rafmacalaba/data-use-mentions-extended huggingface dataset:
Chatterjee et al. (2022) combine surveys and income tax data to estimate wealth inequality in South Africa, using a replacement method. They use incomes from household surveys and tax data and replace the top of the distribution in the surveys with tax data at the point where survey data incomes are lower than in the tax data, which they find to be around the 25<sup>th</sup> to 30<sup>th</sup> percentile. The authors then use the income data to capitalize asset classes where capitalization can be applied and use survey and tax data to estimate capital for other asset classes. The key finding is that South Africa has an extremely unequal wealth distribution. The top 1% own 55% of total wealth, whilst the net wealth held by the bottom 50% is negative due to liabilities exceeding assets.

Gaps in the Research on Measuring the Upper Tail of the Income and Wealth Distributions In this section we document gaps in the research that has been conducted on measuring the top tail of the income distribution in LMICs. In the next section we discuss how these gaps can be filled by outlining a possible research agenda.

Chancel et al. (2023) highlighted the limited availability of household surveys with income data in Africa, as well as the lack of harmonized consumption data. This is very clearly an important gap- there is no way to directly investigate the top of the income distribution without income data from surveys.

We have noted that a few papers using tax data from LMICs have shown important differences between administrative and survey data at the top of the income distribution. But there is a need for more studies in a wider variety of LMICs to further investigate the top of the income distribution in surveys and admin data. This requires the availability of administrative tax data, at least in tabulation form for different income groups. This type of data is not easily accessible and may best be pursued on a country-by-country basis.

The use of other forms of data to check
the language and mathematics assessments for teachers included test items starting at grade 1 level (simple spelling or grammar exercises, addition and subtraction) and included items up to the grades at the upper primary level (Cloze passages to assess vocabulary and reading comprehension, interpretation of information in a diagram and/or a graph, and a more advanced math story problem).<sup>13</sup> The teacher tests have good reliability, with a reliability ratio (estimated by Cronbach’s alpha) above 0.85 in both subjects on the teacher test. Like the student tests, the teacher test scores used here are based on IRT-derived ability scores for each of the assessments, and they are normalized to have a mean of 0 and standard deviation of 1 across all grade 4 teachers in these six countries.

In addition to these tests, we also incorporate data from a teacher roster, a classroom observation of a lesson, and from a module that measures teacher absence. The teacher roster collects basic demographic and education background information on teachers: age, gender, highest education level completed, whether the teacher holds a teaching certification,<sup>14</sup> whether they were born in this district, as well as the teacher’s contract status.<sup>15</sup> We also use information on whether the school is in an urban or rural area.

In each school, one grade 4 lesson was observed for its entirety. During this classroom observation, enumerators recorded minute-by-minute what the teacher was doing against a set of predefined activities. Activities include descriptors such as “Teacher interacts with all children as a group” or “Teacher supervises pupil(s) writing on the board” or “Teacher in class - not teaching.” We use these data to construct a measure of the share of time the teacher is teaching. After the lesson, enumerators record what they observed along three main dimensions: the teacher’s general demeanor; the use of what are generally
than average chances of downward mobility. For example, the likelihood of upward mobility is significantly higher and that of downward mobility lower when the household head has secondary or higher level education as opposed to education at middle‐school or lower levels.

# Data uncertainties cloud the assessment of poverty, shared prosperity, and (especially) inequality

Important caveats apply to India’s estimates of inequality: when income rather than NSS consumption data are used, inequality in India appears to be lower than high‐inequality countries like South Africa, Brazil and Colombia, but comparable with income inequality in Peru and Ecuador, and higher than in the Russian Federation, Turkey, and the United States.<sup>13</sup> Why the gap between India’s income and consumption Gini measures of inequality is so large remains to be explained, but this at a minimum casts doubt on the oft‐rehearsed notion that inequality is low in India. In addition, it is likely that income (or consumption) distribution from household surveys can underestimate the true extent of inequality due to under‐reporting of top incomes (or consumption). Research using Indian tax return data over the period 1922‐2000 shows a rising share of top income earners in total income from the mid‐1980s to 2000.<sup>14</sup> This would have led to greater underestimation of inequality over time if top income earners are insufficiently captured in the NSS data. Under‐reporting of top incomes, however, is likely to have less bearing on the lower end of the distribution and therefore on poverty and shared prosperity estimates and trends.

One symptom of under‐reporting is the large gap in levels and growth rates of mean consumption per person from the NSS and the private consumption component of the National Accounts Statistics (NAS), which have been diverging since the early 1990s (the 2010 to 2012 period is an exception). In levels, aggregate household consumption implied by the NSS is less than half that of the household
**District Characteristics** We use additional data to control for district heterogeneity. We use population data from the Global Human Settlement Layer (GHSL),<sup>11</sup> land categories from the European Space Agency land cover (see Defourny (2017)), distance from the coast from the Global Self-consistent, Hierarchical,<sup>12</sup> High-resolution Geography Database (GSHHG), distance to the border,<sup>13</sup> access to a city larger than 50,000 inhabitants from the Malaria Atlas Project,<sup>14</sup> temperature from Land Processes Distributed Active Archive Center,<sup>15</sup> and elevation from CGIAR-CSI.<sup>16</sup>

# **4 Empirical Strategy and Results**

## **4.1 Ordinary Least Squares**

Our first empirical strategy uses panel ordinary least squares (OLS) regressions that include year and country fixed effects and a battery of initial district-level controls. The OLS specification is _Sectori,c,t_ is the share of employment in agriculture, manufacturing or services for district _i_ in country _c_ , at year _t_ . _Paved Roadi,c,t_ is a dummy variable that takes a value of one if location _i_ in country _c_ contains a paved road at year _t_ . _Electricityi,c,t_ is a dummy variable that takes a value of one if location _i_ in country _c_ has more than 50% of its population with lights at night at year _t_ . _Interneti,c,t_ is a dummy variable that takes a value of one if location _i_ in country _c_ has a node on the internet backbone fiber network at year _t_ . _Paved Roadi,c,t_ ∗ _Electricityi,c,t_ captures the interaction of the road and electricity infrastructure, and _Paved Roadi,c,t_ ∗ _Interneti,c,t_ the interaction of the road and internet infrastructures. We add interaction effects between the dummies to better understand
stems from the control group being more likely than the treatment group to report outlandishly large numbers.

- The regressions also have enumerator fixed effects, community-level controls (Irbid/Mafraq governorate and population quartile), refugee household-level controls (shelter program, baseline number of children, baseline number of children plus adults), the Marlowe–Crowne scale, and respondent gender, education, and age. In the parentheses are robust standard errors clustered at the locality level.43 Statistical significance represented by * (10%), ** (5%), and *** (1%). Q-values are calculated per Anderson (2008) and correspond to Family 5 in the pre-analysis plan.
_∗_</sup>|
|||(0.491)|(0.112)|(0.166)|
|||[0.590]|[0.134]|[0.204]|
|Observations||510|510|215|
|Control mean||2.42|0.67|0.70|
|Robust p-value||0.563|0.001|0.086|
|MSE-opt. bandwidth||16.2|17.3|17.1|
|Effective obs.||205|219|90|
|High vs. low p-value||0.245|0.145|0.144|

_Notes_ : This table reports RD estimates of _γ_ in equation (1) obtained via the non-parametric method from Calonico et al. (2014). Units of observation are villages in column 1, bureaucrats in columns 2 and 3, and village heads in column 4. In column 1, the dependent variable is an indicator for whether a village is below or above the median of a standardized index of transportation costs constructed using the 2014 Podes data. In column 2, the dependent variable is bureaucrats’ rating of road quality measured on a 5-point Likert scale. In column 3, the dependent variable is an indicator equal to 1 if the bureaucrat reports receiving complaints about roads from village citizens. In column 4, the dependent variable is an indicator equal to 1 if the village head took action on roads. Road quality is measured by a standardized index of transportation costs to the subdistrict office and the regent or mayor’s office, where lower transportation costs indicate better road quality. Panel A restricts the sample to villages with high baseline road quality (transportation costs below the sample median), while Panel B restricts the sample to villages with low baseline road quality (transportation costs above the sample median). The bottom row reports the p-value from the test of Calonico
- Dijk, D. V., Franses, P. H., Lucas, A., and Lucas, A. (1999). Testing for Smooth Transition Nonlinearity in the Presence of Outliers. _Journal of Business & Economic Statistics_ , 17(2):217.

- Domowitz, I. and White, H. (1982). Misspecified models with dependent observations. _Journal of Econometrics_ , 20(1):35–58.

- Gasser, T., Guivarch, C., Tachiiri, K., Jones, C. D., and Ciais, P. (2015). Negative emissions physically needed to keep global warming below 2<sup>_◦_</sup> C. _Nature Communications_ , 6:7958.

- Granger, C., King, M. L., and White, H. (1995). Comments on testing economic theories and the use of model selection criteria. _Journal of Econometrics_ , 67(1):173–187.

- Grossman, G. M. and Krueger, A. B. (1995). Economic Growth and the Environment. _The Quarterly Journal of Economics_ , 110(2):353–377.

- Hainmueller, J. and Hazlett, C. (2014). Kernel regularized least squares: Reducing misspecification bias with a flexible and interpretable machine learning approach. _Political Analysis_ , 22(2):143–168.

- Hansen, M. C., Potapov, P. V., Moore, R., Hancher, M., Turubanova, S. A., Tyukavina, A., Thau, D., Stehman, S. V., Goetz, S. J., Loveland, T. R., Kommareddy, A., Egorov, A., Chini, L., Justice, C. O., and Townshend, J. R. G. (2013). High-Resolution Global Maps of 21st-Century Forest Cover Change
per vehicle mile. Marginal congestion costs for other vehicles are obtained by scaling up according to their passenger car equivalents.

_Accidents_ . According to police-reported data, there were 730 road deaths in Cairo in 2006 (CAPMAS 2010).<sup>19</sup> Given the breakdown between pedestrian/cyclist versus vehicle occupant deaths is unavailable, we use the same share of pedestrian/cyclist deaths as reported in Parry and Strand (2010) for Santiago, namely 55 percent. We make the common assumption that all pedestrian/cyclist deaths are external. For single-vehicle accidents we make the usual assumption that fatality risks are internalized. To what extent injuries in multi-vehicle collisions are external is unsettled. All else constant, the presence of an extra vehicle on the road raises the likelihood that other vehicles will be involved in a collision, but a given collision will be less severe if people drive slower or more carefully in heavier traffic. To be conservative, we omit deaths in multi-vehicle collisions from external costs.

Multiplying external deaths (401.5) by the VSL ($600,000) gives a total cost of $241 million.<sup>20</sup>

> 19 We do not make any adjustment for possible under-reporting and in this regard are estimate of accident externalities may be conservative.

> 20 To be on the conservative side, we assume the same VSL for accident deaths as for local pollution deaths. In contrast, Small and Verhoef (2007) use a higher value for road deaths to account for the lower average age of someone killed by traffic as opposed to pollution (seniors are most at risk from local pollution).

33
beyond deliberate state action) and eliminates the requirement that a person establish an individual risk of persecution.

In Latin America, the **1984 Cartagena Declaration on Refugees** (Cartagena Declaration), which was heavily influenced by the OAU Convention, similarly expands the definition of refugees to include “persons who have fled their country because their lives, safety or freedom have been threatened by generalized violence, foreign aggression, internal conflicts, massive violations of human rights or other circumstances which have seriously disturbed public order.” Statistics on refugees do not always conform to legal definitions. UNHCR leads global efforts to collect and compile data on refugees, relying on individual registration or data provided by host governments. Data are generally provided to UNHCR by host governments based on their own definitions and methods of data collection, which tend to vary across countries and can lead to substantial variations in aggregate numbers. For example, people of certain nationalities may be recognized as refugees by some host countries but not by others and some countries consider the children of refugees to be refugees themselves, while other countries accord them a different status.<sup>16</sup> Moreover, countries do not methodically report statistics on voluntary returns, naturalization and resettlement.

> 14 The 1967 Protocol expanded the application of the 1951 Convention beyond the protection of European refugees following World War II.

> 15 Adopted by the Organization of African Unity, now the African Union (AU) on September 10, 1969, and entered into force on June 20, 1974. There are 54 AU member states.

> 16 In addition to persons recognized as refugees under the 1951 Convention, 1967 Protocol and OAU Convention, UNHCR data also include persons recognized as refugees in accordance with the UNHCR Statute, persons granted a complementary or subsidiary form of protection and those granted temporary protection. UNHCR statistics also include people in refugee-like situations, i.e. individuals outside their country or territory of origin who face protection risks similar to those of refugees, but for whom refugee status has
# **1. Introduction**

A number of important policy and research issues concern the relationships of banks with nonfinancial firms. Theory suggests that such relationships may play key roles in resolving information problems and mitigating financial market imperfections more generally, and empirical analysis has shown important benefits from strong banking relationships.

Much of the research focus is on the nature of relationships formed by different types of banks and on the rationale for multiple banking relationships. To our knowledge, however, existing studies of relationship forms and multiple relationships have not directly addressed the potentially important role of bank ownership types – foreign, state-owned, and private domestic banks. In addition, very few of the empirical relationship studies are applied to data on developing nations, where relationships may be particularly important because of financial system inadequacies.

This paper directly addresses these under-researched issues by formulating hypotheses about the effects of bank ownership type on relationships and testing these hypotheses using data from India, one of the largest and most important developing nations. In particular, we test for patterns in firms’ main banking relationships across ownership types, and investigate the impact of bank ownership type on the number of banking relationships and the likelihood that firms diversify across ownership types.

The hypotheses are based on the extant theory and knowledge about the effects of bank ownership type on bank-firm relationships. Theory suggests a number of reasons why ownership type might affect relationships. Information-based theories of banking relationships (e.g., Stein 2002) suggest that foreign banks – by virtue of their inability to process “soft” information about opaque local firms and market conditions – may be more likely to use their advantages in “hard” information and serve as the main relationship bank for transparent firms. In contrast, state-owned banks often have direct mandates to serve certain types of businesses, such as small firms with limited access to private-sector credit, stateowned firms, firms in rural areas, or those in “priority sectors.” Other research emphasizes that multiple banking relationships might be a consequence of firms’ need to
using electronic transaction technology tend to pay higher taxes on POS assignment. That leaves regular POS (RPOS) as the only POS type with consistent statistically insignificant impact on tax receipt in both districts.

Our study contributes to the literature on tax compliance by providing unique insights into how various transaction-monitoring technologies could affect tax compliance in developing economies’ subnational settings. In doing so, we connected several sets of studies. The first comprises a set of studies discussing tax enforcement (Andreoni et al., 1998; Slemrod and Yitzhaki, 2002; and Slemrod, 2019). Second are those evaluating policyinduced third-party reports on value-added tax (VAT) liabilities (Bellon et al., 2019; Lovics et al., 2019; Fan et al., 2018). The third is a group of studies on consumers’ use of electronic payment technologies (Brockmeyer and Somarriba, 2022; Agarwal et al., 2007; Bolt et al., 2010). Unlike existing literature, which mostly focuses on the acceptance aspect of POS devices, this study is the first to connect the POS distribution program and local tax payment data to examine the program’s overall efficacy.

The remainder of this paper is organized as follows. Sections 2 and 3 present the background and data used in our study together with some conceptual foundations. Sections 4 and 5 explain the empirical strategy employed and results, respectively. Section 6 extends the interpretation of the results, and Section 7 concludes.

2
A similar trend is observed for firms in services, with the number of such firms in the north gradually converging with that in the south except between 2005 and 2014.

# **Regional Trends in Mining and Manufacturing in the North and the South**

We look at regional trends for subsectors within industry, specifically mining and manufacturing. For this, we use data from 2003 NIC and 2014 IBES. The 2003 NIC only surveyed industrial firms. Given this, it is not feasible to examine the number of firms and employment in agriculture or services over time.

Table 1 reports the numbers of firms and employees in 2003 and 2013 at the national level, in the north, and in the south. At the national level, the number of firms engaged in mining increased from 125 in 2003 to 294 in 2013 and the number of firms engaged in manufacturing increased from 23,797 in 2003 to 101,789 in 2013. The numbers of firms and employees remain overwhelmingly large in the south in both manufacturing and mining, compared to numbers for the north. The change in the number of mining firms and of employment in the south is higher than in the north by 14 times and 62 times, respectively. The south’s share of mining firms increased from 82 percent (103 out of 125) in 2003 to 89 percent (261 out of 294) in 2013, and its share of mining employment continued to dominate, totalling an overwhelming 98 percent in both years. This is also evident from figure 3 (panels b and c). On the other hand, the north’s share of manufacturing firms grew from 19 percent (4,623 out of 23,797) in 2003 to 31 percent (31,281 out of 101,789) in 2013. Similarly, its share of manufacturing employment grew from 9 percent (10,386 out of 116,774) in 2003 to 16 percent (44,507 out of 271,863) in 2013. The evidence suggests manufacturing
043)|(0.037)|(0.066)|(0.069)|(0.048)|
|Observations|103,839|103,010|101,221|97,326|103,839|51,483|23,345|23,345|103,839|
|Importer-Industry-Yr FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Exporter-Importer-Yr FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|

Source: Author's analysis based on data described in text. Notes: See Table 1.
|**Agro-climactic**|||
|---|---|---|
|Precipitation|CHIRPS*|Wilmott and Matsuura(2018)|
|Elevation and slope|ASTER sensor|Jarvis(2008)*|
|Global forest cover<br>slope|<br>Hansen (2013)||
|Climactic region||Kottek et al(2006)|
|Crop yield estimates||IFPRI Harvest-Choice (Wood-<br>Sichra et al,2016)|
|Normalized Difference<br>Vegetation Index|<br>Sentinel 2|Sentinel 2*|
|**Market access**||Belghith et al(2020)|
|**Natural Disaster Risk**||UNEP/DEWA/GRID-Europe|

Note: * indicates that at least one variable from this set was selected in the model of welfare

# D. Geographic structure of Sri Lanka and Tanzania

Before describing the methodology used in the study, we briefly review the geographic structure of the two countries. Table 3 shows the subareas, areas, and super-areas for each country. Subareas are the lowest level for which shapefiles were obtained, and are therefore the most disaggregated level for which geospatial data could be linked with the survey. Areas are the target domains for the small area estimates, which are at a higher level than the subareas, but below the level at which the survey is considered to be representative. Finally, super-areas are the lowest level for which the household budget survey is considered to be representative.

_Table 3: Geographic structure of Sri Lanka and Tanzania_

||Sri Lanka|Tanzania|
|---|---|---|
|Sub-areas|||
|Name|GN Division|Village|
|Number in country|13,978|16,438|
|Areas|||
|Name|DS Division|District|
|Number in country|331|159|
|Super-areas|||
|Name|District