rafmacalaba/data-use-mentions-extended huggingface dataset:
Bratsberg et al. (2018) provide evidence that migrants from less developed countries have more precarious jobs and are more exposed to the adverse consequences of firms’ bankruptcy and closure. Although displaced workers are the last hired and first fired and earn much less than natives, they may stay and work even longer during a crisis in the destination because conditions at home could be even worse. Danzer and Dietz (2018) find that Tajik workers stayed longer in Russia during the crisis of 2007-09, even though they earned less, became more exposed to illegal work, harassment, and deportation.

Turbulent economic conditions may also escalate social tensions between host and displaced communities by increasing competition over scarce resources. Given the lack of evidence for anti-refugee sentiments during a crisis, we refer to works on change in attitude toward migrants during bad economic times. Studies using European Social Survey suggest that the crisis of 2007-09 raised negative attitudes toward migrants, especially in countries more influenced by the crisis (Hatton, 2016; Isaksen, 2019; Turner & Cross, 2015). More recent papers explore the impact of the Covid-19 outbreak on the rise of negative attitudes towards migrants (Daniele et al., 2020; Dipoppa et al., 2021).

The papers focusing on displaced populations mostly struggle with scarce and unreliable data (Ruiz & Vargas-Silva, 2012). Refugees are normally excluded from household budget surveys, and the available data is mostly collected by humanitarian organizations without sufficient experience in measuring welfare and poverty (Verme, 2017). The scarcity of reliable data can explain the relative absence of refugees-related articles in the literature on the impact of economic shocks. However, the advantage of our paper is having access to the two national surveys conducted by the Statistical Center of Iran, the Labor Force Survey (LFS) and the Household Expenditure and Income Survey (HEIS), with a suitable coverage of Afghan refugees.

# **4. Research design**

In this section, we first describe data sources and the matching strategy. Then we
**Table A.9:** Robustness Checks on Nepotism (Table 4)

||**Village head survey**|**Bureau**|**crat survey**|
|---|---|---|---|
||Employs relative|Parent was leader|Parent served ingovt|
||(1)|(2)|(3)|
|1. Baseline|-0.385<sup>_∗∗∗_</sup><br>(0.178)|-0.066<br>(0.051)|-0.168<sup>_∗∗_</sup><br>(0.082)|
|2. No controls|-0.191<br>(0.179)|-0.040<br>(0.046)|-0.165<sup>_∗∗_</sup><br>(0.084)|
|3. Region fxed effects|-0.208<sup>_∗_</sup><br>(0.161)|-0.051<br>(0.046)|-0.164<sup>_∗∗_</sup><br>(0.084)|
|4. 3rd-Degree polynomial|-0.516<sup>_∗∗_</sup><br>(0.237)|-0.096<br>(0.065)|-0.209<sup>_∗_</sup><br>(0.120)|
|5. 1/2 MSE-opt. bandwidth|-0.456<br>(0.401)|-0.080<br>(0.073)|-0.188<br>(
producers, journal editors, and policymakers in addition to estimating the frequency with which efficiency analysis currently appears in published impact evaluations for the first time.

This paper combines results from exploratory research that queried individual impact evaluators, VFM experts, policymakers, as well as representatives of international development multilateral, bilateral, philanthropic, and research institutions and academic peer-reviewed journals using semi-structured and unstructured interviews, surveys, a small randomized control trial, and portfolio analyses of World Bank and worldwide impact evaluations.

# **Data**

Data for the evaluation were procured through five data collection activities:

**NEAR CENSUS OF WORLD BANK IMPACT EVALUATIONS** – A data set of 30 World Bank impact evaluations with VFM analyses was constructed from a sample of 168 World Bank impact evaluations produced between 2000 and June 2010.<sup>4</sup> Of the 168 impact evaluations, 40 were found to contain “a simple comparison of costs with benefits, cost-benefits analysis, economic rate of return, or cost-effectiveness analysis across treatment types or programs” (IEG 2012). Three were dropped because they were falsely tagged as including efficiency analysis when they did not, and two because they did not report the information needed to make a determination. Five IE publications could not be found. The remaining 30 World Bank IEs with VFM analysis were reviewed in detail.

**SAMPLE OF GLOBAL IMPACT EVALUATIONS FROM THE DATABASE OF THE INTERNATIONAL INITIATIVE FOR IMPACT EVALUATION** – We obtained a random sample of 236 impact evaluations from the 3ie’s Impact Evaluation Repository (IER), representing 10 percent of IER studies that were published between 1986 and 2012. The IER records all published impact evaluations of the effectiveness of development interventions that were identified through systematic search of over 30 databases, search engines and websites. Included interventions must have been carried out in low- or middle-income countries using recognized experimental or quasi-experimental estimation strategies.

**SEMI-STRUCTURED AND UNSTRUCTURED INTERVIEWS WITH IE PRODUCERS AND CONSUMERS, INDIVIDUAL RESEARCHERS, INSTITUTIONAL REPRESENTATIVES, AND POLICY MAKERS** –
we have seen for income poverty. In Mexican cities that are larger than 500,000 inhabitants, the incidence of stunting and of underweight among children is 9%. In the case of stunting the incidence rises monotonically as cities decline in size. Amongst the smallest cities (of less than 10,000 inhabitants) the incidence is as high as 16%. In the case of underweight, the incidence also rises, but less markedly: from 9% in the largest cities to 11% in the small cities. The higher incidence of child malnutrition in small towns also translates into more malnourished children: 27% of stunted children in urban

> 20 Fujii (forthcoming) adapts the Elbers et al procedure for the estimation of anthropometric outcomes and applies this methodology to Cambodia. Rascón adapts this procedure further to apply it to Mexican data.
in USD (base year 2009). Exchange rates and GDP deflators have been obtained from the World Development Indicators.

We apply the following rules to the dataset: (i) We include only the most recent Enterprise Surveys for each country. (ii) We only cover the years 2010 to 2017, to account for the shock of the global economic crisis of 2008. (iii) We exclude services firms from the sample, i.e. for which the ISIC Rev. 3 classification of the main product or service is higher than 36 and those that have been classified as services firm by the Enterprise Surveys, as information on female employment is hardly available. (iv) We drop countries for which female employment data were spotty<sup>9</sup> and firms with missing observations for

> 9 These include Lao PDR, Madagascar, Malaysia, the Philippines, Turkey, and Thailand.

6
|-0.067*|-0.075|-0.059|-0.059|-0.071|
||(0.046)|(0.041)|(0.054)|(0.044)|(0.040)|(0.052)|
|Initial log GDP|-0.130*|-0.021|-0.065|-0.135**|-0.037|-0.075|
||(0.067)|(0.075)|(0.070)|(0.064)|(0.073)|(0.069)|
|Exits|61|61|61|61|61|61|
|Spells|74|74|74|74|74|74|
|Years of Decline|489|489|489|489|489|489|
|Log-_L_|-105.691|-102.837|-97.312|-104.042|-101.201|-96.122|
|Pseudo-R<sup>2</sup>|0.083|0.108|0.156|0.097|0.122|0.166|

_Notes_ : All models include a constant. The standard errors are clustered at the country level to account for repeated spells. Some countries are not included in the Polity IV data, so that we analyze at most 54 (out of 57) or 74 (out of 77) spells.<sup>_∗_</sup> _p <_ 0 _._ 1,<sup>_∗∗_</sup> _p <_ 0 _._ 05,<sup>_∗∗∗_</sup> _p <_ 0 _._ 01 _Source_ : Authors’ analysis based on data from the Penn World Tables version
The IRR of 1.62 on the change in domestic unemployment rate in the second row of column (1) is greater than 1 and indicates that import protection increases when the domestic economy is weakening through rising unemployment. The IRR of 0.88 on foreign real GDP growth is evidence that additional import protection is used against trading partners that are going through their own periods of weak economic growth. Furthermore, an estimated IRR of 1.02 indicates that a real appreciation of the domestic currency is associated with increased import protection through TTBs. This evidence is consistent with related results from a 1980-1998 sample of data on antidumping policy use reported by Knetter and Prusa (2003). Finally, the time trend estimate of 0.99 indicates that, on average, import protection through these policies has been declining over the sample period.

Specification (2) provides our first sensitivity analysis by replacing the variable capturing the domestic macroeconomic shock – the change in the domestic unemployment rate – with a variable defined as the growth rate of domestic real GDP in _t-1_ . Theory predicts an IRR that is less than 1; i.e., weak domestic growth is associated with a higher incidence of TTBs. The estimated IRR is 0.93 and statistically significant. The IRR estimates for the other determinants of interest are qualitatively unaffected by this alternative indicator of the health of the domestic macroeconomy.

Before proceeding with additional robustness checks, consider next the economic magnitude of the estimated IRRs; Figure 2 provides a graphical interpretation. We present the percent increase in the count of imported products per trading partner per quarter subject to new TTBs that are associated with a one standard deviation change in each macroeconomic determinant. Specifically, we compute the median of the model’s predicted estimates of import protection evaluated using the sample data; and we then introduce (one at a time) a one standard deviation shock to each of the macroeconomic determinants of interest, holding everything else constant, and regenerate the model’s predictions of import protection.

The grey
# **8. Works cited**

ACOSTA, M., VAN WESSEL, M., VAN BOMMEL, S., AMPAIRE, E. L., TWYMAN, J., JASSOGNE, L. & FEINDT, P. H. 2020. What does it Mean to Make a ‘Joint’ Decision? Unpacking Intra-household Decision Making in Agriculture: Implications for Policy and Practice. _The Journal of Development Studies,_ 56 **,** 1210-1229.

AGARWAL, B. 1997. ''Bargaining'' and gender relations: Within and beyond the household. _Feminist economics,_ 3 **,** 1-51.

ALKIRE, S. 2008. 455Chapter 24 Concepts and Measures of Agency. _In:_ BASU, K. & KANBUR, R. (eds.) _Arguments for a Better World: Essays in Honor of Amartya Sen: Volume I: Ethics, Welfare, and Measurement._ Oxford University Press.

ALKIRE, S., MEINZEN-DICK, R., PETERMAN, A., QUISUMBING, A., SEYMOUR, G. & VAZ, A. 2013. The women’s empowerment in agriculture index. _World development,_ 52 **,** 71-91.

ANNAN, J., DONALD, A., GOLDSTEIN, M., MARTINEZ, P. G. & KOOLWAL, G. 2021. Taking power: women’s empowerment and household well-being in Sub-Saharan Africa. _World Development,_ 140 **,** 105292.

ARUGAY, A., DONALD, A., JARVIS, F., JOHNSON, H. C. & VALENCIANO, A. 2024. When Does Decision-Making Reflect Agency?

ASABA, R. B., FAGAN, H., KABONESA, C. & MUGUMYA, F. 2013. Beyond distance and time: gender and the burden of water collection in rural Uganda. _wH2O: Journal of Gender and Water,_ 2 **,** 31-38.

BANDURA, A. 2001.
-- End of picture text --> <!-- Start of picture text --> Rural Boy 5-17<br><!-- End of picture text --> <!-- Start of picture text --> Rural Girl 5-17<br><!-- End of picture text --> <!-- Start of picture text --> Rural Girl 5-17<br><!-- End of picture text --> Source: Authors’ estimates based on MICS 2016/17 Note: Individuals with missing age were dropped from the analysis. For Ebonyi the shares for urban boys and girls are excluded because of too limited sample size. The threshold for exclusion is set at less than 30 observations per demographic group.

23
estimate impacts on exit/entry, we use all facilities operational at randomization (1,348) and/or endline (1,319) regardless of whether they have a completed survey.

# **III.3 Data Sources and Description of Main Outcomes**

Our primary data sources are surveys of health facilities and their staff, exit surveys of patients, and direct clinical observations. At endline (baseline) we surveyed 1,285 (1,027) health facilities, 11,098 (8,577) patients, 2,098 (1,625) healthcare workers, and observed 19,178 (18,758) clinical interactions. We augment these survey data with additional administrative information on licensing status. Section 6 in the Supplemental Material lists the outcome variables and key covariates, along with details on how they were constructed.

In our study counties, 70% of facilities were private and 30% public, although higher patient

> 17At endline all facilities in 5 markets had closed, reducing the total number of markets to 268. We also exclude 3 of 1,322 facilities that were more than 4km from our existing markets, which results in a total of 1,319 facilities at endline.

> 18A difficulty with undertaking a census of this magnitude is that many of the facilities were small, one-roomed clinics and not included in administrative databases. In addition, 23 of the 90 facilities that we had “missed” were closed during the initial surveys, but during the endline survey, the facility in-charge gave us a facility opening date prior to the randomization. If we exclude these facilities, the market share of facilities that were missed is 2.7%. We assign these 90 facilities to a market using a closest-neighbor algorithm preserving the 4km clustering rule. Therefore, in total, there were 1,348 facilities in the 273 markets at randomization.

11
increase as _caused by_ the crisis, given the measured increase is consistent with pre‐crisis trends and one not far from forecasts of what may have taken in place even in the absence of the crisis.<sup>5</sup> My second main contribution of the paper is to provide a thorough measurement of the exporter incidence of the growing use of antidumping and its implications for discriminatory patterns of import protection across the trading system. Section 4 provides evidence of substantial heterogeneity; this time across affected exporting economies. Over time, the main impact of the foreign use of antidumping is increasingly on developing economy exporters. First, China’s exported products face the largest stocks of foreign‐imposed antidumping barriers, at nearly four times the amount of the second most‐targeted economies – South Korea; EU; Taiwan, China – more developed economies whose stocks of exports affected by foreign antidumping have remained relatively stagnant over recent history. Overall, in 2009, 2.6% of China’s exported products to developing economies were subject to antidumping and 1.6% of its products to developed economies were subject to antidumping, percentages that have accelerated since its 2001 WTO accession (Messerlin 2004, Bown, 2010c). I illustrate additional data that shows this feature of antidumping as increasingly a “South‐South” phenomenon is not unique to China’s exports. A number of other developing countries face trends similar to China in that the share of their products exported to other developing economies that is targeted by foreign antidumping is higher than the share of their products exported to high income economies.

In addition to these two main empirical contributions, my approach allows us to address a number of other important questions raised by the data. For example, while focusing on annual stock measures of TTBs is an important and previously underemphasized area for research, my approach also allows for construction of other measures of TTB policy activity, including more precise “flow” measures based on product coverage. Capturing more information on the rate
the value 1 if the firm is exporting and importing at the<br>same time and if the share of private foreign ownership of the firm is greater than<br>10%|
|GVC4|Dummy variable that takes the value 1 if the firm is exporting and importing at the<br>same time, if it has an international quality certification and if the share of private<br>foreign ownership of the firm is greater than 10%|
|Gender Provisions|The number of gender-related provisions in regional trade agreements.|

Source: Constructed by the authors using the WBES.

31
data, accounting for all the emissions of the firm (including subsidiaries), spanning from 2012 to 2021. The ICC includes detailed information on companies’ Scope 1, Scope 2, and Scope 3 emissions and emissions’ intensity, sourced from official reports such as Sustainability and ESG reports, the Carbon Disclosure Project (CDP), as well as estimated emissions from ISS ESG when official data is not available. For the analysis, we focus specifically on Scope 1 emissions since these are direct GHG emissions from sources owned or controlled by the firm.

# **2.4 List of green technologies and associated keywords**

In line with the approach of Bloom, Hassan, Kalyani, Lerner, and Tahoun (2021), we use the WIPO green patent data and the ECTs to identify biagrams or a set of two words that represent each green technology. We follow several steps. First, we extract the summary of the green technologies included in each patent in the WIPO database. Second, we transform the summary’s text into biagrams by applying a text recognition algorithm, removing numbers, special characters, and stop words (e.g. of, the, from, etc.) and converting each plural word into its singular form (eg. batteries, battery). Third, we drop bigrams that are not included in at least 20 patents and prior to the year 2000. The rationale behind this choice is to include not only the most influential but also the most novel technologies in our analysis. Fourth, we perform a human audit of the resulting biagrams. Fifth, we train a word embedding algorithm (word2ovec) with the patent’s summary to identify similar biagrams to each biagram and dropped nontechnical bigrams using Google Bigrams. Sixth, we perform a human audit of the new set of biagrams. Seventh, we train the word embedding algorithm with ECTs to capture business-like language referring to technical bigrams. Finally, we restrict the focus to technologies mentioned in more than 50 ECTs during the 2012-2021
|
||||(0_._008)||||(0_._008)||
|Employed in Industry|||0_._159<sup>_∗∗∗_</sup>||||0_._149<sup>_∗∗∗_</sup>||
||||(0_._007)||||(0_._007)||
|Employed in Services|||0_._232<sup>_∗∗∗_</sup>||||0_._225<sup>_∗∗∗_</sup>||
||||(0_._006)||||(0_._006)||
|Country FE|_Y es_|_Y es_|_Y es_|_Y es_|_Y es_|_Y es_|_Y es_|_Y es_|
|Adjusted R2<br>|0_._632<br>|0_._720<br>|0_._726<br>|0_._653<br>|0_._637<br>|0_._722<br>|0_._728<br>|0_._657<br>|
|Nr. of countries|19|19|19|19|19|19|19|19|
|Nr. of hh<br>4|18586<br>4|18586<br>4|18586<br>2|92538<br>4|18586<br>4|18586<br>4|18586<br>2|92538|

Source: International Urban Poverty Database.

Note: *** p < 0.01, ** p < 0.05, *** p < 0.1. Robust standard errors in parentheses. WorldPop 250m is used for the DOU method. The dependent variable is the log of per capita consumption expenditures, expressed in PPP and not spatially deflated.
Bound, John, Clint Cummings, Zvi Griliches, Bronwyn H. Hall, and Adam B. Jaffe. 1984. “Who Does R&D and Who Patents?” National Bureau of Economic Research Working Paper 908, in Z. Griliches ed., _R&D, Patents, and Productivity_ , (Chicago: University of Chicago Press, 1984), 21-54.

Branstetter, Lee G. 2001. “Are Knowledge Spillovers International or Intranational in Scope? Microeconomic Evidence from the U.S. and Japan.” _Journal of International Economics_ 53: 53-79.

Caselli, Francesco, Gerardo Esquivel, and Fernando Lefort. 1996. “Reopening the Convergence Debate: A New Look at Cross-Country Growth Empirics.” _Journal of Economic Growth_ 1:363-390.

Chamberlain, Gary. 1992. "Comment: Sequential Moment Restrictions in Panel Data." _Journal of Business and Economic Statistics_ 10: 20-26.

Evenson, Robert E.. 1984. “International Invention: Implications for Technology Market Analysis.” In Zvi Griliches, ed., _R&D, patents and productivity._ University of Chicago Press. Chicago.

Evenson, Robert E., and Yoav Kisleve. 1976. “A Stochastic Model of Applied Research.” _Journal of Political Economy_ 84:265-281.

Furman, Jeffrey L., Michael Porter, and Scott Stern. 2002. ”The Determinants of National Innovative Capacity.” _Research Policy_ 31:899-933.

Griliches, Zvi. 1990. “Patent Statistics as Economic Indicators: A Survey.” _Journal of Economic Literature_ 28(4):1661-1707.

Grossman, Genne E., and Elhanan Helpman. 1991 “Quality Ladders in the Theory of Growth.” Review of Economic Studies, 58(1) 43-61.

Hall, Bronwyn H, Jerry Hausman and Z. Griliches. 1986. “Patents and R&D: Is There a Lag?” _International Economic Review_ 27(2):265-83.

Hausman, Jerry, Bronwyn H. Hall, and Zvi Griliches. 1984
2019) analyzed how individual labor market trajectories responded to the trade-induced labor demand shocks. Following individual workers over time using the Relação Anual de Informações Sociais (RAIS) data, they show that tradable sector workers initially employed in harder-hit locations were more likely to switch to nontradable sectors in response to liberalization. However, this response did not oset the large losses in employment in tradable sectors. Finally, they document that the formal employment trajectories of workers initially employed in nontradable sectors were aected almost as much as those of tradable sector workers, indicating the presence of important spillovers from tradable to nontradable sectors locally. Dix-Carneiro and Kovak (2019) also investigate how the structure of local labor markets responded to the trade-induced local shocks over longer time periods. In the medium run (1991 to 2000), nonemployment and informal employment increased in harder-hit locations relative to the national average. Yet in the long run (1991 to 2010), nonemployment did not respond to local trade shocks, but informal employment strongly 24
et al., 2018). The year 2014 also saw the passing of carbon tax legislation with the _General Tax Reform Bill_ .<sup>8</sup>

# **4 Data**

The Chilean _Annual National Industrial Survey_ (ENIA from now on) is made available by the _National Statistical Institute_ (INE, 2015). ENIA data is publicly available and spans the period 1995-2019. From 1995 to 2007, ENIA offers a harmonized panel data set (referred to as the _combined_ data) which identifies the same manufacturing firm with a unique ID linking plants over time. At the time of this study, unique firm IDs linking panel data were unavailable and, until recently, ENIA had microdata available from 2016-2019 in separate annual waves (without a unique firm ID over time). We refer to these separate waves as the _raw_ version on ENIA.<sup>9</sup> The combined ENIA data set suffers from a lack of regional and manufacturing sector-level identifiers. To recover information on the location and the ISIC 4-digit codes for each plant, we resort to the raw data version of ENIA, which contains both desired variables. We match plants in the combined and raw data by their common characteristics and apply the same matching techniques to expand the panel structure in the post-2007 period. This procedure yields a final data set containing 100,803 observations on 12,169 plants, with an average of 4,200 unique firms observed across survey years. We provide a more extensive discussion on the matching algorithm in Appendix C. As illustrated in Figure A.2, the matching algorithm achieves a stable firm sample over the entire sample horizon and a low attrition rate.

The combined data also exhibit irregularities potentially stemming from misreported electricity and fossil fuel profiles. We address this issue by employing a simple cleaning algorithm, which provides a rule-based procedure to identify and correct inter-temporal changes in reported quantities and unit prices of pre-defined magnitudes. We provide a more extensive discussion on the cleaning algorithm in Appendix D.