rafmacalaba/data-use-mentions-extended huggingface dataset:
87

|**Country**|**P**<sup>**a**</sup>**Diversification**|**Conservation**|
|---|---|---|
||installation targets for wind, solar, hydropower, geothermal, biomass<br>and ocean energy.<br>Government approved a Renewable Energy Act in 2008 and issued<br>implementing regulations and rules in 2009.|,<br>than 4 P.M., and switched to the fan mode<br>during lunch hours. Since 2003, the Manila<br>Electric Company has had an appliance<br>calculator on its Web site, showing the cost<br>of operating different appliances, total<br>electricity bill, and tips for EE improvement.<br>In 2011, the Department of Energy urged the<br>public to conserve energy under its banner,<br>“Bright Now!”.<br>**Automotive fuel conservation**: The<br>Department of Energy has been promoting<br>automotive fuel conservation for more than<br>a decade. There is a vehicle-use reduction<br>program as part of national energy<br>efficiency and conservation efforts. The<br>department’s Web site posts fuel-saving<br>driving habits for awareness-raising. “Bright<br>Now!” in 2011 advertised fuel-saving tips<br>for drivers. A Shell market research survey<br>in the first quarter of 2009 found 90% of<br>those surveyed were willing to change<br>driving habits to save fuel. Three most<br>common fuel-saving driving habits adopted<br>by Philippine drivers were (1) keeping the<br>engine properly tuned, (2) keeping the tires<br>properly inflated, and (3) driving sensibly<br>and within speed limits
national participation rate of 53%.<sup>14</sup> However, only 53% of wives of male respondents worked.

Of the female respondents who were working, only 41% worked exclusively outside the home (45% of the wives of male respondents). Of female respondents who worked, 47% were wage workers, with the remainder being self-employed with no employees (37%), self-employed with employees (7%) or casual or family workers (9%). If we define the formal sector to consist of wage workers and self-employed businesses with staff, 54% of working women in our sample are employed in the formal sector. The formal sector is not particularly family friendly with flexible and part-time work rarely being available (Cameron et al., 2023).<sup>15</sup>

> 10Of Indonesian women aged over 25 years in 2018, 31% had completed upper secondary school, compared to only 3.4% in 1980. World Bank Databank. Accessible at https://databank.worldbank.org. The service sector accounted for about 29% of employment in 1991 and 49% in 2019. See https://data.worldbank.org/indicator/SL.SRV.EMPL.ZS?locations=ID&view=chart.

> 11Halim et al. (2023) show that the expansion of public pre-school provision from 2003 has not changed this situation.

> 12A Gallup survey found that 43% of Indonesian men prefer women to not engage in paid work outside the home, the same as Saudi Arabia and more than India (35%), (Gallup and ILO, 2017).

> 13Gender norms are becoming more conservative in Indonesia over time. In the 2006 World Values Survey 65% and 42% of men and women agreed with the above statement respectively.

> 14As reported in the World Bank data bank, see https://data.worldbank.org/indicator/SL.TLF.CACT
average than in urban areas, in part due to less economic opportunity in rural areas, a lack of data means that research to date has not been able to parse how returns vary across the spectrum of urbanization. New estimates of the returns to schooling, combining household survey data with geolocated data, could illuminate how these returns vary not only across the urban–rural divide but also as students get further from roads that provide access to urban centers (and thus, the formal job market). It could also illustrate the extent to which these returns are mediated through complementary behaviors like migration or inputs like virtual technologies.

A deeper understanding about how policy makers, educators, caregivers, and students see the role of education across the urban–rural continuum can also help improve the quality of education and reduce potential inequalities across children in different geographic spaces. For instance, rural areas often face teacher-specific challenges related to attendance, resources, training, and recruitment. Students in rural areas also access post-secondary education at much lower rates. Rural and remote-area schools also have less access to modern construction materials, transportation, electricity, water, and proper infrastructure; good infrastructure can promote attendance and foster a productive environment that is conducive for learning, particularly when designed to serve the basic needs of its beneficiaries (Beteille et al., 2020). Likewise, teacher and student absenteeism may vary non-linearly and even nonmonotonically across the urbanization spectrum. However, very few research studies examine absenteeism across the full spectrum of urbanization. This would be possible, for example, by 24
# 1. Introduction

Welfare<sup>1</sup> disparities are generally found to play an important role in explaining the frustration and dissatisfaction that people may have with the societies in which they live; in the most extreme cases, these disparities are directly linked to the outbreak of civil conflicts (Hirschmann and Rothschild, 1973; Muller, 1985; Esteban and Ray, 1999, 2008, 2011). In this regard, the Middle East and North Africa (MENA) region represents an important exception, as in the face of apparently low and stagnant inequality, dissatisfaction is rampant ( _inter alia_ , Verme et al., 2014, Cuesta Leiva et al., 2016, and Devarajan and Ianchovichina, 2018). What mechanisms might explain this puzzle? Drawing on Moroccan data, we show that absolute measures of welfare disparities—notably absolute polarization—are significantly correlated to perceptions of deprivation, and hence more suitable in explaining the growing discontent characterizing the central and lower deciles of welfare distribution.

At the end of the last decade, the MENA region was the only region in the world with a high incidence of large declines in average subjective well-being, with steeper declines for the top 60 percent of the population, representing mostly the middle class, than the bottom 40 percent, representing the poor and vulnerable (Arab Barometer, 2019). This pattern was especially pronounced in the countries that had experienced the Arab Spring—the Syrian Arab Republic, Libya, Tunisia, the Arab Republic of Egypt, and the Republic of Yemen—but the ranks of the unhappy swelled in nearly all Arab countries (Dang and Ianchovichina, 2016).

However, at first glance, a mismatch is apparent between this generalized dissatisfaction of Arab world citizens and inequality. The latter has been stagnant or declining since the end of the 1990s (Hassine, 2015; Hlasny and Verme, 2018), although some works question the veracity of the data arguing that inequality is underestimated (Alvaredo et al., 2018; van der Weide
To empirically apply these methods, we utilize data and definitions from the Global Multidimensional Poverty Index (MPI) developed by the Oxford Poverty and Human Development Initiative (OPHI) with the United Nations Development Programme (UNDP). The Global MPI is a robust measure that evaluates multidimensional poverty, using 10 different poverty indicators across three dimensions (health, education, and living standards). The 10 indicators include nutrition, child mortality, years of schooling, school attendance, cooking fuel, sanitation, drinking water, electricity, housing, and assets. A household is considered deprived in an indicator if it falls below a defined threshold (see Table A1.1 in Appendix 1 for the deprivation criteria used for each indicator). The underlying survey data for the indicators draws primarily from the Demographic Health Survey (DHS) program and the Multiple Indicator Cluster Surveys (MICS). The Global MPI was chosen for its comprehensive coverage and consistency in measuring poverty indicators.

However, this paper makes an important deviation from the standard Global MPI measure. While the OPHI/UNDP MPI assigns different weights to each of the ten indicators,<sup>6</sup> this paper weighs all of the indicators equally. The reason for this departure lies in the nature of our research question. By assigning equal weight to each indicator, we ensure that no single dimension is prioritized over another, allowing us to investigate the complex relationships in an agnostic manner. It is important to note that this weighting scheme may not necessarily reflect the relative importance of different dimensions of poverty in real-world scenarios, but it is a methodological choice that allows us to delve deeper into the structural relationships among these dimensions. Throughout the analysis, we assume that a household is multidimensionally poor if it is deprived in at least one of the 10 indicators used by the MPI. This means that any household that is deprived is automatically considered multidimensionally poor. To test the robustness of the results, we also replicate the analysis under different thresholds for multidimensional poverty. In Appendix
- Baier, Scott L., Yoto V. Yotov, and Thomas Zylkin (2019), “On the Widely Differing Effects of Free Trade Agreements: Lessons From Twenty Years of Trade Integration.” _Journal of International Economics_ 116, 206–226. url: https://doi.org/10.1016/j.jinteco.2018.11.002.

- Baldwin, Richard E., Simon Evenett, and Patrick Low (2009), “Beyond Tariffs: Multilateralizing Non-Tariff RTA Commitments.” _Multilateralizing Regionalism: Challenges for the Global Trading System_ . Ed. by Richard E. Baldwin and Patrick Low. Cambridge University Press.

- Bergstrand, Jeffrey H., Mario Larch, and Yoto V. Yotov (2015), “Economic Integration Agreements, Border Effects, and Distance Elasticities in the Gravity Equation.” _European Economic Review_ 78, 307–327. url: https://www.sciencedirect.com/science/article/pii/ S0014292115000847.

- Borchert, Ingo, Mario Larch, Serge Shikher, and Yoto V. Yotov (2020), “The International Trade and Production Database for Estimation (ITPD-E).” _International Economics_ .

- Breinlich, Holger, Valentina Corradi, Nadia Rocha, Michele Ruta, J.M.C. Santos Silva, and Tom Zylkin (2021), “Machine Learning in International Trade Research.” _World Bank Policy Research Working Paper 9629_ April. url: https://openknowledge.worldbank.org/handle/ 10986/35451.

- De Melo, Jaime and Alessandro Nicita (2018), “Non-Tariff Measures: Data and Quantitative Tools of Analysis.” _F.R.E.I.T. Working Paper_ . url: UNCTAD/DITC/TAB/2017/2.

- Dhingra, Swati, Rebecca Freeman, and Hanwei Huang (2021), “The impact of non-tariff barriers on trade and welfare.” 17.

- Dür, Andreas, Leonardo Baccini, and Manfred Elsig (2014
Fasani, and Frattini (2018) find that immigrants who are potentially eligible for legal status are more likely than ineligible undocumented immigrants to be employed. Using the 1990 Legalized Population Survey, Rivera-Batiz (1999) estimate a 52% wage penalty for Mexican undocumented immigrants in the United States, while Kossoudji and Cobb-Clark (2002) report a wage penalty that ranges between 14% and 24%. Phillips and Massey (1999) find that the Immigration Reform and Control Act in the United States, which made it illegal to hire unauthorized immigrants, is associated with a larger wage penalty for undocumented migrants as employers pass the costs of unauthorized hiring on to workers. Accounting for selection, Schluter and Wahba (2009) also find a significant effect of illegality on wages in the host country but mixed effects on remitting behavior. On the other hand, Dustmann, Fasani, and Speciale (2017) find that undocumented migrants consume about 40% less than documented migrants in Italy, partly due to their lower incomes relative to documented migrants.

The literature has put forward several explanations for the worse outcomes experienced by undocumented migrants compared with documented migrants. First, several studies show that undocumented migrants have weak bargaining power due to the lack of legal work options. Without viable outside options, undocumented migrants are confined into poor jobs (Gleeson & Gonzales, 2012; Gonzales, 2011). For instance, undocumented migrants are more likely to work in physically exhausting and dangerous jobs and receive no compensating differential, which is analogous to receiving lower wages than documented workers with similar education levels (Hall & Greenman, 2015). Second, the occupational choices of undocumented migrants might be limited by fear of exposure to apprehension by immigration enforcement agencies, and relatedly, being undocumented may limit workers’ ability to maximize their potential, which may lead to lower productivity (Ortega & Hsin, 2018). Third, several studies highlight the increased risk of depression and anxiety among undocumented migrants—due to the threat of deportation and poorer labor market conditions—which is
Finally, this study shows that nutrition interventions are able to substantially contribute to the reduction of malnutrition. In showing this, the paper fills an important gap as impact evaluations of nutrition interventions are scarce (see Mkenda 2004 for a review for Tanzania). Even the much studied Iringa project is inconclusive about the effectiveness of various strategies such as growth monitoring and promotion, integrated care and nutrition, communications for behavioral change, supplementary feeding for women and young children, school feeding, health related services, micronutrient supplementation and foodbased strategies (Allen and Gillespie 2001). Though our study suggests that the described interventions are effective, their (cost)-effectiveness remains an unknown. A recent review on cost effectiveness suggests that some interventions are more cost effective than others, particularly vitamin A supplementation for children under age five, iron supplementation for pregnant women, and some types of nutrition education and behavioral change (Gillespie and Haddad 2001; Behrman, Alderman and Hoddinott, 2004). In this light it is of importance to take the next step and to assess the cost effectiveness of the programs considered in this paper.

# **References**

- Ainsworth M. and I. Semali, (2001), “The impact of Adult Death on Children’s Health in Northwestern Tanzania”, World Bank working paper, n. 2266.

- Abdulai A. and D. Aubert. (2004). A Cross-Section Analysis of Household Demand for Food and Nutrients in Tanzania. _Agricultural Economics_ .

- Alderman H. (2000), “Anthropometry”, in _Designing Household Survey Questionnaires for Developing Countries_ in Glewwe P. and M. Grosh, vol.1

- Allen, L., and S. Gillespie, (2001), “What Works? A Review of Efficacy and Effectiveness of Nutrition Interventions”. Asian Development Bank, Nutrition and Development Series # 5, Manila.

- Arellano, M. and S. Bond. (1991).  Some tests of specification for panel data: Monte Carlo evidence and
. Data

The analysis uses four data sources. Household-level data on consumption and non-monetary indicators come from the 2018/19 Nigerian Living Standards Survey (NLSS) and the General Household Survey (GHS) available over four waves in 2010/11, 2012/13, 2015/16, and 2018/19. National accounts and other macroeconomic data are taken from the World Bank MFM-MOD tool, which incorporates data from NBS, and from the World Development Indicators (WDIs).

> 6 The World Bank's macroeconomic and fiscal model (MFMod) consists of individual country models for 181 countries. The models are used by country economists within the World Bank's Macroeconomics, Trade and Investment Global Practice to (i) generate country forecasts and (ii) simulate various policies.

> 7 Backcasting poverty rates beyond 2009 proves challenging for two main reasons. First, the backcasting exercise relies on strong assumptions that are likely not to hold for a period longer than a decade. Second, sectoral GDP data for the early 2000s shows some inconsistencies and large year-on-year volatility, which could bias the backcasted poverty estimates.

> 8 Moreover, the GHS survey is not representative at the state level, which makes it unsuitable to be used for official poverty measurement purposes.

6
Services liberalization and productivity of manufacturing firms: Evidence from Ukraine Oleksandr Shepotylo<sup>_∗_</sup> and Volodymyr Vakhitov<sup>_†_</sup>

**Abstract**

This paper brings new evidence on the impact of services liberalization on the performance of manufacturing firms. Using a unique database of Ukrainian firms in 20012007, we utilize an external push for liberalization in the services sector as a source of exogenous variation to identify the impact of services liberalization on total factor productivity (TFP) of manufacturing firms.

The results indicate that a standard deviation increase in services liberalization is associated with a 9 percent increase in TFP. Allowing services liberalization to dynamically influence TFP through the investment channel leads to an even larger effect. The effect is robust to different estimation methods and to different sub-samples of the data.

In particular, it is more pronounced for domestic and small firms.

**JEL codes: F14, G28, L80**

**Keywords: services liberalization, productivity, trade**

> _∗_ Kyiv School of Economics and Kyiv Economic Institute, oshepotylo@kse.org.ua

> _†_ Kyiv School of Economics and Kyiv Economic Institute, vakhitov@kse.org.ua

1
time, even if it was already produced domestically, is itself an entrepreneurial act that requires discovery (Ibeh 2003). Using export data allows us to consider all of the market failures associated with this entrepreneurial act, including those discussed in section 1 that are specific to exports.

Worldwide export data are taken from the United Nations COMTRADE<sup>3</sup> database, which contains global exports by country under multiple classification systems. These data are used in many publications to analyze export dynamism and growth as well as geographic patterns in export growth (UNCTAD 2003, Mayer, Butkevicius and Kadri 2002, Lall 1998 & 2000), but have not been used to study the emergence of new products.

Export data at a highly disaggregated level are available from COMTRADE under the Harmonized Commodity Description and Coding System (HS) beginning in the early 1990s. There are over 55 countries reporting export data under the first revision of the Harmonized System (HS 1988/1992) by 1992, and 70 countries reporting by 1993 (see Appendix II for sample composition).

Data are available at multiple levels of disaggregation under the Harmonized System, and it is not obvious what the level of disaggregation for the analysis of discovery events should be. Greater disaggregation allows for the study of more specific products, and it is at this individual product level where uncertainty of production costs and market demand may require experimentation. However, there is a tradeoff when using higher levels of disaggregation, as at a

> 3 United Nations Department of Economic and Social Affairs (UN/DESA) Commodity Trade Statistics Database, accessed via the World Bank World Integrated Trade Solution (WITS) tool, July 2004.

12
-1.8|704|686|-18.3|32|
|South Asia|43.4|43.2|-0.2|763|759|-3.6|54|
|**World**|**29.6**|**29.6**|**0.0**|**1738**|**1738**|**0.0**|**130**|
|Europe and Central<br>Asia|4.6|4.7|0.1|23|23|0.4|4|
|Other High Income|0.9|1.1|0.3|9|12|2.7|3|
|Latin America &<br>Caribbean|9.3|10.6|1.2|54|62|7.1|8|
|East Asia & Pacific|18.4|21.2|2.8|118|136|17.6|20|

<u>Source: Authors’ calculations from the Global Monitoring Database (GMD) and Luxembourg Income Study (LIS) Notes: This table reports poverty estimates for the 2017 reference year. For countries with no surveys conducted</u> exactly in 2017, the poverty estimates are extrapolated from the latest survey if conducted before 2017, otherwise extrapolated or interpolated from the closest surveys before and after 2017. The extrapolation and interpolation rules applied are the same ones the World Bank applies when “lining-up” global poverty estimates for every year (see the - Poverty and Inequality Platform (PIP) Methodological Handbook via <u>https://worldbank.github.io/PIP Methodology/lineupestimates.html). The table is arranged in ascending order of change in poverty (column 4). These</u> results are based on the full sample of 162
for each institution and loan type by creating a dummy equal to one if the amount outstanding of either the DNI or RUC associated with a loan applicant increases by any amount. The immediate post-application period allows six months for loans to be processed and disbursed and provides some time for applicants to potentially shop around with other financial institutions for other loan offers. For a placebo test, we define an analogous outcome measuring whether each loan applicant took out a new loan from each financial institution in the six months immediately preceding his or her loan application with our partner bank.

We also purchased Equifax credit scores for the month when the SME applied for the loan from our partner bank. Here, Equifax included a dummy variable indicating whether this score was primarily based on their credit history, i.e. a “thick file,” or on demographics and other sources, such as the Peruvian tax authority (SUNAT), i.e. a “thin file”.<sup>4</sup> Our sample includes 1,517 thick file borrowers and 366 thin file borrowers.

We utilize two measures of credit market competition: the log of per capita NBFI lending at the district level, and the per capita number of NBFI branches at the district level. We focus on NBFIs as our results indicate that the increase in loans for thin-file applicants comes from NBFIs, not banks. We note that as our partner bank operates in only eight districts, this is a fairly coarse measure of competition. Total NBFI lending for February 2012 was obtained from the Peruvian Bank Supervisor (SBS) and the 2012 population size from the Human Development Index of the UNDP.

Though we do not directly observe the profits that lenders derive from each loan applicant, we compute an imperfect measure of profits from new loans at the applicantinstitution level. If the applicant does not have a new loan with a given financial institution six months after the loan application, profits are defined to be zero. If they have a new loan within six months of