rafmacalaba/data-use-mentions-extended huggingface dataset:
human work time to review at a cost of $3,113,244, which assumes a cost of $3 per article as was paid to MTurk workers.

A model is next trained on the 3,500 labeled articles. We use a distilled version of the BERT (bidirectional Encoder Representations for transformers) model to encode raw text into a numeric format suitable for predictions (Devlin et al. (2018)). BERT is pre-trained on a large corpus comprising the Toronto Book Corpus and Wikipedia. The distilled version (DistilBERT) is a compressed model that is 60% the size of BERT and retains 97% of the language understanding capabilities and is 60% faster (Sanh, Debut, Chaumond, Wolf 2019). We use PyTorch to produce a model to classify articles based on the labeled data. Of the 3,500 articles that were hand coded by the MTurk workers, 900 are fed to the machine learning model. Nine hundred articles were selected because of computational limitations in training the NLP model. A classification of “uses data” was assigned if the model predicted an article used data with at least 90% confidence.

The performance of the models classifying articles to countries and as using data or not can be compared to the classification by the human raters. We consider the human raters as giving us the ground truth. This may underestimate the model performance if the workers at times got the allocation wrong in a way that would not apply to the model. For instance, a human rater could mistake the Republic of Korea for the Democratic People’s Republic of Korea. If both humans and the model perform the same kind of errors, then the performance reported here will be overestimated.

> 3 We ended up not using this in the paper.

6
and health (contraceptive acceptance, antenatal care, deliveries by skilled attendants, 2008/092011/12) from woreda-level sectoral administrative data; and information on ethnicity, the frequency of droughts, and other control variables. In addition, regional data on capital expenditures and zonal data on crop yields have been linked to woreda data.

An important caveat is that our horizontal equity analysis is based on woreda-level administrative data. The capacity of regional governments to collect this data varies. Some regions that are more adept at collecting it have more reliable data than others. But discrepancies – which do exist – between administrative data and more reliable household survey data affect means and not trends over time. The Ethiopia Demographic Health Survey (DHS), and other internationally accepted surveys, show trends over time similar to those shown by administrative data, albeit with lower means. Hence the direction of our findings, which uses both DHS survey data and administrative data, are reliable.<sup>10</sup> Another limitation of administrative data is its scope. There is no information at the local level for the years before decentralization to woredas, limiting the time scale we can analyze.

By contrast, our vertical equity analysis focuses on households and individuals, and so uses available DHS data, from 2000, 2005, 2011 and 2014. The 2000-2011 surveys are from the DHS program, and the 2014 survey is a mini-DHS implemented by the Central Statistical Agency. These are far more reliable, representative data, generated according to internationallyrecognized household survey standards.

One reason an analysis of this sort has not been undertaken for Ethiopia until now is the absence of woreda-level data on local economic, demographic, fiscal and other characteristics. Indeed, it is difficult to overstate the difficulty of doing subnational empirical work on Ethiopia. When we began this project, relatively little subnational data was collected, the data was often of poor quality, and few attempts were made to systematize the results into any obviously

> 10 Questions have been raised about the quality of administrative
**Figure 1 - Reservation and market wage distribution**

<!-- Start of picture text --> Employed (market wage)<br>Unemployed (reservation wage)<br>2 3 4 5 6 7<br>Log(wage per hour)<br>1.5<br>1<br>Density<br>.5<br>0<br><!-- End of picture text --> _Source: Authors’ calculations based on LFS. Population weights accordingly used._

**Table 2 – Basic statistics of wages**

|||**Confiden**<br>|**ce interval**<br>|<br>||
|---|---|---|---|---|---|
||Average|Lower<br>bound|Upper<br>bound|Median|Observations|
|**Market wage per hour**|93.30|92.74|93.87|79.55|28,995|
|**Reservation wage per hour**|90.54|89.90|91.18|79.55|10,195|
|**Ratio of reservation wages**<br>**to market wages**|0.97|0.97|0.97|1.00||
|_Source: Authors’ calculation_|_s based on_|_LFS. Popu _|_lation weig_|_hts accordi_|_ngly used._|

The same conclusion could be reached if reservation wages are contrasted with market wages at the skill level. **Figure 2** reveals that reservation wages grow with education, similarly as market wages do. In addition, tertiary-educated job seekers may be even more rigid in setting their expectations than what the market rewards them. What the figures do not reveal, though, is
Accounts System], a yearly census of firms run by National Statistics Institute (INE). The main objective of SCIE is to characterize the economic and financial behaviour of firms. This data set includes information on total sales, investment, employment, value added, wage bill, industry, location, among several other variables.

**International trade statistics** : We merge the above data sets with yearly data on firms’ export transactions from _Estat ́ısticas do Com ́ercio Internacional (ECI)_ [Foreign Trade Statistics] from INE. This is the country’s official information source on imports and exports. It comprises

> common shocks associated with a single global recession (e.g. financial factors), which may plausibly have heterogeneous impacts across firms. The focus of our analysis is also distinct in that we separately identify the effects of these unexpected shocks on the different components of pay, show that wage effects apply mainly to top earners, and to firms initially led by highly skilled top executives. This enable us to make further progress in identifying specific mechanisms whereby firm-specific unexpected demand shocks impact wage setting.

5
and non-food items over reference periods varying from the past week to the past year. Such expansive modules are unable to be implemented via mobile phone. However, the Food Insecurity Experience Scale (FIES), which is a subjective measure of food access, is composed of an 8-question module that is amenable to phone surveys (e.g., Ballard 2013).

> 3A separate literature investigates the degree to which food security indicators, including subjective food security indicators, overlap with each other. There is growing evidence of a lack of alignment between subjective measures of food security and more traditional measures, such as undernourishment, obtained from the same respondents. However, there is also a lack of alignment between some traditional measures and other non-subjective food security indicators (e.g., Headey and Ecker 2013; Maxwell et al. 2014; Broussard and Tandon 2016; etc.). Similarly, Beegle et al. (2012) report subjective welfare assessments by expenditure per capita, and demonstrate that the two measures do not perfectly align. However, these differences could be driven by imperfections in either the subjective welfare question or in the measurement of expenditure.

2
secondary school age over the first decade of the program’s life, from its inception in 2004 until 2015. This examination is made possible by a sharp RD design based on an observed, numerical program assignment variable (the district adult literacy rate) and government administrative data from annual censuses of government schools that capture school-level data on girls’ enrollment in secondary grades. The local randomization-based RD approach that we apply to estimate program effects improves on the parametric and nonparametric empirical approaches used in previous evaluations of the program by better fitting the structure of the program assignment variable.

We find that the program had significant, large positive effects on girls’ enrollment in secondary grades in government schools across cohorts throughout the period of observation—and that the effects were of similar size. Moreover, these program effects were observed even though the program’s economic incentive effect on households weakened substantially over the observation period, as a result of a marked decline in the real value of the program benefit (which was not indexed to inflation) accompanied by a rise in average real household income. The effects were also observed despite the potential re-optimization of behavior by various agents in later years of the program. (Note that the documented RD effects, which relate to the RD program district of Khanewal, may not apply to program districts more generally.) The finding of sustained program effects on girls’ secondary school enrollment over the study’s observation period despite a loss in economic incentive suggests potential behavioral explanations. The program may have generated durable changes in household and community behavior relating to girls’ secondary education. For example, independent of the value of the benefit, the program may have made girls’ secondary education salient to households, leading to a change in perceptions by households and communities about the acceptability and value of such education. More generally, greater exposure to girls’ 22
P a g e <u>|</u> **42**

The housing market in Ahmedabad ( _<u>Figures 10 and 11</u>_ <u>), as might be expected,</u> shows a very different pattern.

- 1) Prices are much lower overall. The lowest priced unit is 30 times less expensive than its equivalent in Mumbai.  A number of informal solutions are available around INR100,000, and all informal housing prices are below INR 400,000, putting the highest priced unit of informal housing at 8 times less than the equivalent in Mumbai.

- 2) There is practically no overlap in price of informal and formal housing. Only subsidized EWS housing and units provided by the Housing Board overlap with both the prices and floor area of the informal sector.  This lack of overlap is due to the relatively lower premium paid for well located slums and the lower costs of land overall.

- 3) Like Mumbai, in the formal sector the price per square foot is tightly correlated with distance from the city center (crossing of Ashram Rd and Gandhi Bridge)  while this is not the case for slums. Ahmedabad’s slums are relatively small pockets distributed close to the city center. Analysis of transport patterns as in the transport survey should be done to confirm the preference for short commutes in Ahmedabad’s slums.

- 4) Unlike Mumbai, Ahmedabad is far less spread out. The furthest distance from the city center for formal housing in Mumbai is roughly six times higher than in Ahmedabad, for slums, it is three times higher.

- 5) There is an overlap between the floor size at the low end of the formal market and the high end of the informal market, which is not the case in Mumbai. Again, this indicates less pressure to economize on expensive land to live in a good location.

- 6) _<u>Figure 11</u>_ suggests that in Ahmedabad  an
activity.<sup>5</sup><br>The employment rate is the number of persons in employment as a percentage of the<br>total labor force.|
|Unemployment rate|A person is unemployed if he/she is of working age, is not in employment during the<br>reference period, and has been seeking employment over the past 4 weeks.<br>The unemployment rate is the number of persons in unemployment as a percentage of<br>the total labor force.|
|Outside the labor force/or<br>inactivity|A person is outside the labor force (or “inactive”) if he/she is of working-age and<br>neither employed nor unemployed, according to the preceding definitions. An inactive<br>person is not necessarily idle, especially in the context of a developing economy. The<br>data breaks this group down into those who are inactive because they do household<br>work, those who are enrolled in education, those who are discouraged, etc.|
|**Hunger**||

4 The labor market statistics presented in this paper follow closely the international standard set as per the International Labour Organisation’s (ILO) Key Indicators of the Labour Market (KILM). There are two key reference periods: (a) the short observation period defined as 7 days, and (b) the long observation period defined as 12 months. Following ILO guidelines, statistics are reported for the short observation period unless explicitly stated. All persons aged 15-64 are defined as being of working age.

5 The five employment activities are: (i) working as an apprentice, (ii) working on the household’s farm, raising livestock, hunting or fishing, (iii) conducting paid or commissioned work, (iv) running a business of any size for oneself or for the household, (v) helping in a household business of any size. The definition further
# **The Impact of the Global Food Crisis on Self-Assessed Food Security**

_By_ DEREK D. HEADEY<sup>1</sup>

**JEL Codes:** I32; O11.

**Keywords:** Food crisis, food security, poverty, subjective indicators.

**Sector Board:** Poverty Reduction (POV)

1 Derek Headey, Research Fellow, International Food Policy Research Institute, PO Box 5689, Addis Ababa, Ethiopia. <u>D.Headey@cgiar.org. The author particularly wishes to thank Angus Deaton for the introduction to the GWP data as well as very</u> detailed comments on an early draft. Thanks also to Gallup staff for answering a number of questions and to Shahla Shapouri of the USDA for providing comments and answering questions regarding the USDA model. John Hoddinott, Olivier Ecker, Paul Dorosh, Bart Minten, Maggie McMillan, Maximo Torero, and Shenggen Fan contributed useful comments and suggestions. Participants at various seminars at the FAO and IFPRI provided insightful comments. The author also thanks USAID for financial support and Yetnayet Begashaw, Teferi Mequaninte, and Sangeetha Malaiyandi for excellent research assistance. Any errors are the author’s own.
# **II. DATA**

This paper uses the latest National Survey of Employment, Wages, Technology and Training (ENESTYC), fielded in 2001. The ENESTYC is a periodic establishment survey fielded by INEGI ( _Instituto Nacional de Estadística Geografía e Informática_ ) on behalf of the Ministry of Labor and Social Welfare. The ENESTYC’s universe is the manufacturing establishments numbered in the Economic Census updated with the EIM – _Encuesta Industrial Mensual_ – and with information from the petrochemical industries. The sampling design is random and stratified for each of the 54 activity branches of the _Clasificación Mexicana de Actividades y Productos_ (CMAP) 1994 and by firm size. The sample size for 2001 was 8,179 firms, which represents 333,647 firms. As its name suggests, ENESTYC contains detailed information about the firm – production, output and input cost, capital assets, size, location, division of activity, ownership, technology<sup>3</sup> , quality control methods and ISO 9000 certification, and research and development – and attributes of the workforce – gender, wage and non-wage compensation by occupation and gender, and training, both in-house training and external formal training.

In both 1993 and 1999, INEGI selected a sub-sample of the establishments interviewed in the ENESTYC. From each of those firms, a randomly selected sample of 10 workers from different occupational categories was interviewed to elicit the National Survey of Employment to Workers in the Manufacturing Sector (ENTRAM). This is a worker level dataset which includes information on basic data of the worker, educational level, job conditions, remunerations, training, and work history. Though the linked ENESTYC-ENTRAM yields an employee-employer dataset with very rich information at the worker level, it was not suitable for our analysis because the methodology in Section IV requires data for the entire workforce and the ENESTYC-ENTRAM only has only a small sample of workers by firm, especially for the larger firms, which would produce an incomplete characterization of the firms’ workforce. The excellent quality of the ENESTYC and
Total|100|100|100|100|100|100|100|100|100|100|100|
|_Standardized Consumption_|_Aggr_|_egate_||||||||||
|- Food|57|59|52|44|46|51|34|29|53|48|50|
|- Non-food non-<br>durables|20|20|20|30|28|28|28|28|20|23|24|
|- Health|3|3|3|3|5|5|4|7|9|3|5|
|- Education|6|5|8|2|2|2|3|3|4|3|5|
|- Durable goods|2|2|2|3|2|2|2|12|7|2|3|
|- Housing|14|13|19|19|17|12|29|21|8|21|18|
|Total|100|100|100|100|100|100|100|100|100|100|100|

Source: Authors’ own estimates based on South Asia Harmonized Micro Dataset (SARMD). India’s consumption aggregates reported are based on the Uniform Recall Period (URP).

For the most part, the standardization process preserves the poverty rankings. When we use the national consumption aggregates to estimate the international extreme poverty rate, Sri Lanka has 16
Moreover, the same way that GVCs generate spillover effects and amplify those effects, global inflation synchronization might require more coordinated international policies (IMF, 2018).

Finally, beyond inflation synchronization, GVCs also create strong interdependence of GDP fluctuation at business cycle frequency (de Soyres and Gaillard (2019), Gunnella et al. (2019)). Moreover, the segmentation of production across countries has a significant impact on trade elasticities (Amiti et al. (2014), de Soyres et al. (2018)). GVCs also impact the extent to which regional trade agreement divert trade flows with the rest of the world (de Soyres et al. (2019)).

# **3 Data Sources**

The analysis is based on annual inflation data for more than 126 countries depending on the specifications, for the period 1970-2017.

## **3.1**

Inflation is measured at the annual frequency and the analysis are performed using two different measures: Consumer Price Index (CPI) and Gross Domestic Product Deflator (GDPD). There are key differences between those two indices. On the one hand, CPI includes all goods bought by consumers including foreign goods if the country imports final goods. As such, it is an interesting measure of price changes when one wants to focus the analysis on consumers only. On the other hand, GDP deflator measures price changes in goods and services produced in an economy, but not those imported. Moreover, the two measures have different shares of tradable goods, being higher for the CPI. Importantly, since there are production linkages between importers and non importers within each country, GDP deflator has an _indirect_ exposure to imported inputs. However, GDP deflator has no exposure to imported final goods that are simply consumed by households.

When building the data set on which we perform our analysis, we are particularly interested in covering as many countries and as many low income countries as possible. The use of CPI and GDPD allows to have two ( _very_ )
# **Green Investment by Firms: Finance or Climate Driven?**

Fotios Kalantzis, Helena Schweiger and Sofia Dominguez<sup>1</sup> Keywords: finance constraints, green management practices, mitigation strategies JEL classification: D22, L23, G32, L20 1 Kalantzis: Senior Economist, European Investment Bank. Email: <u>f.kalantzis@eib.org. Schweiger: Associate Director,</u> Lead Research Economist, European Bank for Reconstruction and Development. Email: <u>schweigh@ebrd.com.</u> Dominguez: Associate Economic Affairs Officer, United Nations Conference on Trade and Development (current affiliation). Email: <u>sofia.dominguezdelgado@unctad.org (Sofia contributed to this research during her traineeship at</u> the European Investment Bank). This paper was commissioned by the World Bank Middle East and North Africa Chief Economist office as part of the activity “Assessing the MENA formal Private Sector.” The activity is task managed by Asif M. Islam. The study is a background paper for the Chapeau report titled “Unlocking Sustainable Private Sector Growth in the Middle East and North Africa: Evidence from the Enterprise Survey.” The opinions expressed in this paper do not represent the views of the World Bank Group, its Board of Directors or the Governments they represent. The opinions also do not reflect the views of the European Bank for Reconstruction and Development (EBRD), the European Investment Bank (EIB), and the United Nations Conference on Trade and Development (UNCTAD). All errors and omissions are the authors’ responsibility. We are thankful to Ralph De Haas, Sergei Guriev and Simon Savšek for comments. We would also like to thank the participants of two workshops hosted by the World Bank, EBRD, and EIB for their feedback.
500% of GDP per capita could exclude significant parts of the progressive PIT schedule in some IDA countries. This will become clear as data on IDA countries PIT schedules is collected, and the methodology may be adjusted accordingly, if necessary. An alternative option that could be progressed is the simplified “transplant and compare” approach of Vellutini and Benítez (2021). While slightly more complicated to implement, this approach does not impose any ceiling on the income distribution and reduces the likelihood of country rankings being affected by the underlying ‘base’ distribution.

# 7 Conclusion

Measurement of tax progressivity is a challenging issue for several reasons. Most significantly, data limitations have a large impact on what taxes can be examined, what measurement approaches can be adopted to examine those taxes, and how reliable those approaches are likely to be. This is particularly important for IDA countries where data limitations are typically most significant. Additionally, there is no single definition of progressivity, or single method for measuring the degree of progressivity of a tax, thereby necessitating a range of decisions and value judgements to be made in any analysis. Contingent upon the definition chosen, there are several approaches that can then be adopted to measure the degree of progressivity of a tax, each with benefits and drawbacks. Finally, measurement of tax progressivity – particularly when based on microdata – can be extremely resource intensive, and this must be taken into account, particularly in the current context where the progressivity of a large number of countries needs to be assessed.

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