rafmacalaba/data-use-mentions-extended huggingface dataset:
Transport costs (national and international) account for approximately 13% of global CO2 emissions (about two-thirds of emissions related to manufacturing activity). With the relocation of manufacturing activity to Asia, shipping volume has increased by 50% over the past 20 years. Shipping now accounts for about 90 percent of the volume of global trade and about 3 percent of global CO2 emissions. Also, oceangoing ships use bunker fuel and emit about 15 percent of global nitrogen oxides and between 5 and 8 percent of global sulfur oxide emissions, all contributing to respiratory illnesses, cardiopulmonary disorders and lung cancers, particularly among people who live near heavy ship traffic (70% of emissions are within 400 km of land).<sup>10</sup> Two studies give orders of magnitude of transport-related GHG emissions related to international trade. Using detailed emission for 7 sectors and 62 countries covering the period 1990 to 2000, Grether et al. (2010) compute an order of magnitude of the contribution of trade to SO2 emissions. Having defined a no-trade equilibrium, adding up emissions coming from trade-related composition effects and trade-related transport activities, they estimate that global worldwide SO2 manufacturing emissions were increased through trade by 16 percent in 1990 and 13 percent in 2000.<sup>11</sup> Cristea et al. (2011) produce a more systematic ‘bottom-up’ estimate of trade-related emissions (emissions from producing what is traded, i.e. traded goods in manufacturing activities, plus transport-related emissions from international transport disaggregated according to mode of transport. They show that world-wide, one third of trade-related emissions come from international transportation with a great variation in contribution across countries. They estimate 1.2 Gt of CO2 for 2004 or 146 grams per dollar of trade while output of traded goods generates 300 grams per dollar of trade, i.e. total trade-related emissions of 446 grams per dollar with 33% due to international transport. This implies that a $100/ton carbon tax would be equivalent to a 1.5%
# **_I. Introduction_**

The aim of this paper is to identify the role of urban policies in augmenting the natural advantages of cities to initiate virtuous spirals resulting in the emergence of innovative industrial clusters. The paper compares the efforts of Beijing and Shanghai to develop knowledge-based industries. It concludes that the innovative sectors in Beijing and Shanghai are still small but very dynamic with bright prospects. However, they are developing in different ways. The problem is the gap between the intention of public policy-makers and the real need of innovators living in the cities, particularly when public policy has to balance several controversial objectives in urban development.

The research was based on field studies conducted in Beijing and Shanghai from August 2003 to April 2004, and an extensive review of published material relevant to this topic. The primary sources were quasi-structured interviews with company executives in 100 firms (50 in Beijing and 50 in Shanghai). The firms interviewed varied greatly in size and sector, but all were engaged in producing creative outputs covered by intellectual property (IP) laws or in knowledge-based value adding services for businesses. The interviews inquired about the internal and external factors influencing the innovative capacity of firms, such as knowledge workers, IP laws, cluster dynamics, service businesses, innovative infrastructure, etc., in each city.

The next section presents the state-led policy directions that provide the background for the innovation needs of Beijing and Shanghai. The third section gives an overview of their economic development, as well as provides selected indicators on innovative capacity, such as R&D investment, creative industries, high-tech outputs, etc. based on published statistics. It is followed by an assessment of the quality of the business environment in each city from the perspective of the enterprises. This is followed by a critical discussion of the effects of government policies targeting high-tech industries and knowledge-based economy, including preferential policies toward key sectors, special treatment for inducing human talent, planning of parks for specific creative sectors, and so on.
of the household or patients, relative to the living standard of the other households in the sample. In this study, the wealth index for households was calculated by an index using self-reported data including consumer item ownership, amount and value of owned land, the value of rentals, household infrastructure, the source of water, heating, and electricity, and number household members per room. The resulting index is calculated from the standardized first component of principal component analysis, apportioned into quintiles. This method is also used by the Demographic and Health Survey; however, results will differ as the population in this survey is entirely rural. The wealth index was used as a covariate in the household survey indicator regressions.

44
the original vulnerability measures from the Probit model. The details and results of these robustness checks are in Appendix B.

We also performed one more robust check constraining the hazard coefficients to positive values. Vulnerability to poverty using our approach might be higher or less than the actual poverty. The empirical strategy of estimating vulnerability to poverty conditioned to hazards, exposure, and coping factors may predict a high probability of being poor even if households do not experience considerable hazards. Further, it may not identify households with a high susceptibility to hazards as vulnerable when the potential damage of hazards correlates negatively with districts' poverty. The latter is a critical caveat of the model, and we explore the effects of this miss-allocation using a restricted non-linear model where we force hazard coefficients to be positive. We estimate a non-linear probability model, which we named the restricted model, where we set the hazard coefficients as positive. At first, it is not an appealing option to impose structure on the model forcing the sign of the coefficients, but it can be an appropriate robustness check exercise. The results of this exercise show a high correlation between the vulnerability rates computed with the restricted and Probit model (see Figure B.1 in Appendix B).

# **5. Conclusions**

We merge nationally representative household datasets of seven countries in the LAC region with aggregate natural disaster and area data to estimate the household vulnerability to poverty in the context of climate risk. We find considerable heterogeneities in terms of the vulnerability to poverty among events. For example, cyclones in Mexico and earthquakes in 25
18 Coronavirus Disease 2019 (COVID-19) Outbreak in China: Summary of a Report of 72 314 Cases From the Chinese Center for Disease Control and Prevention’, _JAMA_ , 323(13), p. 1239. doi:10.1001/jama.2020.2648.

Yang, C. (2020) ‘Does hand hygiene reduce SARS-CoV-2 transmission?’, _Graefe’s Archive for Clinical and Experimental Ophthalmology_ , 258(5), pp. 1133–1134. doi:10.1007/s00417-02004652-5.

Zachreson, C. _et al._ (2021) ‘Risk mapping for COVID-19 outbreaks in Australia using mobility data’, _Journal of The Royal Society Interface_ , 18(174), p. 20200657. doi:10.1098/rsif.2020.0657.

Zvobgo, L. and Do, P. (2020) ‘COVID-19 and the call for “Safe Hands”: Challenges facing the under-resourced municipalities that lack potable water access - A case study of Chitungwiza municipality, Zimbabwe’, _Water Research X_ , 9, p. 100074. doi:10.1016/j.wroa.2020.100074.
<mark>Jordá, O. (2005). Estimation and inference of impulse responses by local projections.</mark> _<mark>American Economic Review</mark>_ <mark>, vol.</mark> **<mark>95</mark>** <mark>, pp. 161– 82.</mark>

- Leduc, S. & D. J. Wilson. (2012). _Roads to prosperity or bridges to nowhere? theory and evidence on the impact of public infrastructure investment_ . Cambridge, Mass: National Bureau of Economic Research.

- <mark>Leeper, E. (1991). Equilibria under ‘active’ and ‘passive’ monetary and fiscal policies.</mark> _<mark>Journal of Monetary Economics</mark>_ <mark>, volume 27, Issue 1, 129-147.</mark>

- <mark>Leeper, E. M.</mark> & <mark>Leith C. (2016). Understanding inflation as a joint monetary‐fiscal phenomenon, in</mark> _<mark>Handbook of Macroeconomics</mark>_ <mark>, vol.</mark> **<mark>2</mark>** <mark>, eds J. B. Taylor and H. Uhlig,</mark>

- Leeper, E. M., Traum, N. & Walker, T.B. (2017). Clearing up the Fiscal Multiplier Morass. _American Economic Review_ 107(8): 2409–54.

- Leigh, A. (2012). How Much Did the 2009 Australian Fiscal Stimulus Boost Demand? Evidence from Household-Reported Spending Effects. _The B.E. Journal of Macroeconomics_ , 12(1).

- Li, S., & Spencer, A. (2016). Effectiveness of the Australian Fiscal Stimulus Package: A DSGE Analysis. _The Economic Record_ , 92
“ _Append will add 185 observations to the workfile and remove any structure. Do you want to continue? Yes or no_ ?” We choose “yes”, and rename the new workfile page: “COMBINED”. This page now holds a total of 2675 observations. It resides in the overall wokfile called “NSWDATA”.

# **_Estimating the Propensity Score_**

# _Underlying Model_

The first step in propensity score matching is to estimate the probability of participation (receiving treatment) conditional on some covariates **x** . This can be based on a model of the probability that **d=1** given **x** , **Pr{d=1|z}** . This probability is also equal to the conditional expectation of the dummy variable **d** . Let π **(z)** stand for this conditional expectation, then we can write:

By analogy to standard regression analysis, we can write a _participation model_ as the sum of the conditional expectation and a random disturbance term, **u** . In other terms, It can be shown that **E(** ε **i)=0** , and **var(** ε **i)=** π **(zi)[1-** π **(zi)]=var(di)** .

To further specify the model, we assume that **di=1** only when the underlying function, **h(zi,** ε **i)** , of observable and unobservable characteristics is greater than zero, otherwise **di=0** . Let _h_ ( _zi_ , ε _i_ ) = _zi_ γ + ε _i_ , and **F()** stand for the cumulative distribution of ε **i** , then we have the following expression.

23
Ethiopian data the Singh-Maddala ranks first (being observationally equivalent to the GB2). For income data, the results in table 4 are somewhat mixed. The GB2 is clearly the best model for Burkina Faso, Ethiopia, Ghana, Malawi (for 2017 income data), Nigeria, Rwanda, Tanzania, and Uganda, whereas for Kenya, the Malawi’s 2013 household incomes and the Niger’s 2011 data, the Singh-Maddala seems to be as good as the GB2. A similar conclusion applies to Niger’s 2014 data, but in this case the Dagum model fits the data better than the alternatives. In general, the GB2 model gives the best fit to both consumption and income data in 7 of the 14 cases analyzed: Ethiopia 2014, Ghana 2005, Nigeria 2004, Rwanda 2014, Tanzania 2009, Tanzania 2013, and Uganda 2005. For the remaining country-year pairs, the following parametric distrbutions are instead selected as imputation models:

- Burkina Faso 2014: Dagum for the consumption distribution and GB2 for the income distribution

- Ethiopia 2014: Singh-Maddala for the consumption distribution and GB2 for the income distribution

- Kenya 2005: GB2 for the consumption distribution and Singh-Maddala for the income distribution

- Malawi 2013: Dagum for the consumption distribution and Singh-Maddala for the income distribution

- Malawi 2017: Dagum for the consumption distribution and GB2 for the income distribution

- Niger 2011: Dagum for the consumption distribution and Singh-Maddala for the income distribution

- Niger 2014: GB2 for the consumption distribution and Dagum for the income distribution

The suitability of fit of the functional forms chosen according to the model selection methods is evaluated by comparing the sample values of distributional indicators reported in table 1 with their counterparts implied by the fitted models—see the last four columns of table 3 and table 4.<sup>21</sup> Specifically, in figures 3 and 4 the comparison relies on checking for overlap between 95 percent confidence intervals of theoretical and sample indicators to draw conclusions about the accuracy of selected distributional statistics deduced by parameter estimates. The results
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We find that firms in the lower 25<sup>th</sup> percentile of sales and the 75<sup>th</sup> percentile of employment were more affected in the aftermath of Russia’s invasion of Ukraine than other firms. We try to explain the heterogeneity by examining two candidate structural factors: firms’ export links to Russia and digital gains prior to the invasion. Firms with export links to Russia are estimated to have suffered significantly more in terms of dropping sales and employment. We do not find any significant mitigating effect of digitization after the invasion shock that the existing literature would suggest. However, the results suggest that digitization helped firms increase employment on average during the studied period. While export links to Russia appear to increase sales on average during the studied period, this effect does not survive all the robustness tests we apply.

The remainder of the paper is organized as follows. Section 2 describes the employed data and their summary statistics. Section 3 explains the estimation approach and methodology. Section 4 discusses the results. Section 5 concludes and highlights some policy implications.

# 2. Data

We use data from the Business Pulse Survey (BPS) developed by the World Bank to measure the impact of the COVID-19 pandemic on the private sector. The Business Pulse Survey (BPS) is a novel dataset that tracks the potential impact of the pandemic on the private sector along critical dimensions of business performance, such as sales revenue, liquidity, and solvency, labor adjustments, adoption of technology, business expectations and uncertainty, and access to government support programs. In most countries, the survey interviews were conducted over the phone. The data include micro, small, medium, and large businesses across all main sectors (i.e., agriculture, manufacturing, retail, and other services, including construction). The sampling frame excluded micro-firms and/or businesses in agriculture in some countries. In other countries, when micro-businesses were included, the survey instrument offered simplified versions of some questions
medication (1=Yes)|0.46|0.63|0.17***|
||(0.49)|(0.48)|(0.04)|
|**5. Community support**||||
|Parental involvement index (z-score)|0.00|0.13|0.13|
||(1.02)|(1.07)|(0.08)|

* p<0.10,  ** p<0.05,  *** p<0.01

Note: Data from Indonesia ECED Surveys. Sample size is 245 project playgroups in each year. Cells with . indicate that the variable was not collected that year. Difference column is the difference between 2013 and 2010. Robust standard errors clustered by center. Classroom observation is measured using the ECERS-R. Parental involvement is constructed from a principal component analysis of four items: parents are involved in activities with teachers and students in the center; parents are involved in cleaning the center; parents are involved in supplementary food program in the playgroup; parents are involved in acquiring toys and materials in the playgroup. The index is normalized to have mean 0 and standard deviation 1 in 2010. A higher value of the index means more parental involvement.
<!-- Start of picture text --> “8, Wo +<br>rt a Pilea .<br>A% Ny , Roy 8S<br>. Ot;<br>. ,<br>we? wy tH S ba F)<br>eee IT<br>Sth<br><!-- End of picture text --> <!-- Start of picture text --> aa “~\<br>aN, rane<br>fp :<br> @ ° L<br>—<br><!-- End of picture text --> <!-- Start of picture text --> (%) Household Tax Debt (4%) Household Paid<br>-11<br>1i<br>i) i<br>31<br>11<br>:+qi)<br>1 i<br>1<br>q ! a 1<br>ae i = i<br>.1i<br>i<br>71 1<br>::4<br>:: lt<br>4.11i1<br>11<br>rr<br>and 2016 2018 2020 2022 ani4 2016 2018 2020 2022<br>Fiscal Year Fiscal Year<br>— Not Visited — Visited — All — Not Visited — Visited me Al<br><!-- End of picture text -->
would take Syria at least two decades to meet its pre‐conflict GDP level and close to three to surpass its pre‐conflict GDP per capita. Respectively for the optimistic and pessimistic scenarios, the average GDP per capita growth rate over the next two decades is projected to be 8.2 or 3.1 percent.

While the reconstruction and expansion of physical infrastructure is essential, the importance of strengthening human capital and the factors underlying TFP growth cannot be overstated. In this paper, we have only accounted for population and enrollment effects on human capital growth. However, the quality of education and health (as illustrated in the World Bank’s human capital index) will also likely be impeding factors that would have to be addressed in Syria’s quest for growth.

34
is a synthetic indicator of the strictness of regulation on the dismissal of workers on regular contracts and the hiring of workers on temporary contracts. It ranges from 0 to 6, where 6 measures the strictest regulation. Then, a larger number indicates more job security. The OECD product market regulation measures the degree to which policies promote or inhibit competition in markets. It ranges from 0 to 6, where 6 captures policies that most inhibit competition. The OECD social expenditure indicator is presented as a share of GDP, and it includes social expenditure on old age, survivors, incapacity-related benefits, health, family, active labor market programmes, unemployment, housing, and other social policy areas. When looking at 2010 data on migration policies from the Immigration Policies in Comparison (IMPIC) database (Helbling et al. 2017), the previous indicators correlate in the expected direction. For example, social expenditure tends to be higher in countries with less restrictive labor migration policies (e.g., on work permit validity, labor market tests, loss of employment, or age limits).

6
with tax authorities,” _Annual Review of Economics_ , 2019, _11_ , 755–781.

- **Porta, Rafael La and Andrei Shleifer** , “Informality and development,” _Journal of Economic Perspectives_ , 2014, _28_ (3), 109–26.

- **Redonda, Agustin, Christian von Haldenwang, and Flurim Aliu** , “Global Tax Expenditures Database (GTED),” March 2022.

- **Serrato, Juan Carlos Su ́arez** , “Unintended Consequences of Eliminating Tax Havens,,” _R&R, American Economic Review_ , 2022.

- **and Owen Zidar** , “Who benefits from state corporate tax cuts? A local labor markets approach

- with heterogeneous firms,” _American Economic Review_ , 2016, _106_ (9), 2582–2624.

- **Slemrod, Joel** , “Tax Compliance and Enforcement,” _Journal of Economic Literature_ , December 2019, _57_ (4), 904–54.

- **Souillard, Baptiste** , “Profit Shifting, Employee Pay, and Inequalities: Evidence from US-Listed Companies,” _Working Paper_ , 2022.

- **Tørsløv, Thomas, Ludvig Wier, and Gabriel Zucman** , “The Missing Profits of Nations,” _The Review of Economic Studies_ , 07 2022, (0), 1–36. rdac049.

- **Ulyssea, Gabriel** , “Firms, informality, and development: Theory and evidence from Brazil,” _American Economic Review_ , 2018, _108_ (8), 2015–47.

- **Wei, Shang-Jin, Jianhuan Xu, Ge Yinz, and Xiaobo Zhang** , “Bureaucratic bean counting and patent subsidies: A welfare evaluation of China’s pro-innovation program,” Technical Report 2021. Working Paper.

- **Wu, Wenfeng, Chongfeng Wu, Chunyang Zhou, and Jun Wu** , “Political connections, tax benefits and firm performance: Evidence from China,”
strongly regressive across districts. The strong cross-district regressivity in applications is effectively neutralized by the formula that allocates funding across districts, which fixes the amount of money available per district at the outset of the entire program. The progressivity injected into project selection is entirely cross-district, suggesting that it is in fact the _centralized_ component of this decentralized program that makes it mildly pro-poor. It is also noteworthy that variables, such as access to information and political activity are not correlated with the funding decisions conditional on applications, consistent with the fact that the centralized funding formula to the districts did not include these characteristics.

Figure 1 illustrates how the regressive nature of the decentralized applications process is counteracted by the progressive centralized district funding formula. Here, we plot the smoothed number of applications and the acceptance rate over the distribution of poverty headcounts across wards. The heterogeneity is tremendous: the richest wards submit almost six applications per 1,000 people compared with less than two among wards with the highest poverty rates. The acceptance rates, on the other hand, increase from below 12% to over 15% across that same span. This finding suggests that the heterogeneity in applications is not simply arising from the expected probability of funding: application rates are highest in places where the funding rates are lowest and vice versa.

# **6. Targeting Performance at the Village Level**

Our analysis of within-village targeting focuses on the Vulnerable Groups component (VG projects) of TASAF II because the data collected for this study was part of baseline data collection for the randomized study designed to assess the impact of this component on the creation of small enterprises and household welfare.<sup>13</sup> The data at the household level allow the cross-examination of the earlier findings from the institutional data that we have used up to this point, because the surveys were conducted by an independent firm. To the extent that data coming from the program itself may contain accounting irregularities (ghost projects, efforts to cover up the misuse of funds, etc.