rafmacalaba/data-use-mentions-extended huggingface dataset:
urban areas).<sup>10</sup> Mean statistics for all variables used in the regressions are shown in Table A4 for the urban and rural subsamples.

Besides the DHS data for the couple sample, the paper draws on the following country-level databases (see Table A1 for data definition). The _Women Business and the Law_ (WBL) program provides data on laws and regulations constraining women’s property ownership and economic opportunities (World Bank 2020a).<sup>11</sup> The _World Development Indicators_ (WDI) provide data on GDP per capita (World Bank 2020b). Data on historical cultural attitudes and social norms are from Alesina et al (2013), while the data on the country’s largest religions are drawn from _The Association of Religion Data Archives_ (Harris et al 2019).

# **4.2 Results**

Considering the WBL variables we see that the equal remuneration law has the lowest coverage in the sample -- only about 15 to 18 percent in urban and rural areas, respectively (Table A4). Effectively, the share of the population benefitting from such laws is likely even lower, as employment-related legislations do not extend to the informal sector that employs a large proportion of workers in developing countries. Equal ownership rights to immovable property has the widest coverage and applies to more than 90 percent of the sample. Women’s incidence of housing ownership is higher than land ownership, and a greater proportion of rural than urban women own some property. Turning to our regression analysis, we find that women living in countries with more gender-egalitarian legal regimes are more likely to own property in both urban and rural areas. We first discuss the results for housing ownership (Table 1). In urban areas, three legal variables, except for the laws providing for equal inheritance rights, are positively and significantly associated with women’s chances of owning a house across all models. More genderequitable legislation providing for equal ownership rights for men and women is
the United States: Aspects of PreWar and Post-War Changes, NBER, New York.

- J.C. Musgrave (1986), "Fixed Reproducible Tangible Wealth in the United States: Revised Estimates", Survey of Current Business, January.

- J. Nyers (1981), "Comparison of Productivity Level of the Austrian and the Hungarian Industry: Methods and Ro-ults", IARIW, processed.

# OECD (1979), Measuring Employment and Unemployment, Paris.

- H. Van Ooststroom and A. Maddison (1985), "An International Comparison of Levels of Real Output and Productivity in Agriculture in 1975", revised version, Groningen, processed.

- D. Paige and G. Bombach (1959), A Comparison of National Output and Productivity, OECC, Paris.
.. 35**|
|**Annex**|**IIA: Stamp Duty Rates for States and Union Territories FY81-FY2003.................. 66**|
|**Annex**|**IIB: Stamp Duty Rates for States and Union Territories FY81-FY2003 .................. 67**|
|**Annex**|**IIC: Stamp Duty Rates for States and Union Territories FY81-FY2003.................. 68**|
|**Annex**|**IID: Stamp Duty Rates for States and Union Territories FY81-FY2003.................. 69**|
|**Annex**|**III: Time Series Analysis of Stamp Duty Revenues in West Bengal.......................... 70**|
|**Annex**|**IV. Actual vs. Predicted Growth of Stamp Tax Revenues.......................................... 71**|
|**Annex**|**V: Stamp Tax Revenue Data Across States.................................................................. 72**|

i
equation (23): We also construct a range of price indices at the quarterly level using alternative methods. The first index weights each product group using the expenditure shares of only products chosen to construct the price index, that is, products that satisfy the consistency criterion illustrated above, as opposed to all products in the data. The second index uses the Tornqvist index mentioned above. The third index constructs the price index in one stage instead of two stages. The fourth index uses a weighted geometric average in the first stage instead of a weighted arithmetic average. The fifth index uses weights that are fixed over time at the base period to ensure that the results are not driven by shifts in the consumption bundle over time as opposed to actual price changes. To construct such an index, only products that appeared consistently over the entire sample period can be used. The sixth index again uses fixed weights but also base observations that are sampled from the last observable posted price for each store-product-quarter.<sup>63</sup> The seventh index uses weights constructed from households using the Nielsen Consumer Panel instead of the Nielsen Retail Scanner, which allows us to construct weights by household income brackets. We present results focusing on SNAP-eligible households as classified above. All indices are highly correlated and results are robust to using any of the above methods. We show these indices for New York City in Appendix Figure O9, which are nearly identical across construction methods.

# **E Plausible Exogeneity**

We relax the identifying assumption by allowing the IV to be plausibly exogenous ala Conley et al. (2012) with the IV having a direct impact on outcomes even after controls.

We obtain a back-of-the-envelope estimate of how large this impact would be quantitatively. Since SUA depends on both energy costs and idiosyncratic state policies, we allow the IV to reflect energy costs, which may have an impact on the outcomes even after energy controls if the controls are not perfect. Pass-through formulas imply that the effect of energy costs on retail prices
or 12 female agents (86 agents in total) that operated in 20 markets. All transactions were made in West African CFA francs and the values were converted to US dollars as of January 2015.

As shown in Table 8, among the 936 transactions, 34.8% were made by a female client and only 9.6% were made at a female agent. The average transaction amount was USD$105 and 46.6% of all transactions were withdrawals. Consistent with data from the Democratic Republic of Congo, most of the transactions made by female customers were made at a female agent.

Table 9 presents the estimates of the probability that a transaction was done with a female agent, predicted by the gender of the client, using the same specification from Columns 1 and 3 of Table 4 with data from FINCA DRC. Following the Democratic Republic of Congo specification, we cluster the standard errors at the client level. Since we have data on only 98 clients in 20 markets, the inclusion of

> 8 For more details on the experiment design, see Section 2 of Buri et al, 2018.

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# **C Data Appendix**

The main outcome variable of interest in this paper is juvenile catch: the number of individual anchoveta that vessels catch that are less than 12 cm. There are two challenges in calculating juvenile catch in an unbiased and accurate manner.

First, vessels may underreport percentage juvenile in the electronic logbook data in order to avoid triggering a closure in the area they are fishing. If I only used raw electronic logbook data to calculate juvenile catch and underreporting is correlated with closures declared by the regulator, my treatment effect estimates would be biased.

Second, even if percentage juvenile reported by vessels in the electronic logbook data is unbiased, percentage juvenile and tons caught are not sufficient for calculating juvenile catch because the number of individuals caught depends on the length distribution of those individuals. For example, consider two sets that both catch 40 tons of anchoveta that are 20% juvenile. In the first set, 20% of individuals are between 11.5 and 12 cm and 80% of individuals are between 12 and 12.5 cm (actual length distributions are much more diffuse; see Figure C1b). In the second set, 20% of individuals are between 10 and 10.5 cm and 80% of individuals are between 14 and 14.5 cm. The weight of an anchoveta in grams equals _._ 0036 _length_<sup>3</sup><sup>_._238</sup> (IMARPE, 2019). Therefore, 683,137 juvenile anchoveta are caught by the first set and 469,685 are caught by the second set, even though both sets caught the same tons and percentage juvenile.<sup>43</sup> Recall from Section II that vessels report percentage juvenile to the regulator in the electronic logbook data, but not the length distribution. I obtained a supplementary electronic logbook dataset for a group of vessels that report length distribution data to their owners. These vessels represent 56% of landings and I received their data
updated preferential trade agreements, whereas they are often not updated in WITS in cases of phasing in and out. Fourth, it includes information on actual imports entering under different trade regimes (e.g., GSP and AGOA) so preference utilization rates can be computed. The detailed tariff information allows us to examine how the trade effects of AGOA or GSP LDC preferences vary with the magnitude of the preference margin offered to beneficiary countries and in response to the reciprocal and non-reciprocal preferential tariff rates granted by the US to other countries.

For the econometric analysis, we make the following adjustments to the US database. First, in order to capture trade flows before the GSP LDC expansion in 1997, we augment the import data to include years from 1992 onwards. Second, for computational feasibility, trade and tariff data are aggregated from the 8 to the 6-digit level for the econometric estimation. Third, in order to account for zero trade flows, we expand the database such that it is a balanced panel where all countries exporting to the US have observations for all products in all years. Fourth, we exclude from the estimating sample oil products (HS chapter 27), though they account for a large share of AGOA-eligible exports from African countries to the US, to focus on the impact on other products. To account for several HS revisions between 1992 and 2017, we convert all codes into HS 1996 revision 6-digit codes using concordance tables provided by WITS.

The sample used for the econometric analysis includes 27,420,560 observations, 87 percent of which have zero imports. AGOA-eligible countries - described in Section 4 - export to the US substantially fewer HS 6-digit products and smaller values than control countries, whether the products are AGOA-eligible or not. On average, AGOA-eligible countries have positive exports to the US in only 97 products whereas for control countries that number is 734.<sup>16</sup>

> 16 The summary statistics on the estimating sample are provided in Online Appendix B.

12
days per child, and children exhibited symptoms on average on 18 days. Cold symptoms, coughing, and unusually hot skin were most frequent. Section 3 below gives more detail on how symptoms translate into illness spells and care-seeking recommendations. Appendix B provides a description of how the health calendar information was collected.

**Consultations.** At each surveyor visit, mothers were asked if they discussed their child’s health with anyone in the preceding week, and the surveyors recorded role or occupation of the person seen, facility, waiting time, treatments received and costs incurred.<sup>12</sup>

Table 2 provides an overview of these health care “consultations”, grouped into CSCOM visits; private doctors, hospitals or clinics; pharmacies; and purchases from informal providers. Parents occasionally see several providers in the same day, for example when they get a prescription at a clinic and then purchase medication elsewhere. We group visit records that are linked in this manner and classify them by the “most formal” consultation in the group. The value of care is approximated using the full price of care.<sup>13</sup> Note that the sizable difference in costs and value of care even without a subsidy is due to families seeking outside help to pay for the child’s care, e.g. from relatives or friends.

The table shows that the subsidy program reduced the average cost of a visit to the household by 71%, from CFA 2850 to 933 (476 CFA equaled 1 USD in 2013). Among the formal-care visits in the control and CHW-only groups, 14% and 12% were reported as free, vs. 70% and 77% in the subsidy groups, respectively. Parents paid if they visited other providers than the two partner clinics or did not claim the AfH benefit, or the child received services not covered under AfH.

CSCOMs provide similar value per visit with and without the subsidy (CFA 3652-3807),

> 12In order to facilitate
measures in residential and commercial buildings and also for natural gas consumption for heating. The demographic and economic projections (e.g., projections of population, household size and GDP) and projections of natural gas prices and average electricity grid emission factor are obtained from the MARKAL- Georgia model. All data sources used by the MARKAL-Georgia model are identified in Appendix B. National energy balance and statistical year book were used for fuel consumption values (National Statistics Office of Georgia, 2014)). Electricity prices were obtained from the local electricity distributor Telasi Ltd. (http://www.telasi.ge/en/customers/tariffs). The detailed data for Georgia Energy efficiency MAC analysis and their sources are presented in Appendix B.

# **3.3. Selection of Energy Efficiency Measures and Associated Assumptions**

The estimation of MAC curve for the residential sector depends on several factors including number of households, demolition rate of existing households, rate of new household construction, percentage of existing households with efficient appliances, percentage of newly built households with efficient appliances, energy consumption rates of both efficient and inefficient appliances, costs of those appliances and other variables. There are variety of energy utilizing devices and process which could be considered for GHG mitigation in the buildings, such as space heating devices (kerosene stoves, electric heaters), water heaters, refrigerators, washers and dryers, cooking appliances, electronic devices (TV, computers). Two options are normally available to reduce GHG emissions from these device or processes: improving their thermal efficiency and replacing more carbon intensive fuels used by these devices with less or none carbon intensive fuels. For space heating we considered the insulation improvement measures, such as increasing wall insulation, reducing heat leakage from windows and improving roof insulations. We did not consider energy efficiency improvements of space heating devices (e.g., kerosene stoves, electric heaters) because the efficiency of these devices is already relatively higher (> 87%) thereby leaving a small margin to
Mancini (2019).

Finally, the segmentation of production across countries does not only impact the synchronization of GDP but also inflation (Auer et al. (2017), Auer et al. (Fort)). GVCs also impact the trade elasticities (Amiti et al. (2014), Gunnella et al. (2019), de Soyres et al. (2018)) as well as the extent to which regional trade agreement divert trade flows with the rest of the world (de Soyres et al. (2019)).

# **2 Data Sources**

**GDP.** We use GDP data from the Word Development Indicators of the World Bank. It is measured using constant 2010 prices in US dollars.<sup>8</sup> For our analysis, GDP series need to be filtered in order to extract the business cycle component from the trend. We use three different filtering methods that capture different but related information. Our main filter is the standard Hodrick-Prescott filter (HP) with a smoothing parameter of 100 which is consistent with the yearly frequency of our data. Such a transformation allows us to capture the standard business cycle fluctuations.<sup>9</sup> Second, we also use a Baxter and King filter (BK) to isolate medium-term fluctuations in the spirit of Comin and Gertler (2006a).<sup>10</sup> Finally, we also present results with a more “agnostic” transformation which is a simple log-first difference. Such a measure of growth rate accounts for both the cyclical and the trend components embodied in any year-toyear fluctuation, but it is sometimes considered as less sensitive to researcher’s assumptions and preferences regarding the parameters of the filtering method.

**Trade Proximity.** We collect data on bilateral trade flows from the Observatory of Economic Complexity (MIT). This database covers 215 countries over the period 1962-2014. The data are classified according the 4-digit Standard International Trade Classification (SITC),
or “expenditure” interchangeably.

# **Official poverty lines**

The definition of the official poverty lines is relatively complex, which makes the interpretation of the poverty trend challenging. The main complexity comes from the way in which the poverty lines are computed and updated.

The constitutive elements of Egypt’s official poverty lines are related to the cost of basic needs approach (Ravallion 1998, 2015a). These lines are computed as the sum of two components: a food poverty line and a non-food poverty line. For a household h living in region r, the poverty line zhr is thus computed as F The food poverty line zhr for household h living in region r is computed as:

> 4 The publicly available microdata of HIECS 2015 only contains 50 percent of the total sample.

> 5 As we explain in Appendix 2, the households’ total expenditures slightly differ from their official welfare aggregates, because the latter also include (small) food subsidies.

4
. First, women are identifying themselves (and being identified by others) primarily as housewives, and the concepts of housewife and employment are taken as mutually exclusive. Second, given this duality between housework and employment, women define ‘employment’ based on a set of necessary characteristics that exclude many of their own activities. For it to be employment, work needs to i) be conducted physically outside the home; ii) be in exchange for money, this applies to both services and goods; and iii) entail sufficient time commitment. Importantly, these conditions are not binding constraints for men to identify their own activities as economic activity. These results have implications for understanding the low labor force participation of women in rural communities in countries beyond Honduras, suggesting that the low rates hide a significant amount of economic and non-economic work.

# **Literature survey**

Employment and labor force indicators are designed to measure the percentage of the working age population engaged in economic activities, both those who are employed and those who wish to be employed. There is a long literature on the mismeasurement of women’s engagement in economic activities and the labor force, both due to underreporting of women’s participation in some activities as well as the exclusion of activities undertaken primarily by women from the definitions used for labor force statistics (Boserup 1970, Anker 1983, Beneria 1981, Dixon 1982, Donahoe 1999, Mata-Greenwood 2000). In this study, we focus on the first of these reasons – the underreporting of women’s participation in activities that are counted towards employment statistics.

3
condition saw revenues of household business amounting to approximately US$70 PPP in the past month and those in the Personal Initiative intervention saw revenues of US$83 PPP (SE=21.56, pPI=.527), those in the Interpersonal Initiative intervention saw revenues of US$111 PPP (SE=24.53, pII=.093).<sup>13</sup> Although directionally positive, no significant effect on household-owned businesses was observed among households in which women were assigned to Personal Initiative intervention (business omnibus index: dPI=0.19, SE=0.13, p=0.142).

Responses to one question in the Interpersonal Initiative intervention may help explain why household-owned businesses also grew in that condition. As part of the guided exercise, women were asked about strategies for navigating conflict in the household and maintaining harmony, as follows: “If a woman’s husband was resistant to her engagement in business, what would you advise her to say to her husband?”. One woman responded, for instance, “I would advise her to negotiate with her husband and explain to him the advantages of business development for the education of their children and the well-being of their household.” This suggests that this intervention condition helped women become better equipped to engage in negotiated agency (Adjei, 2019), building shared aspirations with their husbands and family members and possibly inspiring them to engage in more economic activity as well.

Notably, while there was some evidence that the Interpersonal Initiative intervention led to an increase in other household members’ economic activity, there was no evidence that either agency intervention affected other aspects of how women ran their business. For instance, women were no more likely to be more collaborative in their businesses in the Interpersonal or Personal Initiative condition. In the control condition, on average women worked with 0.24 other people outside the household on their business, and this did not differ in either intervention condition ( β PI=0
and FB families, we first show that our results survive controlling for indicators for the number of children (column (2) of Table 5) and controlling for the proportion of sons (Table 6).<sup>34</sup> Second, we explicitly estimate the effect of expected dowry on differential fertility and sex-selection behavior in FG relative to FB families. To do so, we use data on fertility and sex-selection from the 199899 and 2005-06 rounds of the Demographic Health Survey of India, also known as the National Family Health Survey (NFHS).<sup>35</sup> We restrict our analysis to the rural sub-sample of NFHS since our dowry data from REDS does not cover urban India. We prefer using NFHS for this analysis as it reports complete birth histories, including the dates of live births and of any child deaths, for each surveyed woman, where as in REDS we need to impute birth year and birth order from children’s ages. Moreover, NFHS is substantially larger in sample size than REDS.

We estimate the following equation by using the number of children and the proportion of sons among second and higher parity births at the time of survey for mother _i_ from caste _c_ in state _s_ and whose first child was born in year _t_ as the dependent variables: The vector **X** _i_ comprises parents’ years of schooling, parents’ ages, and indicators for religion, month of survey, and household standard of living at the time of survey. The inclusion of the FG main effect allows us to control for any changes in fertility or sex-selection due to firstborn sex that could result from factors unrelated to dowry; for instance, higher fertility among FG families due to the desire for at least one son. If certain castes are richer and therefore can more easily afford sex-selection technology, the caste fixed effects ( _ωc_ ) would capture this confounding variation. If there is state or time variation in wealth status of various castes that can explain the differential fertility
Campello et al. (2010) find that financially constrained firms planned deep cuts in employment and spending. Giroud and Muller (2017) find that more highly levered firms experienced significantly larger employment losses in response to decline in local consumer demand.

5 See Reuters (2020a); Washington Post (2020).

6 Early in the pandemics, bankruptcies among oil and gas companies in North America have risen (Reuters, 2020b; Haynes and Boone LLP, 2020). Given the concern, the Trump administration even considered giving direct aid to oil firms (Wall Street Journal, 2020).

7 Data from US Energy Information Administration website show that US crude oil output has more than doubled from about 5 million barrels per day (mb/d) in 2008 to over 12 mb/d in 2019. 8 See for instance Nguyen et al. (2020) and Bjørnland and Zhulanova (2019).

2