rafmacalaba/data-use-mentions-extended huggingface dataset:
&D (% GDP) < 1 (year 2002) Source: Authors’ calculation from the World Bank’s World Development Indicators (WDI).

With a medium level on R&D and NBER, Italy has an robust small and medium enterprise sector (77.8% of total firms), but it has not been as successful in establishing multinational corporations<sup>8</sup> .

# 4.1. Efficiency and Productivity Analysis

When comparing efficiency levels, we can see large differences between countries (see Appendix Tables I and II). The above differences are greater when comparing High-Medium and Medium-Low-Tech countries (see Table 4).

Using a TransLog production function to estimate Technical Efficiency, we observe important differences across countries (see Appendix Tables I and II). We can see that the U.S. has a high level of technical efficiency while Bolivia has a very low level of technical efficiency, and can/should significantly improve its performance, making better use of its resources. In between we have the cases of Japan,<sup>9</sup> United Kingdom<sup>10</sup> and Canada<sup>11</sup> (high efficiency), Georgia,<sup>12</sup> ,Mexico<sup>13</sup>

> of outsourcing activities in the areas of technical support and customer services for some of the major economies of the world).

> 7 In Germany’s economy, the average annual growth rate of GDP has been on a decline since the 1980s. Germany’s economy is currently recovering, ending a phase in stagnation on the back of its traditionally strong, competitive and innovative export-oriented manufacturing sector.

> 8 Many of these companies do not have a high level of technology sophistication. Italian services today make up 69% of the economy, industry 29%, and agriculture 2%.

> 9 Japan's economy is the second largest economy in the world and
<mark>Figure 5. Change in the Share of Occupied Adults per Household</mark> <!-- Start of picture text --> 2.0%<br>1.5%<br>1.0%<br>0.5%<br>0.0%<br>-0.5%<br>-1.0%<br>percentage change<br><!-- End of picture text -->

**_<mark>Source:</mark>_** <mark>Authors’ calculations with data from SEDLAC, RIGA, and National Household Surveys.</mark>

# Figure 6. Subsidies and Other Social Transfers

<!-- Start of picture text --> 25%<br>Circa Initial<br>Circa Final<br>20%<br>15%<br>10%<br>5%<br>0%<br>Source: World Development Indicators, 2012.<br>* The reported subsidy and transfer data are for a year that is one year earlier than the household survey.<br>percent of GDP<br><!-- End of picture text --> 22
'4|55'122'4|743|773|0.207|0.215|
|Peru|6'400|4'545'455|308'230|2'088'63|6'518|5'554|1**.**018|0.868|
|Philippines|4'791|13'360'12|818'896|8'682'46|2'681|3'103|0.560|0.648|
|**Average **|**4'558**|**19'676'22**|**3'683'8**|**26'993'3**|**3'281**|**3'188**|**0.816**|**0.753**|

_Notes:_ Calculation of life-years saved (LYS) are based on a 3% discount rate, uniform age weights and an average life expectancy of 75 years at the age of 5 (premature death). Same life expectancies and mortality effects are assumed for males and females. Income per capita numbers are in real 2007 dollars at PPP from the Penn World Tables 6.3 (Heston et al, 2009).

26
bias problem for us. As Ruhm (1998) notes, since women use virtually all parental leave in most countries, men constitute a reasonable comparison group, and the “natural” experiment examines how changes in leave entitlements affect the gap between female and male outcomes.

# _Main explanatory variable_

Our main explanatory variable is the (log of) the number of weeks of paid maternity leave as mandated by law in the country ( _Maternity Leave_ ). The data source for the variable is the World Bank’s Women, Business and the Law project (WBL). The mean value of the variable equals 2.6 and the standard deviation equals 0.31. Across countries, _Maternity Leave_ has the highest value of 3.9 (or over 52 weeks) in Albania and lowest value of 1.9 (7 weeks) in Lebanon.

As discussed above, we explore how the relationship between maternity leave and female workers varies with who pays for maternity leave. To this end, we define a dummy variable equal to 1 if the employer pays for the entire maternity leave and 0 otherwise ( _Employer Pays_ ); a dummy variable equal to 1 if the government and the employer jointly pay for maternity leave and 0 otherwise ( _Gov.-Employer Pays_ ); and a dummy variable equal to 1 if the government alone pays for maternity leave and 0 otherwise ( _Government Pays_ ). The data source for these dummy variables is WBL. In our sample, the employer alone pays for maternity leave in 18 countries, government and employer jointly pay in 13 countries while the government alone pays in 34 countries.

# _Other explanatory variables_

Note that our dependent variable varies at the firm-level while the main explanatory variable, _Maternity Leave_ , varies at the country level. Thus, reverse causality is unlikely to be a problem for 8
**Figure 1:** Timeline of the experiment and of the 2017 and 2018 cotton seasons

<!-- Start of picture text --> Timing of oxen distribution<br>By treatment status<br>Harvest<br>Begin End<br>Planting<br>Land<br>Prep<br>Planting<br>Land<br>Prep<br>1/2016 7/2016 1/2017 7/2017 1/2018 7/2018<br>Date<br>Control Treatment<br>.6<br>.4<br>.2<br>Fraction received oxen<br>0<br><!-- End of picture text --> Note: No date reported for 13% of the sample that received oxen. These households are assumed to receive the oxen at the mean delivery date within their given strata.

seasons in Cˆote d’Ivoire. Delays compiling the lists of eligible farmers and procuring the oxen pushed the delivery of the oxen to April-November 2017, meaning that most treatment households received the oxen after the 2017 land-preparation and planting activities but before harvest. With the delivery timeline and the timing of the survey in mind, we focus on agricultural outcomes that span two seasons: agricultural production and harvest for the 2017 season, and agricultural inputs for the 2018 season.

# **3.2 Sample characteristics**

At endline, we tracked and administered a survey to 91% of farmers in the sampling frame, resulting in a sample size of 2,314 producers: 1,146 producers in the control group and 1,168 producers in the treatment group.<sup>9</sup> We focus on the 2,113 producers who reported comprehensive agricultural production, sales, and time use data.<sup>10</sup>

> 9The main survey respondent was the household head, with specific questions on the receipt and use of oxen addressed to the producer in instances where
results with administrative data, which contains transaction-level information on the full universe of program accounts over time.

# **4 Empirical Strategy and Results**

Treatment was randomly assigned at the individual level, so that we can estimate the impact of payroll accounts using simple treatment effect regressions of the form: where _Yi_ is an outcome of interest measured post-treatment, _Ti,acct_ is a treatment indicator equal to one if an individual was assigned to the bank or mobile account treatment and _Ti,payroll_ is a treatment indicator equal to one if individual _i_ was assigned to the bank or mobile payroll account treatment condition.<sup>35</sup> _X_ is a vector of controls which, where we use survey data includes the value of the outcome variable at baseline _Yi_<sup>_BL_</sup> as well as strata dummies, and _εi_ is a stochastic error term. In this specification, the coefficient _γpayroll_ measures the impact of payroll accounts with direct deposit wage payments.

Where we use administrative data rather than survey outcomes to estimate treatment effects we use regressions where the treatment indicators _Tit,acct_ and _Tit,payroll_ are zero in the periods before the randomly-assigned account opening date and equal to one thereafter. This means that in these specifications, the treatment effects are estimated relative to a control group whose outcomes are zero by construction. Because we do not observe transactions for the control group, the comparison of interest in these specifications is between _γpayroll_ and _γaccount_ , the treatment effect estimates for the payroll account, compared to the account only treatment groups, respectively.

## **4.1 Account Use and Learning**

In this section, we examine the effect of payroll accounts on account use and learning. The main interest of our study is to understand whether consistent use of a formal account enables inexperienced consumers to learn how to navigate the financial technology more effectively and avoid common consumer protection risks. Examining this question requires variation on the _extensive margin_ of account ownership as well as the _intensive margin_ of how
gent (2005) and a generic version of what is suggested in Lenza and Primiceri (2020) as a solution to the COVID-19 shock.

# **3 Data**

## **3.1 Earning call transcripts**

The empirical analysis is based on two main data sets. These are the earning conference call transcripts of publicly listed firms, and the lexicon dictionary of Loughran and McDonald (2011) for the identification of positive, negative, and uncertainty sentiment words.

Our primary dataset is composed of transcripts of quarterly earning calls from publicly listed firms in the United States stock market, obtained through Factiva’s Fair Disclosure Wire. In these calls, senior management discuss the company’s performance in the previous quarters as well as provide forward-looking guidance for the future conditions. Market participants can ask questions and more widely debate key topics with management on the calls, which are held in conjunction with earnings announcements.

We collect 169,891 available earning call transcripts from 2006Q1 to 2021Q2, of 5,901 firms headquartered in 80 different countries including both advanced economies and emerging markets. The dataset covers a large number of earning calls from all major sectors and countries (figure 1). This equates to over 11,000 earning calls per year for about 3,100 firms on average. The earning calls are mostly from firms headquartered in the United States, accounting for 77 percent of the total. The next largest is Canada accounting for about 7 percent of the earning calls. The data covers all sectors with the most earning calls, about one-third, from manufacturing followed by finance and ITC–about 13 percent each.

We clean the textual dataset using standard NLP techniques by removing stop words, and apply tokenization and stemming (Gentzkow et al., 2019). Tokenization splits sen9
affects household welfare, measured by consumption and food insecurity. We also find that property-related events are more detrimental to household welfare than are violent events. Both types of acts are perpetrated by different groups. Insurgents, bandits and criminals, and clashes between farmers and herders all involve both violence and property-related crimes. However, these different perpetrators might have different motivations for their acts, which might lead to different consequences for the household. Indeed, we find that consumption and food insecurity are

> 28 For the analysis with the wave-based consumption aggregate measured in 2010–11, 2012–13, and 2015–16, we consider events that took place in 2010, 2012, and 2015. For the food insecurity analysis, the outcome variables are measured in 2011, 2012, 2013, 2015, and 2016. Therefore, we use conflict information from 2010, 2011, 2012, 2014, and 2015, and we are only dropping 2013 conflict events. As in the main analysis, here food insecurity in 2010 also is not part of the analysis as we do not have conflict data preceding that year .

26
for soap and detergents, paints, agricultural fertilizers, petroleum refining and products. The PPP for stone, clay and glass products is a weighted average for cement and bricks. In all cases value added figures (US census concept) were used as weights. The cruzeiro/US$ PPPs and peso/US$ PPPs for "Other Manufacturing" and "Total Manufacturing" are derived from the sum of the values for all other branches from tables 3.15 and 3.16 respectively.
|
|State land|0.16|0.85|-||-||0.16|0.15||
|Previously customary land|0.03|0.15|-||-||0.03|0.03||
|<br>Parcel with chief certificate|0.17|-|1.00||-||0.15|0.19|***|
|State land with no document|0.09|-|-||0.17||0.11|0.07|***|
|Customary land with no document|0.46|-|-||0.83||0.45|0.47||
|<br>Number of parcels|2,737|503|456||1,512||1,444|1,293||

_Source:_ Own computation from 2020 Zambia Eastern Province Farm Survey.

22
Table 3. Responsiveness of Catch to Price: Intensive Margin

||Dep. varia<br>Pichilingue<br>|ble: log catch<br> <br>|<br>|Abreojos<br>|||
|---|---|---|---|---|---|---|
|Variable|(1)|(2)|(3)|(4)|(5)|(6)|
|Log coop price|0.563**|0.563|0.782**|0.670***|0.670***|0.704***|
||(0.220)|(0.345)|(0.352)|(0.029)|(0.051)|(0.046)|
|Log mkt price|-0.599|-0.599|0.364|0.310|0.310|1.124**|
||(1.319)|(1.244)|(1.335)|(0.221)|(0.274)|(0.505)|
|Obs|2618|2618|2618|23586|23586|23586|
|Fixed effects|Sp-M-FT|Sp-M-FT|Sp-wk-FT|Sp-M-FT|Sp-M-FT|Sp-wk-FT|
|Clustering|none|Sp-M-FT|Sp-wk-FT|none|Sp-M-FT|Sp-wk-FT|
|Num. groups|1493|1493|2110|3169|3169|7153|
|Within-R2|0.006|0.006|0.018|0.025|0.025|0.041|

Note: All specifications use linear fixed effects estimation. First three columns use only Pichilingue observations, and next three columns use only Abreojos observations. Columns 1, 2, 4, and 5 include fixed effects at the
[Online] CPEC Secretariat. http://cpec.gov.pk/map-single/1.

78. Arora, Rajat. New Silk Route? China plans Kunming-Kolkata railway link. The Economic Times. June 18, 2015.

79. Nag, Devanjana. Bullet train service to connect Kolkata in India to Kunming in China? Why China is pushing for the project. FinancialExpress.com. s.l. : Financial Express, September 13, 2018.

80. UNESCAP. Trans-Asian Railway Network Map. November 1, 2016.

81. Frontier Myanmar. The grand plan for the China-Myanmar Economic Corridor. FrontierMyanmar.net. [Online] Frontier Myanmar, September 26, 2018. https://frontiermyanmar.net/en/the-grand-plan-for-the-china-myanmar-economic-corridor.

82. Chaudhury, Anasua Basu Ray and Basu, Pratnashree. India-Myanmar Connectivity: Possibilities and Challenges. Kolkata : Observer Research Foundation, 2015.

83. Arora, Rajat. Railways plans Rs 40k crore projects to improve connectivity in North East. EconomicTimes.IndiaTimes.com. s.l. : The Economic Times, May 15, 2018.

84. Ul-Karim, Naim. Roundup: China-funded bridge rail link contributes to Bangladesh-India connectivity project. XinhuaNet.com. [Online] Xinhua, May 9, 2018. http://www.xinhuanet.com/english/2018-05/09/c_137167483.htm.

85. Paul, Joshy M. Myanmar in China’s Push into the Indian Ocean. [Online] Institude for Defence Studies and Analysis, March 14, 2016. https://idsa.in/backgrounder/myanmar-in-chinas-push-into-the-indian-ocean_jmpaul_120316.

86. MarEx. China and Myanmar Agree to $1.3 Billion Port Project. Maritime-Executive.com. [Online] Maritime Executive, November 8, 2018. https://maritimeexecutive.com/article/china-and-myanmar-agree-to-1-3-billion-port-project.

87. Lee, Yimou and Aung, Thu Thu. China to take 70 percent stake in strategic port in Myanmar - official. Reuters.com. [Online] Reuters
priors. To address these challenges, we use a machine learning method implemented in a two-stage estimation approach. First, we select the set of explanatory variables for student outcomes by using the adaptive Least Absolute Shrinkage and Selection Operator (LASSO) technique.<sup>6</sup> Second, we estimate OLS regressions of academic and labor market outcomes on the set of explanatory variables selected by LASSO to estimate the associations between outcomes and quality determinants while accounting for student, program, and HEI characteristics.

Our findings highlight four main conclusions. First, our program-level survey data shows great variation—within and across countries—in program outcomes and quality determinants. While some practices—such as providing labor market information to students or updating the curriculum to meet firms’ needs—are commonly reported, others are reported by less than a fifth of the programs (for example, requiring a second language for graduation).

Second, outcomes are generally associated with quality determinants from multiple categories. The most frequently selected determinants—and those associated with the greatest outcome improvements—correspond to the T&C, LPS, faculty, and other practices categories. The specific predictors of outcome improvements are different in the regional and country-level analyses. In the regional analysis, improving academic and labor market outcomes is associated with practices such as teaching a fixed curriculum, training students to work under pressure, teaching numerical competencies, running an employment center, and providing labor market information. In Brazil, three practices are associated with an improvement in graduation and all labor market outcomes— frequent analysis of student performance, use of tests as a graduation requirement, and students’ access to bank loans—and one practice—teaching numerical competencies—is positively

> 6 See Zou, 2006; Bühlmann and Van de Geer, 2011; Chetverikov et al., 2019. A recent application of machine learning methods (including LASSO) is Filmer et al. (2021), which seeks to identify predictors of teacher effectiveness.

5
for stock markets to act as alternative sources of finance when banking systems are in crisis. As discussed in the introduction, several of these characteristics could become even more relevant during a financial crisis when instability and uncertainty are heightened. To identify countries that experienced a systemic banking crisis since the onset of the global financial crisis, we use the indicators constructed by Laeven and Valencia (2010). Other country characteristics are measured as of 2008.

We consider real GDP per capita as an indicator of overall economic and institutional development. As indicator of the efficiency of the legal system in enforcing contracts and addressing insolvencies, we consider two indicators from the World Bank Doing Business Database: an indicator that estimates the average duration of bankruptcy proceedings, and an indicator that estimates the average recovery rate on assets during a bankruptcy. As indicators of investor protection, we consider the index of anti‐director rights and the index of disclosure, taken again from the World Bank Doing Business Database. Last, better transparency and information sharing among lenders may also help support firms’ access to external finance in general and in particular during a financial crisis. To measure this we consider the Doing Business index of depth of credit information.

Financial development has been found to exert a significant impact on the capital structure and debt maturity of firms (Demirguc‐Kunt and Maksimovic, 1996, 1999) and on their performance (Demirguc‐Kunt and Maksimovic, 1998), and tends to disproportionately benefit the use of external finance by smaller firms (Beck, Demirguc‐Kunt, Laeven and Levine, 2005). As indicators of financial development, we consider the ratio of private credit to GDP, the ratio of stock market capitalization to GDP and the private (domestic and international) bond market 19
# Annex 1: Application of the Residual Expenditure Method in Urban Pakistan

This annex provides a detailed explanation of how the Residual Expenditure Methodology (REM) was adapted from the Residual Income Methodology (RIM) for application in Pakistan. It presents the key assumptions used in the analysis, and one way in which the REM methodology can be applied to lowincome countries where reliable income data is often not available, but expenditure data is.

Pakistan’s housing shortage is currently estimated at approximately 10 million units and growing. About half of this shortage occurs in urban areas, where 47 percent of households live in over-crowded housing units in informal settlements ( _katchi abadis_ ) with inadequate infrastructure and services.<sup>18</sup> In response to the growing housing shortage, the Government of Pakistan launched the ambitious Naya Pakistan Housing Program (NPHP) in April 2019 with the objective of providing 5 million housing units across the country over five years. In the context of the NPHP and the World Bank’s housing sector engagement in Pakistan,<sup>19</sup> housing affordability analyses were conducted to inform policy dialogue and project design that would support the better targeting of housing subsidies to those in lower-income brackets for whom affordable housing is out of reach.

Using data from the Pakistan Household Integrated Economic Survey (HIES) 2018/19, the REM was used to estimate housing unaffordability for households in urban parts of the country. The findings suggest that approximately 25 percent of households are unable to cover their basic food and non-food, nonhousing expenses after accounting for housing costs, with the poorest segment facing the highest affordability challenge (94 percent). In contrast, the EIR approach suggests that housing is unaffordable for only 44 percent of households, and that housing is least affordable for the highest quintile, with close to 60 percent of households spending more than 30 percent of their household income on housing. The REM approach highlights the need for a much more accurate methodology to facilitate nuanced understanding of affordability in the housing sector
Table A.12: Parental health investments, NFHS data

||Months breastfed|Received _≥_1 vaccine|No. of antenatal checks|
|---|---|---|---|
||(1)|(2)|(3)|
|_Firstborn girl_ * _Female_|-0.373**|-0.028*|-0.076|
||(0.172)|(0.014)|(0.061)|
|_Firstborn girl_ * _Female_ * _Post2_|0.056|0.022|0.061|
||(0.291)|(0.015)|(0.071)|
|N|59,654|56,430|67,001|
|_Xijt_|x|x|x|
|_1st Girl_ x Birth year FE|x|x|x|
|_Female_ x Birth year FE|x|x|x|
|_Female_ x State FE|x|x|x|
|_Female_ x Birth order FE|x|x|x|
|Birth order x State FE|x|x|x|
|State-specifc time trends|x|x|x|
|_Firstborn girl_ x State FE|x|x|x|
|_Firstborn girl_ x Birth order FE|x|x|x|

NOTES: NFHS data. Sample include second and higher order births. Investments are only queried for cohorts born in a few years before each survey so there are no pre-ultrasound cohorts. The comparison here is therefore across the two post-ultrasound periods. The estimates are from the following specification for child _i_ of birth order _b_ born to mother _j_ in year _t_ : _Iibjt_ = _α_ + _βGj ∗ Fi ∗ Post_<sup>2</sup> _t_<sup>+</sup><sup>_γGj∗Fi_+</sup><sup>_ωtGj_+</sup><