rafmacalaba/data-use-mentions-extended huggingface dataset:
parent has a clear preference for one type of organizational form or the other. The Bankers’ Almanac is the source used to construct this variable.

_Home-Country Regulations_ refers to restrictions placed by home regulators from country j on parent banks’ organizational form. In particular, it measures restrictions to open branches relative to subsidiaries. This dummy was constructed on the basis of the information gathered from laws and regulations in the parent countries. More details on this dummy can be seen in Table 3.

We also control for several characteristics of the affiliate bank. _Affiliate Bank Size_ refers to the size of the local affiliate in the host-country, as measured by the number of employees.<sup>13</sup> This data comes mainly from Bankscope and Bankers’ Almanac. _Affiliate Bank Business Orientation_ is a dummy equal to 1 if the affiliate bank has a retail business orientation. The dummy is zero in the case of trade finance and corporative and investment banks. This data come from Bankscope, Bankers’ Almanac and individual banks’ WebPages. We also include a dummy (labeled _Acquisition Dummy_ ) for whether the bank is a greenfield operation (0) or the result of an acquisition (1). Finally, we control for the year in which the affiliate was established ( _Year-ofentry_ ). Information to construct both of these variables was obtained from numerous sources such as central banks, banks WebPages, Bankers’ Almanac and Bankscope.

_Host-Country Factors_ include measures of banking regulations, corporate taxation, country size, level of development and risk. Among the host country banking regulations, _HostCountry Bank Entry Requirements_ is an index that takes values from 0 to 8, depending on the number of legal submissions required to obtain a license to operate as a bank in the host-country.

> 13 We use size as measured by employees because data on assets for branches was lacking in many cases given that balance sheets are typically consolidated at the parent level.

10
divorce." Each item is normalized and then the resulting index is normalized again where higher values indicate views in accordance with more gender equality. Religiosity Change in Community (Z-Score) is defined as a normalized index from the FR’s responses to whether he or she would say their community has become more religious, stayed the same or become less religious in the last 12 months, where higher values indicate increasing religiosity. Religion Importance (Z-Score) is defined as a normalized index from the FR’s response to whether religion is very important, somewhat important or not very important to the lives of most of their neighbors, where higher values indicate more importance of religion. Religious Identity Salience is defined as an indicator variables that equals unity if the FR chooses "Above all I am a Muslim" among other identities that include Syrian, Arab, or Christian. Economic Optimism (Z-Score) is defined as an index of whether in two years from now, the FR things that their own personal economic situation will be the same, better, or worse, with higher values indicate better economic situation.

- The independent variable of interest is the TOT treatment indicator, which is the predicted value from a first-stage regression of treatment implementation on treatment assignment.

- The regressions also have assessment month-by-year fixed effects, enumerator fixed effects, community-level controls (Irbid/Mafraq governorate and population quartile), and household-level controls (vulnerability-assessment quartile, shelter program, baseline number of children, baseline number of children plus adults, respondent gender, and respondent age). In the parentheses are robust standard errors clustered at the locality level. Regressions are weighted by the number of people interviewed in each household. Statistical significance represented by * (10%), ** (5%), and *** (1%). Q-values are calculated per Anderson (2008) and correspond to Family 9 in the preanalysis plan.

25
compared to smaller countries at comparable levels of per-capita income.<sup>4</sup> This is the case for all other populous countries it is compared with in graphs below. What is striking, however, is that Pakistan’s average trade-to-GDP ratio for the period 2007-09 was smaller than what it was a decade earlier (1997-99), in contrast to the shares of its peers. In 1997-99, Pakistan’s trade-toGDP ratio was as high as China’s, and much higher than India’s. Ten years on, shares of both China and India almost doubled while Pakistan’s fell.

**Figure 3**

**Openness to trade**

<!-- Start of picture text --> Openness to Trade 9799 Openness to Trade 0709<br>MYS<br>MYS<br>VNM<br>THA<br>VNM THA<br>EGYCHN<br>EGY TUR BGD IND IRNTUR<br>BGD INDPAK IRN USA PAK BRA USA<br>BRA<br>6 7 8 9 10 11 6 7 8 9 10 11<br>Log of GDP per capita (PPP, av. 9799) Log of GDP per capita (PPP, av. 0709)<br>Source: Authors’ calculations based on data from WDI.<br>Table 1<br>Export Composition by Market<br>– Selected Economies-<br>Region 2009 CAGR(%)*<br>Exports (US$ '000) % of Total 00-07 00-09<br>EU 4,416,240 31.1 10.3 -7.0<br>USA 3,345,885 23.6 7.3 -6.4<br>CHINA 1,632,546 11.5 7.5 -3.1<br>BRAZIL-RUSSIA-INDIA 588,617 4.1 21.1 1.0<br>TURKEY 618,949 4.4 30.6 7
under-performance has cultural and economic antecedents, but it is starting to change. Women are making economic gains in the Indian economy, and further progress represents a tremendous growth opportunity for the country. Our work contributes to this debate by utilizing procompetitive policies as a possible channel for promoting greater participation of women in economic activity and reducing gender segmentation.

# **III. Data**

Our work examines the pattern and evolution of female labor force participation rates and gender segmentation in the manufacturing and services sectors. We begin by describing the manufacturing and services data, followed by a discussion on constructing varying measures of gender segmentation.

# **_Manufacturing_**

We use the same plant level information as much of prior research on the Indian economy, including Duranton et al. (2015). This project primarily draws upon two major sources of data – the National Sample Survey Organization (NSSO) for unorganized manufacturing and services and Annual Survey of Industries (ASI) for organized manufacturing. Manufacturing activity undertaken in the unorganized sector, such as households (own-account manufacturing enterprises, or OAME) and unregistered workshops, is covered by the NSSO. Following the first Economic Census 1977, small establishments and enterprises not employing any hired workers (that is, OAME) that engaged in manufacturing and repair activities were surveyed on a sample basis in the 33<sup>rd</sup> round of the NSSO during 1978-79. Subsequent surveys covering OAEs and NonDirectory Manufacturing Establishments (NDME) were conducted in the 40<sup>th</sup> and 45<sup>th</sup> rounds of the NSSO during 1984-85 and 1989-90, respectively. In 1994-95, the first integrated survey on unorganized manufacturing and repair enterprises, covering OAMEs, NDMEs, and DMEs, was undertaken during the 51<sup>st</sup> round of the NSSO. Subsequently, surveys of manufacturing enterprises in the unorganized sector were conducted in the 56<sup>th</sup> (2000-01), 62<sup>nd
|||||
|Value at third quartile start||14,765|29,900,000|5,596|||||
|Value at highest quartile start||19,024|57,100,000|9,184|||||
|Observations|103,839|103,839|103,839|103,839|103,839|103,839|103,839|103,839|
|Importer-Industry-Yr FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Exporter-Importer-Yr FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|

Source: Author's analysis based on data described in text. Notes: See Table 1. Interactions in Column 4 use Great Circle distances between capital cities. To give a feel for these demarcations, the distances from Beijing, China, to the capitals of Bangladesh, United Arab Emirates, and Spain are 3029 km., 5967 km., and 9229 km., respectively. Columns 6-8 test for the Rybczynski effect within manufacturing. Industries are grouped into quintiles by their US capital-labor ratios, mean wage rates, and non-production worker wage bill shares. Table S1b lists industry groupings. Linear time trends for each country by industry quintile are included in the estimation.
5 2011PPP-US$ and 13 2011-PPP-US$ to identify the vulnerable population. The concept of probabilistic vulnerability, on the other hand, is based on two subcomponents: The average level of welfare across many periods and the deviation of welfare across these periods (that is, the variance of welfare). Vulnerability is defined as the probability of becoming poor in the future or the "poverty risk." It is, therefore, a forwardlooking extension of the static concept of poverty at a given point in time (Skoufias and Baez, 2021). Instead of considering poverty from a purely ex-post viewpoint, it incorporates a forward-looking perspective on poverty.

**Especially in countries with high fluctuations and income volatility, looking at the current state of poverty falls short of painting a complete picture of the situation.** High fluctuations mean that a poor

> 7 The coping capacity dimension of the INFORM risk index measures which issues the government has addressed to increase the resilience of the society and how successful their implementation is.

5
In our analysis, we will also distinguish between the effects of reform in individual services sectors. To do so, we will construct indices capturing the reform in a particular services sector. For instance, we will define where _αj,banking_ reflects the proportion of inputs sourced by the manufacturing sector _j_ from the banking sector, according to the input-output matrix, and _reformbanking,t_ is the state of reform in the banking industry at time _t._ We will follow the same approach to construct indices for telecom, insurance and transport sectors.

For the banking sector, an alternative measure of financial dependence will help us to test the robustness of the main measure. This alternative is based on Rajan and Zingales (1998), who compute sector averages of financial dependence based on US data and argue that this is a suitable measure for firms‟ technologically induced demand for external finance in an environment with well-developed financial markets. The measure is based on a comparison between firms‟ investment outlays and own cash flow.

# **_Measuring the Performance of Manufacturing Firms_**

Our goal is to provide a fuller explanation of the remarkable improvement in the performance of the Indian manufacturing sector following the post-1991 economic reforms. We use firm-level data from the Capitaline database, a commercially available database including balance sheets, profit and loss statements, and ownership information on large private and public firms operating in India to measure the performance of manufacturing firms. The database covers 62 percent of India‟s manufacturing output during the period considered by the analysis, and includes 11,939 firms, of which 5,236 operate in the manufacturing sector. The data set forms an unbalanced panel covering the period 1993-2005. Firms‟ industry affiliations follow India‟s National Industry Classification (NIC) which encompasses the manufacturing sectors. After cleaning the data and discarding firms not reporting information on output or production inputs, we are left with 3,771 firms or 22,558 firm-year observations.

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2018, more than doubling the number in 2015 (Hallward-Driemeier and Nayyar 2017). This would give China the distinction of having the highest number of installed industrial robots in the world, accounting for about one-fourth of total industrial robots projected to be installed globally. While other large emerging markets (including Brazil, India, Indonesia, Malaysia, Mexico, Thailand, and Turkey) also had nontrivial stocks of industrial robots in 2015 (appendix figure 2), Thailand leading the pack at approximately 14,000 robots was a fraction of China’s at more than 200,000.

What matters more than the absolute stock of industrial robots is how the stock has changed, relative to labor input. These data on robots are combined with data on labor input taken from the World Input-Output Database Socio-Economic Accounts to compute the intensity of robot use, as measured by the stock of industrial robots per 1,000 employees. The intensity of robot use varies widely not only across sectors, but also across countries. Figure 3 plots the evolution of robot per 7
|||
|||(0.230)|(0.229)|(0.229)|
|How Much Of An||0.027|0.041|0.051|
|Obstacle: Labor||(0.300)|(0.300)|(0.300)|
|Regulations?|||||
|Population (logs)|||-37.758***|-37.641***|
||||(10.909)|(10.849)|
|Urbanization (%)|||1.709***|1.723***|
||||(0.423)|(0.427)|
|WBL Index|||16.988*|17.157**|
||||(8.755)|(8.677)|
|Women can work|||2.962|2.967|
|the same night|||(2.814)|(2.807)|
|hours as men Y:1|||||
|N:0|||||
|Women can work|||-0.860|-0.608|
|in the same|||(2.009)|(2.053)|
|industries as men|||||
|Y:1 N:0|||||
|Ratio of Female to|||2.352***|2.486***|
|Male Population|||(0.572)|(0.566)|
|Fertility Rate|||-15.380***|-15.262***|
||||(3.649)|(3.646)|
|Starting a Business|||0
risky task due to their large size and potentially erratic behavior. According to Alesina, Giuliano and Nunn (2013), the plow requires significant upper body strength, and bursts of power, both to pull the plow and control the animal that pulls it. Women are thus commonly considered less suitable for the task–though there is substantial heterogeneity in adherence to this norm. We find that our observed negative treatment effects on the time women and girls spend on household plots are predominantly driven by households in districts with relatively lower female involvement in oxen tasks. Nationally-representative data shows that matrilineal ethnic groups are less prevalent in these districts, and norms are less gender-egalitarian. In these more conservative communities, women shift to working off-farm once oxen are introduced. These empirical patterns align with predictions from our simple model of time allocation under social prescriptions, which builds on Kevane and Wydick (2001).

There are three main contributions of this paper. First, we provide the first experimental evidence on the impacts of animal traction, and the first experimental evidence on the effects of agricultural mechanization as a whole in Sub-Saharan Africa–building on a rich literature that has examined the adoption and use of labor-saving agricultural technology in the region (Pingali, 2007; Owolabi et al., 2012; Guthiga, Karugia and Nyikal, 2007; Williams, 1997). The existing non-experimental research lays out several key stylized facts about animal-traction mechanization, including minimal yield gains, an increase in land under cultivation and devotion of the expanded land to market crops, as well as a reduction in labor required during the land-preparation period (Pingali et al., 1987). More recently, studies of rice production in Cˆote d’Ivoire highlight how mechanizing land preparation can contribute to agricultural intensification, thereby raising productivity (Mano, Takahashi and Otsuka, 2020), especially when agro-chemicals and mechanized land preparation are combined (Aihounton and Christiaensen, 2023). Within the limited literature demonstrating experimental evidence in developing country settings, our paper is most closely related to a recent
Klingebiel, and Krozner (2005), we restrict our analysis to the first systemic banking crisis in a country to avoid identification problems in case of multiple crises.

We took the industry-level external dependence measures for mature, young, and all firms directly from RZ. Tabel IV lists the data. External financial dependence of a firm is measured as the fraction of capital expenditures not financed with internal cash flows of US firms using COMPUSTAT. RZ take the median external financial dependence of all firms in the industry to reflect the extent to which the whole industry is dependent on external finance. RZ do not report values for Industrial Chemicals, ISIC code 351, so we draw this information from Laeven, Klingebiel, and Krozner (2005). Note that younger firms are often significantly more reliant on external finance.

We collected institutional indices for the period 1984-2004 of Accountability, Corruption, and Law and Order from the International Country Risk Guide (ICRG), an independent source, widely used by both academia and business (see for example, La Porta, 1998). All indices are on a scale from 0 to 6, where a higher value means a better institution. We also constructed an institutional index as a composite of the individual accountability, corruption, and law and order indices, to obtain a robust estimate of the quality of institutions. Accountability is concerned with _“how responsive the government is to its people.”_ The Corruption index measures the extent to which _“excessive patronage, nepotism, job reservations, ‘favor-for-favors‘, secret party funding and suspiciously close ties between politics and business”_ are found in a country. Importantly, it also takes corruption practices regarding loans into account. The Law and Order index assesses _“the strength and impartiality of the legal system and [...] popular observance of the law.”_ For all indices, we calculated simple yearly averages, since ICRG reports monthly data.

16
only once. If such selection is correlated with our main

waves: 42 have two waves, 45 have three, and two have four. Across these country-waves, the sample includes 40,884 firm-year observations, corresponding to 16,608 firms observed twice and 2,556 observed three times. Dropping firms without sales data yields a final sample of 36,087 firm-year observations across 89 countries.

> 8The ORBIS dataset lacks meaningful coverage for 63 countries in our sample. Most notably, geolocalization data—a central component of our analysis—is sparsely populated in ORBIS across the majority of countries, significantly limiting the usable sample and raising concerns about sample selection. When we require the availability of both sales, costs, and profits (as needed for the baseline regression) and geolocated information, WBES provides superior coverage in all cases except Kazakhstan, where ORBIS still includes a comparable number of firms relative to WBES. In addition, ORBIS does not collect data on several key variables necessary to investigate the mechanisms underlying our results—such as informality, access to imported inputs, power outages, and other operational constraints. See Appendix B for further discussion.

> 9Profits are not directly provided in the WBES. We construct _Profits_ as _Sales_ minus _Total costs_ , defined as the sum of labor costs and raw materials and intermediate inputs costs, including energy expenditures (for details on the variable definitions, see Table C1). For input costs, the WBES questionnaire administered to service firms only collects information on electricity expenses. As a result, for service firms, the _Total costs_ variable may underestimate overall production expenditures (e.g., by excluding costs related to equipment, telecommunication services, etc.), potentially affecting the comparability of _Profits_ across sectors. To address this, we exclude from the sample all service firms with a _Total costs_ -to- _Sales_ ratio below the fifth percentile of the distribution. This restriction improves the reliability of economic performance comparisons across sectors. In any case, our results are virtually unchanged when focusing
the Coast Guard (5,200).

Consolidated Central Government wages and salaries are for 1991 and are taken from IMF Government Finance Statistics Yearbook, 1995. Data on wages in manufacturing (monthly basis) is taken from the International Labor Office's Yearbook of Labor Statistics 1995 and refers to 1993.

# **Denmark**

Paid employment in non-agricultural activities is drawn from the ILO's Yearbook of Labor Statistics 1995 and refers to 1994. Data on Central Government, Non central Govemment, Education and Health employment are taken from Publi Management: OECD Country orofiles, OECD 1992 and relate to 1990. Responsibility for education is shared between three levels of government. Primary schooling is assigned to municipalities, secondary schools are funded by the counties, whereas the central government is responsible for the higher education. Vocational training is also funded by the central government. Health care is exclusively the responsibility of non-central government. Data on military employment are taken from the International Institute for Strategic Studies: The Military Balance Survey of 1995-96. Data include conscripts (8,300), but exclude personnel of paramilitary units.
case of Vietnam, the data show virtually no change in manufacturing shares of exports until the zone program was established (along with a range of other policy initiatives linked to the opening of Vietnam’s economy), followed thereafter by a radical shift in export structure toward manufacturing. Bangladesh experienced a similarly large shift, which only became apparent between 5 and 10 years after its EPZ program was launched. This was driven almost entirely by the growth of the garment sector, which grew from only about 50 firms in the 1980s to more than 4,500 firms employing over 2 million by 2009. In the case of Dominican Republic and Honduras, the structural changes were equally dramatic, although they only occurred after the programs had been operational for 10 years.

A glance at the African country data shows a very different story. Countries like Lesotho (a garment export mono‐economy) and Nigeria experienced no change in their national export structure. Export structures in Senegal and Ghana have fluctuated somewhat over recent decades, with no clear patterns discernable (at least none that appear to have any relationship with their zones programs).Only Kenya and Tanzania appear to show any broad patterns of sectoral adjustment over time. In Tanzania’s case, it is a story of increasing growth of non‐traditional exports, particularly services and minerals; again, with no real link to the EPZ program. Kenya is the only country to have shown a steady rise in manufacturing exports, with a corresponding decline in agricultural exports. However, most of this adjustment appears to have taken place prior to the establishment of their EPZ program, and, in fact, manufacturing share growth appears to have stagnated in the years following the launch of the EPZ program.

One factor at play here may simply be the timing of the launch of EPZ programs in the context of global trade and investment trends. With the exception of Senegal and Lesotho, the other African programs in the study were only operationalized well into the 1990s – probably too late to take advantage