rafmacalaba/data-use-mentions-extended huggingface dataset:
an line was initially an isolated route, serving only passengers between these two cities. However, after connecting to the BeijingGuangzhou HSR line in 2013, its passenger volume surged by 43 percent, and passenger-kilometers increased by 72 percent. By 2016, almost half of the passengers on the Zhengzhou-Xi’an line were traveling from stations beyond the immediate route (Lawrence et al., 2019).

Given the network characteristics of HSR, the effect of an HSR opening in a city on firms’ DVAR depends not only on direct connections but also on the extent of indirect links across multiple lines. It also hinges on how many firms in those connected prefectures have potential IO linkages with the initial firm. Figure A1a illustrates the number of prefecture pairs connected via HSR, both directly and indirectly, over the sample period. As shown in Figure A1b, HSR construction surged after the financial crisis, steadily increasing the number of prefecture pairs connected both directly and

> 7Ouyang et al. (2024) present recent evidence from a survey of corporate managers, revealing that site visits are crucial for both suppliers and customers in overcoming information asymmetry. These visits play a key role in building and maintaining supplier-customer relationships, especially for middle-to-long distance travel, ranging from 200 to 500 km. 8In 2013, the average travel distance on the Beijing-Shanghai and Beijing-Guangzhou lines, two of the most important HSR routes in China, was around 500 km. Amos et al. (2010) suggests that HSR holds a comparative advantage over airlines for trips under 800 km.

> 9Fang et al. (2023) highlights that delays are a persistent issue in China’s airline industry, making the punctuality and convenience of HSR a significant competitive threat to air travel.

> 10Business interviews conducted in cities newly connected to HSR, as summarized by Lawrence et al. (2019), reveal that while HSR has limited influence on the location choices of manufacturing firms, it significantly boosts business travel. Sales, procurement, and professional service staff, in particular, heavily rely
Table A.1: Relevant features of household survey over time (2001-2019)

|Year|Self-emp. <br>Module|Ag.<br>Imputed<br>rent|13/14vo<br>identifer|Formality<br>(contributes<br>to IHSS)|Self-emp.<br>reference<br>period|Census<br>weights|
|---|---|---|---|---|---|---|
|2001||Estim ptile|||3 months|2001|
|2002||Estim ptile|||3 months|2001|
|2003||Estim ptile|||3 months|2001|
|2004|✓|Estim ptile|||3 months|2001|
|2005|✓|Estim ptile||✓|3 months|2001|
|2006|✓|Estim ptile|✓|✓|3 months|2001|
|2007|✓|Estim ptile|✓|✓|3 months|2001|
|2008|✓|Estim ptile|✓|✓|3 months|2001|
|2009|✓|Estim ptile|✓|✓|3 months|2001|
|2010|✓|Estim ptile|✓|✓|3 months|2001|
|2011|✓|Model|✓|✓|3 months|2001|
|2012|✓|Model|✓|✓|3 months|2001|
|2013|✓|Model|✓|✓|3 months|2013*|
|2014||Model|✓|✓|6 months|2013|
|2015||Model|✓|✓|6 months|2013|
|2016||Model|✓|✓|6 months|2013|
|2017||Model|✓|✓|6 months|2013|
|2018||Model|✓|✓|6 months|2013
). Only with panel data can one investigate the effects of policy interventions and the process of economic growth. Consider, for example, the

> ' See Blank and Grosh (1999) for a complementary paper on household surveys. They suggest that political will, appropriate resources and careful planning are key requirements for developing analytical capacity. With these prerequisites in place, policy-makers must concentrate on four areas to strengthen the analytical capacity of their country: appropriate training of policy analysts; use of external technical assistance to work closely with local counterparts; establishment of fully staffed policy analysis units; and direct participation of staff members in the development and implementation of the research agenda. Our paper builds on Blank and Grosh by assuming the will and resources are in place, and discusses the best methods for gathering the data.

3
profitability. Other independent variables enter more or less with the same sign and magnitude as in column 1.

In columns 4, 5, and 6 we use an adjusted ROA data to take into account the impact of inflation on bank profitability. We reduce nominal profits by the amount of reduction in real bank capital as a result of inflation. In another words, we keep bank capital constant in real terms. As shown in the table, this leads to important differences. Foreign ownership dummy is still highly significant. However, the real interest variable, in contrast to earlier results, becomes highly significant. The fact that it enters with a positive sign suggests that higher real rates of interest increase ROA. Inflation becomes insignificant. In columns 5 and 6 we look at the impact of FMS and FNS on the performance of domestic banks. In contrast to column 2, FMS becomes highly significant after adjusting ROA for inflation. It enters with a negative sign. The significance of FNS improves and it also enters with a negative sign.

These findings suggest that foreign bank entry had reduced ROA in the system, an indication of increased competition in the banking market.

# **VI.** QUALITATIVE **IMPACTS** OF FOREIGN BANK ENTRY

The analysis so far has focused on the quantifiable impact of foreign bank entry. However, there are qualitative effects as pointed out by Levine (1996). Pehlivanli (1991), in her assessment of foreign entry during the 1980-1990 period, reports that foreign banks contributed to the quality of bank management in at least three ways: (i) planning; (ii) credit evaluation and marketing; and (iii) recruitment. She points out that financial and operations planning were not a standard and serious process in Turkey before the 1980s. Indeed, Turkish banks during the pre-1980 period were characterized 16
some of the treatment GPs could have seen them withdrawn due to a shortage of funds. The attrition of control GPs was thought to be more likely than the attrition of treatment GPs. Accordingly, it was decided to sample 60 GPs from each of the treatment arms and 80 GPs from the control arm. So, the final sample had 260 GPs of 24 households each, for a total number of 6,240.

# 4.2 Sampling Strategy for GPs

Baseline SBMG data on GP-level toilet coverage were used to select the GPs in the sample. Of the 12,786 GPs from Punjab listed in the SBMG data set, those excluded (i) had already received interventions under the SBMG (Tranche 1 and 2); (ii) were going to benefit from improvements in water service delivery; (iii) had water quality problems that were going to be addressed in the near future under the project or the National Rural Water Development Program; (iv) were GPs where political interests might make it difficult to withhold interventions, as identified by the officials of the DWSS; and (v) lacked data on toilet coverage. Once we excluded these GPs, the total number of GPs in our target population was reduced to 7,764.

Further, to ensure that a full-fledged BCC campaign would be undertaken in the sample GPs, a construction requirement of at least 25 toilets was set as a minimum threshold for being selected. Similarly, to ensure that the project interventions would be completed within six months, GPs where the number of households without individual latrines exceeded 300 were excluded from the sample. After imposing these conditions, the remaining 4,868 GPs were identified as our baseline population from which the samples were drawn. A computer program was used to draw random samples stratified at the district level and maintain proportional representation of GPs in districts in each of the treatment and control arms similar to the baseline population.

# 4.3 Sampling Strategy for Households

Up to 250 households were listed in each of the selected
with data from real-world settings. Cabral et al. (2021) used both survey and administrative data to conclude that self-employed people in New Zealand underreport their income by around 20%. They also found that men are more likely to underreport their income than women.

In the context of developing countries, there may be implicit gender biases in tax administration that affect how tax administrators interact with women-owned businesses. These biases can lead to differential treatment, such as intense scrutiny in assessment or women-owned business may face challenges in accessing tax incentives. Moreover, a well-established pattern suggests that women are less likely to resort to bribery when dealing with government employees to influence their decisions, according to Agerberg (2014). This will likely be the case even in tax-related affairs. This observation highlights the importance of looking deeper into the gender-based dynamics at play in such contexts. We could not find studies that investigate the presence of differential treatment by tax administrators of women-owned businesses in tax enforcement, but we consider it as a potential source of distortion in the tax burden.

In the context of Ethiopia, Yimam and Asmare (2020) found that womenowned businesses are less likely to be penalized for tax evasion after audit than male-owned businesses, indicating that women are more tax compliant. On the other hand, Ambel et al. (2021) conducted the first gender disaggregated business income taxation survey on households in Ethiopia. They found that women pay lower business income tax than men, but the study did not distinguish between taxpayers whose tax liability is determined by the authority and those who self-report their income and tax liabilities. In contrast, Mascagni and Mengistu (2016) and Bachas et al. (2022) focus on the tax burden without considering gender.

A significant gap remains in understanding the distribution of tax burden by gender. This paper addresses the under-explored issue of gender bias in business taxation for self-employed individuals, which is particularly relevant 5
Policy Research Working Paper 8628

# **Abstract**

This paper develops a tractable method to identify urban areas and applies it to India, where urbanization is messy. Google Earth images are assessed subjectively to determine whether a stratified large sample of Indian cities, towns and villages, as officially defined, are urban or rural in practice. Based on these assessments, a regression analysis combines two sources of information—data from georeferenced population censuses and data from satellite imagery—to identify the correlates of units in the sample being urban.

The resulting model is used to predict whether the other units in the country are urban or rural in practice. Contrary to frequent claims, India is not substantially more urban than implied by census data. And the speed of urbanization is only marginally higher than official statistics suggest. But a considerable number of locations are misclassified in the midrange between villages and state capitals. The results confirm the value of combining subjective assessments with data from these different sources.

This paper is a product of the Office of the Chief Economist, South Asia Region. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/research. The authors may be contacted at yli7@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent
<!-- Start of picture text --> 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%<br>Brahman/Chhetri 22 43 33<br>Terai Middle Caste 35 50 15<br>Dalits 51 42 7<br>Newar 8 35 51<br>Hill Janajati 34 45 20<br>Terai Janajati 35 52 12<br>Muslim 32 58 10<br>Other minorities 18 74 7<br>Poor Vulnerable Middle class Upper class<br><!-- End of picture text --> Source: World Bank staff estimates based on data from three rounds of NLSS data for 1995/96, 2003/04 and 2010/11

# **4. Perceptions of Mobility**

In the preceding sections, we introduced, defined, described and characterized various notions of economic and social mobility in Nepal. A question that we attempt to answer next is the extent to which our findings square with perceptions about mobility. Do Nepalis feel and experience this mobility? Do they feel that they have necessarily done better than their parents? Do they expect their children to do better than their parents? Even within their lifetimes, do they expect to do better than their current economic and social position? We can answer some of these questions using data collected specifically for the purpose in Nepal and data from other global data sources such as the Gallup World Survey.

## **_4.1. Conceptual Background_**

How inequality is perceived can often be very different and disconnected from the actual level of inequality in society. Perceptions naturally have a strong correspondence with every individual’s actual experience with inequality, and are likely to be influenced and shaped not just by the extent of the magnitude of inequality but also by beliefs about the underlying processes that generate the observed inequality. For example, inequality generated by growth processes that are dynamic, broad based and rooted firmly in meritocratic principles of rewarding
...<br>49<br>Foreign Reserve Accumulation<br>and Debt Management<br>...................................<br>51<br>Foreign Asset Accumulation<br>.52<br>Domestic Capital Investment<br>.55<br>Monetary Aspects of Coffee Booms .58|
|VI.|Conclusions<br>.62|
|REFF|RENCES<br>..<br>63|
|APPE|NDICES|
|A.|The Impact of Capital Controls on the Management of Export Booms.66|
|B.|Macro Effects of the 1976-80 Coffee Export Boom in Colombia<br>......<br>68<br>Inflation and the Real Exchange Rate<br>........................<br>68<br>The Monetary Policy Response<br>................................<br>69<br>Foreign Reserve Inflows, Exchange Race Policy,<br>and Capital Controls<br>...................................<br>73<br>The Fiscal Policy Response<br>..................................<br>74|
|C.|Data on Individual Country Experiences<br>with Booms<br>.78|
appears to be out of reach for many countries (see World Bank (2010)).

Related in part to gender bias in education, in today's low- and middle-income countries the labor force participation rate for women is only 57 percent, compared to 85 percent for men (International Labour Office (2010)). In most regions female employment is concentrated in either services or agriculture, with fewer women than men employed in industry. Gender inequality in health remains large as well; maternal mortality rates in many developing countries have only declined marginally (United Nations (2010)). Combined with gender bias in the work place, the consequence is large pay differentials. According to data compiled by UNIFEM (2008), the gender pay gap ranges from 3 percent to 51 percent, with a global average of 17 percent. Seven out of ten of the world's poor are women or girls, so a focus on poverty reduction involves necessarily addressing their plight.

This paper focuses on two aspects of the debate on the role of women in growth and development: the role of inter- and intra-generational health externalities (namely, how mothers affect their children’s health, and how health in childhood affects health in adulthood) and constraints on women’s time allocation—particularly lack of access to infrastructure—and how, in turn, policy-induced changes in such allocation affect growth.<sup>2</sup> In line with the above evidence, much research on gender and growth in developing countries

> 1See Blackden and Bhanu (1999), Blackden et al. (2006), Morrison, Raju, and Sinha (2007), Nallari and Griffith (2009), and Momsen (2009). Some of the recent evidence is further discussed in subsequent sections. 2Lazear (1983) provides one of the first systematic discussions of the concept of intergenerational externalities. A key point of his contribution was to draw attention to the negative externalities that may result when parents underinvest in themselves (both
equivalent to about US$4 per day in 2011 purchasing power parity (PPP). As a comparison, poverty rates based on the international poverty line of US$1.90/day at 2011 PPP was 1.35 percent.

> 4 Estimate refers to the consumption per capita distribution, while official figures use consumption at the household level. It is important to note that distributions estimated using household surveys are typically truncated, as surveys do not adequately cover rich households. For Egypt, “correcting” for this truncation using real estate housing prices (Van der Weid, Lakner, and Ianchovichina 2016) or regional information on wealth, national accounts, and taxes (Alvaredo et al. 2018) suggest the Gini may be higher.

2
dening the circumstances is not a clean measure of eort as it partly reects the eects of family background.<sup>14</sup> As measure of eort, they use the sterilized residual from the regression of the residual squared (the residual from the earlier stage) on circumstances.

There is a small literature on intergenerational mobility that exploits the information in conditional variance using quantile regressions. See, for example, Grawe (2004) on the United States and Kishan (2018) on India. The focus in this approach on estimating dierent conditional mean functions corresponding to the quantiles of children's education. Grawe (2004) provides an interesting analysis of the pitfalls in relying on functional form of the CEF to learn about credit constraints in the context of income mobility. His analysis suggests that a quantile regression approach can be useful in understanding the existence of credit constraints.

# (3) Data and Variables

We use the following household surveys for our empirical analysis: China Family Panel Studies (CFPS) for China, India Human Development Survey (IHDS) for India, and Indonesia Family Life Survey (IFLS) for Indonesia. These data sets are suitable for our analysis because they do not suer from any signicant sample truncation arising from coresidency restrictions commonly used to dene household membership in a survey. A truncated sample is likely to underestimate the conditional variance, for example when the data miss observations on highly educated children who left the natal house for college.

The data for China come from the China Family Panel Study (CFPS) 2010 wave, which has a unique T-Table design that presents a complete family network, in which household members' education information is also available. For more detailed discussions about the unique advantage of CFPS in analyzing intergenerational mobility related questions, please see Fan, Yi ang Zhang (2021) and Emran, Jiang, Shilpi (2020). The data for India come from the India Human Development
Berhe et al. (2022)|SRM of 15 cross-<br>sectional studies,<br>Ethiopia<br>(includes 2 national<br>surveys in 2016 –<br>EDHS & MNS)|Adolescent girls 10-19<br>y (n=9,669)|Dietary diversityScore,<br>low (DDS <4 of 9 food<br>groups)|Anemia (using WHO Hb<br>cutoffs)|Low diet diversity (n=2 studies) was<br>associated with anemia (OR=2.81;<br>1.33, 5.90)|
|Habtegiorgis et al.<br>(2022); Endale et al.|SRM of 10 cross-<br>sectional studies,|Adolescent girls|Dietary diversityScore,<br>low (DDS <4 of 9 food|Anemia (using WHO 2011<br>Hb cutoffs by age and|Low diet diversity (n=5 studies)<br>associated with anemia|
|(2022)<br>(both articles based on<br>the same 10 studies)|Ethiopia||groups)|pregnancy status)|(OR 1.35; 95% CI 1.00, 2.34) or<br>(OR 1.56; 95% CI 1.05, 2.32)|
|Jensen (2023)|SRM of 11 cross-<br>sectional studies<br>(most in HICs, 3 in<br>India, 2 in China)<br>8 studies included<br>in meta-analysis|Children and<br>adolescents 5-18 y<br>(n=1545 in meta-<br>analysis)|Adherence toplant based<br>diet(vegan, vegetarian,<br>macrobiotic, etc)|Vitamin B12 level|Vegan or macrobiotic diets<br>associated with lower vitamin B-12<br>levels
5 explained variation, probability of rejecting a linear relationship between dependent and independent variables, and significance of the coefficients on saving).

The other decision required for estimation concerns the discount rate. The underlying theory (see Ch. 3 and Ferreira _et al._ (2003)) suggests that the rate should be the marginal product of capital less depreciation rates for produced capital, less population growth rates, which argues for a low value. We use a uniform rate of 5%, and tests of alternatives suggest that the estimates are fairly insensitive to small changes in the discount rate.

Allowing for the sparse early-1970s savings data<sup>4</sup> , therefore, expression (4) was estimated using OLS for consecutive 20 year periods from 1976-1980. These results, as well as more informal methods, are reported below.

# **Empirical results**

To give a feel for the data, we first scatter the present value of changes in consumption against the four different savings measures for 1980 in Figures 1-4. The broad picture which emerges is that there is no monotonic improvement in the fit with theory as more stringent measures of saving are applied. The coefficient on saving actually drops going from gross saving to net saving, and the explained variation drops considerably. For genuine saving the coefficient on saving is very near 1 and the explained variation is the highest of the four saving measures. Finally, for Malthusian saving the coefficient on saving drops to the lowest level of the four measures, while explained variation reaches its highest value.

Figure 5 presents the same scatter for high income countries only. As seen in Ferreira and Vincent (forthcoming) and Ferreira _et al._ (2003), the model fit is particularly poor for these countries – further tests show the coefficient on saving to be insignificant, while the explained variation is very low.

Table 1 presents the results of the individual OLS estimates of the model for each of the five years and four measures of saving. This table reports the
∗</sup> tt←tt<sup>∗</sup> tt←tt<sup>∗</sup> SS . So, the enumerators form longitudinal sample SS , with mm households and nn . tt←tt<sup>∗∗</sup> tt←tt<sup>∗</sup> The samples for the estimation at time tt are SS and SS

# 3.2. Movers and representativeness of the samples

Movers create two types of problems in longitudinal surveys. First, there are additional costs. Surveys that employ face-to-face interviews use cluster and multi-stage sampling designs to control costs. Following a mover to a new address may incur considerable extra expenses if the new address is not in one of the original sample areas. Also, there is a risk that no interviewer may be available to visit a mover if the move is discovered during the field operations. Thomas et al. (2001) discuss the cases of certain surveys in low-income countries that suffered significant attrition and bias because they did not track movers. Therefore, new contact information should be obtained to facilitate collecting data on the mover through phone or online interviewing. Another solution is to collect the data of interest on movers through proxy interviews; however, using proxies requires implementing adjustments in the estimation phase to reduce bias.

Based on the above, tracking protocols should be defined to determine a reasonable approach for balancing increased costs on one hand and distortion or bias on the other.

The target current population of households can be defined as the union of the three disjoint subpopulations: ∗ ∪E ∗∗ ∗ ∪E ∗∗ where tt tt←tt tt ←tt tt←tt (3.1) HH = FF 9