rafmacalaba/data-use-mentions-extended huggingface dataset:
correlation between all of our variables of interest and Entry Density (Table 2). In addition, many of our control variables are significantly correlated with each other. For this reason we do not include multiple variables simultaneously in the regressions. However, because many of the control variables are also correlated with GDP per capita, we control for GDP per capita in all regressions.

# **_3.2 Entrepreneurship and the Financial Crisis_**

Business Entry, for the most part, increased gradually between 2004 and 2007 (, as shown for the complete (unbalanced) sample of 95 countries in figure 5).<sup>11</sup> Looking at a balanced panel of 57 countries for an even longer horizon, entry density in high-income countries increased from less than 3 percent in 2002 to about 4.5 percent in 2008 (Appendix 2). With the onset of the financial crisis in 2008, however, the trend reversed and Entry Density declined. While the drop appears to be most pronounced for high and upper-middle income countries, a closer look at other income groups shows that the crisis also impacted new business entry in other countries, though notably, entry density generally did not decrease in many lower income countries until 2009. For example, in the United Kingdom, the number of newly registered limited-liability firms steadily declined from 449,700 in 2007 to 372,400 in 2008 and 330,100 in 2009. In comparison, new firm registrations in Madagascar were 1,050 in 2007 and 1,183 in 2008, and then declined to 724 in 2009.

> 11 Entry Density data is trimmed for outliers at 99%.

12
**In addition to critical socioeconomic information, the EU-SILC data provides valuable information on gross and net labor incomes. Still, it is important to acknowledge its limitations, particularly about non-response bias and recall errors.** The EU-SILC (Statistics on Income and Living Conditions) survey relies on voluntary participation, whereas anyone with taxable income must file a tax return. This means there may be an under-sampling of households with high incomes, leading to underestimating top incomes and inequality. Furthermore, self-reported incomes in the EU-SILC may be subject to recall errors, with respondents potentially misremembering their income sources over the past year (the reference year). Incomes in the EU-SILC are also subject to imputations by the National Statistical Office, which might introduce some inaccuracies.  Despite these concerns, the EU-SILC remains the only reliable source of household self-reported incomes, with sufficient sample size and detailed demographic information. As a result, we base our analysis on EU-SILC data to estimate true income.

**Another primary concern with the labor income data in the EU-SILC is the lack of information on monthly incomes; to overcome this, we perform the analysis with annual income estimates rather than monthly.** The income information is only obtainable every year and does not correspond to the same period as the labor market status and additional labor variables. Yearly income measures cannot be used as a substitute for monthly income measures because yearly income may accumulate in only a few months of employment. Therefore, to accurately assess monthly income, we must consider the duration spent in different employment statuses during the year. The retrospective primary economic status data provides some information that can help divide the income into monthly parts, but to do so, we need access to longitudinal data. As a result of the different periods, the income divided by 12 may differ from the monthly income of the job at the time of the interview in the previous or current year. This issue is particularly significant for individuals with unstable careers, including job changes and unemployment interruptions, as the varying
relations and trust building are critical to explain co-inventorship—where contracts are usually more tacit, contract enforcement is difficult, and diaspora networks may play a referral role—but are less important in explaining more formal and hierarchical relationships—where probably explicit, written contracts are the rule. Technical highlyskilled workers, such as scientists and engineers, are possibly not the best placed to boost R&D offshoring networks to their countries of origin, but other skilled workers, such as executives, managers, trade agents, or entrepreneurs, may play a more critical role. Although some preliminary evidence seems to give support to this hypothesis, further research, possibly at the firm and inventor levels, will shed more light on these particular issues.

Of course, using patent data for migration research does not come without limitations. One important caveat is that inventors are only observed when they seek patents. However, not all inventions are patented; indeed, the propensity to patent for each dollar invested in research and development differs considerably across industries. In addition, studies have documented a skewed distribution of patent values, with relatively few patents yielding high economic returns. Another limitation of the dataset used in this study is that it misses inventors with a migratory background that have become nationals of their host country. To the extent that it is easier to gain citizenship in some countries than in others, this introduces a bias in the data. A related bias stems from the possibility that migrants of some origins may be more inclined to adopt the host country’s nationality than migrants from other origins. Unfortunately, the data do not allow the severity of these biases to be assessed. Notwithstanding these caveats, we believe that this database meaningfully captures a phenomenon of growing importance.

37
Policy Research Working Paper 7764

# **Abstract**

To estimate the impact of weather on rural income changes over time, this study combines data from the panel subsample of the latest Vietnam Household Living Standard Surveys 2010, 2012, and 2014 and gridded weather data from the Climate Research Unit. The analyses show: (i) crop cultivation, livestock management, forestry and fishing activities, and agricultural wages remain important income sources in rural Vietnam—especially for poorer households; (ii) rural communes are exposed to substantial inter- and intra-annual weather variation, as measured by annual, seasonal, abnormal, and extreme weather conditions and weather events; and (iii) these types of weather variation are indeed related to income variation. In particular, warmer temperatures and heat extremes can have negative income effects in all climate contexts and for all socioeconomic groups and most income activities. Only staple crops, forestry, and fishing seem to be less sensitive to hotter conditions. The effects of rainfall are more difficult to generalize. Some findings indicate that more rainfall is beneficial in drier places but harmful in wetter places. Interestingly, the incomes of poorer households seem to be negatively affected by wetter conditions, while those of wealthier households are more impacted by drier conditions. An increase in rainfall levels and flood conditions between 2012 and 2014, which were relatively wet years, is related to reduced income growth between these two years. Altogether these findings suggests that greater attention has to be paid to making rural livelihoods more resilient to weather variation which, is very likely to increase because of climate change.

This paper is a product of the World Bank Environment and Natural Resources Global Practice Group and the Climate Change Cross-Cutting Solutions Area and is a background paper for the World Bank work on “Climate Change and Poverty in Vietnam.” 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
be that the new metric is indeed poorly correlated with the underlying dimensions of food access that it purports to capture. Better understanding the degree to which different metrics complement each other and knowing which metrics might be poorly suited to specific contexts will help to weight metrics properly and, in turn, to characterize overall food access more fully.

The FIES is an experiential food access metric that incorporates information on anxiety over poor food access, food coping strategies, and household behaviors that are consistent with low levels of consumption and poor diet quality through eight yes-no 8
lower rates of empty mileage, with median 20 percent empty trips compared to 30 percent empty trips for other countries. Under the Noortman and van Es (1978) model, this is roughly equivalent to a 20 percentage point difference in backhaul probability. As many of the High Income studies use U.S. data from the 1970s and 1980s, if I condition on more recent surveys, the gap between High Income and Non-High Income countries grows.

**Meta-Analysis** To formalize these comparisons, I conduct a meta-analysis of the 54 primary studies. I begin with a univariate random-effects analysis using a restricted maximum likelihood (REML) model, where the weights are estimated using the inverse-variance method<sup>5</sup> . I find a mean estimate of 29 percent empty miles, with a 95 percent confidence

> 3This may under-estimate the degree of empty trips if fronthauls are also sometimes empty (e.g., under search frictions), or if trips involve additional empty legs such as traveling from home to a trip origin

> 4This assumption would be violated if, for example, shorter empty trips are used to chain together longer loaded trips.

> 5For studies that did not report a confidence interval, I assume the data was sampled from a Bernoulli distribution and used the implied estimate of the confidence interval. For studies that did not report observation counts, I imputed sample sizes based on study type and region.

7
|**Country**|**Formal**<br>**(% of**<br>**the**<br>**labor**<br>**force)**|<br>**Informal**<br>**Modern**<br>**(% of the**<br>**labor**<br>**force)**|**Informal**<br>**Rud.**<br>**(% of the**<br>**labor**<br>**force)**|**Modern**<br>**Informal**<br>**Wage /**<br>**Expected**<br>**Wage in the**<br>**Modern**<br>**Economy**|**Modern**<br>**Informal**<br>**Wage/**<br>**Min.**<br>**Wage**|**Informal**<br>**Rud. Wage /**<br>**Min. Wage**|
|---|---|---|---|---|---|---|
|Panama|62%|10%|28%|0.42|0.39|0.51|
|Peru|22%|32%|46%|0.61|0.39|0.35|
|Philippines|27%|34%|39%|0.59|0.39|0.37|
|Poland|77%|6%|17%|0.41|0.39|0.53
DC. © World Bank. https://openknowledge.worldbank.org/handle/10986/28498 License: CC BY 3.0 IGO.

- Frankel, Jeffrey, A., and David H. Romer. 1999. "Does Trade Cause Growth?" American Economic Review, 89 (3): 379-399.

- Goutam, Prodyumna, Italo A. Gutierrez, Krishna B. Kumar, and Shanthi Nataraj, Does Informal Employment Respond to Growth Opportunities? Trade-Based Evidence from Bangladesh. Santa Monica, CA: RAND Corporation, 2017.

- Hakobyan, Shushanik, and John McLaren. 2016. “Looking for Local Labor Market Effects of NAFTA.” Review of Economics and Statistics 98(4): 728–41.

- Hanson, Gordon H. 2012. "The Rise of Middle Kingdoms: Emerging Economies in Global Trade." Journal of Economic Perspectives, 26 (2): 41-64.

Harrison, Ann 2007. “Globalization and Poverty,” NBER Books, National Bureau of Economic Research.

33
# 2. Literature

Many analyses of middle class values remain descriptive in nature. The Pew Research Center (PRC), among others, recently carried out two insightful studies analyzing the attitudes of the middle class. PRC (2009) defines the middle class in absolute monetary terms, and explores the hypothesis that the values of the middle classes in emerging countries tends to reflect similar opinions to those of industrialized ones on democracy and social issues, once a certain level of wealth is reached. Compared with the lower classes, the report shows that the middle class assigns a higher importance to democratic institutions and individual liberties, considers religion less central to their lives, and holds more liberal social values. The report fails however in looking at “middle class particularisms,” as it does not report if the observed differences are statistically significant, and does not consider whether middle class values differ with the one of the upper classes. In PRC (2008), a public opinion survey was conducted among a nationally representative sample of US adults, to collect information on which social group people perceive themselves to be a part of (lower, middle or upper class) and their views on different values and perceptions about the future. The results show that 53% of adults in the US state that they are middle class, but roughly 80% indicate that it has become harder to sustain their current lifestyle, which suggests that, while wealthier than the poor, the middle class remains vulnerable to shocks.

The OECD’s Latin America Economic Outlook (2011) makes a relevant effort to analyze the attitudes of the Latin American middle class towards democracy, fiscal policy, and redistribution. It shows that the Latin American middle class supports democracy – yet remains dissatisfied with how democracy actually functions. Regarding taxation and public services, it finds that the Latin American middle class is more likely than other groups to consider that citizens should pay their taxes, and less likely to consider that taxes are too high or justify tax evasion. The middle class is
away from the precise rate‐of‐return calculations toward broader sectoral analyses and public expenditure reviews. They noted three critical areas for proper project appraisal, namely: the rationale for public intervention, fiscal impact of the project and contribution to poverty reduction. These aspects have been incorporated into recent World Bank guidance on doing EA and CBA for investment project financing (IPFs).

The analysis presented in this note suggests that in addition to the elements noted by Devarajan et al (1997), project EA should be seen as a means to think through design and implementation issues with a focus on assessing ways to improve performance and manage risks to achieve targeted goals. This is a particularly practical approach given that the estimated level of ERR is rarely a factor for deciding whether to proceed with a project or not (World Bank 2010). Also, the Bank is putting more attention on how to use project risk ratings to improve the performance of the project portfolio.

The EA could be used to strengthen the discussion in the risk section of the PAD and the rationale for the project risk ratings (or the SORT). In order to achieve this, it will be important to pay closer attention to the assumptions used in calculating ERRs. There may also be a need to more systematically track ERRs reported in the PADs and ICRs to ensure these are systematically and consistently captured, which, along with other readily available country and project level variables, could greatly facilitate gaining a deeper understanding of the factors that determine the ERR.

A number of recommendations could help strengthen the link between the EA and the SORT and thereby more strongly inform project design.

- I. **Automate the process for systematically tracking ERRs reported in PADs and ICRs** .  In the course of this review, it was noted that some of the ERRs estimated and reported in the PADs and ICRs were not always systematically captured in the IEG database.  For example, some PADs/ICRs reported multiple ERRs (sometimes by project component) that were often reflected
spectrum of urban settings (i.e. from small urban centers to megacities) sufficient enough to be linked to adverse health effects.

In fact, one of the first major efforts to contextualize urban air pollution from a global perspective commissioned by the WHO and UNEP found pollution levels that could be associated with serious health effects in 20 megacities (i.e. cities with a population of more than 10 million) across the world. Generally, exceedances of WHO limits were much higher in megacities in developing countries (largely situated in Asia) with Suspended Particulate Matter (SPM) being the most severe, whereas gaseous pollution levels were somewhat spread throughout the sampled megacities. While air pollution seemed particularly more severe in megacities in developing countries, each megacity had at least one pollutant level exceeding WHO health protection guidelines with vehicle traffic being the single most important ambient pollution source accounting for CO, NOx, HC and Pb. This was largely based on data from some of the 270 established monitoring sites under the Global Environment Monitoring System/Air Programme covering 45 countries by 1992 (WHO & UNEP, 1993; Mage et al., 1996).

Likewise, subsequent studies investigating urban air quality on a global scale which also focused primarily on megacities and major urban centers found considerable levels of poor air quality. A snapshot of further insights from the global perspective studies (non-encyclopedic list) have been presented as follows: An assessment of air quality from the years 1990-2000 in the ‘principal cities’ of the world selected from Europe, Africa, North America, Latin America, Asia and Oceania by Baldasano et al (2003) which identified O3, PM10 and NO2 as pollutants of concern. Particularly, nearly half of the cities had maximum daily hourly O3 levels above 200μgm<sup>-3</sup> with North America (Mexico City), and Latin America (São Paulo, Santiago) recording the highest levels i.e. 491μgm<sup>-</sup> 3 -3 -3 , 403μgm
Table 8. Simulated impact of PIT and GST reforms on poverty and inequality

|||Gross market<br>income plus<br>pension|Disposable<br>income|Consumable<br>income|Final<br>income|
|---|---|---|---|---|---|
|Poverty|2017 baseline|8.2|8.2|9.3||
|($3.20/day, %)|2021 simulation|7.2|7.2|8.7||
||2022 simulation|6.7|6.7|9.3||
|Gini index|2017 baseline|38.7|37.8|36.1|31.3|
||2021 simulation|38.7|37.9|36.3|31.4|
||2022 simulation|38.7|37.8|35.9|31.8|

Note: The results for 2017 present the baseline; the 2021 simulation incorporates the PIT reform and the 2022 simulation reflects both PIT and GST reforms. Source: World Bank staff calculations using 2017 BLSS.

Table 9. Concentration of old and new PIT schedule and sales tax/GST

|Old PIT<br>schedule|New PIT<br>schedule|Sales tax|GST|
|---|---|---|---|
|Poorest<br>0.0%|0.0%|2.5%|1.6%|
|2<br>0.0%|0.0%|4.3%|2.6%|
|3<br>0.3%|0.1%|4.4%|3.4%|
|4<br>0.5%|0.3%|5.
in CGE-based assessments of potential multilateral liberalization and regional integration schemes.<sup>17</sup> An implication for empirical efforts to determine the effects of policy is that it is not enough to focus on modeling price-cost margins as that does not allow one to disentangle the two effects. Better econometricallybased decomposition of the various price and cost margins linked to international service transactions is an important and sorely needed building block in guiding policy formation. In addition, recent focus has also shifted to the potential cost burdens imposed by regulations on domestic and foreign firms alike. As Figure 3 illustrates, domestic firms may benefit (or at least not be hurt) from liberalization when it actually involves lifting burdens on both home and foreign firms.

Another question in quantifying the effects of trade agreements, whether WTO or regional, is the extent to which policies on the ground correspond to commitments on paper. This is important if the impacts of new commitments are to be assessed. Both the OECD, with its services trade restrictions database, and the World Bank with a similar exercise, offer evidence on this question. Gootiiz and Mattoo (2009), for example, have created an index measuring the restrictiveness of actual policies in services trade suitable for comparison to the commitments made by countries in the WTO or in regional trade agreements. In the case of the GATS, their work suggests that currently applied policy regimes are much less restrictive than the regimes to which countries are formally committed. That is, the policy bindings negotiated under the GATS allow countries to substantially increase the level of applied trade restrictiveness if they desire to. An implication is not just that there is great scope to lock in the more liberal applied policy regimes, but that modeling the changes in commitments made by countries as a result of a WTO negotiation will overstate the extent of the liberalization achieved in the negotiation. In such cases, new commitments may limit room for policy maneuver, but they imply little change in actual policy.

# **4.2 Approaches to modeling
Status of Women in the Middle East and North Africa Survey Project, 2010).<sup>3</sup> In an already fragile context with a highly complex political, religious and social landscape consisting of 18 religious sects, numerous political parties, and large numbers of refugees, many Lebanese

> 1 On the quality of public services indicator, Lebanon’s score declined from 2.5 in 2006 to 0.8 in 2012 on a 4-point scale.  On the territorial coverage indicator, its score went down from 2.7 in 2006 to 1.5 in 2012. 2In a Gallup World Poll, Lebanese reported low confidence in (a) their national government (37 percent) and the judiciary, (b) the honesty of elections (15 percent), and (c) the honesty of government (4 percent) (World Bank, 2016). 3 According to the SWMENA survey, only 18 percent of Lebanese women are members of an organization, compared to 34 percent of men. Men are more likely to be members of a political organization than women (21 percent of men vs. 7 percent of women), whereas women are more likely to be active in religious groups and charity organizations than men.

4
We leverage this experimental design and original data from 5,080 households, 508 community leaders, and 254 clinics to estimate the causal effects of these interventions on both medium-run outcomes measured one year later, as well as their longer run effects on reporting during the Ebola crisis. The clinics in our study cover nearly 1 million people, just over 15 percent of Sierra Leone’s population in 2011.

In the medium-run, prior to the crisis, we find that both interventions improve the perceived quality of care and confidence in the health system, as reflected in individuals’ satisfaction with health workers and their increased utilization of clinics. Facing a health need, individuals living in treatment areas are 7 percent more likely to seek care in a government-run clinic; and satisfaction increases by 0.1 standard deviations. These effects are similar across both the CM and NFA arms. In CM alone, we find increases in maternal utilization — for example, the probability of delivery in a facility increases by 11 percent — as well as improvements in child health outcomes. The likelihood of under-5 death in the household declines by 38 percent, which we attribute to maternal utilization, as well as increases in vaccinations and improvements in child weight-for length. These effects are similar in magnitude to Björkman and Svensson (2009), who find a 33 percent reduction in under-5 mortality in clinics subject to community monitoring in Uganda. In our experimental context, neither intervention changes the quantity of services provided, nor leads to greater resources at the clinic level. This is not surprising, as the interventions do not attempt to increase inputs to clinics from higher levels of government.

To examine longer-run effects on outcomes related to the Ebola crisis, we use a de-identified database maintained by the GoSL and CDC. We count the number of reported cases (including individuals who test negative for Ebola) in a given week and section — a small administrative unit with a median area of 40 square kilometers.<sup>4</sup> Pooling the treatments, we estimate