rafmacalaba/data-use-mentions-extended huggingface dataset:
sample, information on both the dependent and explanatory variables for them is missing from the data set. This also implies that, in most of the cases, it is not possible to estimate a sample selection equation to correct for the biases, because it is not possible to identify if a household is missing children from the survey. The maximum likelihood approach developed by Bloom and Killingsworth (1985) can be applied in this case if multivariate normality is a reasonable assumption.

Although non-random sample selection due to coresidency has been a major methodological issue in the research on intergenerational mobility, evidence on the magnitude of coresidency bias has been scarce, with the exception of the analysis of occupational mobility in the UK by Francesconi and Nicoletti (2006). In an interesting paper, they use British Household Panel Survey to estimate the extent of coresidency bias in the estimates of intergenerational persistence in occupational prestige between father and son(s). They use the occupational prestige index due to Goldthorpe and Hope (1974), and estimate intergenerational elasticity as a measure of persistence. The evidence reported in their paper shows that the coresidency bias is substantial, ranging between 20-40 percent.<sup>15</sup> They, however, do not address the question whether intergenerational correlation (IGC) and intergenerational regression coefficient (IGRC) are affected differently by the truncation due to coresidency, which is the focus of our analysis.

We are not aware of any analysis of coresidency bias in the context of educational mobility, either in developed or developing countries. Our analysis can also claim broader applicability as we use data from two developing countries with substantial differences in the coresidency rates, and provide evidence on both father-son and mother-daughter links in educational persistence.

# **3. Data and Variables**

We use two rich data sets particularly suited for the analysis of the extent of coresident

> 15They provide an extensive analysis of alternative econometric approaches for selection correction. Their findings indicate that the inverse probability weighted estimator is the most reliable to tackle coresident sample
household survey, is the main source of information on the measurement and the monitoring of the living standards of the Peruvian population. Among the wealth of individual information, the survey also collects detailed information on the food consumed away from home by each adult household member. By meal event (breakfast, lunch, dinner or others), the survey collects information on the frequency of consumption, the place of consumption (street vendor, markets, restaurants, or soup kitchens), and the costs of the food. Important for this study, the survey allows inferences at the Lima Metropolitan Area.

Last collected in 2008–2009, ENAPREF is the main source of information on the family consumption basket and, thus, the computation of the consumer price index and the measurement of household consumption in national accounts. The household expenditures reported in the survey are the basis for the stratification of Metropolitan Lima into five socioeconomic strata, which constitutes the basis for the sampling design of the ENCONUT survey.

All data sets cover the same 40 districts and neighborhoods in the Lima Metropolitan Area. Thus, the sample of 2,106 women ages 15 to 49 living in these neighborhoods is combined with information from ENCONUT, ENAHO and ENAPREF at the neighborhood level. For each neighborhood, the average quality of the diet of FAFH, the average menu price and the average number of menu servings are calculated using the information on 1,605 restaurants (ENCONUT).<sup>8</sup> Furthermore, the share of women

> 7 A la carte restaurants were disregarded.

> 8 While the ENCONUT data set covers 48 districts in the Lima Metropolitan Area, the continuous demographic and health survey covers only 40 districts, which are the ones used in the analysis.

6
cds.68d2bb30. Copernicus Climate Change Service (C3S) Climate Data Store (CDS).

- [101] Schlenker, W. & Roberts, M. J. Nonlinear temperature effects indicate severe damages to US crop yields under climate change. _Proceedings of the National Academy of Sciences_ **106** , 15594–15598 (2009).

- [102] Burn, R. W., Underwood, F. M. & Blanc, J. Global trends and factors associated with the illegal killing of elephants: a hierarchical Bayesian analysis of carcass encounter data. _PLoS one_ **6** , e24165 (2011).

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encourages people to move; conversely, the promise of inherited land encourages young people to stay.<sup>4</sup> Finally, controlling for other covariates, we continue to find that household wealth is strongly correlated with moving, for both migration measures. Moreover, there appears to be some relationship with wealth and the effect of being children of household heads when moving is modeled using the distance measure in the OLS model. While suggestive that young people who lack financial resources will be constrained in their ability to move for economic or social opportunities and potentially trapped in poverty or remote rural areas, further analyses are needed to better interpret this finding.

# **IV. Conclusions**

Migration studies in Sub-Saharan Africa often focus on prime-age adults and their international moves, with few studies on the patterns of mobility among adolescents and young adults. Yet, young people are often highly mobile, with moves that are often domestic and local. This study attempts to fill this gap by drawing on household survey data from Malawi. It is a first step toward developing a framework on the role of spatial mobility in the transition to adulthood for young men and women in Southern Africa.

Drawing on new, panel data from Malawi, three important patterns on the migration experiences of young adults emerge. First, we find that geographical movement of adolescents and young adults is common, but young women are significantly more likely to migrate than are young men. Within a 24-month span, our data showed that just under a third of young women

> 4 As noted earlier, a dominant reason for moves was following parents/relatives. The lower propensity for children of the head to move shown in Table 4 suggests that these young adults move to relatives as opposed to these being family co-moves (entire groups or subgroups of households moving to new dwellings).

14
networks for promoting female entrepreneurs. We further confirm these results when instrumenting for current incumbent structures using lagged 1994 incumbent structures.

Our empirical methodology draws from the work on spatial determinants of entrepreneurship in advanced economies, most often related to the manufacturing sector, that are reviewed later in this paper. In related work, Ghani et al. (2011c) and Mukim (2011) evaluate spatial factors that explain a high rate of entrepreneurship in a district-industry generally and irrespective of the owner’s gender. These earlier studies also contrast entry in the organized sector with that in the unorganized sector. Our current paper focuses instead on factors that explain higher female entrepreneurship shares independent of aggregate levels of entry. We also specifically focus here on the local industrial conditions of female-owned businesses. In discussing these results, we identify central findings fromGhani et al. (2011c)and Mukim (2011) where appropriate.

In addition to the conditional estimations that provide the tightest econometric framework for our study, we also consider unconditional estimations that include industry-year effects only to identify general district-level attributes that correlate with high rates of female entrepreneurship. These estimations emphasize several factors. First, relative rates of female entrepreneurship do not appear strongly linked to the district’s population or to the districtindustry’s overall employment levels, although relative female entrepreneurship rates do decline with population density. More strikingly and relevant for policy makers, better local infrastructure strongly connects with higher relative female entry in both manufacturing and services. Local education and the female literacy rate further matter for services entry. Stricter labor regulations are also associated with higher rates of female entry.

These findings are very important for Indian policy makers and business leaders. Despite many economic advancements since liberalization began, the role of women in the Indian economy still lags well behind that of advanced economies (e.g., Dunlop and Velkoff 1999, Mammen and Paxson 2000, Ghani 2010a, World Bank 2011). Cross-country data from the World Bank Entrepreneurship Snapshots find that India’s rate of entrepreneurship is lower than
Policy Research Working Paper 10667

# **Abstract**

Despite the efforts made to increase women’s inclusion in the economy, they are still underrepresented in trade in general and in global value chains in particular. Thus, this paper aims at examining the impact of global value chains on women’s trade participation as entrepreneurs and employees. It also analyzes how this effect is moderated through external (gender provisions in trade agreements) and internal (investment climate variables) factors. The analysis uses firm-level data for 154 developing economies and emerging markets with a special focus on the Middle East and North Africa region, being one of the regions with the lowest female labor force participation. The main findings show that global value chains 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 for 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, access to finance, and corruption). These results remain robust after controlling for the endogeneity of global value chains using an instrumental variable approach and a propensity score estimation method where the treatment is being part of a global value chains. Thus, global value chains can be perceived as a tool that boosts women’s empowerment in emerging economies, especially in the Middle East and North Africa region.

This paper is a product of the Office of the Chief Economist, Middle East and North Africa Region. 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 chahir.zaki@feps.edu.eg.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about
requirements which will gradually be phased in over the next years to accommodate the adjustment process and avoid disorderly deleveraging.<sup>25</sup> These measures are intended to strengthen and shrink the banking sector in order to make it more stable (i.e. “good” deleveraging). A recent survey of European banks found that a large majority of banks indicated higher capital and liquidity requirements as the main drivers of deleveraging and divestment plans.<sup>26</sup> Consistent with this finding, a recent ECB bank lending survey finds that regulatory requirements—Basel III, but particularly the short-term recapitalization directive by the European Banking Authority (EBA) in addition to national measures—have prompted banks to deleverage on both sides of the balance sheet and has already tightened credit conditions, particularly for large firms (Exhibits IV.3 and IV.4). Basel III was mostly designed with developed banking systems in mind although there is widespread support under EMDEs for its objectives. However, the new regulatory framework might have

> 25 There are additional considerations and implications of deleveraging for “too big to fail” institutions and global Systemically Important Financial Institutions (GSIFIs) (e.g. Blundell-Wignall and Atkinson (2012)). 26 Deloitte (2012). The survey included 18 large European financial institutions representing €11 trillion in assets.

17
For the most part, empirical studies investigating international conflict and cooperation over water have utilized static measures of water availability, which often proxy for scarcity. Interestingly, the work by Brochmann (2012), which focuses on the impact of treaties on basin cooperation, does not include any variables proxying for water scarcity or variability. Given that this study focuses on whether basin country dyads governed by treaties evince more cooperation than those basin country dyads that are not governed by treaties, specifically under conditions of variability, we use the coefficient of variation (CoV) for precipitation to measure climate-driven water variability in each basin dyad. Precipitation data from the Climate Research Unit TimeSeries (TS) Version 3.23 (Harris et al. 2014)<sup>7</sup> are the input values for the precipitation CoV. The CoV is calculated to measure inter-annual precipitation over all monthly observations for the relevant time-period (1901-2008).<sup>8</sup> Our rationale for calculating the CoV as a long-term measure is based on our empirical assumption that water variability is embedded in the basin’s history and that it is long-term water variability that affects conflict and cooperation over time in a given basin. Our CoV measure likewise follows precedent set by previous studies (Dinar et al., 2010; Dinar et al., 2011; De Stefano et al., 2012).

The literature touting a relationship between climate change and conflict would suggest that high water variability is not conducive for cooperation. However, some research has found that water scarcity can facilitate treaty formation (Tir and Ackermann, 2009). In fact, some studies have identified a more nuanced relationship between water scarcity (and variability) and treaty formation, whereby formal cooperation is enhanced only up to a certain level of scarcity (and variability), after which formal cooperation is negatively affected as scarcity and variability continue to increase (Dinar, 2009; Dinar et al., 2010; Dinar et al., 2011). In light of the
**Table 3: Indices measuring provincial segregation**

|Year|Dissimilarity Index|Gini Index|
|---|---|---|
|2002|0.21|0.30|
|2004|0.26|0.36|
|2006|0.28|0.38|
|2008|0.23|0.32|
|2010|0.24|0.34|
|2012|0.28|0.38|
|2014|0.32|0.45|
|2016|0.40|0.53|
|2018|0.50|0.66|
|2020|0.50|0.61|

Note: i) Authors’ estimation based on VHLSSs data 28
Country-year fixed effects control for any national-level time-varying economic conditions. In the context of the model, the relevant factors are prices in the primate city. Empirically, they also include the level of industrialization, oil production, and terms of trade, as well as policies, including gasoline subsidies and preferential trade pacts with developed countries like the American Growth and Opportunity Act (AGOA) and the European Union’s Everything but Arms (EbA) regulation. They also control for global macroeconomic fluctuations, including commodity prices, as well as differences across satellites in the lights data. City fixed effects control for initial size and all other fixed city characteristics. City-specific time trends allow each city to be on its own growth path.

The identifying assumption for _β_ is thus that there is no other time-varying withincountry variation net of linear growth correlated with network distance to the primate times the change in oil price that affects city growth, or more specifically, In specification checks below, distances to other large cities are tested in combination with distance to the primate to determine whether it is actually the cost to the primate city that matters, as opposed to other correlated transport costs.

In order for these regressions to pick up these effects, it must be the case that transport costs substantially affect contemporaneous economic activity, and that oil prices affect contemporaneous transport costs. On the first point, Gollin and Rogerson (2010) find that in Uganda, internal transport costs for crops can easily exceed their farmgate price. It is hard to imagine that this does not affect cropping decisions. Using national trade flows data, Lim ̃ao and Venables (2001) find that transport costs affect international trade substantially, with an elasticity of around _−_ 3. The World Bank Enterprise Surveys of establishments ask respondents whether “transportation of goods, supplies, and inputs...present any obstacle to the current operations of your establishment?” In the most recent (2006–2009) round, in all 15 countries studied, over half of respondents said that
21 _Qualifications.A_ word of caution is warranted in the interpretation of trends outlined in the paper. The above conclusions are preliminary in several respects. First, the sharp decline in agricultural employment on Java derives from one data source, namely the most recent National Labour Force Survey.<sup>3 4</sup> Evidence of this marked change needs to be corroborated with data from other surveys. Second, the analysis has been conducted at a highly aggregate level. There are still many sub-regional pockets of poverty and labour immobility in Indonesia. Clearly, special programs such as _Inpres Desa Tertinggal_ are still needed to help overcome these problems.

_Further Research and Policy Issues._ Further research is needed to examine local labour market processes and to substantiate some of the tentative conclusions outlined in the paper. Very little is known about the relationship between changing patterns of inter-regional labour mobility and their impact on wages and labour welfare in Indonesia. As labour mobility increases, the impact on changing economic structure and employment patterns on the job opportunities of migrants compared with local, non-migrant workers has become an important social and economic issue. This is particularly true in less developed regions where in-migration both to urban and rural areas has been intense - as in Southern Sumatra, parts of Kalimantan, Maluku, Irian Jaya and East Timor.

Two sets of issues are in urgent need of further investigation in a regional context. The first relate to the apparent accelerated shift out of agriculture in recent years. The second set of issues pertain to the factors driving high rates and new patterns of manufacturing employment growth, especially outside Java. One key question is the extent to which these developments have been increasingly influenced by national or sub-national rather than local structural change. Another is the extent to which different regional labour markets are affected by contrasting stimuli associated with economic growth and policy. Such factors include export-oriented industrial growth, rising agricultural productivity, and inter-regional and international migration.

With regard to specific regions, examples of key issues in need of further investigation include:
</sup> Unfortunately, standardized tests were not available in the country at the time of the intervention. As with our socio-emotional skills measures, we standardize GPAs to have mean zero and standard deviation one (i.e. z-scores).

It should be noted that our administrative data do not include the precise dates when exams or tests were taken, but only the (precise) dates when the resulting grades were recorded by teachers. Our approach to construct GPAs is, therefore, agnostic, in that we exploit sharp cut-offs based on official school year and quarter beginning and end dates to determine whether a specific grade is pre or post treatment. This is a conservative approach, as pre-treatment grades based on exams or tests taken just before the start of the treatment quarter may only be recorded in the treatment quarter, and may thus be intermingled with posttreatment grades, deflating these and resulting in lower-bound estimates.<sup>17</sup> While this type of bias works in favor of positive results, we believe that it is quantitatively rather minor, for several reasons: first, teachers are strongly encouraged to record grades as soon as possible, ideally within two weeks after the respective exam or test was taken. Second, the vast majority of assessments are taken at the end of the second and fourth quarter of the school year (i.e. after the first and second semester, before winter and summer holidays), and only a small fraction in-between. Our intervention, however, was implemented in the third quarter while our post-treatment GPAs are constructed from fourth-quarter grades only. That said, there was only a small fraction of assessments occurring just before the fourth quarter which could have spilled over. Finally, note that, even if spillover would occur, it is unlikely to cause systematic bias, as such spillover is likely to be balanced across groups due to randomization, unless teacher grade-reporting behavior has changed systematically between groups. However, we find little evidence for this.

We test the latter formally by
The addition of 25.9 million female workers during 1999-00 to 2004-05 was mainly due to a surge in FLFP in rural areas. The drop in FLFP during the later period of 2004-05 to 2009-10 was also mainly due to the withdrawal of rural females. There was a decline of 0.98 million workers in urban areas between 2004-05 and 2009-10, while 2.7 million joined the labor force during the period of 2009-10 to 2011-12, leading to an absolute decline of 19.16 million during the period of 2004-05 to 2011-12.

2.3 Comparison of Estimates of FLFP with Prior Studies Over the past few years, several papers have examined the puzzle of this unprecedented drop in FLFP during 2004-05 and 2011-12 in India. To begin, we first discuss prior estimates of FLFP in India and then compare our estimates with those of previous studies. We use various ‘ _EmploymentUnemployment_ ’ rounds of the NSSO as the principal data source for estimating trends in FLFP. From prior studies based on Usual Principal and Subsidiary Status (UPSS), participation is understood to have increased by 27.0 million female workers between 1999-2000 and 2004-05 (Rangarajan et al., 2011). Estimates by Himanshu (2011) and Abraham (2013) closely follow the above figure at 26.7 million and 26.5 million, respectively. Our own estimates follow suit, suggesting an increase of 25.9 million. In the following period, 2004-05 to 2009-10, female labor participation was reported to have declined to the extent of 21 to 22 million workers (Rangarajan et al., 2011; Himanshu, 2011; Kannan and Raveendran, 2012). Overall, there is consensus among various studies that there was a relatively large rise in labor participation — to the tune of about 26 million to 27 million during the period of 1999-2000 to 2004-05 — followed by a considerable decline during the period of 2004-05 to 2009-10 (Table 5). These studies corroborate our estimates in Table 4, of the rise and drop in FLFP between 1993-94 and 2004-05