rafmacalaba/data-use-mentions-extended huggingface dataset:
migration, undercutting the incentive to migrate and to accumulate savings. Moreover, we show that providing accurate information on wage levels in destination countries can reduce both emigration rates and durations, leading to lower levels of repatriated savings and less domestic business creation after return.

Our model is closely tailored to the institutional context. Temporary migration is pervasive in Bangladesh, and domestic economic activity strongly depends on migrants’ remittances and repatriated savings. Our model captures the key determinants that together influence the decisions about whether to emigrate, the age of departure, the destination, the duration, and the employment decision upon return. Demand for migrant labor in the main destination countries varies with oil revenues, which in turn limits individual migration opportunities. In addition, labor contracts may be canceled prematurely, creating income and employment risks for migrants. Migration choices are further influenced by pre-migration employment outcomes, migration fees and other costs, access to credit, and the costs and returns of entrepreneurship. For instance, the duration of a worker’s migration is affected by migration costs and the extent to which these are covered on credit, but also by the level of assets required to start better-paying self-employment activities in Bangladesh. In turn, credit conditions influence a worker’s ability to access self-employment in Bangladesh, but also the incentive to migrate, and the amount of savings repatriated. Thus, cost-benefit analyses of implemented policies must account for potentially offsetting changes in migration, savings and investment behavior.

Our main data source is a new and unique survey of returning migrants that was specifically developed to answer these questions. The Bangladesh Return Migrant Survey (BRMS) was designed with the idea that temporary migration is an integral part of workers’ life-cycle career. Hence, it provides very detailed information on the entire employment and migration history of a sample of 5,000 migrant workers who had returned to Bangladesh at the time of the data collection. The survey is one of the very few comprehensive surveys on temporary migrants globally, and to the best of our knowledge,
change from a forest to non-forest state)”: _forloss_ and _forest_ .

To measure pollution, we use the fine particulate matter (PM 2.5) of air pollution data estimated from a model that excludes dust and sea-salt particles (van Donkelaar et al., 2015). The model uses geographically weighted regressions along with monitoring and satellite data, including: aerosol composition and land use information. We obtain the mean ( _pm25_ ), sum ( _pm25_ _~~s~~ um)_ and standard deviation ( _pm25_ _~~s~~ d_ ) for each administrative unit.

# **Socio-economic: Night time lights**

Night time lights (NTL) data, the visible light emitted from earth at night captured by satellites, are found to correlate strongly with population density and economic activity (e.g. Ebener et al., 2005; Henderson et al., 2012). The NTL high gain data come from the Defense Meteorological Satellite Program DMSP-OLS, which are made

> 19See Table 2 in CGIAR-CSI _Global Aridity Index (Global-Aridity)_ and _Global Potential EvapoTranspiration (Global-PET)_ Climate Database (2009)

46
(such as payment of electricity bills), and development (such as construction of classrooms). Policy options and targets for ECE were then defined based on best practices and in line with national and provincial policies and implied service standards. Enrollment estimates from the PSLM were updated. Using data on population growth and enrollment in public and private ECE we use an estimated enrollment rate of 31% for all scenarios in this paper. Enrollment rates were then projected for 2023-2035 using the population projections contained in the most recent published census files.<sup>12</sup> Infrastructure and human resource

> 4https://www.ece-accelerator.org/resources/unicef-west-and-central-africas-regional-prototype-supporting-costeffective-policy-development-early-childhood-development

> 5 https://www.humanitarianlibrary.org/sites/default/files/2014/02/ECD-CoNo30-rev.pdf

> 6 https://www.costtoolkit.org/

> 7 https://www.brookings.edu/articles/the-childhood-cost-calculator-c3/

> 8 https://www.ilo.org/globalcare/?language=en#simulator 9https://bangkok.unesco.org/sites/default/files/assets/article/Early%20Childhood%20Care%20and%20Education/file <u>s/Costing%20and%20Financing%20SDG%204.2%20Webinar/2.1_Kristy%20Bang.pdf</u>

10 https://www.pbs.gov.pk/content/final-results-census-2017 11 <u>http://www.emis.gob.pk/; https://semis.rsu-sindh.gov.pk/; https://open.punjab.gov.pk/schools/; https://ese.kp.gov.pk/page/education_management_information_system_emis; http://hrmis.fde.gov.pk/;</u> 12 The rates used for each province are available online at https://www.pbs.gov.pk/content/final-results-census-2017- <u
to either hide money from their spouses (in wave 1) or whether to control money themselves (in wave 2).

I also investigate possible heterogeneity of effects along two dimensions that have been suggested as important in the intra-household bargaining literature, namely women’s initial bargaining power and women starting to earn more than the husband.<sup>59</sup> I am underpowered to identify interaction terms between the selected variable and the heterogeneity variables, but the sizeable coefficients provide suggestive evidence that the increase in decision-making and female assignable expenditures is larger for selected women with above median initial decision-making involvement. In contrast, the selected women who started earning more than their husbands between factory baseline and household surveys appear to spend less on female expenditures and remittances. This is in line with the results in Bertrand et al. (2015), which found that US couples in which a woman earns more than her husband are less satisfied with their marriage, more likely to divorce and more likely to have a higher gender gap in non-market work.

# **4.4 Robustness checks**

The large positive effects on expenditures on women’s assignable goods and remittances are robust to different estimation approaches, placebo tests and checks for attrition, as well as different specifications, variable definitions and data sources, though the impacts on women’s involvement in decision-making in wave 1 and girls’ expenditures are somewhat more sensitive to those checks. I also demonstrate that the effects are not mainly driven by the training that was delivered as part of the promotion program.

I implement a regression discontinuity design (RDD) relying on the factory rankings of candidates<sup>60</sup> as well as a matching approach<sup>61</sup> as robustness checks. Figure 1 compares the coefficients between the PDS Lasso results and these two different approaches for the impacted variables, where the decision-making index is expressed in tenths of a standard deviation for exposi-

> 59Results are shown in Tables C.16 and C
The World Bank _Doing Business_ report measures to a certain degree the quality of secured transactions systems through the Legal Rights Index<sup>19</sup> which is part of the Getting Credit indicator. While not every aspect of a comprehensive secured transactions system is covered by the criteria of the Legal Rights Index, it covers a number of aspects related to: (i) the scope of the law (possessory and non-possessory security rights, general description of assets, debts and obligations, security interests in future assets, extension of the security interests to the products and proceeds); (ii) the priority scheme for creditors (outside and in bankruptcy proceedings); (iii) the publicity mechanism through the registration of notices in a collateral registry, and; (iv) the enforcement mechanisms, including out of court enforcement. The Legal Rights Index has some important gaps in how security interests are created and perfected, in measuring which countries have a good registry system and also in determining the types of transactions and the types of parties that should be covered by the secured transactions law. In addition to this, there is not complete consensus among international experts in a couple of the criteria, including the exempt from an automatic stay on enforcement and the absolute priority of secured creditors. Other minor aspects are also missing given the limitations of the methodology. However, despite these gaps, the Legal Rights Index proves to be a good benchmarking tool to assess creditors’ rights and secured transactions in a given jurisdiction and the ranking of the MENA countries reflect relatively well the regional overview that has been validated by some stand alone diagnostics in specific countries.

An overview of the Doing Business’ Legal Rights Index indicator for the region in 2009 illustrates that secured transactions systems are still hugely underdeveloped. Individually, there is not a single country in the region that has introduced reforms in its secured transactions system, which is illustrated by the position of individual countries in the ranking (see Table 2). The first group of countries in the
targeting the poor.

We also find that the VAT scheme is strongly progressive, with urban households bearing a higher tax burden than rural households. This finding echoes results from most studies on VAT impact (N. Gemmell and O. Morrissey, 2003, S. M. Hossain, 1995, D. Newhouse and D. Zakharova, 2007, N. Salti and J. Chabaan, 2008). What is important in the context of this study is the robustness of the VAT progressiveness even when we do not take into consideration the indirect effects of the VAT.

Overall, exempted goods constitute a large budget share among the poor (at least 79.4 and 79.8 percent of the food budget shares of the rural and urban poor, respectively). Although we have not implemented a very detailed analysis of exempted goods, it is hoped that exemptions can be refined to better help the poor by further subsidizing luxury goods that are important on nutritional grounds (for example, meat and fish, which tend to be luxury goods). In addition, the adoption of the VAT led to a worsening of the food poverty headcount by 1.2 to 1.4 percentage points.

The current analysis provides nationally representative estimates, which does not fully reflect regional specificities. Nor does it account for general equilibrium effects resulting from the VAT reform. These two points highlight the need for an analysis of the data disaggregated at the regional level. They also underscore the need for an analysis that accounts for macro level interactions with respect to the impacts of the VAT reform.

23
**Figure 2: Children receiving stimulation at home and attending early childhood education by country income**

<!-- Start of picture text --> 100%<br>0.092 0.228 0.421 0.607<br>90%<br>0.14<br>High stimulation & preprimary<br>80% 0.29 Low stimulation & preprimary<br>0.539 High stimulation & no preprimary<br>0.471<br>70% 0.658 Low stimulation & no preprimary<br>0.708<br>60%<br>50%<br>0.834<br>40% 0.875<br>30% 0.386<br>20%<br>0.226<br>10%<br>0.116<br>0% 0.074<br>Low Income Low-Middle High-Middle High Income<br>Income Income<br><!-- End of picture text --> _Notes_ : Data from the Multiple Indicator Cluster Surveys, reported in McCoy et al. (2018)
this paper fills the gap by examining how HSR expansion may enable firms to choose domestic over imported materials, thereby driving up firm DVAR and moving China up the GVCs.

This paper first proposes a new methodology to estimate firm-level DVAR from the export revenue function, based solely on customs transaction data. With the rise of GVCs, where production stages span multiple countries, traditional trade statistics are inadequate to measure a country’s contribution, leading to the need to properly measure DVAR in exports. Previous studies, such as Johnson and Noguera (2012) and Koopman et al. (2014), relied on industry input-output (IO) tables to estimate aggregate DVAR. However, IO tables tend to over-represent large firms with higher import-to-sales ratios (Amiti and Koning, 2007), which causes aggregation biases and may underestimate DVAR.3 Kee and Tang (2016) taking a ground-up approach, address the aggregation bias by measuring firm DVAR directly from customs transaction data and constructing industry- and country-level DVAR as the weighted averages of firm DVARs. However, while only customs transaction data is necessary for measuring firm DVAR for the exporters that use all their imports for exports, such as the processing exporters in China, for exporters who also produce for domestic markets, their approach still requires matching customs data with industrial census data to infer the proportion of imports used for exports. The matching requirement severely limits the applicability of their approach in countries without industrial census data. This paper proposes a new method to estimate the proportion of imports used in exports directly from firms’ export revenue function. Relying solely on customs transaction data, this approach eliminates the need for additional industrial census data, making it more applicable for future research and policy work on GVCs for countries with such data limitations.

To estimate the proportion of imports used for exports in the firm-level export revenue function regressions, this paper builds on the classic approach to estimate production function introduced by Olley and Pakes (1996), who use firm investment as a proxy for firm productivity. In
_TREATi,t_ takes value one if unit _i_ has been assigned to receive treatment by time _t_ (that is for t=1,2,...r), and zero otherwise. The treatment effect of interest is then given by γ.

Note that γ does not contain a _t_ subscript: either the treatment is assumed to have a constant level effect, or the treatment effect of interest is taken to be the average treatment effect over the _r_ post-treatment rounds. Clearly a further, very important, use of multiple postintervention surveys is to collect information on the trajectory of treatment impacts (e.g. Woolcock, 2009), since the short and long-run impacts of some policies may differ dramatically. However, unless one imposes some structure on the form these impact trajectories can take, collecting more rounds of data merely increases the number of points in time at which these impacts can be measured, but does not improve the power of an experiment for measuring these impacts. Even in this case where impacts at different points in time are of interest, one can consider multiple measurements located around each time horizon of interest, and thereby improve power. For example, if a study is interested in knowing whether the impact of microfinance differs at a one year horizon versus a three year horizon, taking measures of microenterprise profits at 11, 12 and 13 months after treatment, and at 35, 36, and 37 months after treatment can be used to improve the accuracy of impacts at around these horizon points, by assuming the treatment effect is constant or considering an average treatment effect in the neighborhood of the horizon of interest. The analytics and ideas of the remainder of the paper can then be applied to estimating the impact at a particular horizon by taking more measurements in neighborhoods around these horizons.

# **2.1.The case of a single baseline and single follow-up**

Consider first the case of a single baseline and single follow-up. Then we see that the least squares estimator of γ is simply
)|(0|.0315)|(0.0293)|(|0.0313)|
|Constant|‐0.0190|‐|0.0212|‐0.0194|‐|0.0206|
||(0.0135)|(0|.0143)|(0.0135)|(|0.0143)|
|Country fixed effects|Yes||Yes|Yes||Yes|
|Year fixed effects|Yes||Yes|Yes||Yes|
|R‐squared|0.562||0.581|0.563||0.582|
|Number of observations|906||906|906||906|

_Sources_ : International Monetary Fund World Economic Outlook, World Bank Global Economic Monitor, International Monetary Fund International Financial Statistics, World Trade Organization, World Input Output Database (WIOD), <u>www.PolicyUncertainty.com, Ahir et al. (2019), and authors’ calculations. See Table 1 for details. Notes: Heteroscedasticity-</u> robust standard errors corrected for clustering at country level. * p<0.10; ** p<0.05; *** p<0.01. Results in grey indicate that difference between coefficients of GVC-related and non-GVC related indicators is not statistically different from zero. Panel covers period 1995-2018. Growth indicators are computed as log-differences.

# **6. Conclusion**

Trade growth has been subdued since the global financial crisis; the evidence presented in this paper suggests that the increase in policy uncertainty in recent years may have been an important overlooked factor contributing to the slow growth of world trade. Specifically, we use new data on economic policy uncertainty for a sample of 18 countries and 24 years from Baker et al. (2016) and find a statistically significant relationship between this measure of policy uncertainty and growth in trade, including trade linked to GVCs.

15
Servicio de Rentas Internas, 38-81. https://repositorio.flacsoandes.edu.ec/handle/10469/3867</mark>

- <mark>Angrist, Joshua D., and Jörn-Steffen Pischke. "Parallel worlds: fixed effects, differences-in-differences, and panel data." Mostly harmless econometrics. Princeton University Press, 2008. 221-248.</mark>

- <mark>Atkeson, A., & Burstein, A. T. (2010). Innovation, firm dynamics, and international trade. Journal of political economy, 118(3), 433-484.</mark>

35
percent level.

Using the production function elasticities from Table 1, we calculate sector-specific factor intensities, defined as the capital-to-labor PF elasticity ratio, and examine the pairwise correlations between the results obtained using the WBES database and those from De Loecker (2013). We found a correlation coefficient of 0.55 between factor intensities, which is significant at the 5 percent level. The correlation coefficient between median productivitypremium from exporting is 0.36. This is high given that we only have 15 observations and there is a lot of cross-country variation in the WBES database.

20
gives the concentration curve for worker participation in the program. This was estimated by locating the families of a random sample of 3,500 Trabajar workers within the national distribution (based on a sample of 22,000 families).'<sup>0</sup> For example, 76% of people living in the households of participating workers had a household income per capita that placed them amongst the poorest 20% of Argentineans nationally.

How does this incidence of income gains compare to other social spending in Argentina? Table 3 gives the concentration curves for the Trabajar program and both aggregate social service spending and social insurance, all on a household basis (to assure that the Trabajar concentration curve is comparable with the numbers in Table 1). Since there is very little variation in the Trabajar wage rate, the concentration curve based on participation is also the benefit incidence curve for gross wage payments." It can be seen from Table 3 that the direct income gains from the program were far better targeted than social insurance and social services as a whole. The program's targeting also appears to be better than any other targeted programs in Argentina. Amongst the programs for which incidence calculations are given in World Bank (1999, Table 3.7), the next best performance was for programs directed at pregnant mothers and children, for which 70% of the benefits went to the poorest quintile of households, which was itself an unusually good performance compared to other programs.

Of course, targeting performance is only one factor in assessing the performance of such programs in reducing poverty (Ravallion, 1999b). A program such as Trabajar is designed to help in one dimension of poverty, while programs directed at health and nutrition of the poor help in quite different dimensions; both types of programs can have important

**10** Identical surveys were used for the program participants and the national sample, and the surveys were implemented at approximately the same time; for details see Jalan and Ravallion (1999).
Table 1

# Growth Rates in Developing Regions

(average annual % growth)

||2000-2009|2010|2011|2012|
|---|---|---|---|---|
|East Asia and the Pacific|9.4|9.7|8.2|7.8|
|China|10.9|8.5|9.1|8.4|
|Europe and Central Asia|5.8|5.2|5.3|4.0|
|Turkey|4.9|9.0|8.2|2.9|
|Latin America and the Caribbean|3.8|6.0|4.2|3.6|
|Brazil|3.6|7.5|2.9|3.4|
|Colombia|4.5|4.3|5.6|4.4|
|Mexico|2.2|5.5|4.0|3.2|
|Argentina|5.4|9 .2|7.5|3.4|
|Middle East and N. Africa|4.7|3.6|1.7|2.3|
|South Asia|7.3|9.1|6.6|5.8|
|India|7.9|8.7|6.5|6.5|
|Sub-SaharaAfrica|5.1|4.8|4.9|5.3|

Source: World Bank (2012) and World Bank World Development Indicators 2011 Finally, the entire region underperforms in competiveness. The region’s participation in world exports of manufactures has grown little and its participation in services has fallen (ECLAC 2011). Meanwhile, only Chile and Barbados are in the top 50 of the more than 140 countries in the World Economic Forum’s Global Competitiveness Index
# Figure 8. Services trade restriction policies

A. Services trade restrictions <!-- Start of picture text --> A. Services trade restrictions B. Services trade restrictions in EMDE<br>regions<br>Score, 1 = completely closed Score, 1 = completely closed<br>0.6 0.6<br>0.4 0.4<br>0.2 0.2<br>0.0 0.0<br>Advanced EMDEs<br>economies<br>EAP ECA LAC SAR SSA<br><!-- End of picture text --> Sources: Organisation for Economic Co-operation and Development; World Bank. Note: EMDEs = emerging market and developing economies; EAP = East Asia and Pacific, ECA = Europe and Central Asia, LAC = Latin America and the Caribbean, SAR = South Asia, SSA = Sub-Saharan Africa.

A. B. Services trade restrictions index (STRI) helps identify which policy measures restrict trade. The STRI takes values from 0 to 1, where 0 is completely open and 1 is completely closed. They are calculated on the basis of information in the STRI database which reports regulation currently in force. Bars show the unweighted average and orange whiskers indicate the minimum and maximum range. Sample includes 31 advanced economies and 17 EMDEs in 2020.