rafmacalaba/data-use-mentions-extended huggingface dataset:
2 causality.' Financial development may simply be a leading indicator, rather than an underlying cause _of_ economic growth. This paper uses new data and new econometric procedures to shed considerably more light on the issue of causality while also illuminating a close empirical association between key legal and accounting characteristics and financial intermediary development. In conducting this analysis, we evaluate the predictions that emerge from an array of theoretical models.7 Methodologically, the paper uses two econometric techniques to study causality. First, we use a pure cross-sectional estimator, where data for **71** countries are averaged over the period **1960-1995,** with one observation per country. As in much of the cross-country growth literature, the dependent variable is the growth rate of the real per capita Gross Domestic Product **(GDP).** The regressors include a variable of particular interest, in our case financial intermediary development, along with a set of conditioning information. Unlike much of the literature, we use instrumental variables to extract the exogenous component of financial intermediary development. Specifically, LaPorta, Lopez-de-Silanes, Shleifer, and Vishny **(1997, 1998;** henceforth LLSV) note that most countries can be divided into countries with predominantly English, French, German, or Scandinavian legal origins and that countries typically obtained their legal systems through occupation or colonization. Thus, we view legal origin as an exogenous "endowment." After extending the LLSV sample from 49 to **71** countries, we use the legal origin indicators as instrumental variables to extract the exogenous component of financial intermediary development.

**' King and Levine (1993a,b) and Levine and Zervos (1998)** show that measures of financial intermediary development predict economic growth over the next 18 to **30** years in a broad-cross section of countries. Further, Neusser and Kugler **(1998)** and Rousseau and Wachtel **(1998)** find that financial development Granger-causes economic performance in timeseries
Section J provides concluding remarks.

# **D Background**

In India, limited infrastructure and transport services restrict mobility for both men and women. Still, women frequently experience extra socio-cultural and economic factors that negatively affect their commute patterns (Srinivasan and Rogers, 2005; Tripathi et al., 2017; Alam et al., 2021). Given the sizeable gender wage gap (Duraisamy and Duraisamy, 2016; Deshpande et al., 2018) and the additional barriers in accessing the financial system compared to men (Khera, 2018), paid access to transportation is plausibly harder for Indian women. Besides, the low rate of female usage of public transport might raise a perception problem since more female presence in public transportation makes women feel safer (Sajjad et al., 2017). In a survey of 3,800 students at Delhi University, Borker (2021) found that women are willing to travel 27 minutes more per day or 40 % more than their daily travel time if they can use a perceived safer transport route. These factors put women at a disadvantage regarding access to transport services and infrastructure (Astrop et al., 1996; Dominguez Gonzalez et al., 2020), potentially affecting their participation in labor markets (Patacchini and Zenou, 2005; ILO, 2017; Sajjad et al., 2017; Martinez et al., 2020).

In light of these challenges, the Delhi government introduced a scheme offering free bus rides to all women in the city from November 2019 onward (Kejriwal, 2019). The scheme makes bus travel free for women in all Delhi Transport Corporation (DTC) and Cluster buses. On each ride, bus operators provide a pink ticket to each woman. Afterward, Delhi’s government compensates the bus operators with |10, the equivalent of $0.14 (all currency conversions use the 11/2019 exchange rate), per pink ticket ride (The Economic Times, 2019; Durai, 2021). The program showed an early
—users and non‐users—split by gender. Because the objective of this study was to examine how social norms and cultural schema influence latrine usage among rural latrine owners, we sampled a cluster of villages where latrine penetration was higher than average (based on data from SaniFOAM‐UP, 2016). Our survey sample came from five villages in the Ghazipur District of Uttar Pradesh—Adelabad, Gauspur, Jhakrauli, Keshopur, and Saleempur. Latrine penetration rates in the sample villages ranged from 31.5%</mark>

> 3 Prior to asking questions about social empirical and social normative expectations, respondents were told that the three people who give the most accurate answers (i.e. closest to the actual number of people engaging in the behavior) will receive a prize of Rs. 500 a few weeks after the survey is completed.

6
GNB GIN SEN BGDGHACIVMRT VNMEGY COLGAB MRT VNMEGY GAB<br>7 7.5 8 8.5 9 9.5 7 7.5 8 8.5 9 9.5<br>Log of GDP per capita Log of GDP per capita<br>R2 = 0.4879 R2 = 0.3893<br>Poverty rate in urban areas Poverty rate in rural areas<br>Poverty rate in urban areas Poverty rate in rural areas<br>Poverty rate in urban areas Poverty rate in rural areas<br><!-- End of picture text --> Source: International Urban Poverty Database. Note: GDP per capita is measured in PPP (constant 2017 international $). For the DOU and DB methods, WorldPop 250m is used. Urban areas include the categories “Urban center” and “Urban cluster” for the DOU method, and the categories “Core” and “Suburb” for the DB method. Poverty is measured using the $2.15 poverty line.

78
# **Housing expenditure**

_Housing expenditures._ In the CMte d'Ivoire surveys, as in other surveys, statistical techniques are used to impute housing expenditures for the households that do not pay rent. Using the sample of households that do pay rent, regression analysis was used to estimate a hedonic rental equations relating the amount of rent paid to different household characteristics, with regional dummy variables added to capture spatial variation in rental prices. The small sample size was a problem in 1995; only 9 households in West Forest and 12 households in the Savannah rented lodging in **1995.22** In the initial processing of the data, housing expenditures were estimated separated for the 1993 and 1995 using different specifications of the hedonic rental equation. Real housing expenditures increased substantially in rural areas. The increase in the Savannah of 47 percent was particularly suspect. There is little reason to think the imputed value of housing would have increased significantly, especially since the housing costs rose less rapidly than other goods after the devaluation.

Another approach to deriving imputed housing costs is to pool the 1993 and 1995 data and use the same equation to estimate housing costs, with dummy variables for each region allowed to vary between 1993 and 1995. This improves comparability but does not eliminate entirely the problem caused by the small sample size in 1995. With the pooled sample, real housing expenditures fall in all regions but the Savannah, where they remain unchanged. The two methods yield different estimations of the headcount index of poverty in 1995 (table 23). While recognizing that housing expenditures in the Savannah may be overestimated even with the pooled estimate, it is the better of the two estimates.

Table 23 Headcount Index of Poverty Using Different Estimates of Housing **Expenditure**

|_(poverty line_|_= 75,000 C_|_FAF in 1985_|_CFAF)_||
|---|---|---|---|---|
||_Separate E_<
Turning now to the control variables in the regressions, country/year dummy variables control for any possible omitted effect that operates at the country level, such as macroeconomic shocks, the systemic component of the shock to bank equity prices, the policy response to the crisis, differences in accounting and regulatory definition of capital across countries and so on. In other words, what our model seeks to explain is just the cross-sectional, within-country dispersion in stock returns in each quarter.

To isolate the effect of capital on this dispersion, we control for other bank-specific characteristics that may affect stock returns. Specifically, we control for bank liquidity using liquid assets/assets; the bank’s reliance on deposits for funding (deposits/total assets), asset quality (loans loss provisions), the banks’ business model (net loans/assets), and the bank size (log of total assets). Also, following standard asset pricing models, we include in the regression the stock’s beta (computed as the five-year covariance between the bank’s monthly stock return and the country stock market return) and the market-to-book value of equity.<sup>8</sup> The price-earnings ratio (PE) measure possible mispricing of bank equity during the boom. Summary statistics for the explanatory variables are in the Appendix, Table A2.

Explanatory variables computed from bank balance sheet information, including the variables measuring bank capital, are available on a yearly basis rather than a quarterly basis, while our dependent variable is quarterly. For these variables, we use the last available (but not contemporaneous) observation. For example, stock returns during each of the four quarters of 2007 are regressed on the capital/asset ratio at the end of 2006.

In Table 3 we report correlations among stock returns, the various (lagged) capital ratios, and the other explanatory variables. Interestingly, there is a strong negative correlation between capital and bank size, particularly the Tier 1 leverage ratio. The regulatory ratio (RWR)
# 5. Results

## _Sample characteristics_

The selection of roads for RTP3 interventions was carried out at the start of the program and was strictly based on criteria linked to the development objective of the program: to _improve year-round access to markets, economic opportunities, and social services for rural populations_ . The criteria included the number of poor and total population affected by the road, defined as the population living within 2 km of the road, and whether or not the commune previously had an all-weather road. Our study sample differs from the broader VARHS 2008 sample, which was constructed to be representative of the rural population in the provinces it covered, because we restricted our study to the communes that were selected to receive RTP3 interventions.

A comparison of households from the two sets of communes suggests that the program achieved its targeting objectives:<sup>15</sup> our study sample is poorer than the full VARHS sample and this difference is quite large in some measures. There are few statistically significant differences in terms of household size and household head characteristics (Table A.3), but there are significant differences in income, consumption, and indicators of wealth like assets and quality of housing. For instance, households in RTP3 communes are less likely to have a brick wall or a cement floor. Total incomes among the RTP3 sample are approximately 40 percent lower than in the full VARHS sample. The differences appear to come from wages, agriculture, and income from non-farm sources, all of which are suggestive of lower levels of economic opportunity in these villages. Indeed, considering land ownership, we see little difference in the size of land owned, but significant differences in the amount of land cultivated, and perhaps unsurprisingly, in the value of land (Table A.3).

## _Male- versus female-headed households_

A second relevant comparison to make at the outset is between gender differences at baseline: mainly differences between male- and female-headed households since most of our regressions are at the household level. Anecdotal evidence
30 30 30<br>20 20 20<br>10 10 10<br>0 0 0<br>50 50 50<br>40 40 40<br>30 30 30<br>20 20 20<br>10 10 10<br>0 0 0<br>50<br>40<br>30<br>20<br>10<br>0<br>Burkina Faso (2015)<br><!-- End of picture text --> _Source:_ Establishment censuses obtained from the statistical agencies of the select countries; see section 3. _Note:_ The figure is constructed based on data for establishments in the manufacturing sector. Establishments with missing employment data are excluded.

15
and women to the latter. Increased access for rural groups to skills training was, however, an ubiquitous secondary justification: it was mentioned in connection with at least one (but not necessarily each) component in each country in the sample. Access for women figured prominently in the justification of seven of the 76 projects; most of these were in the EMENA region (Jordan, Turkey, Egypt, Pakistan, YAR, Barbados).

# Planning

3.10 Virtually all of the sampled component investments were planned or. the basis of projected manpower requirements. In the majority of cases these were forecast from admittedly inadequate data, with appropriate caveats regarding the indicative nature of the estimates. Such estimates were then compared with the output of existing training systems and investments rationalized as (usually partially) closing the gap. In a majority of projects these estimates were made by Bank staff or UNESCO consultants.

3.11 In a number of cases manpower requirements were derived not from global forecasts, but from enterprise surveys (Ecuador I, Uruguay I, Cameroon II), and other indicators of employment needs. In this
# **_Brazil and Mexico_**

Between 1990 and 2012, the number of people living in extreme poverty in Brazil fell from almost 31 million to less than 5 million. At $3.10 a day, poverty has been reduced from 54 million to 18 million. That millions are still living in abject poverty in a country that reached upper middle‐income levels almost 50 years ago is testament to an ineffective development strategy. But the progress during the last two decades has lessons for countries in Asia and Africa. It shows that for countries that have reduced poverty to low levels, a combination of macroeconomic stability with more effective social policies can help end extreme poverty. But it also shows that without higher growth rates, it is difficult to sustain this progress. What exactly has Brazil done well, and what will it have to do better?

First, between 1994 and 1997, Brazil stabilized its economy. Inflation had stayed above 100 percent annually since 1982 going up to 2,500 percent in 1993. It was brought down to less than 10 percent by 1998. Between 1993 and 2004, using the national poverty line, the share of people living in poverty dropped by a third, from 33 to 22 percent. Brazil’s experience with inflation is a reminder for others that poor macroeconomic management is one of the most anti‐poor things a government can do.

Second, Brazil invested in agriculture. Brazil’s support for agricultural research has helped both large and small farms. Despite the publicity that mega‐farms have received, more than 80 percent of rural Brazilians continue to till small farms. Their productivity has grown due to better seeds and farming techniques made available by EMBRAPA, Brazil’s agricultural research agency. But it is also clear that small farmers need more assistance than they have received and this has led to rural poverty remaining high, prompting an exodus to cities. Extreme poverty is evenly distributed between rural and urban areas in Brazil, unlike Asia and Africa where it is largely
|75|
|R-Squared|0.0549|0.3084|0.0574|0.3115|

All specifications are estimated using the panel fixed-effects estimator. District casualties are expressed per 1000 inhabitants. Standard errors clustered at the district level are in parentheses. District casualties are expressed per 1000 inhabitants. Sample includes only individuals aged 6-9 (treatment group) or 16-19 (control group) years old at the start of conflict in 1996. *p<0.10, **p<0.05, ***p<0.01.

Table 9: Impact of Conflict during Marriageable Age on Female Marital Status – DHS and NLSS.

|Explained Variable|(1)<br>Married by 21|(2)<br>Married by 21 -<br>here since 1996|(3)<br>Married by 18|(4)<br>Married by 21|(5)<br>Married by 21 -<br>here since 1996|
|---|---|---|---|---|---|
|Specification|Eq. (1)|Eq. (1)|Eq. (1)|Eq. (1)|Eq. (1)|
|Dataset|DHS|2006|NLSS 2003|DHS|2006|
|=1 if 13-17 in 1996 x|-0.0334|-0.0274||-0.0339|-0.0229|
|district casualties|(0.0274)|(0.0263)||(0.0278)|(0.0266)|
|=1 if 13-17 in 1996 x<br>district casualties up to<br>NLSS|||0.0442<br>(0.0423)|||
|=1 if 13-17 in 1996 x||||0.0001|-0.0009|
|
another service or product provider in a structured readable format.<sup>23</sup> More in detail, the right to portability has been adopted approximately by 14 percent of high- and lower-middle-income countries, 9 percent of upper-middle-income countries, and 6 percent of countries in the low-income group.

Another issue investigated under the private intent data dimension is the voluntary licensing on FRAND terms, which enables companies and patent holders to share technology and data. On the one hand, holders of data-related intellectual property rights (IPRs) are encouraged to invest in products and markets, as they can control access to licensed products and receive returns on their investments. On the other hand, sectoral or international standard setting organizations may require patent rights to be applied on FRAND terms when these are considered standard essential patents. The diagnostic results show that voluntary licensing to access essential data on FRAND terms is regulated in 14 percent and 9 percent of high- and uppermiddle-income countries, respectively, while the matter is not regulated by any lower-middle- and lowincome country.

As more and more economic transactions occur online, the ability of private sector actors to digitally verify the identity of users against the ID system becomes essential for efficient digital transactions. High-income countries are taking the lead on adopting such practices, with more than 79 percent of them allowing so. For instance, Canada’s SecureKey Technology digitally identifies users through the online credentials used with private partners.<sup>24</sup> A comparable solution is found in approximately half of the upper-middle-income countries. However, no low-income country allows private sector actors to digitally verify the identity of users against the ID system.

# _4.4 Existence of laws vs. robustness of laws_

The details presented above on data regulation frameworks seek to capture the existence of foundational laws, the robustness of these laws, and data governance administration. These additional details comprise the value this diagnostic provides compared with efforts like UNCTAD’s e-transaction database and DLA Piper’s data protection law tracker. Besides collecting
Relative growth (Ei,t-3 - Ei,t-3)) / Ei,t-3) is biased towards small firms, as small units are much more likely to exhibit high rates of proportional growth than large firms.

Birch (1981 1987) and Schreyer (2000) suggested a synthetic indicator – in the following called a ‘Birch’ indicator: (Ei,t-3 - Ei,t-3) * (Ei,t-3 / Ei,t-3) ). The purpose is to control for the bias of indictors towards larger or smaller firms.

Only growth of existing firms will be considered. Mergers and acquisitions are not included. Employment can be generated by start-ups or destroyed by firms exiting the market. The INTERIM ES does not contain any information on firm entry and exit, which is why the analysis focuses on existing establishments. Furthermore, it is important to differentiate firm’s expansion strategies. Firms can grow from within, or through the acquisition of another business. It is important to differentiate between these two. While internal growth is achieved by creating new employment, growth by acquisition is achieved by adding existing employment. This paper solely considers internal growth.

Furthermore, firm age is defined as t-(t-3); dummy variables will be used for urban areas –defined as localities with more than one million inhabitants, and for conflict regions, defined as Afghanistan and Pakistan.

# **Descriptive employment growth diagnostics**

## **Employment growth at the industry level**

**Employment growth differed greatly across industries.** The expansion patterns of the economies of South Asia have been quite different. The annual average relative growth rate of the entire INTERIM ES dataset is approximately 9.9%, which is high by international standards. The median growth rate is nil, which is a common finding in growth literature (see for instance Coad and Holzl, 2009 for an overview of distribution of growth rates). There is great variance of growth rates, both across industries and across countries.
Figure 2: MPL used to measure WTP for prepaid electricity in South Africa

||Electricity vs.|cash|
|---|---|---|
|Binary choice|Electricity voucher value|Cash value|
|_t_|Option A|Option B|
|Choice 1|R100|R60|
|Choice 2|R100|R80|
|Choice 3|R100|R92|
|Choice 4|R100|R97|
|Choice 5|R100|R99|
|Choice 6|R99|R100|
|Choice 7|R97|R100|
|Choice 8|R92|R100|
|Choice 9|R80|R100|
|Choice 10|R60|R100|

Notes: choices 1-10 were randomly shown in order (decreasing value difference) or in reverse order (increasing value difference). In addition, option A (electricity) and B (cash) were randomly shown either on the left or the right of the screen. Each binary choice was shown separately, accompanied by images showing the amounts either on a cash bill or on a stylized voucher. The respondent made choices by tapping directly on the image on the screen and then confirming the selection. See also figure S1 in appendix S2.

of those exhibiting MSB) have three switches, consistent with one error and subsequent correction.

The rate of MSB is relatively high, although not completely out of line with other MPL data from low-income populations. As discussed, one reason could be that within-choice randomization reveals errors that may otherwise remain undetected (e.g., in the form of NSB). That said, it is possible that the within-choice randomization increased error due to comprehension issues or difficulty using the screens.

The share of never-switchers in the data is also high. Since options were randomized within binary choice, it is unlikely that this is due to inattention that made the subject simply repeat the same choice many times. Only one individual always chose the same side of the screen, that is, exhibited
many industrial countries. Along with the decomposition exercise, this paper also estimates the magnitude of developing country exports (aggregate and 3 digit) within the domestic absorption and production in these selected countries (both industrial and developing) and their evolution over the last two decades.

Finally, the study analyzes two periods with very different trade growth rates. World trade growth in current US dollars accelerated for all countries during 2001/02-2007/08 period but the acceleration of export growth of developing countries was greater and reached 18.2 percent per annum during this period. As a result developing countries have increased their

> 2 It would be desirable to undertake this analysis for aggregate developing and industrial countries but manufacturing production data is not available in the same format and definitions for many countries. Sample countries account for more than 50 percent of world trade (Table 1).

> 3 This decomposition was undertaken for selected industrial countries for the 1990s (World Bank 2005).

3