rafmacalaba/data-use-mentions-extended huggingface dataset:
- Bazzi, S. (2017). Wealth heterogeneity and the income elasticity of migration. _American Economic Journal: Applied Economics_ , **9,** 219–255.

- Bedi, T., Coudouel, A. and Simler, K. (2007). _More than a pretty picture: using poverty maps to design better policies and interventions_ . World Bank Publications.

- Bell, K. and Bockstael, N. (2000). Applying the generalized-moments estimation approach to spatial problems involving microlevel data. _Review of Economics and Statistics_ , **82,** 72–82.

- Berg, C., Blankespoor, B., Li, Z. and Selod, H. (2017). Travel time to major cities. mimeo. The World Bank.

- Blumenstock, J., Cadamuro, G. and On, R. (2015). Predicting poverty and wealth from mobile phone metadata. _Science_ , **350,** 1073–1076.

- Blumenstock, J. E. (2016). Fighting poverty with data. _Science_ , **353,** 753–754.

- Bosco, C., Alegana, V., Bird, T., Pezzulo, C., Bengtsson, L., Sorichetta, A. and Wetter, E. (2017). Exploring the high-resolution mapping of gender-disaggregated development indicators. _Journal of The Royal Society Interface_ , **14** .

- Bourguignon, F. (2017). _The globalization of inequality_ . Princeton and Oxford: Princeton University Press.

- Bright, E. A., Rose, A. N. and Urban, M. L. (2013). _LandScan 2012: High Resolution Global Population Data_ , US Department of Energy: http://www. ornl. gov/landscan edn. UT-Battelle, LLC. Oak Ridge National Laboratory.

- Burke, M., Driscoll, A., Lobell, D. and Ermon, S.
working in Peru and 69% in Bolivia. In the SSA region estimated shares are as low as 8% in Madagascar and 11% in Burkina Faso and shares as high as 50% in Nigeria and 62% in Kenya.

Upper-middle-income countries (41% on average) and lower-middle-income countries (37%) had the most work stoppage. High-income countries had 26% of respondents on average stop work, followed by low-income countries at 19%. (We note that the set of countries in our data is not representative of country income groups.) In the LAC countries, respondents were also asked about whether they were planning to return to work if they stopped working. For these countries, we break down the overall share of workers who stopped working by whether they were planning to return to work in Panel (c) of Figure 1. It suggests that the majority of workers who stopped working were planning to return to work, though there is some variation across countries.

In addition to stopping work altogether, we examine measures of reduced payment. Figure 2 shows the share of respondents reporting partial or no payments for work performed among wage employees. This question on partial or no payments for work performed is available mostly in countries in the LAC region. The share reporting partial or no payments in this region ranges from 17% in Chile to 30% in Peru. This indicates that in addition to stopping work, reductions in pay due to reduced economic activity was an important challenge to workers. The workers nominally kept their jobs but were not receiving the full payment for the work performed, either possibly due to some furlough type of arrangements or employers delaying or reducing the pay in response to the crisis. Workers may also have adjusted working hours, but with the HFPS data we cannot measure reduced working hours directly.

The large disruption in the labor market is also apparent from the high share of workers changing jobs during the pandemic (Figure 3). Where data are available, job changing ranged from 2% to
in<br>thousands) of country_i_in_t-1_(1990). Docquier, Marfouk and Lowell (2007).|3052|1.6870|11.1509|
|TotalMig_ij,1960_|<br>Proxy of emigrant population from country_i_living in country_j_in year 1960.<br>Constructed as described in text, based on data from the United Nations Population<br>Division|3052|1.6120|20.5570|
|Dist_ij_|.<br>Log geodesic distance in km between countries_i_and_j_. Mayer and Soledad (2006).|3042|8.5170|0.9313|
|ComLang_ij_|<br>Dummy variable capturing if same language is spoken by at least 9 percent of the<br>population in country_i_and_j_. Mayer and Soledad (2006).|3052|0.1311|0.3375|
|Contig_ij_|<br>Dummy variable capturing whether countries_i_and_j_are contiguous. Mayer and<br>Soledad (2006).|3052|0.0269|0.1617|

_Note:_ The ranges, means, and standard deviations are not weighted and are based on the respective number of observations. Destination countries are the 30 OECD members. The total number of observations depends on the data availability for destination and source countries. An observation is excluded if bilateral data are not available or the source country does not have any emigrants in the destination country.

36
Policy Research Working Paper 9829

# **Abstract**

Preferential trade agreements are growing in number and deepening in content by incorporating disciplines that go beyond market access. They increasingly encompass nontrade-related disciplines as diverse as intellectual property rights, environment laws, or labor market regulations. Moreover, because investment is complementary to trade, preferential trade agreements provide relevant institutional frameworks to partner countries that wish to regulate their foreign investments. This paper studies the impact of deep trade agreements on foreign direct investment and examines three sub-questions. First, is the impact of trade agreements on foreign direct investment heterogeneous across types of business activity? Second, is this impact heterogeneous across disciplines covered in the agreements? Third, does the level of development of home and host countries matter for this impact? The analysis exploits the World Bank’s data set on the content of preferential trade agreement and data on announcements of bilateral greenfield investment at the activity level. The findings show that deep trade agreements matter for investment: every additional discipline in a preferential trade agreement increases foreign direct investment by 1.4 percent, on average. Deep agreements do not impact foreign direct investment in natural resources and extractive activities and have heterogeneous effects across manufacturing- and services-related activities. The results also reveal that disciplines that go beyond the mandate the World Trade Organization matter more for foreign direct investment. Disciplines related to investment liberalization and protection, intellectual property rights, or migration increase foreign direct investment, whereas disciplines on labor market regulations reduce investment. The results are mostly driven by investment between developed and developing countries.

This paper is a product of the Macroeconomics, Trade and Investment Global Practice. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at nrocha@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings
27.5% in new member states over the period 1999-2007, the respective share in the old member states stood at only 17.9%.

Shadow economy is a complex phenomenon, determined by numerous economic, institutional, regulatory, social and cultural factors. Generally, these are the factors affecting decision-making of individuals and firms whether to stay formal or turn informal, based on financial motives with potentially different moral evaluation of both situations. In our research, we focus on labor market institutions as these have been considered one of the main forces driving economic agents to informality in existing economic research (see e.g.. Schneider and Enste, 2000, OECD, 2004, Oviedo et al., 2009). Substantial differences in institutional frameworks exist across the European countries, although some convergence could be observed recently (see Fialová and Schneider, 2009).

In this paper, we present a multiple country, aggregate level econometric analysis of the impact of labor market institutions and institutional reforms on the size of shadow economies in European countries and various trends in their development in period 2000-2007. We analyze changes in labor market institutions and their impact on the share of the labor force in shadow employment and on the shadow economy production. Furthermore, we address the

> 6 In contrast, Schneider (2003) argues that informal sector exhibits higher level of productivity compared to the official economy. One of the reasons he mentions is stronger work effort of informal workers, whose pay is not burdened by huge taxes, social contributions and other regulations.

> 7 For a detailed survey of costs and benefits considered by individuals and firms in decision-making about turning informal, see Djankov et al. (2003).

> 8 For the purpose of this paper, we consider old EU countries group as Belgium, Denmark, Germany, Greece, Spain, France, Ireland, Italy, Luxembourg, the Netherlands, Austria, Portugal, Finland, Sweden, United Kingdom and non-EU Norway (sixteen countries). New member states group (―NMS
(contact@esalandcover <u>cci.org).</u>

**Protected areas** : UNEP & IUCN. 2018. The World Database on Protected Areas (WDPA). Cambridge: UNEP‐ WCMC. <u>https://protectedplanet.net/</u>

**Pangolin hub & trafficking routes** : Chouvy, P‐A. 2013. An Atlas of Trafficking in Southeast Asia. London: I.B. Tauris.

**Intact Forest Landscape 2013** : Potapov, Peter, et al. "The last frontiers of wilderness: Tracking loss of intact forest landscapes from 2000 to 2013." _Science Advances_ 3.1 (2017): e1600821.

<u>http://www.intactforests.org/</u>

**Centers of Endemism** : Li BV, Hughes AC, Jenkins CN, Ocampo-Peñuela N, Pimm SL (2016) Remotely Sensed Data Informs Red List Evaluations and Conservation Priorities in Southeast Asia. PLoS ONE 11(8): e0160566. <u>https://doi.org/10.1371/journal.pone.0160566</u>

**Species distributions** (clouded leopard & tiger): Goodrich, J., Lynam, A., Miquelle, D., Wibisono, H., Kawanishi, K., Pattanavibool, A., Htun, S., Tempa, T., Karki, J., Jhala, Y. & Karanth, U. 2015. _Panthera tigris_ . The IUCN Red List of Threatened Species 2015: e.T15955A50659951.

<u>http://dx.doi.org/10.2305/IUCN.UK.2015-2.RLTS.T15955A50659951.en.</u> Downloaded on 10 September 2018.

Grassman, L., Lynam, A., Mohamad, S., Duckworth, J.W., Bora, J., Wilcox, D.
noise introduced into the data to result in rainfall metrics being uncorrelated with outcomes. ERA5 is also inferior to CHIRPS, CPC, ARC2, and TAMSAT. As is evinced by the descriptive statistics, ERA5 tends to report much higher volumes of rainfall, which suggests the existence of non-classical measurement error. This conclusion is supported by our specification charts for mean and total rainfall, where coefficients derived from ERA5 data tend to be more positive than coefficients from other remote sensing products. This bias in results for ERA5 does not carry over to the number or share of rainy days, since these metrics are not based on the volume of rainfall, which is where ERA5 deviates from the other remote sensing products. Based on this evidence, we conclude that MERRA-2 and ERA5 are poor choices when seeking a remote sensing source for precipitation to match with household data.

Dissimilar from rainfall, for temperature we are unable to draw strong conclusions. In general, results for all three temperature products are broadly alike, suggesting one could use any of the three. But this general similarity is disturbed by differences in highly specific cases. Unlike rainfall, we cannot identify patterns of differences, such as with MERRA-2 and ERA5. Therefore, we caution 42
|0.24|
||Sugar<br>0.15|0.59|

_Source_ : World Bank for international commodity prices and 2010 GDP, IMF for international reserves and USDA for import demand.

Dependency ratios give important insights into MENA‟s overall exposure to price shocks. It shows that, from a macroeconomic perspective, the impact of high international food prices on GDP and international reserves is sizable for most developing MENA countries, mainly because of the big role of grain imports for domestic consumption. This finding is consistent with MENA‟s high grain import dependency, the high grain import share, and the large increase in world grain prices compared with world meat prices. International prices of oils and sugar jumped, in some cases, by more than the prices of grains but their lower share in imports implies a smaller effect on the import bills.

This analysis gives an idea about the relative importance of different food commodities on macroeconomic indicators such as aggregate imports, yet it ignores second-round effects and may heavily misrepresent the impact on consumer prices,

-11-
between road infrastructure and labor outcomes often suffer from endogeneity and reverse causation problems. Road quality is usually not exogenous. Other than topographic characteristics, it is often driven by economic, social or political factors (Banerjee et al., 2012; Datta, 2012; Nguyen et al., 2011; Burgess et al., 2015). To address the endogeneity and reverse causation issues, this study uses a (historical) instrumental variable strategy. The instrumental variable has been obtained by georeferencing and digitizing a militarytopographic map of the Caucasus region printed by the Russian Empire in 1903. The study uses historical, primary military and post roads to instrument the existing road quality a century later.

The results show that a shorter distance to the nearest good quality road has statistically significant positive impact on overall non-agricultural employment, on skilled manual employment for rural men, and non-agricultural employment and cash earnings for rural women. People are more likely to work outside of their villages if they have access to good quality roads, and also tend to work for more hours. The analysis has been carried out on two different datasets, the Demographic and Health Survey and the Integrated Living Conditions Survey of Armenia, using different estimation methods. The results are similar and robust from both datasets.

This paper contributes to three groups of literature: (1) estimating impact of road infrastructure improvement, (2) examining rural employment and structural transformation, and (3) using historical setting for causal inference. There is a growing number of studies evaluating road construction or improvement programs in various countries, like in India (Aggarwal, 2018; Asher and Novosad, 2020; Bell and van Dillen, 2014; Duranton et al., 2014; Datta, 2012; Ghani et al., 2016; Herrera Dappe et al., 2021), Bangladesh (Khandker et al., 2009; Khandker and Koolwal, 2011), Papua New Guinea (Gibson and Rozelle, 2003; Wiegand et al., 2017), Ethiopia (Dercon et al.
, and Rafkin (2021) study mobility among different marginalized groups and analyze geographic differences in India; Card, Domnisoru, and Taylor (2022) use 1940 census data to study the role of school quality in mediating upward mobility in the US; Van der Weide, Ferreira de Souza, and Barbosa (2020) study mobility at the sub-national level in Brazil; and most closely related to this paper, Alesina et al. (2021) document patterns of IGM in Africa using

> 2The former documents mobility for 42 countries (7 from LAC) and the latter 153 countries (16 from LAC).

> 3Narayan et al. (2018) and Van der Weide et al. (2021) allow regional comparison but pooling together estimates generated with retrospective information and those with coresident samples, which may be problematic (see Munoz & Siravegna, 2021).

4
--|---|---|---|
||**ANC**<br>|**OVA**<br>|**P**<br>|**ooled**<br>|
||**5 weeks**<br>**WHO‐5 score**|**3 months**<br>**WHO‐5 score**|**Pooled**<br>**WHO‐5**<br>**score**|**Clustered SEs**<br>**WHO‐5 score**|
|Treatment|0.751|4.732*|2.710|2.710|
||(2.147)|(2.637)|(1.698)|(1.958)|
|Baseline score|0.442***|0.338***|0.392***|0.392***|
||(0.056)|(0.067)|(0.0436)|(0.0580)|
|Round‐ ERKF|0.105|3.669|1.924|1.924|
||(2.197)|(2.742)|(1.750)|(1.845)|
|Age|0.134|‐0.135|‐0.00123|‐0.00123|
||(0.103)|(0.125)|(0.0810)|(0.0926)|
|Education|‐0.735***|‐0.207|‐0.480**|‐0.480**|
||(0.265)|(0.327)|(0.210)|(0
Policy Research Working Paper 8915

# **Abstract**

At 50.9 percent, female labor force participation in Indonesia is far below the regional average of 60.8 percent. Is it being hindered by a lack of affordable childcare services in the country? This paper exploits the joint variations in preschool age eligibility and access to preschool across regions and over years in a difference-in-difference-in-differences framework. With a longitudinal survey that tracks individuals for an average of 22 years, a panel of mothers was constructed to estimate the elasticity of maternal employment to preschool access. The analysis finds that an additional public preschool per 1,000 children increases the work participation of mothers of preschool age eligible children by 11–16 percent from the baseline mean. Private preschools do not increase work participation at the extensive margin, but they increase the likelihood of holding a second job. The availability of preschools induces mothers to informal sector occupations that do not require full-time commitments.

This paper is a product of the Gender Innovation Lab, East Asia and the Pacific Region and the Gender Global Theme. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at dhalim@worldbank.org, hjohnson1@worldbank.org, and eperova@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
, were economic (‘looking for opportunities’ or ‘work') and family motives. I deal with migration arising from women moving out of municipalities exposed to violence as a robustness check.

# **V. Results**

# **A. Summary statistics**

Across the three DHS waves there are 37,500 observations of children born in the five years before each survey and whose mothers lived in the municipality of interview during since conception. Of those, 27,183 (72.49%) reported the baby’s weight at birth, 75% of which were from the mother’s recall, and 24.7% from a birth certificate or hospital record.<sup>43</sup> The average baby weighs 3.222 kg; girls are on average 110 gr lighter than boys _(_ Table 2 _),_ and their whole birthweight distribution is slightly to the left (Figure 3). 8.17% of babies are born weighing less than 2.5 kg, but because of the tendency to report birthweight in round multiples (heaping), it is preferred to include the threshold cases in a low birthweight indicator when using household survey data (Blanc and Wardlaw 2005). This adjustment brings the share of low birthweight babies in the sample to 11.79%. Less than 1% of babies die within the first 28 days of life in the sample of babies with birthweight data, and 1.1% in the full sample. In both cases, the boys’ rate of neonatal mortality is

> interpretation of this estimate would be slightly different, returning an intention to treat rather than an average treatment effect. Results do not significantly differ from the ones presented here and are available upon request. 39 Because DHS surveys have no information on household income or expenditure, I calculate the Multidimensional Poverty Index (MPI) following United Nations Development Programme guidelines for the international version of the index. Colombia has a local version (Angulo
# **Will Market Competition Trump Gender Discrimination in India?**

Ejaz Ghani, Arti Grover Goswami, Sari Kerr, and William Kerr

## **JEL Classification** : J16; F13, F14, F16, J7, J21, O12, O25, O38, O53

**Keywords** : Trade liberalization, India, De-reservation, Manufacturing, Services, Segmentation, Discrimination, Competition, Gender inequality, Female labor force participation

**Acknowledgments** : We thank Alexis Brownell for excellent research assistance. We also thank Petia Topalova and Ishani Tewari for respectively sharing their trade policy and domestic policy data with us. The views expressed here are those of the authors and not of any institution they may be associated with.
980 2,033 3,861 8,123 77,011

# **5.  Priority Area Monitoring – Illustrations**

This exercise aims to use methane data from the Sentinel-5P platform to track changes in emissions-intensive areas identified by EDGAR. In the previous section, we developed a methodology for sampling S5P data for those areas. We illustrate our approach for three areas dominated by important CH4 sources: Madrid Province, Spain [two large landfills]; Karnal District, Haryana State, India [irrigated rice production]; and Al Amarah District, Maysan Governorate,

> 14 https://www.meeo.it/

> 15 - The data are available in NetCDF format at the ESA’s AWS repository, https://meeo <u>s5p.s3.amazonaws.com/OFFL/L2__CH4, with filenames catalogued at</u> https://scihub.copernicus.eu/catalogueview/S5P/

> 16 For further discussion, see Dasgupta, Lall and Wheeler (2022, 2023).
OCHA 2019a), with evolving geographic concentrations. Initially, much of the violence was focused on the eastern border of the territory held by Houthi forces. However, over time, the violence extended to the southern border, and then, eventually, to both the northern border with Saudi Arabia and the portion of Houthi-held territory that borders the Arabian Sea (e.g., Tandon and Vishwanath 2020). Furthermore, there were reports of Houthi forces targeting civilians and political opponents, and there was also an additional violent secessionist movement taking control of territory from the IRG (e.g., BBC 2019; OCHA 2020).

As the violence has evolved, official records suggest new households continued to become displaced at roughly an equal rate to the number of households that returned, leaving the total share of the population that was displaced at any point during the time period under analysis similar to the share displaced after the conflict initially escalated (e.g., OCHA 2019a). Importantly, violence was a pivotal factor in the decision to become displaced. Violence was escalating in districts from which these newly displaced households migrated more so than for households that did not become displaced (e.g., D’Souza et al. 2022); and in interviews with displaced households during the period under analysis, the majority of households reported that violence was the cause of their displacement (e.g., TFPM 2019).

However, in addition to the escalation in violence, the conflict has had economic impacts that might have also forced displacement. First, coalition naval forces have significantly restricted imports in ports that supply regions in the north where Houthi forces have a strong presence. These blockages necessitated the delivery of imports from ports farther away via trucks, which is one of the factors that has led to significantly higher food prices in the country (e.g., ACAPS 2020; etc.).<sup>5</sup> Although it is difficult to precisely gauge the intensity of