rafmacalaba/data-use-mentions-extended huggingface dataset:
Second, we analyze how this effect is moderated through external (gender provisions in trade agreements) and internal (investment climate variables) factors. To do so, we use firm-level data for 154 developing economies and emerging markets with a special focus on the Middle East and North Africa region. Our main findings show that GVC integration increases the likelihood of being a female owner and the share of female employees, especially production ones. A less robust negative effect is found regarding the impact on being a female top manager. These effects are moderated by the inclusion of gender provisions in trade agreements and by the characteristics of the investment climate (especially tax policy, courts, access to finance and corruption). These results remain robust after we control for the endogeneity of GVC using an instrumental variable approach and a propensity score estimation method where the treatment is being part of a GVC. Thus, GVCs can be perceived as a tool that boosts women’s empowerment in emerging economies, especially in the MENA region.

The remainder of the paper is organized as follows. Section 2 presents the data we use and some stylized facts on GVCs and women’s participation. Section 3 is dedicated to the methodology and the econometric specification. Section 4 presents the empirical results. Section 5 provides robustness checks. Section 6 concludes and provides some policy implications to increase women’s employment through trade and GVC channels.

# **2. Data and Stylized Facts**

To explore the nexus between firms’ integration into GVCs and women’s trade participation as entrepreneurs and employees, firm-level pooled data from the WBES is used. These surveys cover a broad range of business environment topics such as access to finance, trade, corruption, competition, and infrastructure for 143,598 firms in 154 developing economies and emerging markets. The manufacturing and services sectors are the primary business of interest in these surveys conducted in a range of time that varies from 2006 to 2021 (see Appendix 1). The objective of this section is to provide some
://www.bis.org/publ/bppdf/bispap21l.pdf .

- Lyons, R. C, (2019). Can List Prices Accurately Capture Housing Price Trends? Insights from Extreme Market Conditions. Finance Research Letters 30, 228-323.

- Mack, A. & Martínez-García, E. (2011). A Cross-Country Quarterly Database of Real House Prices: A Methodological Note. _Federal Reserve Bank of Dallas Globalization and Monetary Policy Institute_ ~

- _Working Paper No. 99_ <u>https://www.dallasfed.org/ /media/documents/institute/wpapers/2011/0099.pdf</u>

- McGreal, S. & Taltavull de La Paz, P. (2013). Implicit House Prices: Variation over Time and Space in Spain, _Urban Studies_ 50 (10), 2024-43.

- McKinsey Global Institute (2014). A Blueprint for Global Affordable Housing Challenge. McKinsey Global Institute.

- Mian, A., Sufi, A., & Verner, E., 2017. Household Debt and Business Cycles Worldwide. _The Quarterly Journal of Economics_ 132(4), 1755-1817.

- Montero, J.-M., Mínguez, R., & Fernández-Avilés, G. (2018). Housing Price Prediction: Parametric Versus Semi-parametric Spatial Hedonic Models. _Journal of Geographical Systems_ 20, 27-55.

- Mohd, T., Masrom, S., & Johari, N. (2019). Machine Learning Housing Price Prediction in Petaling Jaya, Selangor, Malaysia. _International Journal of Recent Technology and Engineering_ 8(2S11), 542–546.

- Mullainathan, S. & Spiess, J. (2017). Machine Learning: An Applied Econometric Approach. _Journal of Economic Perspectives_ 31(2), 87-106.

- Mullins, D. & Moore, T. (2018). Self-organised and Civil Society Participation in Housing Provision. _International Journal of Housing Policy_ 18, 1-14.

- Nairametrics (2018). Deal
**Figure 10: Perception of corruption within government by ethno-religious groups and time (% of adults)**

<!-- Start of picture text --> 2010-2012 2014-2016 2018-2019<br>92% 91% 92%<br>87%<br>84%<br>76% 75% 76% 74%<br>69% 67%<br>61%<br>Baghdad Sunni Shia Kurds<br><!-- End of picture text --> Source: Gallup World Poll.

# **_4.3 Leadership approval rating_**

Recent years saw most adults in Iraq lose confidence in the job performance of national leadership. In 2013, 46 percent of adults in Iraq approved of the national leadership's job performance; by 2019, the share dropped to 13 percent, which is the lowest share among comparator countries. By contrast, approval ratings were 70 percent in Indonesia, 65 percent in Malaysia, and about 34 percent in Nigeria. Again, this is consistent with the calls from popular protests, which started in October 2019 and pushed for the resignation of the incumbent government and for early parliamentary elections. In Iraq, approval ratings fell among all groups except Sunnis, whose approval rose from 13 percent in 2010 to 21 percent in 2019 (figure 11).

While approval ratings fell among both the rich and poor, the drop was deeper among wealthier adults. In 2010, 52 percent of adults in the richest 60 percent of households approved of national leadership. By 2019, this share had fallen to 9 percent. By contrast, the approval ratings among adults in the poorest 40 percent of households fell from 25 percent in 2010 to 17 percent in 2019.

13
, allowing us to assess recent health status accurately.

To measure exposure to environmental hazards, we use two datasets, the Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS) and its analogous version for temperature (CHIRTS), published by Funk et al. (2015). These data products have better resolution and are better calibrated for regions near the equator than existing alternatives, including ERA5r (Copernicus Climate Data, 2024), which is crucial for our study in Cambodia. Moreover, their daily frequency enables us to estimate the acute impacts of extreme 5
Policy Research Working Paper 9306

# **Abstract**

To overcome the constraints for policy and practice posed by limited availability of data on crop rotation, this paper applies machine learning to freely available satellite imagery to identify the rotational practices of more than 7,000 villages in Ukraine. Rotation effects estimated based on combining these data with survey-based yield information point toward statistically significant and economically meaningful effects that differ from what has been reported in the literature, highlighting the value of this approach. Independently derived indices of vegetative development and soil water content produce similar results, not only supporting the robustness of the results, but also suggesting that the opportunities for spatial and temporal disaggregation inherent in such data offer tremendous unexploited opportunities for policy-relevant analysis.

This paper is a product of the Development Research Group, Development Economics. 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 kdeininger@worldbank.org and dali1@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
Table 2 – Uncompensated own-price elasticities (continue)

|**Authors**|**Niimi,**<br>**2005**|**Niimi,**<br>**2005**|**Niimi,**<br>**2005**|**Niimi,**<br>**2005**|**Niimi,**<br>**2005**|**Niimi,**<br>**2005**|**Meyerhoefer et al.,**<br>**2007**|**Le, 2008**|**Mergenthaler et al.,**<br>**2009**|**Gibson and Kim,**<br>**2013**|**Gibson and Kim,**<br>**2013**|**Gibson and Kim,**<br>**2013**|
|---|---|---|---|---|---|---|---|---|---|---|---|---|
|**Demand System**|AIDS|AIDS|AIDS|AIDS|AIDS|AIDS|LAIDS|LA/AIDS|AIDS|AIDS|AIDS|AIDS|
|**Price method**|MP|UV|Deaton|MP|UV|Deaton|MP|MP|CW|UM|SPM|UV|
|**VHLSS wave**|1992|1992|1992|1997|1997|1997|1997|2004|2005 (own survey)|2010|2010|2010|
|Rice|-0.84|-0.95|-0.83|-1.01|-0.89|
Limits on land availability are evident from the fact that only 11% or 31% of villages still have uncultivated or unallocated land available and that in 11% of villages, households have moved out to escape land shortage. Village land is no longer exclusively used by villagers either, as 37% have village land owned by outsiders and 34% rent village land to them (21% where outsiders own and rent village land). Higher outside demand for land is also visible from the fact that 11% and 15% of villages had, in the last 5 years, sold or given land to outsiders, sometimes (14%) failing to respect the village head’s authority. Land conflict is becoming more frequent: 29% and 20% of villages reported conflict among village families or with neighboring villages, respectively. Some 24% of villages are located within walking distance from estates and in more than a fifth of these cases, land conflicts with neighboring estates are reported. Infrastructure access remains limited with only 35% of villages connected to an all-season road.

# **3.2 Comparison of land use and productivity between smallholders and estates**

Our survey comprises small and large farms. We can thus compare the intensity and productivity of land use between smallholders and estates to assess the long-term impact of land transfers to estates as well as the extent to which rental or sales markets can attenuate dualistic structures and equalize returns between different types of producers. Doing so allows us to see if, as some have claimed, studies of the relationship between farm-size and productivity that rely on household samples may miss a significant part of the distribution and thus come to erroneous conclusions (Jayne _et al._ 2015).

Comparing smallholder with estate data provides interesting insights in a number of respects. Mean age for the 868 estates with data is 19 years with largest estates the oldest (table 4). Most (68%) are owned by natural persons of Malawian nationality, 16% by ‘others’ -most
and completeness of the school’s name across the two databases; hence, matching on school name frequently required matching on key words and word patterns. In cases where we suspected that the combination of district name and school name (even with keyword and word pattern matching) did not yield a unique school record in a database, where possible, the set of matching variables was extended to include school address. This extension helped resolved a number of cases where we were tentative. On the basis of this exercise, 94% and 97% of school records in the SLQAT databases were linked with school records in the application databases for phases 3 and 4, respectively. Only the linked school records are used in our analysis, which yield sample sizes of 747 and 830 schools in phases 3 and 4, respectively. We refer to these samples as the SLQAT samples.

_Follow-up telephone interview data_ : We collected follow-up outcome data via telephone in October 2008 from phase-3 and phase-4 schools with SLQAT pass rates between  /  15 percentage points of the cutoff. This information was gathered by a small team of independent interviewers. We were able to collect the data using this mode as schools were required to provide the telephone number of the school owner or administrator when they applied to the program, and the information was included in the school application electronic databases for both phases.

We obtained data on the following outcomes: the number of students, teachers, classrooms, blackboards, and toilets (the data on the last two outcomes were collected only for the phase-4 schools).<sup>24</sup> We also obtained information on the program participation status of the schools, and crosschecked this information against PEF’s records of program schools as of September 2008. The school sample sizes within the selected pass rate range from the cutoff are

> 24 We are unable to speculate on the accuracy of these data vis-à-vis the same data collected through, for example, face-to-face interviews in the field, as we are unclear what context
# **Using Registry Data to Assess Gender-Differentiated Land and Credit Market Effects of Urban Land Policy Reform: Evidence from Lesotho**<sup>*****</sup>

Daniel Ayalew Ali and Klaus Deininger The World Bank, 1818 H Street NW, Washington, DC _JEL Classification:_ G21, H41, J16, K11, O18, Q15 _Keywords:_ Urban land policy reform, formal land market, credit market, poverty, gender, Lesotho

* We thank the Lesotho Land Administration Authority (LAA), especially Nts’ebo Putsoa, for supporting the project, administrative data access and insightful discussion; the Lesotho Bureau of Statistics for collecing the endline household survey and Innocent Pangapanga-Phiri and Ezra Rwakazooba for support in leading and supervising the administrative and survey data collection. We thank Xuwen Gao and Ziwei Ye for excellent research assistance work. Special thanks are due to Thea Hilhorst and Jennifer Lisher for the overall support of the project, valuable comments and discussions. We also would like to thank the World Bank Lesotho country team particularly Yoichiro Ishihara and Jana El-Horr for useful comments and suggestions. Funding support from the US Millennium Challenge Corporation (MCC) is gratefully acknowledged. The views presented in this paper are those of the authors and do not represent those of the World Bank or its member countries.
<sup>_∗∗∗_</sup>||||0.008<sup>_∗∗∗_</sup>|
|||(0.001)||(0.001)|(0.001)||||(0.001)|
|constant|-6.047<sup>_∗∗∗_</sup>|5.664<sup>_∗∗∗_</sup>|8.999<sup>_∗∗∗_</sup>|9.874<sup>_∗∗∗_</sup>|6.038<sup>_∗∗∗_</sup>|0.000|0.000|11.597<sup>_∗∗∗_</sup>|12.083<sup>_∗∗∗_</sup>|
||(0.763)|(0.969)|(0.142)|(0.046)|(1.253)|(0.001)|(0.001)|(0.090)|(0.080)|
|Quarter FEs|YES|YES|YES|YES|YES|NO|NO|YES|YES|
|N|28|28|28|28|28|28|28|28|28|
|r2|0.953|0.994|0.775|0.992|0.994|0.238|0.477|0.846|0.958|

Standard errors in parentheses _∗ p < ._ 1, _∗∗ p < ._ 05, _∗∗∗ p < ._ 01

> 12The beginning of the sample coincides with the availability of the electricity daily data.

9
risk of greater mortality as a result of drought; HRD beneficiaries are therefore also called emergency beneficiaries.

The collection of LIAS data has been impacted by changing ownership of the data collection process and capacity constraints with respect to the implementation of the HEA methodology. In particular, between 2008 and 2011, LIAS data were collected systematically in all _woredas_ . However, in 2011, the USAIDfunded LIU project closed, leading to changing ownership of the data collection process. While the LIU continued to be located within DRMFSS until 2015, data collection after 2011 has been less systematic, with LIAS data collected in some _woredas_ between 2011 and 2015 and not in others. A key constraint impeding the collection of LIAS data after 2011 has been a shortage of qualified technical staff. Moreover, a lack of familiarity with the HEA methodology among seasonal assessment teams has led to the increasing use of checklists and decreasing use of the HEA methodology in the determination of beneficiary numbers (for an overview of available LIAS data, refer to Annex 1). As of 2015, the use of LIAS data has been discontinued, as it was recognized that LIAS baseline data are outdated for the highlands regions.

The HEA methodology and LIAS tool have formed key inputs into the seasonal needs assessment process. Moreover, as LIAS data are collected in June and November respectively, which is approximately mid-way through the _meher_ and _belg_ harvest seasons, LIAS data may provide an early indication of drought conditions, allowing for a timely drought response. In the absence of the LIAS tool / HEA method, seasonal needs assessments are based mainly on checklists, leading to a less systematic and transparent data collection process. Recognizing the need for a renewed use of LIAS data, USAID has issued an RFP during 2015 to update LIAS baselines for the highlands regions. Work on updating baselines will be conducted during 2016, with a view to obtaining updated baselines for all regions in Ethiopia.

# IV.3 Complementarities between the LEAP and LIAS tools

The LEAP and LIAS tools complement each other in enabling a
</sup>|
|Rail,<br>Metro,<br>Light<br>Rail|3.8 – 5.8|1.73-2.64|6.05%|These are adjusted based on the IGP-M (General Market Price<br>Index, in Portuguese).<sup>11</sup>|

However, there was limited literature on fuel price increases in Kinshasa. Leveraging information related to historical fare increases, historical prices of fuel locally and observing comparable cities (SSATP, 2001), the share of operating costs that could be attributed to fuel, and hence the expected rate at which fuel prices would impact fares, are documented in Table 2 below. Private minibus operators are more sensitive to fuel prices due to higher burden and risk on drivers, with the public bus service pricing linked to official changes, while the fuel component of moto taxis operating expenses was considered lower. There are no special rates or fare systems operating in Kinshasa where passengers could gain discounts or benefits for using specific services. Despite the system having some regulation through the governor and through a private operator union, there is widespread deviation from these practices, resulting in higher prices. More details on the method used to determine the share of operating costs attributed to fuel price in Kinshasa and on the fare readjustment process in the city are shown in Appendix A – Detailed Data Description.

9 Detailed at https://www.rio.rj.gov.br/dlstatic/10112/4800832/4128528/ANEXOIX.pdf. 10 The IPCA index composition is available at <u>https://www.bcb.gov.br/conteudo/relatorioinflacao/EstudosEspeciais/EE069_Atualizacoes_da_estrutura_de_pon deracao_do_IPCA_e_repercussao_nas_suas_classificacoes.pdf.</u> 11 The IGP-M index is composed of 3 other indexes with different weightings - 60% IPA-M (Wholesale Price Index, in Portuguese), 30% IPA-C (Broad Producer Price Index, in Portuguese) and 10% INCC (National Construction Cost Index
in the classifications of the manufacturing and service sectors in WDI and IEA are insignificant, they should be considered when interpreting the results.

To control for observed-country and sector-specific characteristics, the study includes several control variables from both the WDI and Penn World Tables Version 9 (PWT 9).<sup>9</sup> These control variables need to show variation over time, as the selected econometric approach eliminates country fixed effects that refer to time-invariant observed and unobserved country-specific factors to avoid omitted variable bias in the estimators. This requirement substantially limits the number of available indicators that can be used in the regressions. Among these indicators are countries’ import and export shares in GDP as well as shares

> 7 For a detailed description of these classifications see:

> United Nations Statistics Division. (2002). International Standard Industrial Classification of All Economic Activities (ISIC) Revision 3.1. United Nations Statistics Division. (2008). International Standard Industrial Classification of All Economic Activities Revision 4.

> 8 The IEA’s World Energy Balances 2018 Edition – Database Documentation provides a country-by-country description of sources for energy and electricity generation and consumption.

> 9 https://www.rug.nl/ggdc/productivity/pwt/pwt-releases/pwt9.0.

7
effects on sales, of 63-71 million COP per month (USD $26,500-$29,900) in levels, or 9 to 10 percent in log terms. This treatment effect is not statistically significant compared to the control group (lowest p-value is 0.12 in column 1), but is statistically different from the individual treatment level effect post-intervention. The individual treatment effects have negative point estimates in level terms, and a point estimate close to zero post-intervention for the balanced panel for log sales.

# **6.3 Channels of Production Impact**

The results on employment and sales suggest that the group intervention increased the size of the firm, causing it to employ more people and sell more. In Table 7 we examine different channels through which this increase may have occurred. Column 1 considers the defect rate. Bloom et al. (2013) found quality improvements to be one of the first signs of improvement from better management in their Indian study. We only have defect data in 2017 for 78 of the firms in the study, due to many firms not keeping consistent records on defects. A first point to note is that the defect rates are low (which is one reason some firms do not record them): the control group has a mean defect rate of 0.025 and median rate of 0.007 in 2017, which compares to much higher defect rates in India (5 percent of output was scrapped, _after_ mending of defects was done). The result is that many of the auto parts firms do not have much scope to reduce defects, and we see treatment effects that are all very close to zero and statistically insignificant.

Columns 2 and 3 consider monthly inventories. In India, Bloom et al. (2013) found firms had excess inventory levels, which they reduced when management improved. Large stockpiles of inventories are less common in the auto parts sector, with some firms doing job work and producing upon request. Data are only available for half the
<!-- Start of picture text --> 2 1.0<br>0<br>-2 -1.1<br>-4<br>-3.9<br>-6 -4.8<br>-8<br>-10 -8.9<br>Total Savings Household Public Household Private Corporate<br>Financial Physical<br><!-- End of picture text --> Note: Data is from CEIC. Figures show the difference between the average rate in 2016 and 2017, over 2007 and 2008.

The investment rate has declined as well since the GFC, after registering a rapid increase in prior years. The decline is most evident in the corporate investment and household physical investment rates. There is a divergence in the corporate savings and investment rate, since while the corporate savings rate has increased, its investment rate has declined.

**Figure 18: Trends in Investment Rate**

18 A: The investment rate has declined since the 18 B: ...the decline is evident in household GFC... investments...

<!-- Start of picture text --> Investment as percent of GDP<br>40<br>35<br>30<br>25<br>20<br>15<br>10<br>5<br>0<br>18 C: ...and private corporate sector<br>2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017<br><!-- End of picture text --> <!-- Start of picture text --> Household Sector Investment<br>18<br>16<br>14<br>12<br>10<br>8<br>6<br>4<br>2<br>0<br>2000 2001 2002 2003 2004 2005 2006