rafmacalaba/data-use-mentions-extended huggingface dataset:
proxy for a firm’s capital-intensity. Including the firm’s age serves as a proxy for its experience and strength of its business network.

Guided by the observation of Marques (2015) that the gender of an entrepreneur affects the firm’s decision whether and how much to export, we include a dummy for whether the firm’s owners are women. We control for whether a firm has foreign ownership, as this can be expected to influence export decisions. We control for whether the firm offers training, following Dixit and Pal (2010), who evaluated the effects of training, among others, on firm performance. We include whether the firm uses foreign technology and whether it imports inputs, as we would expect these to influence the firm’s propensity to export. To proxy innovation, we include an indicator for whether the firm has introduced a new product or service in the last 3 years (Srinivasan and Archana, 2011). While the ES data are restricted to formal firms, to include a past indicator of informality we control for whether the firm was registered at the time it began operations. We include a dummy to indicate whether the firm exports directly or if it instead sells to an intermediary that then sells abroad. Finally, we include year dummies. The other parameters are , the overall intercept parameter, is a time-invariant ii unobservable effect, and i is an unobservable time-varying error term assumed to be well αα μμ behaved. εε Next, we turn to the intensive margin to determine how a firm’s employment relates to its export sales. For this, we consider the following model of derived labor demand of the firm: i i i i (5.2) ii ii where, ln NNi is export sales of firm = αα+ ββln EE + XX _i_ ′γγ+ DDat time + μμ _t_ , and the remaining variables are as above. + εε EEFinally, to evaluate the relationship between the change in employment relative to a change Finally, to evaluate the
Indonesia, and Uganda), and the analysis of empowerment for one country poverty assessment (Ethiopia).

Second, while providing very broad boundaries, the concepts of domains, sub-domains, and levels provide a pragmatic solution to the fact that degrees of empowerment vary according to what people are engaged in and where the activity occurs.

Third, the conceptualizations of assets and opportunity structure are useful for understanding the underlying reasons and dynamics that shape the degrees of empowerment of different actors, and for identifying activities and strategies for improving people’s empowerment. Changes in assets or the opportunity structure are likely to precede changes in degrees of empowerment. Identifying asset and opportunity structure indicators is therefore also useful for evaluating intermediate impacts of investments in empowerment. This appears to be the case in Mexico, for example, where investments in education enhance a series of student assets (skills, self-confidence and information) prior to contributing to improved degree of empowerment outcomes. Depending on the nature and components of the intervention, monitoring and evaluation teams have to decide whether they want to focus their studies on assets, opportunity structure, degrees of empowerment, or any combination thereof.

Fourth, distinguishing between different degrees of empowerment is important for two reasons. One, it helps to identify indicators that adequately reflect an empowerment outcome. For example, “voting in the last local elections” is not an adequate empowerment indicator for the political sub-domain at the local level, if, for whatever reason, local elections do not take place in the community where data are collected (the choice does not exist) or if respondents had the choice and knowledge to vote but, due to personal preferences, decided against voting (the respondents decided not to implement the choice). Two, the distinction between different degrees of empowerment makes it possible to capture gradual advancements in the empowerment status of respondents. Before a woman can take effective action against an abusive husband, for example, by getting help from the local police (achievement of choice), she first needs to be
contains many irregular shapes and donuts which make it difficult for the tool to merge the neighboring EAs considering all given criteria. In addition, some region merging techniques are better than others at ensuring all final regions match the desired criteria. In some cases, techniques become stuck in configurations with no solution (e.g. with small unmerged areas along the map borders or surrounded by fully formed regions that cannot be merged more). Such errors could be eliminated by incorporating harder constraints or additional constraints that are more appropriate to the use of many small areas.

Our use of AZTool in the context of Somalia also pointed to potential extensions of the region-merging approach other criteria based on socio-economic variables such that socio-economic metrics are homogenous within the produced EAs. For instance, if the purpose of a survey is to collect data on poverty, the existence of high-resolution poverty maps (Tatem et al. 2014; Steele et al 2017) could be used to provide a weighting constraint which the tool could use to merge regions with the aim of ensuring that poverty levels with EAs were relatively homogeneous. This approach could help to ensure that selected samples are more representative, but still the priority is to have excellent population data, and if only low-resolution socio-economic data are available, the combination still can be made. In addition, a compactness metric based on the comparison of the longest length in a shape to the diameter of a circle of same area would provide a more robust selection criterion during the merging process and lead to more compact EAs, as it is insensitive to shape size, unlike the compactness metric used in the AZ tool. Furthermore, if available data have consistent and reliable labeling, the hierarchy approach will be adopted in terms of prioritizing the features to be merged in the merging process. By doing this, traversability will be accounted as well through ranking of split lines, and additional data sources such as the building delineation derived from satellite imagery (San and Turker 2010; Vakalopoulou et al. 2015) would supplement road data,
such,24Notethe estimatedthat _MAENMAE|_ � _μ_ only _d_ usesis likely larger than the true value since the denominator is underestimatedthe observed data range while the true data range is likely larger. As in equation 6. There are further related challenges to the validation due to the fact that the general objective of imputing unobserved data can only be validated using observations. Some further rationale for25theNote that (1divergence _−NMAE_ metric� and) _≥_ how(1 _−NRMSE_ it�relates), where the second value equals the out-of-sample calculationto the objective is provided in section B.B3. of the _R_<sup>2</sup> . Keep in mind, however, that the data for validation may still contain outliers, see again section� � B.B3, so that (1 _− NRMSE_ ) _>_ (1 _− NRMSE_ ). Regardless, when _NMAE_ is low, such�as in nearly all countries as the results will show and particularly in the outlier case,�the value (1 _− NMAE_ ) approaches the out-of-sample _R_<sup>2</sup> given by (1 _− NRMSE_ ). Moreover, �when _NMAE_ is small, then model fit is good and average bias must be small, which means that 1 _− NMAE_ approaches the in-sample _R_<sup>2</sup> which is the squared Pearson-correlation between the predictions and the observations. It is well known that the arithmetic mean of multiple correlation coefficients underestimates the total correlation and that the distribution of the correlation coefficient must first be normalized before averaging to obtain a less biased estimate, particularly when the number of coefficients is small. See (Corey et al., 1998) on this matter and the use of the Fischer Z-transformation in this context.

> 26The standardization in the units of measurement is automated and works by parsing the text in the WFP data base that describes the food items and inferring multipliers that standardize the prices to comparable units. In particular,
br>(0.000)|
|Observations|2,361|1,199,814|1,199,814|2,361|1,199,814|1,199,814|
|R-squared|0.263|0.101|0.129|0.272|0.111|0.142|

Notes: Robust standard errors in parentheses clustered at the district of birth. (*** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1). Sample corresponds to children born between 1957 and 1962, or 1968 to 1972. Years of schooling (Edu) in Census 2000 was calculated based on the education level completed. Years of schooling (Edu) in the IFLS was calculated based on highest grade completed in an education level. Primary completion takes the value of 1 if a child has completed 6 or more years of schooling and 0 otherwise. Family background is measured by father’s years of schooling. The Inverse Probability Weighting (IPW) estimates are calculated using 1975 district level density interacted with time trend. Data sources: Indonesia’s full count census 2000, IFLS and IFLS-East. 69
financial decisions. Regarding trust, research looks at advanced economies and the role of trust for participation in the stock market and risky financial assets more broadly, on the other hand it shows that the experience of previous economic crises can lead to higher levels of mistrust and therefore reluctance to participate in financial markets – a phenomenon which is particularly relevant for transition economies. We contribute to the literature by presenting new and unique evidence on savings in 10 Central Eastern and Southeastern European countries, distinguishing not only between formal and informal savings but also further distinguishing formal savings by bank savings and contractual savings (life insurance and pension funds) and capital market investments (stocks, bonds and mutual funds). Based on this evidence we can bridge the literature which analyzes the role of mistrust for informality and the literature which analyzes the role of mistrust for lack of capital market participation. Finally, we contribute to the research which investigates transaction costs by simultaneously analyzing the role of physical access and internet access. Our findings are descriptive and have constraints in providing causal explanations but will be of interest to policy makers in their endeavors to promote formal savings in ECA.

While we only focus on a subsample of countries in ECA, the results we obtain are at least to some extent applicable to the region more generally due to the common experience of transition from planned to market economies. Furthermore, our measure of savings is based on survey data and looks at whether households hold certain saving instruments, but we do not know the amount invested in the respective saving instrument. Therefore, our analysis only partially relates to the large body of research which studies savings in terms of flows, i.e. as the residual between income and current consumption, and analyzes saving motives (see Browning and Lusardi (1996) for an overview and Le Blanc et al. (2015) for a recent analysis of saving motives in the euro area, which highlights the importance of precautionary savings, savings for old age and credit constraint). With these limitations in mind,
# **Political Reservations and**

# **Women’s Entrepreneurship in India**

Online Appendix

## **A1.  Empirical Analysis**

This appendix provides additional empirical analyses to support those in the main text. App. Tables 1-5 are mentioned directly in the main analysis. An earlier draft of this paper also confirms the robustness of the household-based response when using estimations based upon cells with four dimensions: state, industry, year, and establishment type. These estimations are available from the authors upon request.

## **A2.  Extended Discussion of Mechanisms**

Section 6 considers the mechanisms that might lie behind substantial growth in women’s entrepreneurship after the state-level implementations of political reservations for women leaders. This appendix provides more direct evidence on three channels: reporting bias, access to government contracts and business, and access to finance. We also provide a more extended discussion of the literature behind the female industrial networks highlighted when discussing the infrastructure and aspiration channels.

### _Additional Background_

As a second background piece to Table 7, Appendix Table 6 presents the breakdown of employment shares and mean wages from five rounds of the NSSO’s household-level Employment-Unemployment survey. These figures highlight two important features for us. First, the share of manufacturing in these surveys fluctuates between 3% and 4% during this period.<sup>1</sup>

> 1 There is an important difference in what the household surveys capture with respect to labor force activities. Employment shares generated from the household data rely on the industry code reported by respondents’

> “usual principal activity”. It is likely the case that the principal activity for many small-scale business owners

1
# Tobacco control policies

<mark>Georgia is a party to the WHO’s Framework Convention on Tobacco Control (FCTC) since 2006. The State Tobacco Control Committee was created in 2013, chaired by the Prime Minister. And the Tobacco Control National Strategy of the Government of Georgia—through the Decree No196, published in July 2013—set the goal of reducing smoking prevalence among the adult population by 2 percent annually.</mark> A major legislative reform on tobacco control was adopted by the Georgian Parliament in May 2017, after unsuccessful attempts in previous years. The main components of the law are summarized on <u>Table 2, and</u> a more detailed description is included in <u>Annex 1. Georgia’s tobacco-control efforts have received</u> international recognition. The new legislation has been called “one of the strongest single comprehensive tobacco control laws in the world” (The Tobacco Atlas 2018). The policy changes have enjoyed popular support among Georgians.<sup>5</sup> However, the implementation of some provisions has been postponed until the end of 2022.

In 2017, Georgia was selected as partner to the FCTC’s 2030 project (the only country from the Europe and Central Asia region). This initiative seeks to provide special assistance to fifteen partner countries to foster the implementation of the Convention (WHO 2017). Additionally, tobacco control has important implications for Georgia’s process towards integration to the European Union (EU). Within the 2014 Association Agreement signed with the EU, Georgia is required to implement the provisions of the tobacco product and tax directives. The EU Directive 2011/64/EU on the structure and rates of excise duty on manufactured tobacco requires Georgia to meet counter-illicit trade and tobacco tax rates obligations— based on the FCTC’s targets—by 2021.

# Tobacco taxation policy

<mark>Taxes on tobacco products in Georgia were introduced in 1997. However, locally manufactured cigarettes enjoyed substantially lower tax
by surveys at highway sites report no change or even a slight increase in the total number of empty trips.

Under the conceptual framework, the data in the above hypothetical case is consistent with a setting where search frictions are important. After search frictions were alleviated in both the fronthaul and backhaul locations, more matches occurred as predicted. However, demand imbalances, regulatory barriers, or returns to scale in the matching technology meant that the proportional increase in matches was higher in the fronthaul market than in the backhaul market. As a result, even though each carrier is more likely to find a backhaul, the overall number of empty trips may remain constant or increase.

For confirmation, the conceptual framework suggests that measuring backhaul probabilities through surveys may show benefits from the platform even if counting empty trips does not. In addition, the number of idle carriers in the fronthaul location may have decreased, which provides another source of economic efficiency.

**Case: United Kingdom** McKinnon and Ge (2006) document that between 1973 and 2003, the share of empty trips fell by 7 percentage points, as can be seen in Figure 5. They attribute the trend to changes in the structure of shipping, improvements in load matching, and the growth in “reverse logistics” demand such as shipping demand for recycled products. Under the framework, these changes map to simultaneous decreases in search frictions and demand imbalances, which predicts a larger decrease in the share of empty trips than either change alone. To tease out which effect dominated, one could have measured the change over this period in idle vehicles as well as empty trips. If the change in idle vehicles was small, this suggests that reverse logistics may be more important, while if the change in idle vehicles was large, then the improvement in load matching may be more important.

**Case: Colombia** Between 1950 and 2005, Gonzalez-Calderon et al. (2012b) report that the percentage of empty trips in Colombia fell by about 0.66 percentage
_For labor or for divorce?_ An institutional environment favorable to women (as illustrated by figure 1), such as nofault divorce laws, coupled with increased chances of success in marriage markets<sup>6</sup> or the possibility of accessing the labor market in the event of a divorce, would influence intrahousehold decision-making processes, even if a divorce does not ultimately occur (Duflo, 2012). Hence, divorce and marriage legislation can exert a substantial influence on these power dynamics and effectively boost women's participation in the labor force. This paper investigates the effects of divorce legislation reforms on women’s labor outcomes in three MENA region countries: Morocco, the Arab Republic of Egypt, and Jordan. At varying dates during the late 1990s and early 2000s, MENA region countries consecutively introduced different forms of unilateral divorce and child custody legislation reforms (Welchman, 2007; Appendix A). These reforms allowed women to easily access divorce without having to prove fault nor require consent of spouse. In past research, Hassani-Nezhad and Sjögren (2014) investigated a similar question using cross-country variation in the timing of introduction of unilateral divorce between 18 MENA countries using aggregate data and focusing on younger women’s labor force participation in comparison to women in relatively older age groups. They showed that the entry into force of unilateral divorce laws effectively increased labor force participation of younger women. Using a different empirical strategy and employing individual panel data, we investigate the effects of unilateral divorce and custody legislation reforms on women’s labor outcomes by exploring differences between women who are more or less affected by the reform. Instead of exploiting cross-country variations, we purposefully isolate estimates for one country at a time to assess how context-dependent the findings can be. In fact, a growing body of literature has shown that the relationship between intrahousehold distribution of bargaining power, social norms on gender roles, and labor supply is context-dependent, especially as far as developing countries are concerned (Duflo and Udry, 2004; Ashraf et al.,
_economic_ distance between two countries (geographic distance adjusted by time penalties caused by additional burdens posed by poor road conditions and congestion) and improving efficiency at border crossings.

# **_2. Preliminary Results:  Part 1_**

The first set of GMT estimations is organized as follows. The model is estimated using intraregional bilateral export flow data for the more integrated part of the European Union (EU15) and, second, for the Central America region. Estimations for both regions were first carried out in terms of total exports (measured in US$) and then for the disaggregated groups of goods: (i) Grains, including rice, maize, wheat, and barley, in both value (US$) and volume (kg) terms; (ii) Steel and Steel products, including ingots and other primary forms, iron and steel castings, tubes, rods, pipes and fittings, universals, iron and steel bars, rails and railway track construction materials, in both value and volume terms; and (iii) Processed Fruits & Vegetables in both value and volume terms. Estimations were carried out over a nine-year period from 2000 to 2008 controlling by country-pair fixed effects in a pooled regression. In the figures, results are presented at the 10-percent level of significance.

# **_2.1. Establishing a Benchmark for Central America’s Spatial Integration_**

The results obtained for the EU15 countries for total intraregional exports (Figure 3) correspond to what is expected according to the intuition underlying the GMT theory: the GDP of both the importing and the exporting countries is significant and positively related to export flows, while the distance between each pair of economies has a significant and negative impact on trade, whereby an increase in distance by 1 percent reduces the expected exports by 0.93 percent.

Similarly, as expected, adjacency represents opportunities for increasing exports within a highlyintegrated region. Expressed as the percentage change in total exports,<sup>8</sup> EU15 countries that share frontiers have
programs in Peru, spent 2.2 billion soles on programs which could be targeted in 1995.10 For the sake of parsimony, I present graphs only for the poverty gap measure. Results for the headcount index and the P2 measure, which are available from the author upon request, are very similar." Once again, because targeting outcomes with different indicators are very similar to each other but are clearly superior to the no targeting regime, I graph the _differences_ in poverty gaps. One way to understand the graphs is therefore as a double-difference: the first difference is the change in the poverty gap when a given budget is transferred-separately, for the no targeting scenario, and for geographic targeting using the FONCODES index, the infant mortality rate, and the measure of imputed poverty. The second difference is the difference between the change in the poverty gap under the no targeting scenario, on the one hand, and the changes in the poverty gap when there is targeting by a given indicator. The value of the y-axis at any given budget and for any given curve is therefore the additional increase in the poverty gap which could be attained from switching from no targeting to targeting with the indicator in question.

Impact on povet: Like the concentration curve analysis, Figures 2.1 and 2.2 show that targeting with any one of the indicators in question is clearly preferable to no geographic targeting: all of the curves are above zero throughout. Figures 2.1 and 2.2 also show that none of the indicators clearly outperforms the others, although transfers based on imputed poverty appear to result in smaller decreases in poverty than the corresponding transfers made on the basis of infant mortality or the FONCODES index.<sup>12</sup> Budget savings: Table 3 presents the same information from a different angle: it considers the impact on the headcount index, poverty gap, and P2 measures of spending the 2.2 billion sol reference budget without geographic targeting, and estimates the budget that would be necessary
.28|42.55|
|Partnership (%)|13.45|10.81|10.33|8.99|
|Limitied partnership (%)|8.28|4.56|15.03|8.07|
|Other status (%)|0.70|0.26|2.00|1.94|
|Exporter (%)|11.09|7.36|18.76|11.30|
|Proportion of total sales that are exported directly (%)|5.16|4.13|9.31|6.01|
|**Sectors**|||||
|Manufacturing (%)|36.91|33.55|63.91|53.94|
|Retail (%)|21.48|26.54|11.51|15.91|
|Other services (%)|41.61|39.91|24.58|30.15|
|Observations|4,302|4,295|31,486|29,230|
|Countries<br>_Source:_Calculations based on World Bank Enterprise Survey.<br>_Note:_Some variables exhibit missing values.||25||93|

32