rafmacalaba/data-use-mentions-extended huggingface dataset:
# **Appendix**

## **Appendix A: Variable Construction**

### **Constructing variables with judge, defendant, and information**

The names of judges, defendants, and plaintiffs were used to remove non-humans and to extract additional information for each case, including gender, ethnicity, and the number of judges and litigants. Cases were identified as non-human and removed if either the plaintiff or defendant name included any of a long list of key words, such as “republic,” “company,” or “medical.” A full list of the keywords can be found in the cleaning scripts in the replication materials posted online.

Afterwards, we could determine the gender of each individual using their first name and the ethnicity of each individual using their last name. To assign gender based on first names we used the genderize.io API and Gender API, both of which use global databases of names and genders to probabilistically assign gender to names.<sup>17</sup> One exception was for the judges, for whom gender was assigned manually.

To assign ethnicity based on last names, we used data available on Harvard Dataverse that links names to ethnicities (Andrew Harris 2014). This data could be used to identify 12 ethnic groups (Meru, Kisii, Kalenjin, Kamba, Luo, Turkana, Mijikenda, Luhya, Kikuyu, Somali, Masai, and Pokot). This includes one ethnic sub-group, the Pokot, which is a sub-group of the Kalenjin. Throughout our analysis, Kalenjin refers to non-Pokot Kalenjin. Together, these groups account for about 91 percent of the population of Kenya. Of the other 29 major ethnic groups (i.e., non-subgroups) identified in the 2019 census, the largest group accounts for only about 0.9 percent of the population.

Gender and ethnicity could not be determined for all individuals in all cases. Gender could not be determined if the first name was either abbreviated (i.e., if
macroeconomy, we document in column (1) of Table 13 that increases in the relative rate of assistance to agriculture significantly increased agriculture’s share of GDP. Controlling for country and year fixed effects, the estimates in column (1) imply that, on average, a ten percentage points increase in the RRA increased agriculture’s share of GDP by nearly 0.3 percentage points. Column (2) of Table 13 shows that increases in the RRA led to significant decreases of the manufacturing sector’s share of GDP. Quantitatively, the estimates in column (2) imply that a ten percentage points increase in the RRA decreased the share of manufacturing in GDP by around 0.15 percentage points. The estimated effects of increases in the RRA on the relative size of the manufacturing sector are negative and, in absolute terms, they are around half the size of the estimated effects that changes in the RRA had on the relative size of the agricultural sector. This difference is not surprising given that, during the sample period, the average share of Sub-Saharan Africa’s GDP from agriculture was nearly three times that of manufacturing. In statistical terms, the estimated effects in columns (1) and (2) are significant at the 10 and 5 percent level, respectively. The results in columns (1) and (2) are therefore a first indication that variations in the RRA have real effects on the economy: they significantly affect the relative size of the agricultural sector.

We provide further evidence that variations in the RRA had real effects on the economy by documenting that changes in the RRA were positively associated with changes in FAOSTAT's agricultural production index. The FAOSTAT agricultural production index is an index of real agricultural production. This index is based on the Laspeyre formula and reflects the relative level of the aggregate volume of agricultural production for each year in comparison with the base period 2004-2006 (FAOSTAT, 2011). All intermediate primary inputs of agricultural origin are deducted. Thus, the FAOSTAT agricultural production
# **Figure 48: Chinese Corporate, Government, and Household Savings to GDP**

<!-- Start of picture text --> 60%<br>50%<br>40%<br>30%<br>20%<br>10%<br>0%<br>1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006<br>Government savings Household savings Corporate savings<br><!-- End of picture text --> Source: Flow of Funds Account Tables, China Statistical Yearbook, various issues; National Bureau of Statistics of China.

101. First, large state-owned enterprises have kept their profits as corporate savings.<sup>78</sup> In 1994, during the reform of taxation, a decision was taken that the government would not collect profit from SOEs for an unspecified period of time. In this context, a number of factors may have played a role. For one, this decision seemed a natural extension of the logic prevailing in SOE reform in the 1980s, which emphasized the independence of SOEs from the government. Second, the central theme of SOE reform during 1992-1994 was to increase managerial autonomy and reduce government intervention, as was evident in the State Council decision on ―Regulation on Transformation of Management Mechanisms of SOEs‖ in 1992. Collecting dividends from SOEs would appear naturally as moving in the opposite direction. Third, without corporatization and some minimum progress in corporate governance, it would be practically difficult for the government to determine an acceptable rate of dividend without reopening the negotiation with the management of each SOE on profit division. Fourth, SOEs as a whole were in a poor financial situation in early 1990s. What the government could collect was not much, while the need for new capital injection was perceived as urgent and large. In 1994, the total profit of industrial SOEs was only 1.8 percent of GDP, which compared with 3.3 percent of GDP in 2003.

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following unexpected contingencies and thus bail out efficient exporters that experience financial difficulties (Huberman and Kahn, 1988). While differentiating these two channels is notoriously hard, in the second part of the paper we try to gain intuition into the mechanisms that drive the baseline results.

In a first set of tests, we build on the prediction of the banking literature that banks’ soft budget constraints are more severe in institutional settings in which banks are more susceptible to the pressure of external parties (e.g., the government) to rollover credit to businesses, and less subject to independent monitoring of bank auditors and supervisors on their decisions to grant such rollover. Matching information from our main databases with data from the World Bank Banking Supervision Survey, we obtain that in countries where banks are less independent from the pressures of external parties and where bank managers are subject to less rigorous scrutiny of bank auditors and supervisors that prevent the release of misleading information, domestic banks reduce the dynamism of the export sector relative to stock market investors and to foreign banks (i.e., they reduce exporters’ entry, exit and turnover, in line with the soft budget constraint hypothesis).

In a second set of tests, we exploit the prediction of the banking literature that, to the extent that banks’ behavior instead reflects long-termism, this benefit should manifest itself especially following negative shocks (e.g., crises or recessions) or in settings where ex ante it is harder to write detailed contracts that can account for unexpected shocks. However, we uncover no evidence that domestic banks moderate the exit of exporters more during recessions or financial crises than in tranquil times. Moreover, the differential effect of foreign banks is no more pronounced for products characterized by higher informational opacity or contractual complexity (measured according to the methodologies in Rauch, 1999, and Nunn, 2007). Overall these findings cast doubts on the benign interpretation that domestic banks are a buffer against the realization of unforeseen shocks over the course of export projects, while
We also estimate the Pareto exponent _α_ , which was not explicitly targeted by the model, for both degree distributions. We obtain an _α_ of 0 _._ 36 from the model and 0 _._ 37 from the data. In addition, since we have previously shown that the formal firm network is spatially concentrated, we also assess how well the model predicts the share of buyer relationships in the economy that originate from firms in Nairobi, Mombasa, to firms in other counties. We find that the model performs well on this dimension too (Table 5). In the administrative data, 69% (11%) of all outlinks in the economy are captured by Nairobi (Mombasa) based firms and the model predicts a share of 70% (11%).

# **5 Predicting a Revised Network**

With the estimated model at hand, we are now able to tackle the question of how including informal firms might affect the spatial patterns of domestic trade. Our proposed thought experiment is the following: suppose we were to observe informal firms. What would happen to the outdegree distribution of various types _θ_ ? To obtain a ‘revised network’ that accounts for informal firms, we rely on updated information on the spatial and sectoral dispersion of economic activity in Kenya – now including the informal sector. In model terms, our exercise shifts the probabilities _p_ ( _θ_ ) with which we observe nodes of certain sector-region-size types _θ_ to be born. Knowing _r_<sup>_∗_</sup> and our updated _p_ ( _θ_ )s, we can then once again predict the type-by-type matrix of firm-to-firm links _π_ , keeping everything else constant. We will also make additional assumptions about the linking probabilities of informal firms.

## **5.1 Predicting the Sector-County Profile of Non-VAT Firms**

To incorporate informal firms into the network, we first update the firm-type probabilities _p_ ( _θ_ ) for each sector-county-size cell, this time accounting for the entire firm size distribution. To update
Note: Estimates are presented as an aggregate for children 0-17 years old based on a balanced household panel (households present in each wave).

High chronic poverty rates in the rural north, especially in the rural North West, suggest the need for more longer-term human capital investments. On the other hand, high transitory poverty rates underline the need for more social protection to reduce the risk for these children and households to prevent them falling back into poverty over hoovering around the poverty line.

# 5.2.2 Inter-generational Poverty

This section tests the hypothesis that education deprivation transmits across generations and correlates not only with children’s education deprivation, but parent’s education deprivation is also correlating with other poverty dimensions of their children. Considering the correlation with monetary poverty the probit models in Table 5 display a negative and significant correlation of the mother’s level of education to the probability of being poor according to the 1.90$ a day international poverty line. The coefficients moreover indicate that the higher the level of education of the mother, the less likely the child being poor. This finding is robust to various types of standard errors and levels of clustering and also holds for the father’s education or any parent education level as well as without using proxies for education level.

Second, parent’s education level also seems to be significantly negatively correlated with different deprivation-based poverty measures. The only exception is food deprivation, which might however be related to the large number of missing observations on food deprivation. It is also worth noting that the mother’s education is only significantly related to sanitation and water deprivations when mothers have completed at least secondary school. The positive significant coefficient of the rural control moreover confirms the descriptive findings that living in a rural area is significantly correlated with the probability of being poor and or deprived.

54
other countries difficult. Bratsberg and Terrel (2002), CIEN (2002) and the World Bank (1995c) argue that the quality of the education system in Guatemala is rather low, and may not have shown much improvement over time.

_(3) Aggregation bias._ Average years of schooling raise human capital by an equal amount regardless of whether a person is enrolled in a primary, secondary or tertiary school. This is an important point because by defining human capital by average years of schooling, one implicitly gives the same weight to any year of schooling acquired by a person. This completely disregards the findings of the microeconomic literature on wage differentials. For example, Psacharopoulos and Patrinos (2002) suggest that the rates of return to education could be decreasing with the acquisition

> 12 The use of labor force instead of total population data is due to problems regarding the Guatemalan population data for the 1980s. By contrast, the labor force proxy used here is assumed to take into account some of the effects of the civil war, i.e. migration and displacement.

-16-
As economic systems are rarely in equilibrium, we will re-specify equation 3 as a short-run or disequilibrium relationship involving lagged values of Y and of the variables on the right hand side (RHS) of the equation as we did for the financial development equation. We will assume the relationship takes the form of equation 4 with first order lags for all variables. The variables in equation 3 are expressed in natural logs and denoted in lower-case letters.

Yt = bo + ,t-y, +bIlrt, + b2lrttl + c1lre, + c21re,, + dlit+ d2it-l + e1sct + e2sct-l + f1tt + f2tt-1 + g1 pt + g2 P1 1 + h1fdt + h2 fd 1l + i1mt+ i2mtl + The variables and their data sources are described in Appendix 1.

# **(c) Empirical Testing and Analysis**

# **(i) Unit Root Tests**<sup>**2**</sup>

More often than not, time series data are non-stationary. In this section, we carry out unit root tests using the Augmented Dickey-Fuller (ADF) method (Dickey and Fuller, 1979) to test for non-stationarity for the key variables in the economic growth and financial deepening equations, using Microfit 4.0 (Pesaran and Pesaran, 1997). The existence of non-stationarity in a data series would mean that, if hit by a shock, a variable would suffer permanent change, since non-stationary variables have a non-constant mean, variance and covariance. On the other hand, faced by a shock, a stationary data series will over time return to its unconditional mean; that is, the effects of the shock will dissipate, leaving only temporary effects on the time series.

T he ADF tests show that only the real deposit rate variable (RD) is stationary at 1(0) while the rest of the variables attain stationarity at I(1) except the per capita income variable (Y) that attains stationarity at 1(2). These ADF results are further verified
ceived training in the past 12 months (column 1), their ability to correctly answer a policy-relevant question about a recent regulation (an objective knowledge measure, column 2), and a standardized index of self-reported knowledge across five domains: development management and accountability, financial management, village regulations, drafting development plans, and the Village Law (column 3). We find no significant effects of electoral turnovers on these outcomes. Furthermore, bureaucrats serving new leaders may also be more skilled at securing government transfers. In Appendix Table A.13, we explore how turnovers impact transfers received by a subset of villages (423 out of 512) for which we could obtain administrative data on Village Funds Allocations (known by the Indonesian acronym ADD), a type of transfer allocated by district governments to fund staff salaries, benefits, and village operations. We find no evidence that turnovers affect the amount of ADD funds allocated or utilized (columns 1–2) or the share of funds spent (columns 3–4). These null effects mirror those we find on the budget amounts reported by village heads in our own survey data (column 5). Together, these results suggest that turnovers reshape bureaucratic organization without materially altering broader institutional know-how.

# **5 Impacts on Service Provision**

Our findings so far indicate that bureaucrats serving new village heads report higher enthusiasm, exert greater effort, and engage more frequently with citizens. This increased engagement helps them better understand community priorities, allowing village governments to respond more effectively to citizens’ demands. We hypothesize that these forces ultimately generate improvements in local service quality.

In this section, we first document effects of turnover on service provision in administrative data, as well as perceived service access and quality and actions taken by village governments to improve service provision, as measured in survey data. We examine the dynamic timing of these effects and we probe the robustness of these results. We then show that these gains are especially pronounced for socially distant citizens and in villages where
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- Shah, Manishah and, S. J., J. Montalvao, and M. Goldstein (2021). Two sides of gender: Sex, power, and adolescence. Technical report.

- Sully, E., A. Biddlecom, J. E. Darroch, T. Riley, L. S. Ashford, N. Lince-Deroche, L. Firestein, and R. Murro (2020). Adding it up: investing in sexual and reproductive health 2019.

- Trussell, J. and K. Guthrie (2011). Contraceptive efficacy. _Contraceptive Technology. 20th ed. New York: Ardent Media_ .

- WHO (2019). Trends in maternal mortality 2000 to 2017: estimates by who, unicef, unfpa, world bank group and the united nations population division. Technical report.

- Wilson, E. K., K. E. Krieger, H. P. Koo, A. M. Minnis, and K. Treiman (2014). Feasibility and acceptability of a computer-based tool to improve contraceptive counseling. _Contraception 90_ (1), 72–78.

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# Figure 39: State Level Information Deprivation

Information Deprivation - by State - MICS 80% 70% 60% 50% 40% 30% 20% 10% 0%

# Source: Authors’ estimates based  on MICS 2016/17.

Note: Individuals with missing age were dropped from the analysis. Estimates are based on children aged 5-17.

Figure 40: State Level Information Deprivation a) b) c) <!-- Start of picture text --> Information Deprivation - by State Information Deprivation - by Information Deprivation - by State<br>in North Central - MICS State in North East - MICS in North West- MICS<br>80% 80% 80%<br>70% 70% 70%<br>60% 60% 60%<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>Children 5-17 Children 5-17 Children 5-17<br>Benue FCT Abuja Kogi Kwara Adamawa Bauchi Borno Jigawa Kaduna Kano Katsina<br>Nasarawa Niger Plateau Gombe Taraba Yobe<br>d) e) f)<br>Information Deprivation - by Information Deprivation - by<br>Information Deprivation - by<br>State in South South MICS State in South West- MICS<br>State in South East - MICS<br>80% 80% 80%<br>70% 70% 70%<br>60% 60% 60%<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>Children 5-17 Children 5-17 Children 5-17<br>Abia Anambra Ebonyi Enugu
For the 19 data sets with graded answer scales, we can compare the extent of the disability gap for the three disability categorizations. Comparing the top and middle graph, the first categorization (No difficulty vs Some difficulty) has less of a disability gap in ever attending school than the second categorization (No or some difficulty vs At least a lot of difficulty). For instance, in Namibia, the disability gap stands at 14 percentage points for No difficulty vs Some difficulty compared to 23 percentage points for No or some difficulty vs At least a lot of difficulty. The bottom graph in fact shows that there is a gradient with persons with at least a lot of difficulty having less often attended school than persons with some difficulty, who in turn have less often attended school than persons with no difficulty.

In Figure 4.2 and Appendices 4 and 9, educational attainment is presented in four mutually exclusive categories: 1/ less than primary school completion, 2/ primary school completion, 3/ secondary school completion and 4/ more than secondary school. In all countries, persons with functional difficulties tend to be overrepresented at the lowest educational level, i.e. less than primary school, while they are underrepresented at higher educational levels.

Figure 4.2: School completion rates across 'Any functional difficulty' status <!-- Start of picture text --> Less than primary school completion<br>No Difficulty Any difficulty<br>Afghanistan<br>Bangladesh<br>Dominican Rep.<br>Ethiopia<br>Gambia<br>Indonesia<br>Kiribati<br>Liberia<br>Malawi<br>Mexico<br>Namibia<br>Nigeria<br>Panama<br>Papua New Guinea<br>Philippines<br>Rwanda (Census)<br> Rwanda (LFS)<br>South Africa (Census)<br>South Africa (GHS)<br>Tanzania (Census)<br>Tanzania (LSMS)<
would be higher in both 2001 and 2011. The speed of urbanization would also be the same in both years when focusing only on the built‐up share, but the urbanization rate would be much lower than using the benchmark specification. And if the lit‐up share were the only key indicator used, the change in the urbanization rate over a decade would be 11.3 percentage points, which is more than double the estimate derived from applying the preferred specification to the probabilistic model.

# **6. Conclusion**

The analysis in this paper yields a more accurate picture of urbanization in India than was previously available. We confirm that India’s urbanization rate in 2011 was close to that reported by the Census of India. However, this finding contrasts sharply with those of several studies reporting much higher urbanization rates (Denis and Marius‐Gnanou 2011; IDFC Institute 2015; Uchida and Nelson, 2009; World Bank 2015). We also find substantial gaps between the official classification and our results for several administrative categories of the census. Overall, the misclassification affects 62 million people, which is roughly equivalent to the population of France or the United Kingdom.

Among statutory urban categories, misclassification is highest for other towns. Although a large share of them have sufficient urban characteristics, many should be considered rural. The misclassification rate is much lower for villages. But given that there are more than half a million of them, the absolute number of misclassifications has non‐trivial implications.

Another important finding concerns the so‐called census towns of India, which are administratively rural but meet three criteria to be identified as urban areas according to the population census. In line with the population census and a growing literature, we confirm that most census towns are urban in practice. But this is not true of all of them.

Finally, our analysis shows that relying only on indicators from the ground or from outer space results in substantially different rates and paces of urbanization. For example, if the lit