43.97|30.51|
|p-value of underidentification LM statistic (And<br>F statistic for weak identification (Cragg-Donald|erson or Kleibe<br>or Kleibergen|rgen-Paap)<br>-Paap)|0<br>29.28|3.33e-08<br>52.01|
|Anderson-Rubin chi-sqared test of significance|of endogenous r|egressors|39.88|13.91|
|p-value of Anderson-Rubin chi-sqared test of en|dogenous regre|ssors|2.19e-09|0.000952|

Source: Authors’ calculations based on World Economic Outlook database 2019.

Robust standard errors in brackets.*** p<0.01,
to February 6, 2020 (the baseline day). We select two components of Google mobility, namely work mobility ( _work mobilityit_ ) and retail and recreation mobility ( _retail mobilityjt_ ) to measure, respectively, the supply and demand shocks of the pandemic. Work mobility measures mobility trends for places of work, while retail and recreation mobility capture m <mark>obility trends for places like restaurants, cafes, shopping centers, theme parks, museums, libraries, and movie theaters. We compute the mean over all daily measures to obtain monthly Google mobility measures.</mark> <mark>Changes in industrial production relative to the previous year are calculated using the industrial production index (IPI) from UNIDO,</mark><sup>16</sup> <mark>which is available on a monthly basis for a set of 60 countries.</mark>

> 13 The 28 exporting countries include all members of the European Union excluding Cyprus, Japan and the United States. While Chinese trade data are also available, the trade data are combined for January and February due to the Chinese New Year. As Google mobility data (our measure of the shock) are not available for China, our baseline regressions do not include China. 14 Trade growth data at the exporter-partner-sector level exceeding the top (99) percentile and those below the bottom (1) percentile of the distribution are set to the growth rates of these percentiles, respectively.

> 15 <u>https://www.google.com/covid19/mobility/</u>

> 16 <u>https://stat.unido.org/database/Monthly%20IIP</u>

12
......<br>27|
|REFE|REN|CES<br>..<br>29|
|Table|1:|Union Densities .31|
|Table|2:|Selected Data on Strikes .32|
|Table|3:|Major Causes of Strikes .33|
|Table|4:|Ratification of ILO Conventions .34|
|Table|5:|Union Registeration, Security, Collective Bargaining Rights .35|
|Table|6:|Dispute Resolution and the Right to Strike .37|
|Table|7:|Minimum Wage Legislation .39|
|Table|8:|Old Age, Disability and Dealth Programs .42|
|Table|9:|Sickness and Maternity Programs .47|
|Table|10:|Work Injury Programs .50|
|Table|1 1:|Other Labor Standards .53|
In the last decade, many researchers have econometrically examined the existence and direction of FDI-generated horizontal and vertical spillovers. Starting with the pioneering study by Caves (1974), early research on the topic examined the effects of horizontal spillovers using industry-level cross-sectional data, and generally found evidence for positive spillovers (see, for an overview of earlier studies, Görg and Strobl 2001). In later work, using panel data at the firm level, Haddad and Harrison (1993) found evidence for negative 6
For very high poverty rates, reducing the Gini increases poverty. Hence, there may be a certain trade-off between decreasing the poverty rate and decreasing inequality for very poor countries. Panels b and c show that reducing inequality almost unambiguously decreases both the poverty gap and the squared poverty gap even for high initial headcount ratios. This indicates that the trade-off is rather about maximizing the reduction in the headcount ratio or the poverty gap – the latter corresponding to a stronger focus on the poorest of the poor.

# 6 Conclusion

Using a global database covering 97% of the world’s population, this paper shows that under assumptions of distribution-neutral growth, the World Bank’s goal of achieving less than 3% extreme poverty by 2030, as well as the United Nations’ Sustainable Development Goal of complete eradication of poverty, will be very difficult to reach by 2030. It also shows that these goals become more viable by reducing inequalities, for any given growth rate of the mean. Conversely, regressive distributional changes can severely limit the way in which growth contributes to poverty reduction.

Motivated by the Sustainable Development Goal 10 on inequality, we modeled inclusive growth in terms of lowering the Gini index in every country. The poverty impact of more inclusive growth defined in this way is different across countries and depends on the initial level of poverty, as well as the shape of the distribution, the precise growth incidence curve used and the growth rate. At high levels of initial poverty, reducing the Gini index could lead to a decrease in the pace of poverty reduction in the short term compared with a distribution-neutral growth scenario. In other words, for a country with a high headcount ratio, the welfare of the marginally poor may be growing slower when lowering the Gini than in a distribution-neutral scenario. This highlights a certain trade-off between focusing on the poor within every country and the poor according to an international poverty line. Nevertheless, in such cases the poorest of the poor still receive a growth premium and thus the poverty gap and severity are
probabilities. Our framework for incorporating ambiguity in the inspection decision is a form of Choquet Expected Utility (CEU) theory, as described in Schmeidler (1989). In CEU theory, inspectors’ beliefs about probabilities of non-compliance are represented as normalized and monotone set functions known as _capacities_ . Importantly, capacities allow the model to separate inspectors’ beliefs from the conditional probability that a good is non-compliant (in contrast to standard assumptions about beliefs in the expected utility framework). We adopt a particular form of capacities: the non-extreme-outcome additive (neo-additive) capacity described in Chateauneuf _et al._ (2007). In addition to a parameter that defines the ambiguity surrounding each type of shipment, the framework describes (in terms labeled _optimism_ or _pessimism_ ) how the agency’s beliefs about levels of compliance among unsearched shipments differ from the underlying probabilities. We show that a resource-constrained agency that is sufficiently pessimistic will inspect shipments with greater levels of ambiguity more frequently. While this is optimal behavior given the agency’s objective function, relative to a no-ambiguity benchmark the agency’s search activity is misallocated.

We develop this framework to illustrate the manner in which ambiguity-reducing investments in IT can contribute to successful risk management reforms. The goals of modern risk management reforms are typically to reduce the overall inspection burden while improving (or at least maintaining) compliance. We calibrate a numeric version of the model to the Serbian customs data (which represent a post-reform equilibrium) and consider the effects of substantially increased ambiguity on equilibrium rates of inspection and compliance (a reverse reform). The agency responds to higher levels of ambiguity by shifting even more inspection resources towards the shipments about which there is greater ambiguity (i.e., the infrequent shipments) and this reduces the effectiveness of search as a deterrent against non-compliance among other types of shipments. In order to maintain compliance in the face of increased ambiguity the agency must undertake increased levels of search. Modern reforms reverse this dynamic; IT investments that reduce ambiguity allow better targeting of search activity
|1,042|969|851|796|
|N (ANCOVA)|989|989|969|969|969|969|||

Note: ***p < .01, **p < .05, *p < .1. The dependent variables are the total number of employees, the number of paid wage employees, the number of unpaid employees, and the average number of days worked in a week (only available at midline). All control variables come from the baseline survey. The list of controls include a dummy that equals one if the firm is already existing (vs new); dummies for whether the firm sector is industry or services, with agriculture being the reference; a share of correct recalls by the owner from a digit span memory test; a dummy that equals one if the owner is female; the age and number of years of professional experience of the owner; dummies for whether the owner is Christian or Muslim; dummies for whether the owner is of the two largest ethnic groups (Bissa or Mossi); dummies for whether the owner reads and writes French and a local language or reads and writes a local language only; dummies for whether the owner attained primary, secondary, or other education levels, with the excluded being no education level; the size of the owner household; the number of assets owned; and dummies for whether the owner is a household head and whether a member in the owner household is a community leader. The control group mean refers to the follow-up survey. The detailed regression results are provided in Table A.6 and Table A.7 in the appendix.

22
This paper presents some new empirical evidence on a series of related questions: _1. Are small plants more pollution-intensive than large facilities?_ _2. Is the share of small enterprises in industry lower in higher income regions?_ _3. Is industry less pollution intensive in higher income regions?_ _4. Do poor areas suffer more from industrial pollution in total, from smallplants andfrom large plants?_ The data for this analysis are drawn from large databases on enterprises in Brazil and Mexico. These data are described in Section 3 after a brief review of the relevant theoretical and empirical literature in Section 2.

The analysis in this paper focuses on air pollution because this poses the most serious threat to human health, and Sections 4 and 5 summarize the results. We believe that this evidence provides the first systematic assessment of the relationships linking economic development, the distribution of plant sizes and industrial pollution, and the paper accordingly draws together both a summary and some broader implications in closing.

# **2. Development, Pollution and Small Enterprises: Previous Research**

**In** this section three aspects of existing research are reviewed:

whether small enterprises are more pollution intensive, with higher emissions relative to their size; the relative role of smaller enterprises in production as development proceeds;

# the share of dirty industries in industrial production in poor regions.

Together these three form key aspects of whether lower income persons are exposed to greater industrial pollution harm. However these three do not represent the entire story as will become apparent in later sections.

2
Environ. Plan. A 33, 141–160.

Forbes, D. J. (2013). Multi-scale analysis of the relationship between economic statistics and DMSP- OLS night light images. _GIScience & Remote Sensing_ , _50_ (5), 483–499. https://doi.org/10.1080/15481603.2013.823732 Freire, S; Pesaresi, M (2015): GHS population grid, derived from GPW4, multitemporal (1975,
relevant in developing countries, which often lack the capacity for comprehensive urban planning and implementation, and the financial resources to invest in transport and protection infrastructure. These challenges are often compounded by fast demographic growth and internal migration, which can mean that risk-informed urban and infrastructure planning cannot keep pace. Moreover, as safe spaces are increasingly occupied, the resulting land scarcity can drive new developments disproportionately into previously avoided areas such as riverbeds, flood plains, or wetlands (figure 1).

This study offers a systematic and global analysis of spatial urbanization patterns and their evolving exposure to flood hazards. It leverages the 2019 high resolution global flood hazard layers by Fathom and the World Settlement Footprint Evolution (WSF-Evo) data set, developed by the German Aerospace Center (DLR) in 2021, which offers high-precision measurements of yearly settlement extents from 1985 to 2015 at 30 by 30 meters spatial resolution and with global coverage (figure 2)<sup>11</sup> . These data allow us to track the speed and shape of urban expansion in large cities through to small rural settlements and estimate flood exposure with high precision, and therefore understand what share of countries’ built-up land is exposed to high flood hazards and how this evolves over time. We show that settlements across the world grew by 85 percent between 1985 and 2015, from 693,000 to over 1.28 million square kilometers. In the same period, settlements exposed to the highest flood hazard level increased by 122 percent – thus highlighting that in many places, risky growth is outpacing safe growth.

2
<u>(2022)</u> argues that “ _a smarter, better-educated citizenry would not necessarily diminish polarization, lead to better policy decisions, or improve democracy._ ” Overall, there is little evidence to suggest that public apathy towards climate change is driven primarily by a lack of knowledge (Hornsey et al. 2016; <u>Kahan et al. 2012; Kellstedt et al. 2008; Malka et al. 2009).</u>

# **3. Data and definitions of variables**

Our data come from the 2023 round of the Life in Transition Survey (LiTS) conducted by the European Bank for Reconstruction and Development (EBRD) and the World Bank and covering the “transition” economies of Europe and Central Asia and several comparator countries in Western Europe, the Middle East, and North Africa (EBRD 2023). The 2023 round of the LiTS was conducted in 37 countries in Europe, Central Asia, and Northern Africa and includes nationally representative samples of around 1,000 households per country.<sup>6</sup>

# _3.1 Perceptions of climate change_

To understand the beliefs and preferences regarding climate change, we examine responses to the question, _“How convinced are you personally that climate change is real?”_ for which we construct a binary indicator that takes the value of 1 if the respondent answers _“quite convinced”_ or _“entirely convinced,”_ and 0 otherwise. We coded the question _“How convinced are you personally that climate change is manmade?”_ in the same way. To capture respondents’ awareness of the potential impacts of climate change, we examine responses to two questions: _“Do you think climate change seriously affects or will seriously affect you during your lifetime?”_ and _“Do you think climate change seriously affects or will seriously affect the children of today during their lifetime?”_ Two dummy variables take the value of 1 if the respondent answers
from other studies using Chinese prefectural-level data (Moreno-Monroy, 2008; Bosker _et al._ , 2010; Hering and Poncet, 2010a). Hence, our assumption  = 3. Studies which have estimated a prefectural-level regional wage equation for China using aggregate wage data have obtained estimates of the elasticity of substitution in the range 5 – 8 (Moreno-Monroy, 2008, p 26; Bosker _et al._ , 2010). Therefore, it was felt reasonable to assume  = 5, on the grounds that, _a priori_ , we might expect there to exist more substitutability between rural varieties than between urban varieties. Again, the possible consequence of these assumptions is to introduce error into the measurement of _RMAU_ and _RMA R_ , thereby further justifying our IV approach to estimation.

Related to the above problem is the need to also specify values for the parameters  _0_ ,  _1_ ,  _0_ ,  _1_ ,  _U_ and  _R_ in the transport cost functions [8] and [9]. Again, this is necessary for the construction of both _RMAU_ and _RMA R_ , as well as our measures of _G U_ and _G R_ . Following Fingleton (2005b, 2007), we assume  0   1   0   1  1 so that the urban and rural transport cost functions reduce to _TijU_  1  ( _tij_ )  _U_ and _TijR_  1  ( _tij_ )  _R_ respectively. This still leaves  _U_ and  _R_ as free parameters whose values need specifying. We, therefore, calibrate these parameters, selecting values which satisfy two criteria: ( _a_ ) a good fit between our "after" model solution and actual 2007 data, and ( _b_ ) satisfactory regression diagnostics, especially relating to the validity of our instruments, for our NEG wage equations. The values  _U_  .045 and  _R_  .075 satisfy ( _a_ ) and ( _b_ ), and imply stronger economies of distance for urban goods transportation than for rural produced varieties.
Jappelli, T., and L. Pistaferri. 2010, The Consumption Response to Income Changes, Annual Review of Economics 2(1), 479—506.

- [20] Kollmann, R. 2012. “Limited asset market participation and the consumption-real exchange rate anomaly”, Canadian Journal of Economics, 45 (May (2)) (2012), pp. 566—584

- [21] Kollmann, R. 2015. "Exchange Rates Dynamics with Long-Run Risk and Recursive Preferences." Open Economics Review, 26: 175-196.

- [22] Kose, M. A., E. S. Prasad, and M. E. Terrones. 2009. "Does Financial globalization promote risk sharing?," Journal of Development Economics, Elsevier, vol. 89(2), pages 258-270, July.

- [23] Lane, P. R. and G. M. Milesi-Ferretti. 2007. "The external wealth of nations mark II: Revised and extended estimates of foreign assets and liabilities, 1970—2004", Journal of International Economics 73, November, 223-250.

- [24] Levin, A., C.-F. Lin, and C.-S. J. Chu. 2002. “Unit root tests in panel data: Asymptotic and finite-sample properties.”, Journal of Econometrics 108: 1-24.

- [25] Lewis, K. K., 1996. “What Can Explain the Apparent Lack of International Consumption Risk Sharing?” Journal of Political Economy, Vol. 104, No. 2, pp. 267—297.

- [26] Lewis, K. K. 1997. “Are Countries with Offi cial International Capital Restrictions Liquidity Constrained?”, European Economic Review, 41, 1079 - 1109.

- [27] Mace, B. “Full Insurance in the Presence of Aggregate Uncertainty.” Journal of Political Economy 1991, 928-956.

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