rafmacalaba/data-use-mentions-extended huggingface dataset:
For example, Deininger and Mpuga (2005) found that knowledge on how to report bureaucrat misbehavior or unsatisfactory service quality reduces the incidence of corruption and improves quality in health and education services in Uganda.

Finally, proof of identification is another crucial aspect of any bureaucratic interaction between citizens and governments. No matter how good the quality of the services, many people are prevented from accessing them by the lack of legal identity.<sup>2</sup> A study in Indonesia found that among the poorest households, individuals with a birth certificate were much more likely to have completed 12 years of schooling and were less likely to get married before age 18 (Sumner 2015).

Identification has been set as one of the United Nations Sustainable Development Goals (SDGs). SDG 16.9 recognizes the importance of identification not only as a proof of existence facilitating access to citizen rights, public services, jobs, financial institutions, and to family and property transactions, but also as a source of reliable health statistics<sup>3</sup> (Asian Development Bank 2007; World Bank and World Health Organization 2014; Dahan and Hanmer 2015). Planning and implementing health care and other social programs require an accurate accounting of population dynamics and health needs. The availability of quality data in these areas relies heavily on efficient civil registration and vital statistics (CRVS) systems. For example, the lack of proper data to track maternal deaths is an obstacle to progress in the related Millennium Development Goal (MDG) and subsequent SDG 3.1 (Danel et al. 2011).

The Identity for Development (ID4D) initiative has identified 10 other SDGs including social protection, disaster relief, women’s empowerment, and ending major epidemics for which identification is a key enabler (World Bank 2016). However, only anecdotal evidence is available on how the inefficiency of CRVS systems may negatively affect their coverage across the world.<sup>4</sup> One example is a recent, small survey
|3.63|5.57|
|Onion|7.99|1.74|1.63|1.71|4.02|
|Passion fruits|13.06|0.87|1.80|0.73|4.95|
|Avocado|25.80|2.84|1.93|0.45|5.72|
|Pineapples|12.94|2.81|0.75|1.47|4.04|
|Cabbage|4.03|1.29|0.60|1.35|4.33|
|<br>Pawpaw|32.71|1.72|1.46|0.22|7.44|
|<br>Oranges|15.85|0.72|1.37|0.13|5.67|
|<br>Mango|18.08|1.82|1.04|0.40|4.56|
|<br>Dodo|6.57|0.65|0.24|0.50|7.20|
|Egg plants|4.03|1.00|0.29|0.26|4.44|
|All fruit & vegetable|||14.87|10.85||
|<br>**_All crops_**|||**615.12**|**372.94**||

_Source:_ Own computation from harvest diary and 2005/06 UNHS Only complete diaries included.

20
standard) are greater for state-owned firms than that for non-state-owned firms in China. This finding, again, boils down to the size of the firms because state-owned firms in China account for a significant proportion of large-scale energy firms, and cleaner production standards have greater effects on the improvement of productivity of state-owned firms.

# **3.3 Effects of innovation vs. adoption of green/clean technology on the productivity**

Whether the firm’s productivity change due to green investment is caused by the investment in the deployment of green/clean technologies or by their development (i.e., R&D investment)? While the increased adoption of green/clean technologies directly affects productivity even in the short run, innovation of new technology through R&D investment does so in the long run. However, the development of new technology does not guarantee its adoption. The discussions earlier are related mostly to investment in the adoption or deployment of clean technologies. In general, the countries with greater energy R&D investment intensity show higher levels of investment in the green technologies (Popp et al., 2011). Here, we briefly highlight the literature that exploits the relationship between R&D investment in clean technologies and firms’ productivity. Some existing studies empirically show that environmental knowledge spillover or the knowledge created through R&D investment on environmental or clean technologies are positively related to firms’ performance.

Based on a survey of small and medium-sized Italian manufacturing firms over the 20042006 period, Aldieri et al. (2021) show a positive but only slightly significant effect of process or product innovation on productivity. They also show positive and strongly significant effects if it is an organizational innovation.<sup>9</sup> Using firm-level data on inputs, outputs, and their patents registered from Japan, the USA, and European countries, Aldieri et al. (2020) show investment in environmental or clean technologies or knowledge, measured in terms of registered patents, affect firms’ productivity significantly
Prefectures, ethnic minority autonomous prefectures and leagues are all less urbanized than prefectural cities. Ethnic minority autonomous prefectures and leagues are also dominated by ethnic minority populations. Districts under municipalities are at the prefectural level administratively, even though districts under prefectural cities are at the county level.

To maximise sample size and geographic coverage, our dataset includes 280 of China's 283 prefectural cities, 11 of its 17 prefectures, all 30 of its ethnic minority autonomous prefectures, and all three of its leagues. The three prefectural cities which are excluded are Tianshui City (Gansu Province), Karamay City (Xinjiang), Lhasa City (Tibet). Meanwhile, the excluded prefectures are Ngari, Nagqu, Qamdo, Xigazê, Shannan and Nyingchi (all Tibet). With the exception of Karamay City, which is located in China's remote western region and whose economic structure is heavily biased by dominance of the oil production and refining industry, all of these exclusions are attributable to a lack of data availability. Also included as "prefectures" in our dataset are three of the four municipalities – Beijing, Shanghai and Tianjin – which operate at the provincial level, whilst we divide the fourth municipality – Chongqing – into three regions ("One-Hour Economic Circle", "Northeast wing" and "Southeast wing"). In terms of population and land area, Beijing, Shanghai and Tianjin more closely resemble the larger prefectural cities than they do the 22 provinces. Chongqing Municipality looks more like a small province, hence our division of it into three regions. These three regions correspond to those identified in Chongqing Municipal Government's _Master Plan for Experimental Reform_ (World Bank, 2009).<sup>33</sup> Shihezi City, a county-level city under the direct jurisdiction of Xinjiang Province, also enters our dataset.<sup>34</sup> Our sample size of 331 regions exceeds that of previous studies in the empirical NEG literature which have made use of prefectural level data (Au and Henderson, 2006
yields using the sub-sample of EACI plots that were subject to crop cutting. The coefficient for plot area is negative but not statistically significant. This may be because the mean difference in plot area between the crop cut plots and the rest of the sample was statistically significant. The plots that were not subject to crop cutting were, on average, larger, and in view of the evidence regarding the underestimation in SR yields on larger plots, the IR is weakened when the regression is estimated using the entire sample. And while the coefficients for key inputs are consistently positive and statistically significant in the regressions of SR yields, the comparable coefficients in the regressions of ML yields are not statistically significant and are consistent with those estimated in the regressions of CC yields. These results are fully consistent with the findings based on the ERIVaS data set and raise flags concerning the analyses conducted with SR yields.

# **5 Implications for crop yield measurement in household and farm surveys**

Having demonstrated the promise of the use of ML to derive imputed measures of CC yields, this section provides a few recommendations on what data to collect and how to collect it in an efficient way.

First, given the importance of using SR yields to improve the quality of the ML-based imputed CC yield predictions, survey practitioners should continue to collect farmer-reported information on crop production, and reduce NCME in the resulting data, including by reducing length of farmer recall and improving the completeness and quality of conversion factors for non-standard measurement units, 32
bias, we match control countries to treatment countries on the following factors –average GDP per capita over the period 2002-2004, average real GDP growth over the period 2002-2004, and propensity to reform. We measure propensity to reform using the 3 year _change_ in the World Governance Indicator (WGI) Regulatory Quality Index over the same period.<sup>17</sup> This Index captures perceptions of the ability of the government to formulate and implement policies and regulations that promote private sector development. By matching on changes in regulatory quality, we are matching countries that show similar trends in propensity to reform.<sup>18</sup> In addition, we seek to keep the treatment and control countries similar on the level and rate of overall development by also matching on GDP per capita and real GDP growth.

We calculate propensity scores for each country using pre-reform values on these variables, and use nearest neighbor one-to-one propensity score matching (with replacement).<sup>19</sup> With a newly matched treatment and control countries, we subsequently estimate the differencein-difference specification of equation 2, taking into account the number of times a control country matches multiple treatment countries with frequency weights.

Second, we introduce within-country variation in dependence on external sources of finance across industries. This approach compares the impact of CB reform on employment growth across industries that are more versus less dependent on external sources of finance. Firms in different industries rely on external financing to a different extent, and we use the varying response of these industries to credit bureau reform as part of the empirical strategy. It

> 17 A three year timeframe provides a more stable regulatory direction of a countries policies that is more likely to smooth out annual jumps.

> 18 Using another measure of regulatory quality, the ICRG Legislative Strength index produces similar matching results.

> 19 We verified that the choice of matching method is not crucial to the overall results. Using Kernel density based matching, for example, produces similar matching and qualitatively similar regression results.
297–321.

- Harrison, A., T. Britton, and A. Swanson. (2003. _Working Abroad: the Benefits from Nationals Working in Other Economies._ Paris: Organisation for Economic Cooperation and Development.

- Integrated Public Use Microdata Series. [2008]. International: Version 6.0 [Machinereadable database]. Minnesota Population Center, University of Minnesota, Minneapolis, MN.

- Mayda A. M. 2007. ―International Migration: A Panel Data Analysis Of The Determinants Of Bilateral Flows.‖ CReAM Discussion Paper Series 0707, Centre for Research and Analysis of Migration, Department of Economics, University College London.

- OECD (Organisation for Economic Co-operation and Development). 2002. Trends in International Migration database. SOPEMI 2002 Edition. Paris: OECD.

- ———. 2008. _A Profile of Immigrant Populations in the 21st Century: Data from OECD Countries_ . Paris: OECD.

- Parsons, C.R., R. Skeldon, T.L. Walmsley, and L.A. Winters. 2007. ―Quantifying the International Bilateral Movements of Migrants.‖ In _International Migration, Economic Development and Policy,_ ed. C. Ozden and M. Schiff. New York: Palgrave Macmillan.

32
20

||fiscal decentralization|log p.c. real GRP|**Tab**<br>log p.c. real GRP, PPP|**le 3. Pai**<br>log population|**rwise cor**<br>log area|**relations**<br>election votes spread|**between**<br>fractionalization|**selected**<br>fuel industry share|**variables**<br>excess budget revenue|<br>privatization rate|share of social transfers|child mortality|corruption|urbanization|number of students|
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
|fiscal decentralization|1.00|||||||||||||||
|log p.c. real GRP|-0.24|1.00||||||||||||||
|log p.c. real GRP,PPP|-0.22|0.86|1.00|||||||||||||
|log population|0.08|0.48|0.18|1.00||||||||||||
|logarea|-0.07|0.40|0.65|0.08|1.00|||||||||||
|electionvotes spread|-0.23|-0.04|-0.10|0.01|-0.14|1.00||||||||
22 We study next the properties of the ECM on different sample periods in order to answer the following questions: Can we quantify the response of POLDIS to a shock to MEXDIS? To which extent the volatility of POLDIS is due to the volatility of MEXDIS?

These questions can be answered through what is generally referred to as _innovation accounting,_ i.e. the _impulse response function_ and _the forecast error variance decomposition._ The impulse response function uses the estimated parameters of the model to trace out the effects of one standard deviation (SD) change in the errors on the time path of MEXDIS and POLDIS sequences; the forecast error variance decomposition uses the estimated parameters and indicates the proportion of the forecast errors on a variable which is due to its own shocks (residuals) as opposed to shocks (residuals) to the other variable. 22 <!-- Start of picture text --> 600<br>400<br>200<br>0,<br>-200,<br>.400 _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _<br>Long Run Residuals<br><!-- End of picture text --> _Figure 8_ The impulse response function in figure 9 shows that: 1) a one standard deviation innovation to MEXDISI (the SD of the residuals on MEXDIS I being of 91 bs. pts.) has a permanent effect of _36 bs. pts_ on MEXDIS I and of _35 bs. pts._ on POLDIS; 2) a one standard deviation innovation to POLDIS (the SD of the residuals on POLDIS being of 44 bs. pts.) has a permanent effect of _29 bs. pts._ on MEXDIS I and of _27 bs. pis._ on POLDIS. We can conclude that a 22 The only problem with this methodology is that an estimated VAR is underidentified: an additional restriction must be imposed on the two variable VAR system in order
admin population, rather than any national business population and represents populations in countries with sufficient internet penetration.

The data, tools, and further details on the methodology of the Future of Business survey can be found on the Facebook Data For Good website https://dataforgood.facebook.com/dfg/tools/future-of-businesssurvey.

In Table A1 we show all the countries that were included in the Future of Business COVID-19 surveys, and for which waves they were surveyed. Most countries were surveyed in all seven waves; however, sample size constraints meant some countries could only be included for a subset of the waves.

|**Table A1: Econo**|**mies include**|**d in the F**|**uture of**|**Business**|**High Freq**|**uency CO**|**VID-19**|**Surveys**|
|---|---|---|---|---|---|---|---|---|
||||**Wav**|**e of Faceb**|**ook Fob Dat**|**a**|||
|_Country Name_|_Wave 1_|_Wave 2_|_Wave 3_|_Wave 4_|_Wave 5_|_Wave 6_|_Wave 7_|_Total_|
|Argentina|1|1|1|1|1|1|1|7|
|Australia|1|1|1|1|1|1|1|7|
|Bangladesh|1|1|1|1|1|1|1|7|
|Belgium|1|1|1|1|1|1|1|7|
|Brazil|1|1|1|1|1|1|1|7|
|Cambodia|1|1|1|
wages. On the other hand, the wage premium of documented migrants remains of the same magnitude.

# **5.2 Underlying Mechanisms: Does the Legal Status of Migrants Matter while Abroad?**

The results in section 5.1 suggest that undocumented migrants fare worse upon return to Egypt compared to documented migrants but also compared to non-migrants. While documented migrants benefit from their migration experience abroad and witness a positive wage premium compared to non-migrants, undocumented migrants witness a wage penalty associated with their experience overseas. In this section, we investigate several underlying mechanisms.

In the ELMPS, individuals are asked about the reasons for return. Interestingly, we find that the primary motives for return migration differ between return migrants who had a documented migration versus those who had an undocumented migration. For example, while the most important cause of return migration is the end of the work contract for documented migrants (26%), the primary reason for return for undocu-

> 25 In Table A.5 in the Online Appendix, we compute the occupational indices following Sicherman and Galor (1990) and Carletto and Kilic (2011).

21
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# **3.2  Satellite Data**

# _Sentinel-5P_

The European Space Agency (ESA) has operated its Sentinel-5P platform since October 2017, with repeat observation frequencies of 5 days in equatorial latitudes and 2-3 days in mid-latitudes. Sentinel-5P provides coverage of nitrogen dioxide (NO2), ozone (O3), carbon monoxide (CO), and sulphur dioxide (SO2). To illustrate, Figure 2 displays spatial variations in average Sentinel-5P NO2 measures for the Dhaka region from September 2018 to August 2019. The ESA has provided open-source access to the Sentinel-5P database since July 2018, and the entire database is now available on Google Earth Engine (GEE) for rapid extraction of daily imagery for specific locations. For comparison with readings from Dhaka’s CAMS-3 monitor, we have used GEE to extract two measures for the cell displayed in Figure 1: daily averages of tropospheric column density for NO2 and an absorbing aerosol index.

Sentinel-5P is a new system, so validation studies remain scarce. In a rare exception, a study for the eastern United States by Kaltenbaugh (2019) finds significant correlations between ground station measures of NO2 and Sentinel-5P’s tropospheric NO2 measure.

**Figure 2:  Sentinel-5P: Average NO2 measure, September 2018 – August 2019.**

5
percent of employees must be nationals, and 70 percent of wages be paid to national employees. Furthermore, Peru allows cross-border supply of reinsurance services which it previously did not.

Some of the most significant developments in financial services reform after the commencement of TPP negotiations were witnessed in Malaysia, and are reflected in its TPP commitments. In 2008, primary entry as the branch of a financial institution was not allowed, and entry through a subsidiary was temporarily not allowed, since no new licenses were being issued. Although there was no limit on foreign ownership in a subsidiary, the limit on foreign ownership was 30 percent for acquisition of a local bank. With the enactment of the Financial Services Act 2013 and the Islamic Financial Services Act 2013 in June 2013, there has been a comprehensive modernization and streamlining of Malaysia’s regime on licensing and foreign equity limits in the banking and insurance sector (conventional and Islamic). In both conventional and Islamic finance, application for a license is now based on the prudential and “best interest of Malaysia” criteria. Similarly, the acquisition of a significant foreign equity interest in Malaysian banks and insurance companies could be up to 100 percent, subject to meeting the aforementioned criteria.

# **Figure 2: STRI for Financial services - Doha Offers, ‘Best PTA’, TPP Commitments and Applied Policy of 2015**

<!-- Start of picture text --> Financial: Doha, PTA, TPP and applied policy<br>BRN MYS CHL SGP MEX CAN VNM USA PER JPN AUS NZL<br>Doha PTA<br>TPP Policy '15<br>Note: Absense of a bar means there are no restrictions (STRI score is zero)<br>100<br>75<br>50<br>25<br>Services trade restrictiveness index<br>0<br><!-- End of picture text --> Source: The World Bank TPP Database, 2016 Malaysia’s TPP commitment enhances transparency and
distance of the school<sup>22</sup> and whose parents would have chosen that particular school for their child if they had sent their child to school in absence of the program. Walking distance for preschool age children is approximately 4 km. Note that, since all enrolled children walk to school, they must belong to the set of targeted children. Characterizing the targeted population is di¢ cult for two reasons. First, there is no household registry with records of where people live, hence there is no ready list of which children live within 4 km of the school. Second, even if there were such a list, one would still not know which school the parents would have chosen to send their children to, since most households live within 4 km of several schools.<sup>23</sup> This evaluation uses the following approach to characterize the targeted population. My basic assumption is that a household’s choice of schools for its children is very correlated between the children. For example, we assume that a household’s school choice for their 10-year old child is a good predictor of their school choice for their preschooler. If households have many children, if the probability that at least one child in the household goes to school is su¢ ciently large, and if all children are bound to go to school within 4 km of their home because they have to go to school on foot, then a baseline survey of all siblings age 4 to 6 of all children currently in the school will give a reasonable approximation of the targeted population.

ICS collected such a baseline survey prior to the program. The NGO ...rst collected a list of all families that had at least one child in the preschool or in the associated primary school. Subsequently, the oldest child was interviewed and asked for the names and ages of her siblings<sup>24</sup> below the age of seven. The list of all families in a school, including the
www.ssrn.com/abstract=4346525` .

- 891 L. Barrage and W. Nordhaus. Policies, projections, and the social cost of carbon: Results from the 892 DICE-2023 model. _Proceedings of the National Academy of Sciences_ , 121(13):e2312030121, Mar. 893 2024. ISSN 0027-8424, 1091-6490. doi: 10.1073/pnas.2312030121. URL `https://pnas.org/doi/` 894 `10.1073/pnas.2312030121` .

- 895 A. M. Bauer, F. McIsaac, and S. Hallegatte. How Delayed Learning about Climate Uncertainty Impacts 896 Decarbonization Investment Strategies. Working Paper WPS10743, The World Bank, Washing897 ton DC, 2024a. URL `https://documents.worldbank.org/en/publication/documents-reports/` 898 `documentdetail/099829103282438373/idu1f2d86d77127091490d1a6df1dc342f15d10b` .

- 899 A. M. Bauer, C. Proistosescu, and G. Wagner. Carbon Dioxide as a Risky Asset. _Climatic Change_ , 900 177(5):72, May 2024b. ISSN 0165-0009, 1573-1480. doi: 10.1007/s10584-024-03724-3. URL `https:` 901 `//link.springer.com/10.1007/s10584-024-03724-3` .

- 902 M. D. Bauer and G. D. Rudebusch. The Rising Cost of Climate Change: Evidence from the Bond Mar903 ket. _Review of Economics and Statistics_ , 105(5):1255–1270, Sept. 2023. ISSN 0034-6535, 1530-9142. 904 doi: 10.1162/rest a 01109. URL `https://direct.mit.edu/rest/article/105/5/1255/107405/` 905 `The-Rising-Cost-of-Climate-Change-Evidence-from` .

46
|0.034|-0.011|
||(0.016)|(0.017)|(0.034)|(0.017)|
|Firm is part of a larger firm|<br>-0.039|<br>-0.039|<br>-0.047|<br>-0.040|
||(0.063)|(0.060)|(0.079)|(0.055)|
|Firm age (years)|-0.001|-0.001|-0.001|-0.001|
||(0.001)|(0.001)|(0.002)|(0.001)|
|Exporting directly at least 10% of<br>sales|-0.029|-0.017|0.216|0.014|
||(0.057)|(0.059)|(0.176)|(0.062)|
|At least 10% foreign-owned|-0.088|-0.091|-0.159|-0.100|
||(0.070)|(0.068)|(0.109)|(0.066)|
|2-digit sector fixed effects|YES|YES|YES|YES|
|Region fixed effects<br>|YES|YES|YES|YES|
|Number of observations<br>|2,255|2,255|2,255|2,255|
|R<sup>2</sup>|0.125||||
|Cragg-Donald Wald F statistic||176.40|9.91|49.86|
|<br>||14883|172|3671|
|Kleibergen-Paap Wald F statistic<br>||.<br>{