rafmacalaba/datause-encoder-data huggingface dataset:
report that the proportion of female homeowners is growing. In contrast, in Karnataka/Bengaluru, where stamp duty discounts to females are not offered, the proportion of female homeownership has not increased.

## 6.1.2 Recurrent property tax incentives do not impact female property ownership

**Unlike for stamp duty,** **_no evidence indicates that property tax incentives for females lead to changes in property ownership_** . Focus group participants and government officials reported that property tax discounts provided to females by the Delhi government did not play a role in their decisions to register a home in the female’s name. Instead, in all the cases where an assessment could be made, respondents felt that recurrent property taxes are only small amounts (a few hundred rupees) and therefore discounts on this were not important in deciding whether to register a property as female owned.

## 6.1.3 Female property ownership does not automatically imply females will have an active role in managing the property

### **_Property ownership does not always translate into a greater role for females in controlling and_**

**_managing the property._** Most female ownerships are driven by the desire to save on stamp duty. When that is accomplished, effective control and property management still remain a man’s domain. That was one reason why Jharkhand, apprehending abuse of the rebate, recently withdrew it. Many males and some females interviewed felt that females were not competent or capable enough to handle property matters. Many property-owning females felt that managing property matters was complicated and best left to their husbands. Some respondents, both males and females, as well as a few government officials, felt that this was not gender-related but depended on education level. Educated urban females felt confident they could handle property matters just as well as men.

## 6.1.4 Other factors are also important in encouraging female ownership

**_Other considerations, such as gender-based loan concessions, ensuring equality, income contributions, and income tax complexities of accounting and compliance, also play roles in female property ownership decisions in various ways_** . While agreeing that stamp duty discounts are indeed powerful incentives for female homeownership, multiple FGD participants outlined additional factors that contributed equally to their decisions to register properties in females’ names. Loan concessions offered to females buying properties in their names or in joint ownership, security of inheritance and providing equal property ownership rights to females, and income contributions all positively contributed to female property ownership.

# 6.2 Limited impact of gender-based incentives on subnational revenues

### **_Gender-based discounts for property-related taxes affect tax revenues, but the impact is small._** In most

of the cities providing gender-based discounts for property-related taxes, government officials reported that gender-disaggregated data for the revenues collected from property taxes and stamp 27
- 37 **-**

Figure 13. Total food production Comparing countries grouped by favorable and unfavorable policy environments <!-- Start of picture text --> Index 1979-81 D 100<br>120<br>110p<br>100<br>90<br>1970 1972 1974 1976 1978 1980 1982 1984 1986 1988<br>FAO production index numbers<br>FPE UPE<br><!-- End of picture text --> Figure 14. Africa's GDP Comparing countries grouped by favorable and unfavorable policy environments Index 1979-81 = 100 <!-- Start of picture text --> 130<br>120 -<br>110 -<br>100<br>90<br>80<br>70<br>1970 1972 1974 1976 1978 1980 1982 1984 1986<br>-FPE -UPE<br><!-- End of picture text -->
Figure 5: Shapley decomposition employment adjustments <!-- Start of picture text --> (a) Extensive margin (b) Intensive margin<br>59.8 72.4<br>22<br>20<br>11<br>5.6<br>1.6 1 2.3 4.3<br>Informality External attributes Informality External attributes<br>Severity of the lockdown (mobility) Severity of demand shock Severity of the lockdown (mobility) Severity of demand shock<br>Constraints Constraints<br>Notes: Firm attributes include sector, age, gender of the CEO, country, and panel firms.<br>Source: Authors’ calculations using data from the World Bank Business Pulse Survey (BPS).<br>60<br>60<br>40<br>40<br>Pseudo-R2 explained by group 20 Pseudo-R2 explained by group<br>20<br>0 0<br>Relative Shapley Value (%) - Probability of adjusting employment extensive margin Relative Shapley Value (%) - Probability of adjusting employment intensive margin<br><!-- End of picture text --> Figure 6: Employment adjustments in crisis and recovery phases (a) Employment adjustments by formal and informal firms (b) Employment adjustments by types of informal firms <!-- Start of picture text --> 0<br>-10<br>-20<br>-30<br>-100 -50 0 50 100 -100 -50 0 50 100<br>Change in sales over waves (p.p) Change in sales over waves (p.p)<br>Formal Informal Necessity De Soto Parasite Fully formal<br>0<br>-10<br>-20<br>Change in employment (%) Change in employment (%)<br>-30<br>-40<br><!-- End of picture text -->

**Notes:** Firm attributes include sector, age, gender of the CEO, country, and panel firms. Source: Authors’ calculations using data from the World Bank Business Pulse Survey (BPS).

20
3 specifically. Martinez and Fernández (2008) estimate the cost of low birth weight and underweight of the members of the current workforce results in a productivity loss to be between 2% and 11% of GDP, but do not look at the aggregate economic penalty associated with stunting. The only paper that does is Steckel and Horton (2013), who use historical data on adult height trends to estimate the cost of adult stunting in the 20<sup>th</sup> century to be around 8% of GDP.<sup>1</sup> The value-added of the first part of our paper is to estimate the economic cost of stunting using a modeling method commonly used in macroeconomics – a development accounting framework (Caselli 2005; Hsieh 2010; Caselli and Ciccone 2013) – that allows to quantify how much childhood stunting among today’s workers can account for cross-country differences in today’s GDP. To do so, we rely on micro-econometric estimates, and factor in the effects of childhood stunting on adult wages through their effects on years of schooling, cognitive skills, and height, parsing out the relative contribution of each set of returns to avoid double counting. We estimate that, on average, the per capita income penalty from stunting is around 7%, i.e. per capita income in the developing world would have been 7% higher if nobody currently working had been stunted in childhood. Africa and South Asia incur larger penalties: around 9-10% of GDP per capita.

In the second part of our paper, we perform a forward-looking exercise, assessing the economic value and the costs associated with scaling up a package of nutrition interventions using the same methodology and set of assumptions used in the first calculation. If the per capita GDP penalty of stunting is so large, a program or intervention that reduces stunting might have an appreciable rateof-return, providing its costs are not too large, and/or its effects on stunting are not too small. There is a sizable literature (synthesized in a series of systematic reviews and meta-analyses) on the effects of various nutrition-specific programs (e.g. breastfeeding promotion) and nutrition-sensitive

> 1 The economic losses in terms of productivity are computed only for those cm of height lost below 170cm.
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satisfies final demand. To date the number of jobs affected by mode 1 trade in services has been small, in part because the share of total services transactions that involve international flows are just a small part of the total market for services that can be provided at arms-length (in ‘disembodied’ form). Bhagwati et al. also note that much of the mode 1 trade that is observed is intra-firm – flows between a parent and affiliate firms – and that what matters is the _net_ effect. Two-way trade in mode 1 services is important, i.e., there is both “insourcing” as well as outsourcing. Moreover, mode 1 imports are often associated with mode 3 ‘exports’, so that the imports of mode 1 services are complemented by an income flow from outward FDI in services that supports the mode 1 imports.<sup>12</sup> Boddewyn, Halbrich and Perry (1986) is an early paper focusing on “mode 3”, analyzing whether what has now become the standard paradigm to explain FDI—ownership, location and internalization incentives—carries over to service multinationals. They argue that it does, but also note that certain features of services are likely to have implications for the form that FDI takes. For example, franchising and leasing type arrangements are likely to be more prevalent, as for some services equity control of affiliates is not needed by the multinational. Empirical research on the determinants of services FDI has concluded that services FDI tends to be market seeking and is positively correlated with prior FDI in the manufacturing and industry sectors. Both explanatory variables are intuitive in that they reflect the characteristics of services (non-storability in particular). Raff and Ruhr (2001), for example, develop a theoretical model of FDI in business services that predicts that US FDI in business services should be affected by informational barriers to entry that are easier to overcome in markets in which there is already a significant U.S. business presence. The implication is that the host market should have a larger effect on service FDI the larger the ratio of U.S. FDI to local market size. They test the predictions of their model using panel data on U.S. FDI in 23 host countries from 1976 to 1995 and find that its predictions cannot be rejected.<sup>13</sup> As mentioned, the GATS/WTO defines trade in services as comprising not just crossborder transactions (mode 1) and payments associated with movement of providers and consumers (modes 4 and 2), but also the _sales_ of foreign affiliates that have been established in host countries (mode 3). This implies a need to complement measurement of transactions on the

> 12 Markusen (2005) is a more extensive analysis of the determinants and effects of offshoring of producer services that comes to similar conclusions regarding the applicability of existing theoretical “toolkits”. Amiti and Wei (2005) and van Welsum (2004) discuss the magnitude of services outsourcing and the relationship with in- and outward FDI in greater depth. Murray and Kotabe (1999) analyze the determinants of domestic vs. international services sourcing decisions of US firms.

7
and map 1 shows the changes in the leave-out wage ratio in Brazil and in the analytical sample between 2011 and 2016.

The raw wage ratio has been closing in Brazil, indicating a convergence between male and female wages in formal sector employment (appendix A, figure A1). Additionally, the dispersion of the raw gender wage ratio across municipalities has narrowed. The leave-out wage ratio has also risen, but dispersion in the leave-out ratio has not increased, suggesting that the narrowing of the distribution of gender ratios across municipalities may be driven by changes in the industrial composition of female employment, which is excluded from the leave-out measure by design. The populationweighted average municipal change in the leave-out wage ratio in 2011–16 was 0.002, which implies that average earnings opportunities among women did not change relative to the opportunities among men over the period. The raw wage ratio increased over the period, suggesting that the share of male employment rose the most in sectors in which earnings were growing rapidly.

That the wage data only include formal sector employees and do not consider the 35 percent of the Brazilian labor force that is self-employed or informal is a limitation (Ulyssea 2018). However, it is the potential wage, not the actual wage that affects a woman’s bargaining position in the household (Aizer 2010). Thus, changes in the gender wage gap in the formal sector may plausibly affect all women. Additionally, to consider how excluding informal workers from the analysis may bias the results, the nationally representative National Household Survey (Pesquisa Nacional por Amostra de Domicílios, PNAD) is used in the analysis to contrast the gender wage ratio between formal and informal laborers. The result is the finding that the informal sector is more equitable than the formal sector in terms of wages, suggesting that the gender wage ratio is underestimated in the case of the country as a whole (appendix A, table A1). The results of the survey indicate that women are only slightly more likely to be in the formal sector than the informal sector (appendix A, figure A2; table A2), and the sex composition of the informal sector is fairly consistent over the period. This suggests that any bias introduced because the informal sector has not been incorporated does not fluctuate over time. (See appendix A for more details on the PNAD analysis.) Ben Yahmed (2018) also provides a detailed cross-sectional analysis contrasting the formal and informal gender wage gaps in urban Brazil in 2015.

# **Control Variables**

Because the outcomes may reflect general trends in crime or changes in reporting, 13
# List of Pigures

1. Relationship between per capita GNP and paved road infrastructure

2. Relationship between per capita GNP and spatial density of paved<sup>roads</sup> 3. Relationship between per capita GNP and paved road density in the<sup>U.S.</sup> 4. Correlation between per capita GNP and paved roads in the U.S. using different time lags 5. Comparison between cross-section and time-series analyses 6. Comparison between the extent and condition of paved road networks<sup>in 98</sup> developing and developed countries 7. Comparison between the extent and condition of paved road networks in<sup>low</sup> and middle-income economies in Africa 8. Sample of African countries with deteriorating per capita GNP and road condition between 1984 and 1989 9. Sample of African countries with improved per capita GNP and road condition between 1984 and 1989

# List of Tablea

1. Sample of relationships between per capita GNP (dependent variable) and road infrastructure variables

# Appndix

List of countries with data available on population, per capita gross national<sup>product,</sup> length of paved and unpaved road networks, and road condition. The vast<sup>majority of</sup> the data was obtained for 1988. However, for a few countries 1988 data<sup>was not</sup> available; then 1987 or 1989 data was used. Countries are sorted by per<sup>capita GNP.</sup> The appendix includes three annexes: (1) data on population, per capita<sup>GNP and</sup> paved and unpaved road density; (2) data on land area, per capita GNP and<sup>paved and</sup> unpaved road spatial density; and (3) U.S. data used in the time-series<sup>analysis.</sup> 15
# **3.2 Infant Mortality Differences**

We now turn to religious differences in mortality risks. Our main specification relies on the child sample, which includes all children ever born to women in the female sample described above. The information was gathered from birth-histories and includes children who are alive as well as those who died, and also includes children living within the household as well as those who do not reside in the household any more. As child-death is a censored variable and since the risk of death appears not to be limited to any particular age group, our empirical analysis of the determinants of mortality is based on the Cox Proportional Hazard model: where the dependent variable is the mortality hazard, or rather, the risk of death for a particular child _i_ . _λ_ 0( _t_ ) is the baseline hazard, _Mi_<sup>_p_isthedummyvariablethatindicates</sup> whether the child’s parents are Muslim, _g_ is a general function and _Xi_ is a vector of observable characteristics that may affect a child’s mortality risk. This model is a semi-parametric model in the sense that it does not impose any functional form on the baseline specification.

Our working sample includes 218,769 children who are born to 79,118 Hindu and Muslim mothers in the NFHS-3. Of the 218,769 children who were included in our sample, 25,784 (10.4 percent of the total) had died. The children’s ages range from 0–37.<sup>13</sup> For children who died, the average age at death was 20 months.

The set of control variables is very similar to what was used in the female-sample. We also include some additional controls: a dummy indicating whether the child is female, his/her birth order, a dummy indicating whether the child born just previously was female, indicators for whether any meat, dairy or plant-protein was consumed in the 24 hours prior to the survey, and cluster-level averages of vaccination rates for measles, polio, BCG and DPT.<sup>14</sup> Cluster-level averages of female labor force participation rates and female selfemployment rates were also included. The subset of observations that has information on

> <u>13For</u> those children who had died, the age variable was coded as age-at-death.

> 14It is important to note that the average immunization rates will only serve as a rough proxy for healthcare services in the cluster. Immunization rates could have changed considerably in many areas (though not in the aggregate for the country) before or after the death of some of the children in the sample. In future work, we will control for immunization levels by year of birth.

10
**Table 4: Evolution Across Birth Cohorts**

||**CH**<br>|**INA **<br>|**IN **<br>|**DIA**<br>|**INDO**<br>|**NESIA**<br>|
|---|---|---|---|---|---|---|
||YrsSch.|**Res. Sq.**|Yrs. Sch.|**Res. Sq.**|Yrs. Sch.|**Res. Sq.**|
|1980-1989|0.420***|**-0.766*****|0.551***|**-0.644*****|0.424***|**-0.478*****|
||(0.034)|**(0.101)**|(0.007)|**(0.034)**|(0.013)|**(0.050)**|
|1970-1979|0.398***<br>(0.021)|**-0.445*****<br>**(0.055)**|0.582***<br>(0.007)|**-0.294*****<br>**(0.041)**|0.492***<br>(0.015)|**-0.299*****<br>**(0.065)**|
|1960-1969|0.292***|**-0.435*****|0.650***|**0.053**|0.586***|**-0.328****|
||(0.015)|**(0.046)**|(0.009)|**(0.056)**|(0.024)|**(0.139)**|
|1950-1959|0.292***<br>(0.017)|**-0.275*****<br>**(0.070)**|0.681***<br>(0.011)|**0.028**<br>**(0.077)**|0.521***<br>(0.036)|**0.462****<br>**(0.228)**|

_Notes_ : (1) The data are from children who were born between 1950 and 1989 using the CFPS 2010 round for China, the IHDS 2012 round for India, and the IFLS 2014 round for Indonesia.

28
leads to a smaller number of clusters. In particular, the 506 villages in the experiment belong to 358 clusters of partially overlapping five-kilometer neighborhoods—the 320 treatment villages belong to 249 such clusters—and this number reduces to 180 when considering clusters formed by overlapping ten-kilometer neighborhoods and 45 with 20kilometer ones.

# _Main Results_

Tables 3 and 4 report the OLS estimates of the spillover effects of the program on eligible children’s school participation decisions. The estimates are obtained using the data for the post-treatment period (October 1998 and November 1999).

The estimates in table 3 correspond to the model in equation (1) with continuous variables for the numbers of beneficiary, _N_<sup>_B_</sup> , and potential beneficiaries, _N_<sup>_P_</sup> . The estimates are obtained with two alternative measures of program frequency _N_<sup>_B_</sup> in the areas surrounding the evaluation villages: the models in columns 1–3 use the numbers of villages treated in a five-kilometer radius, while those in columns 4–6 instead use the numbers of eligible households within the same radius. This second definition takes into account the variations in population density across neighborhoods and, hence, possibly better captures the extent of potential interactions among program beneficiaries. We report and discuss only the estimates of the parameters _α_ 1 and _α_ 2 but, as explained above, the regressions further include controls for the numbers of potential beneficiaries and for baseline characteristics observed in October 1997, notably baseline school enrollment.<sup>16</sup> Column 1 of table 3 reports the estimates for the baseline model in equation (1) when measuring program frequency by the numbers of villages. It indicates that when considering the entire sample of children in treatment and control villages while living in 16. The last rows of table 3 report the estimated coefficients of the conditioning term NP in equation (2), split into its two components, NE and NNE. Those are, in general, negative and significant, suggesting the presence of strong downward biases stemming from the process of geographic targeting of evaluation villages and non-evaluation villages.

19
# **3.2. Occupation-specific task data**

The U.S. Department of Labor surveys workers in all occupations about the activities performed in the occupation. These occupation-specific attributes are maintained in the Department of Labor’s Occupational Information Network. Our paper utilizes this information from the year 2000 for approximately 800 occupations in the U.S. Standard Occupational Classification (SOC).

O*NET offers “importance scores” ranging between 1 and 5 for 41 different “activities” associated with an occupation. A score of 1 means that a given activity is “Not Important” to the occupation, while a score of 5 means that a given activity is “Extremely Important” to the occupation.<sup>10</sup> Our main assumption in using data from the United States is that the same activities are required in similar occupations in Brazil. Even if the levels of such activities may be different across the two countries, we argue that the ranking of the importance of such tasks in similar occupations will not drastically differ. For this reason, we rely on O*NET’s importance scores of different activities in U.S. occupations to characterize activities in Brazilian occupations.<sup>11</sup> In order to better interpret O*NET’s ordinal importance score ranking across occupations, we standardize the scores into a normalized numerical index between 0 and 1, which relies on each occupation’s share of employment in the pre-period year of 1995, following Peri and Sparber (2009). Denote the importance score for activity _a_ in a 5-digit CBO occupation _c_ by _Sc,a_ and the employment share of occupation _c_ in 1995 by _Ec,95_ . We begin with an ordered

> 10 For instance, chief executives score high for activities such as “analyzing data or information” and “making decisions and solving problems” and score low for activities like “controlling machines and processes” and “operating vehicles or equipment”, while jewelers score high for activities related to “handling and moving objects” and “judging qualities of things...” and low for activities such as “assisting and caring for others” and “interacting with computers”.

> 11 We rely on publicly-available concordances from the National Crosswalk Service Center (http://xwalkcenter.org/) and Muendler, Poole, Ramey, and Wajnberg (2004) to convert occupational characteristics from the 2000 SOC to the 1988 International Standard Classification of Occupations (ISCO) to the Brazilian classification of occupations found in RAIS. We calculate the simple average of importance scores in cases where there are multiple SOC codes matched to a single CBO code.

13