rafmacalaba/datause-encoder-data huggingface dataset:
While academics and international organizations have adopted various methodologies to measure multidimensional poverty, as discussed earlier, this study follows the approach taken by UNICEF (2011) to measure severe deprivations of children. This approach is based on operational definitions developed by Gordon et al. (2003a) and relies on the absolute poverty dimensions identified during the World Summit for Social Development in Copenhagen. Given the data available, this analysis distinguishes between three severe deprivations in the areas of food, health and education with information available at the individual child level, and four severe deprivations concerning water, sanitation, shelter and information with only household level information available for each child. See Table 1. While Gordon et al. (2003a) suggest an eighth deprivation, considering the access to basic services, defined as distance to school and health care services >20km, this study restricts itself to the beforementioned seven deprivations. Reasons for this being that there is no such information available from the MICS and GHS datasets, used in this analysis. The datasets only include distance to school at individual level, whereas distance to health care services is available at community level only. However, in both cases, it is defined in time used, rather than kilometers, thus requiring a conversion. Moreover, this deprivation has been criticized as access does not inform about quality of access.

Besides the seven severe deprivations, this study however analyses a few additional, even stricter but milder deprivation definitions relating to education, water and sanitation. The milder educational deprivation counts every child of school going age who is not currently in school as schooling deprived. For water deprivation, the study further analyses protected water deprivation, counting additionally those with only access to unprotected wells/springs and unprotected tap water in any season as deprived.<sup>5</sup> The improved sanitation deprivation definition additionally considers those deprived, who only have access to open pit latrines, latrines without slaps or toilets hanging over the water of any sort. The latter definitions for protected water and improved sanitation are both in line with the definition handled by WHO and UNICEF (2017a).

To inform on the extent of child poverty and its heterogeneity, descriptive statistics of the monetary poverty levels for both poverty lines and deprivation levels for each of the severe and milder deprivation levels are calculated as an aggregate for all children (or for the age group with data available) and for

> 5 An attempt to analyze additionally the share of children deprived of drinkable water, counting only those as non-deprived, who had access to sachet, bottled or pure water, showed very little variation across regions, and deprivation levels close to 100%.

7
for competition forces at this level in our model. As before, this feature of the model could potentially be improved with more data on regional traders (such as numbers of traders per region, their approximate market shares, and so on). Given the unavailability of these data, we work with the simpler version of the model presented here. But, for future work, the model could in principle accommodate regionally segmented trader markets.

We search for intermediate price _pT_ and cost structures that fit the number of market participants ( _nE_ and _nT_ ) and the distribution of market shares (1), (3)-(5) and (8) to real data (for both exporters _j_ and traders, as detailed in Section 2). The solution for each type of exporter involves finding ( _cE_ , _j γE_ ). We calibrate parameters such that the fixed and variable components of marginal cost represent 50 percent each.

This procedure delivers the relevant parameters (the set of _cE_ , _cT_ , _γE_ and _γT_ ) and equilibrium variables (in this case, _pT_ ), which are summarized in Table 5. We use these parameters to simulate comparative static results numerically.

**Table 5. Cost parameters and equilibrium variables used for simulation**

|**Exporters **|**Number **|**Market**<br>**Share**|**Average**<br>**Cost**|**Average**<br>**Profit**|**cE**|**γE**|
|---|---|---|---|---|---|---|
|nCP|1|1.43|692.2|441.1|692.20|-|
|nE1|2|12.66|83.3|90.0|55.17|0.003|
|nE2|3|5.11|112.6|60.7|74.84|0.011|
|nE3|7|2.97|120.0|53.4|79.66|0.019|
|nE4|9|1.77|124.1|49.3|82.46|0.034|
|nE5|11|1.21|125.9|47.4|83.76|0.050|
|nE6|17|0.47|128.4|44.9|85.49|0.131|
|nE|50*|100|119.9|67.2|||
|HHI||510|||||
|**Traders**|**Number **|**Market**<br>**Share**|**Average**<br>**Cost**|**Average**<br>**Profit**|**cT**|**γT**|
|nT1|39|2.56|41.0|34.7|26.51|0.008|
|nT|39|100|41.0|34.7|||
|HHI||256|||||
|p*|600|1560|||||
|pT|427||||||
|pF|351||||||
|X|140,000||||||

* There are 49 exporters. Cashew processors are summarized as one agent.

Appendix B shows sensitivity results to (i) cost structures (75-25 percent of constant an variable marginal cost, and almost-constant marginal cost), and (ii) competition in the trading segment (asymmetric competition and symmetric, but more concentrated, competition).

In sum, in all cases the calibration procedure involves the following steps: 16
The various policy uncertainty measures have some similarities but also differences to another measure of global economic policy uncertainty (EPU) in Davis (2016). The global EPU and the policy uncertainty measures in this paper show peaks of uncertainty in the early 2000s, during the GFC, and the pandemic. Fiscal policy uncertainty reflects rising uncertainty during the European debt crisis of 2012, in the mid-2010s, and during the pandemic in line with EPU peaks. Differences are also evident. Most notably, the global EPU suggests that policy uncertainty rose earlier, in 2018-19, than fiscal policy uncertainty before both reach new highs in 2020. Several factors could explain the differences across these measures. First, this paper uses a measure of policy uncertainty as in Jurado et al. (2015) which is fundamentally different from proxy-measures like the EPU. Second, measures of uncertainty are specific to the fiscal or monetary policy measure and not general policy uncertainty as in Davis (2016). Notable events in the EPU such as the European immigration crisis in 2015, the Brexit referendum, and political turmoil in several countries do not reflect as heavily in the unforecastable part of government spending or interest rate policy which is the focus of this paper. Third, as in Jurado et al. (2015), peaks in uncertainty occur far more infrequently compared to the EPU but have larger and more persistent correlations with economic activity.

While there is no global monetary policy uncertainty measure to compare this paper’s results to, Husted et al. (2020) provide some guidance on the similarities and differences for the United States. Overall, the correlation between the newspaper-based measure of monetary policy uncertainty and in this paper is weak although there are similar spikes: elevated uncertainty can be seen in both measures in the early 2000s and following the COVID-19 pandemic. The measure in this paper, however, better reflects monetary policy uncertainty embedded in the Primary Dealers survey or implied volatility of options on one-year swap rates. Notably, monetary policy uncertainty is highest during the GFC in these measures, and it decreases precipitously through 2013 before rising again. There are also peaks in these measures during 1994/95 (bond market turmoil), and 2002/03.

14
# **Identification of an Expanded Inventory of Green Job Titles through AI-Driven Text Mining**

Michał Paliński Güneş Aşık Tomasz Gajderowicz Maciej Jakubowski Efşan Nas Özen Dhushyanth Raju

**Keywords** : AI, text mining, occupational classification, green jobs, green economy **JEL codes:** J23, Q52, O14

Paliński: University of Warsaw, Warsaw, m.palinski@uw.edu.pl. Aşık: TOBB University of Economics and Technology, Ankara, gunesasik@gmail.com. Gajderowicz: University of Warsaw, Warsaw, tgajderowicz@wne.uw.edu.pl. Jakubowski: University of Warsaw, Warsaw, mjakubowski@uw.edu.pl. Nas Özen: World Bank, Ankara, snasozen@worldbank.org. Raju: World Bank, Washington, DC, draju2@worldbank.org.

We thank Berfu Çopur for research assistance with the literature search. We are also grateful to Burak Baskın, Paolo Belli, Ahmet Kurnaz, René León Solano, and Aivin Vicquierra Solatorio for useful comments.
Policy Research Working Paper 5238

# **Abstract**

Measuring the poverty and distributional impact of the global crisis for developing countries is not easy, given the multiple channels of impact and the limited availability of real-time data. Commonly-used approaches are of limited use in addressing questions like who are being affected by the crisis and by how much, and who are vulnerable to falling into poverty if the crisis deepens? This paper develops a simple micro-simulation method, modifying models from existing economic literature, to measure the poverty and distributional impact of macroeconomic shocks by linking macro projections with pre-crisis household data. The approach is then applied to Bangladesh to assess the potential impact of the slowdown on poverty and income distribution across different groups and regions. A validation exercise using past data from Bangladesh finds that the model generates projections that compare well with actual estimates from household data. The results can inform the design of crisis monitoring tools and policies in Bangladesh, and also illustrate the kind of analysis that is possible in other developing countries with similar data availability.

This paper—a product of the Poverty Reduction and Equity Group, Poverty Reduction and Economic Management Network—is part of a larger effort in the department to analyze the poverty and distributional impact of macroeconomic shocks.. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The authors may be contacted at csanchezparamo@worldbank.org and anarayan@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
opportunities, (2) promote women's social and political involvement at all levels of administration, and (3) establish a legal and institutional framework to protect woman's property rights and to promote equality in employment opportunities, access to basic services, and participation in political life. In principle at least, the implementation of public policies designed to promote women's well-being has been made mandatory nation wide with particular attention paid to rural and poor areas where women's deprivation was deeper and more widespread (Tao and Jian, 1997).4 One of the most significant achievements in of China since 1949 has been the widespread public provision of basic services in education and health care and its focus on equal access. Based on figures published by the Research Institute of All China Women's Federation (1991) between the early 1950s to late 1980s, the number of health clinics (focusing particularly on providing primary health care for women and children) increased from 349 to 2,793 in urban areas, and from 2,880 to 11,795 in rural areas. This clearly has contributed to the improvement of health outcomes for women and children. For example, female life expectancy at birth has increased from 35 years in the early 1960s to 71 in the early 1990s (The World Bank, 2000). The focus on investment in social infrastructure included setting up evening schools and the provision of literacy classes in local communities, and this has undoubtedly been one of the major factors explaining the rapid reduction of adult (particularly female) illiteracy rates. Chinese official statistics show that the female illiteracy rate fell from 64% in 1970 to 24% in 1998 (Research Institute of All China Women's Federation, 1998).

This overview of public policy in China reveals not only its gender focus but also the recognition of the complementarity between different sets of policies. It has been recognised that women's status can only be improved when a supporting legal and institutional framework is

> 4 One expects, however, that the vigour with which these policies are implemented varies depending, for example, on the nature of the policy, on the region, and on the commitment of local leaders to the particular policy.

7
# **<u>References</u>**

Adams, Richard H., and John Page. 2005. “Do International Migration and Remittances Reduce Poverty in Developing Countries?” _World Development_ , 33 (10): 1645–69.

Adams-Prassel, Abi, Teodora. Boneva, Marta Golin and Christopher Rauh. 2020a. “Inequality in the Impact of the Coronavirus Shock: Evidence from Real Time Surveys.” _Journal of Public Economics_ , 189: 104245.

———. 2020b. “Work Tasks That Can Be Done from Home: Evidence of the Variation Within and Across Occupations and Industries.” _Cambridge Working Papers in Economics_ 2040, Faculty of Economics, University of Cambridge.

Ahonen, Emily Q., and Fernando G. Benavides. 2006. “Risk of Fatal and Non-fatal Occupational Injury in Foreign Workers in Spain.” _Journal of Epidemiology and Community Health,_ 60: 424426.

<mark>Albanesi, Stefania, and Jiyeon Kim. 2021. “The gendered impact of the COVID-19 recession on the US labor market.” No. w28505. National Bureau of Economic Research.</mark> Aleksynska, Mariya and Ahmed Tritah. 2013. “Occupation–education mismatch of immigrant workers in Europe: Context and policies,” _Economics of Education Review_ , 36: 229-244.

Alon, Titan, Matthias Doepke, Jane Olmstead-Rumsey and Michele Tertilt. 2020. “The impact of covid-19 on gender equality,” _National Bureau of Economic Research_ , Working Paper 26947 doi:10.3386/w26947.

Amuedo-Dorantes, Catalina, and Susan Pozo. 2010. “Accounting for Remittance and Migration Effects on Children’s Schooling.” _World Development_ , 38 (12): 1747–59.

Autor, David H., and Michael J. Handel. 2013. “Putting Tasks to the Test: Human Capital, Job Tasks, and Wages,” _Journal of Labor Economics_ , 31 (S1): 59-96.

Autor, David H., Frank Levy and Richard J. Murnane. 2003. “The Skill Content of Recent Technological Change: An Empirical Exploration,” _Quarterly Journal of Economics_ , 118(4): 1279-1334.

Avato, Johanna, Johannes Koettl and Rachel Sabates-Wheeler. 2010. “Social Security Regimes, Global Estimates, and Good Practices: The Status of Social Protection for International Migrants,” _World Development_ , 38 (4): 455-466.

Avdiu, Besart, and Gaurav Nayyar. 2020. “When face-to-face interactions become an occupational hazard: Jobs in the time of COVID-19,” _World Bank Policy Research Working Paper_ 9240.

Azlor, Luz, Anna Piil Damm and Marie Louise Schultz-Nielsenc. 2020. “Local labour demand and immigrant employment,” _Labour Economics_ , 63, 101808.

26
# **5. Measures of relative poverty**

Recall that the relative poverty lines rise with the mean above some point, as determined by the alternative calibration methods described in section 2. Table 4 gives the average poverty line by region and year for both methods.<sup>28</sup> Using Method 1, the overall mean poverty line rises from $2.00 a day in 1981 to $2.90 in 2008. The mean poverty lines are similar, though slightly higher for Method 2. In 2008, the mean lines vary from $1.60 in Sub-Saharan Africa to almost $7 a day in Eastern Europe and Central Asia (these are for Method 1, but they are similar for Method 2).

Figure 6 gives the series of implied weakly relative poverty measures for the developing world using both methods, alongside the corresponding series of absolute measures for $1.25 a day. We see that the overall measures of relative poverty are quite robust to the choice of method. Using the survey means (Method 2) gives slightly higher poverty counts in the 1990s and slightly lower at the end of the period. By either method we see an overall trend decline in the incidence of relative poverty, but a trend increase in the proportion of the population who are not absolutely poor but are relatively poor.

The slight upturn in 2008 using Method 1 is probably deceptive, as it appears to largely reflect a short-run gap in the growth rates implied by the national accounts consumption and those found in the surveys. We will rely more on Method 2 using the schedule in equation (3). (The Addendum gives detailed results using Method 1.) Table 5 gives the estimates by region using Method 2, corresponding to Table 2. We see that the proportion fell over time, from 63% in 1981 to 56% in 1990, 53% in 1999 and 47% in 2008. The speed of decline in the incidence of relative poverty has not been sufficient to reduce the number of poor by this measure, which rose from 2.3 billion to 2.5 billion in 1990, 2.7 in 1999 and stayed at 2.7 by 2008.

So we find a simultaneous rise in the numbers of relatively poor, alongside the fall in absolute poverty. Economic growth has clearly pushed many developing countries into the consumption levels at which relative poverty becomes important, by our measure.

The incidence of relative poverty has fallen substantially in East Asia, from 81% in 1981 to 42% in 2008. It has risen in EECA over this period, though falling since the mid-1990s. There

> 28 These average lines are purely for descriptive purposes; they have no analytic role since poverty lines are calculated at country-year level.

17
Cespedes and Velasco (2014) argue that procyclicality for commodity exporters is lower for the 2000s commodity boom relative to earlier booms, due in part to better institutions. The empirical part of our paper is similar in spirit to Cespedes and Velasco (2014), but we seek to explain cross-country variation in cyclicality based on the persistence of commodity price shocks and ER regime rather than due to institutional quality (which for us is a control). While we also construct countryspecific commodity price indices in a similar way as Cespedes and Velasco (2014), we estimate the MPS consistent with our theoretical model rather than estimating a reduced form, which we argue below improves the accuracy of the estimates.

Our paper also relates to the literature on the persistence of commodity price shocks. Although most papers in this literature recognize that the persistence of commodity prices is a key variable for policymaking in commodity-rich countries, they generally do not calculate optimal fiscal policy as we do.<sup>7</sup> Using a medianunbiased estimator on commodity price data for 60 commodities from 1957 to 1998, Cashin et al. (2000) find that the majority of individual commodities have a half-life above 5 years. Using a maximum likelihood approach, Borensztein et al. (2013) find that 7 of 9 important commodities have a half-life of 5 years or higher. Fornero and Kirchner (2014) estimate an unobserved components model for the copper price and find a half-life of the persistent component of 24 years. Fernández et al. (2020) decompose the dynamics of commodity prices into transitory shocks and a permanent global component (the “commodity super cycle”), and find the latter is an important driver of country-level business cycles (but not a dominant one). As an input into our calculations, we estimate the persistence of 61 commodity prices using around a century of data taken from Jacks (2013) and find wide variation in the persistence of shocks across commodities, with many being highly persistent (though still stationary).<sup>8</sup>

> 7 For example, Cashin et al. (2000) argue that highly persistent commodity price shocks will undermine commodity price stabilization schemes, but they make this argument descriptively. 8 The long sample is important to reduce the well-known attenuation bias on the estimation of persistence in short samples. Appendix Table 2 lists the estimated persistence of 61 individual commodity prices.

7
survey for SME taxpayers in South Africa was performed in 2007 with a focus on the previous fiscal year, 2006 (USAID, 2008). This may weaken the identification of benefits from the reform with respect to income taxes, although the benefits from e- filing should have been long realized by VAT and employment tax payers.

In Nepal, the implementation of the system was quite complex. Firms were reportedly required to complete a tedious amount of paper work for their electronic reports to be accepted by the revenue agency, which may increase TCC. The e-filing was legally required, but not comprehensively enforced, and many firms complained about poor IT connectivity. Additionally, e-filing was introduced in the 2009/2010 fiscal year, while the TCC survey was done in 2011, focusing on fiscal tax year, 2009/2010 (IFC, 2012). This can be problematic for the analysis of such data since it was the first year of policy practice, and businesses that used it were still in the process of learning-by-doing.

The Ukrainian government introduced a full e-filing system in 2006 on a voluntary basis, and e-filers were not legally required to submit hard copies of their tax returns. However, certain “background documents” had to be submitted in hard copy; although they could be sent by registered mail, most private sector taxpayers reportedly did not trust the system and the survey showed that the vast majority reported that they had brought their hard copies to the State Tax Committee physically. In addition, SMEs had only one year of e-filing experience prior to the survey (IFC, 2009), which might not have been a sufficient period for “learning by doing” to gain all the benefits. It is likely that a survey conducted shortly after the introduction of e-filing may not be able to provide a complete picture of the medium to long run effects of e-filing on TCC.

# **1.2 Theoretical Thinking and Empirical Identification**

It is important to identify the mechanisms through which e-filing might affect TCC. To address this, we present a very simple theoretical illustration. This is by no means intended to represent a theory of tax compliance, but rather to focus on the basics to clarify the underlying thought process we have in mind. To begin, we think from the perspective of firms. Say the TCC can be written as TCC = f( **s,e,g** ), where 5