rafmacalaba/data-use-mentions-extended huggingface dataset:
%|21.5%|1.1%|
|Change in poverty_ex post_: Skoufias and Di<br>|||||||
|Maro (2008)<sup>1</sup>|-16.5%|1.6%|-24.3%|1.5%|-29.2%|1.4%|
|Change inpoverty _ex ante_: Simulated<sup>2</sup>|-13.1%||-26.6%||-33.1%||

_Source_ : Baseline Survey 1997 and Rounds 1-4; 1: Skoufias and Di Maro (2008), Table 4; 2: authors' calculation using BFL model _Notes_ : Prices of November 1999 (pesos); poverty line: mean of Nov 98 consumption per capita <u>(taken from Skoufias and Di Maro, 2008)</u> The final step in our analysis is the simulation of the cost of the program based on the BFL model. Table 5 shows that 4,558 out of 7,110 (or 64 percent of) poor families in treatment villages would enroll in the program and accept the condition of school attendance. The average cash transfer for these families in treatment villages is estimated at 303 pesos/month, which is very close to the average transfer of 300 pesos in January 2002 for 1.6 million families observed from official program data. Based on an analytical exercise of scaling up the cost of the program

> 31  The poverty line is equivalent to 176.17 pesos per month (for details, see Skoufias and Di Maro, 2008)

> 32 The _ex ante_ results presented in Table 4 remain the same when the simulations were done _without_ incorporating the conditionality of schooling. This suggests that in terms of meeting the poverty reduction objective of safety net programs, conditionality does not necessarily imply better results. In other words, it suggests that if the main objective of a
also find support for the Technology Hypothesis, with mobile phone usage having a significant effect at the 5% level; the GDP effect is attenuated somewhat when we control for mobile phones but remains strong. In contrast to the pre-pandemic SP spending data, we find no significant effect of internet usage in the pandemic responses.

There is support for the Ageing Hypothesis in the linear specification, with the population share over 65 having a significant effect at the 5% level, but this is not robust to allowing for nonlinearity in the SPEC.

> 34 Since this is a generated regressor, we bootstrap the standard errors. We use the country effects estimated in the OLS specification of pre-pandemic data (third column in Table 4).

24
results from four experimental studies (Saint Lucia, Rwanda, Uganda, and Ethiopia). Section IV concludes with an assessment of a combination of reweighting and the SWIFT-based poverty projection model in eliminating the bias in poverty estimation based on the four case studies.

# **II. SWIFT and Reweighting Methodology**

# _II.1. SWIFT poverty projection methodology_

SWIFT is an application of Survey-to-Survey imputation techniques (S2S) to monitor poverty rapidly. SWIFT trains an imputation model in a training dataset by regressing household expenditures/incomes on poverty proxies. Household expenditures and poverty rates are then imputed in another dataset, called “output data,” by plugging poverty proxies of the output data into the model. Figure 1 illustrates the process.

There are two key assumptions in the standard SWIFT methodology. First, the relationship between household income or expenditure and poverty correlates in the output data can be expressed in equation (1): l (1) llwhere hoo = xxhoo′ββoo + uuhoo ) where l refers to a natural logarithm of the household income or expenditure of household uuhoo~NN(0, σσoo _h_ in the output data _o_ . is a vector of poverty correlates of household _h_ in the output data, _o_ . is a vector hoo of coefficients of poverty correlates (ll ). refers to a residual and is often assumed to follow a normal hoo oo distribution of xx (kk× 1) ) .<sup>1</sup> The output data includes the poverty proxy data }h=1 but does not include ββ (kk× 1) hoo hoo xx uu HH oo hoo NN(0, σσ {xx

> 1 This normal distribution and linearity can be relaxed. For the sake of exposition, normal distribution is assumed.

4
institution- to document cross-national investments above a certain size and in a consistent way that draws on national data seems urgent in order to protect rights and initiate more evidence-based dialogue and accumulation of norms and experience. Finally, reports stressing large numbers of failed investments have given rise to legislation or calls for limits on land purchases by foreigners in a number of countries such as Brazil, Argentina, and Ukraine. However, if a sizeable share of relevant deals involves nationals rather than foreigners (Deininger _et al_ 2011), this may exacerbate rather than resolve governance challenges by, for example, limiting competition. Instead of protectionist measures, priority efforts to improve land governance<sup>27</sup> -e.g. by recognizing local rights, educating right holders, and allowing their voluntary and transparent transfer- are likely to be a more appropriate policy response.

> 27 Key areas of emphasis could be (i) demarcation of state land (including forests and protected areas and clarification of the rights on these lands as well as ways in which they can be transferred to investors) and regular monitoring of new encroachment; (ii) provision of a minimum level of information (coordinates, size, projected investments, job creation, taxes and other benefits expected to local communities) to be made available publicly in a way that facilitates third party verification; (iii) clear procedures for contract enforcement and arbitration, including the dissolution of non-performing enterprises by any of the parties involved; (iv) education on rights and ways to enforce them before interests in acquisition materialize.

17
# Figure B.5: Missing municipalities: procurement

<!-- Start of picture text --> Tender<br>10.0<br>7.5<br>5.0<br>2.5<br>0.0<br>Tender-item<br>3<br>2<br>1<br>0<br>Tender-participant<br>10.0<br>7.5<br>5.0<br>2.5<br>0.0<br>CE MG PB PE PR RS<br>2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021<br>%<br>%<br>%<br><!-- End of picture text -->

**Notes:** This figure presents the percentage of municipalities by year for which we do not have data in the procurement dataset.

52
|Share of trade volume identified as vehicle<br>trades,γ(net of False Discoveries)<sup>25</sup>|**3.8%**|
|---|---|
|Cross-border trades<sup>26</sup>/ identified crypto vehicle-<br>trades|17%|
|Cross-border trades / identified crypto vehicle-<br>trades (in USD) Volume)|20%|
|Cross-border trades / identified crypto vehicle-<br>trades (in USD) Volume) when excluding RMB<br>and RUB|27%|

Source: LocalBitcoins.com API, Authors’ Calculations Table 3 also shows that of the 7.4% of all trade we identify as crypto vehicle trades, just under one-fifth are cross-currency/cross border; that is, where two different fiat currencies are used in the two transactions of exactly matching Satoshi size. In Appendix A.2, we perform a Monte Carlo simulation where we draw random samples of trades from the data set.<sup>27</sup> Applying the algorithm on these randomly constructed data sets, only about 1% are individually identified as vehicle trades. And exactly 0% remain identified as vehicle trades after deducting the false discovery control discussed in section II.

Table A.3 in the Appendix looks at the robustness of our results to the time window, using twohour, five-hour and ten-hour windows. As can be seen in the table, as the time-window increases, the number of candidate vehicle trades increases. However, because the number of trades encompassed

> 25 See the Appendix for an explanation of how the methodology is adjusted to calculate the share of the trade volume.

> 26 Note that here we consider only trades with two different currencies as cross-border. However, during limited field experiments one of the authors found the US dollar to be used for cross-border crypto vehicle trades in countries whose primary legal tender is not the US dollar
Bover (1995) and Blundell and Bond (1997), combines in a system regression model (4) in differences and levels. The regression in differences requires differencing model (4) and using instruments to deal with the likely endogeneity of the independent variables, and the fact that in the differenced model the error term is correlated with one of the explanatory variables – the lagged dependent variable. The instruments consist of previous observations of the explanatory and lagged-dependent variables. The GMM estimator assumes that the error term in model (4) is not serially correlated and the explanatory variables are weakly exogenous. The instruments for the regression in levels are the lagged differences of the corresponding variables. These are appropriate instruments assuming there is no correlation between the differences of the independent variables in model (4) and the country specific effect. This GMM estimator is consistent and efficient.<sup>22</sup> To test the robustness of the estimates Loayza _et al_ . (2005) estimate model (4) using various other estimation techniques. They use the ‘pooled’ OLS estimator, the ‘within’ OLS estimator, and the GMM levels estimator for the dynamic model of 5-year and 10-year panel datasets<sup>23</sup> (Appendix Table 1), and the OLS estimator for the pure-cross section model<sup>24</sup> (Appendix Table 2). Each of these alternative techniques has its shortcomings. The ‘pooled’ OLS estimator ignores the presence of country-specific effects and treats all variables as exogenous, while the ‘within’ OLS estimator ignores the joint endogeneity of the explanatory variables. The GMM levels estimator uses instruments to control for joint endogeneity but ignores country specific effects.

> 22 The consistency of the GMM estimators depends on whether lagged values of the dependent variables are valid instruments in the regression. Loayza _et al._ conducted a Sargent test of over-identifying restrictions, which tests the overall validity of the instruments by analyzing the sample analog of the moment conditions used in the estimation
across occupations and levels of education, as wage-earners and salaried workers (most of who live in urban areas) tend to have higher rates of financial literacy than farmers in rural areas.

There are also often other regional differences in financial literacy and financial access within countries. In Malawi, for instance, the Northern and Central regions tend to have better access to financial services than the Southern region. Analogous patterns are found in high income countries as well, for instance in Italy (where the Centre-North region has a higher financial literacy rate than the South) and Germany (where the West does better than the East). Sometimes these patterns are more idiosyncratic: in the U.S., Bumcrot et al. (2011) find that South Dakota and Idaho have higher levels of financial literacy than their demographic make-up would predict, while the opposite is apparently true for Pennsylvania, New Jersey, New York, and Connecticut.

With regard to disparities by ethnicity and race, the FinScope survey in South Africa indicates that blacks (57 percent) are less likely to be banked than whites (91 percent), with coloureds (68 percent) and Asians (85 percent) falling in between. Issues of race may not be as stark in other countries, but it may be important to pay attention to similar ethnic disparities in targeting programs. Unfortunately, many of the other FinScope surveys do not provide breakdowns in this regard.

Among higher income countries, Lusardi and Mitchell (2011b) find that financial literacy scores in the U.S. are lower among Hispanics and African-Americans than among whites and Asians. Relatedly, Lusardi (2005) finds that African-American and Hispanic households in the U.S. are less likely to have checking accounts and hold high-return assets (such as stocks and business equity), and incur higher information and planning costs than the rest of the population. Crossan et al. (2011) find that financial knowledge among Maori is lower than that of the general population of New Zealand, excluding a
of rising (and falling) stars. These 2-digit lines are listed in Tables 5 and A.3, respectively, along with their key underlying 4-digit drivers.<sup>16</sup> The calculations presented in Table 5 suggest that the strength of Syrian exports in the agricultural sector derive from live animals, especially sheep and poultry, as well as edible vegetables. Although not reported, RCA values for many other processed agricultural products are also high. These include products traditionally associated with Syrian agricultural exports, such as animal and vegetable oils (chapter 15), especially olive oil

> 14As for our Herfindahl calculations, since export penetration indices are relatively stable over the period, these values are averages for the period 2001–2007, inclusive.

> 15A more detailed discussion of the aggregated data, in terms of both HS and SITC sections, is provided in the annex.

> 16We identify these drivers by taking bivariate regressions of the 2-digit line on the 4-digit line, and reporting the variables that yielded the top two _R_<sup>2</sup> values. Since this methodology allows for both positive and negative coefficients, it is important to keep in mind that a significant amount of the variation could be due to the negative contribution of a given 4-digit driver.

14
*|**change **|**p.p. (%)**|
|Mean|4.60|4.01|0.33|-0.76|-6|-|-|
|Median|0.51|0.51|0.06|0.08|0|12|-12|
|Std. dev.|13.05|12.19|1.51|18.22|-3|108|-111|
|Skewness|4.76|4.73|2.02|-16.96|0|-|-|
|Kurtosis|27.77|27.87|39.52|293.21|0|87|-87|

_Note:_ Profitability is measured by the banks’ return on equity. The distribution of profitability in the figures is based on the univariate kernel density estimation. DiD is calculated as the difference between the percentage change for the treated and control groups. DiD = difference-in-differences; p.p. = percentage points.

# **6. Regression Framework**

We use the DiD approach to examine the consequences of Basel III implementation for SME financing. We opt for the DiD, given its simplicity, and utilize that some countries adopted the Basel III framework, while others did not. Therefore, our estimation framework shares common elements with Ongena et al. (2013) and Ferrando et al. (2017), who also apply the DiD method. Ongena, Popov, and Udell (2013) examine the consequences of domestic financial regulation on bank risk-taking abroad using data from Europe and the United States. Ferrando, Popov, and Udell (2017) study the effects of sovereign stress on SMEs’ access to finance in the euro area.

As in Ongena, Popov, and Udell (2013) and Ferrando, Popov, and Udell (2017), we also address the sample selection issues. In
relationships only change gradually over time. Therefore, we only used the information for three equidistant years: 1995, 2005, 2015. In the analysis we will look both at the current composition of input-output relationships using the most recent IO table for 2015 and we will consider changes over time. The data file that we make available contains all indicators calculated for all three years.

### <u>Industries</u>

The next step in the analysis is to reduce the sectoral detail. In total there are 26 harmonized industries, but we combine them into the following nine more broadly defined

> 9 A widely-used alternative is the World Input-Output Database (WIOD) (http://www.wiod.org/home) constructed and maintained by researchers at the Groningen Growth and Development Centre. The 2016 release covers 43 individual countries (including the 28 EU member states) and a rest-of-the-world aggregate, the period 2000 to 2014, and a decomposition into 56 sectors and 5 final demand components. A second alternative is the Trade in Value Added (TiVA) database (http://www.oecd.org/sti/ind/measuring-trade-in-value-added.htm). The most recent 2016 edition of this resource covers more countries (63) but at a lower sectoral detail (34) for the period 1995 to 2011.

> 10 http://worldmrio.com/

> 11 In total, the entire database consists of almost 770 million values, most of which are naturally indistinguishable from zero, but not exactly zero.

19
the shares of SADC and EAC for our Africa region.

# **II. Banks in Tanzania**

# **Bank Market Shares**

The data source was Bankscope, an on-line data source for about 29,000 banks worldwide.<sup>29</sup> Through Bankscope, we obtained data on total assets by bank in Tanzania, ownersshareholders of the bank and the percent of the bank owned by each owner-shareholder. Market share of each bank was defined based on the bank’s assets as a share of total bank assets in the country. We divided the regions into the European Union, East African Customs Union (EAC),

> 27 The market share of Benson Informatics, rounded to the nearest one-tenth of one percent is zero. Consequently we ignore Benson.

> 28 Planetel Communications and Caspian Construction are local TZ companies. See the Mail & Guardian Online at: <u>http://www.mg.co.za/article/2007-04-13-vodacoms-elite-network</u> 29 It combines data from the main information provider, Fitch Ratings, and nine other sources, with software for searching and analysis. Each bank report contains balance sheet and income statements with up to 200 data items.

103
**To investigate tax compliance further, we adopt a “cell approach” (Bendek & Lelkes, 2011) and** The **estimate the tax compliance in 40 cells composed of gender (2), regions (4), and age groups  (5).** comparison of “true” and “reported” income across subgroups of the population allows for a better understanding of tax evasion patterns. We report the total outcomes of this exercise in Tables 5 and 6. Our results show that tax compliance is higher among women than men. In all “women” cells, the tax compliance (measured at the median of the income distribution) is higher than 90%; in some cells, the reported income is even higher than the tax income. In the case of men, in all cells except one, the tax compliance is lower than 90%; on average, the tax compliance is close to 80%, and in some cells, the tax compliance is lower than 70%.  D’Attoma et al. (2017) previously identified similar gender differences in tax compliance in the US, UK, Sweden, and Italy based on tax compliance experiments. According to their results, women are more compliant than men.

**Table 6. Tax compliance:“ cel” approach, men (measured at median income)**

|Age/region|<30|30 – 39|40–- 49|50-59|
|---|---|---|---|---|
|RO1 (North)|85.09%|66.82%|69.71%|67.58%|
|RO2 (East)|81.56%|80.34%|83.88%|85.88%|
|RO3 (South)|87.50%|74.71%|72.99%|74.28%|
|RO4 (
line as one could expect, with Sub-Saharan Africa scoring the lowest on average on most of the dimensions, and high income countries scoring the highest on most dimensions. A remarkable number is the relatively low score of Middle East and North Africa on access to finance (Table 3, upper panel). This resonates with the complaints heard during the unrest in the region in 2011.

Much of the differences among regions are correlated with differences in income levels. Countries that have lower income tend to also show lower degrees of financial development as approximated by the 4x2 framework (Table 3, lower panel).

# **_Disparities in financial systems across countries are large._**

Behind these regional and peer group averages are vast differences among individual countries, and in some cases also major differences among different parts of each country financial sector. The data from the Global Financial Development Database demonstrate rather strikingly the large differences in financial systems around the globe. For example, the largest financial system in the sample is more than 34,500 times the smallest one. Even if the financial systems are re-
10 J.C. Duque, N. Lozano-Gracia, J.E. Patino, P. Restrepo, W. Velazquez / LCR Flagship Report on Cities and Productivity Finally, in order to measure the third dimension, the land use pattern, we use the metric proposed by Fallah et al (2011), the _Sprawl_index_ , which approaches the land use pattern by measuring the level of evenness in the distribution of population within the urban extent. When the population is evenly distributed, the _Sprawl_index_ takes values close to 0; when the population is highly concentrated in a portion of the urban extent, the _Sprawl_index_ takes values close to 1. It is calculated as the normalized difference between the share of areas with population density below the regional average density and the share of areas with population density above the regional average density (Fallah et al., 2011). We implemented this metric using population counts at pixel level retrieved from GHS population raster layers for 1990, 2000 and 2015 at 250 meters of spatial resolution (Freire and Pesaresi, 2015; Pesaresi et al., 2016) within the urban extents extracted from DMSP-OLS NTL RC images.

As one of our goals is to generate highly automated methods, we used the “Shape Metrics Toolbox”, which is a Python script that runs in ArcGIS, to calculate two of the three the shape metrics of the urban extents (Exchange index, and Perimeter index). This software is intellectual property of the Center for Land Use Education and Research (CLEAR) at the University of Connecticut (http://clear.uconn.edu/tools/Shape_Metrics/index.htm). We used the OSMnx Python library to compute three network topology variables (Circuity, Intersection_density, and Street_density) (Boeing, 2017). This library uses OpenStreetMap to retrieve, automatically, the street networks within a given polygon, and calculates a series of network-based metrics.
as a reduction in income by US $52.27. We also explore the story that depression and adverse events interfere with productive activities and future investment through lower employment and educational investment.

This paper focuses on Nigeria because the country has been severely impacted by both conflict and economic shocks. In recent years, Nigeria has suffered from security crises caused by conflicts between ethnic groups, farmers, and herdsmen. Some of the more egregious incidents have been committed by Boko Haram, which is responsible for terrorist attacks including the abduction of more than 200 schoolgirls in 2014, and an estimated 20,000 deaths and at least two million displacements.<sup>2</sup> There is also concurrent violence in other regions in the country. Economic and weather-related shocks are also common throughout Nigeria. Recent changes in climate are responsible for the increased frequency of droughts (Abubakar and Yamusa 2013, Shiru, Shahid et al. 2018).

From February to April 2016, the Nigeria National Bureau of Statistics, with the support of the World Bank, conducted the third wave of the nationally-representative household survey called the General Household Survey-Panel (GHS-Panel). Data were collected from about 4,600 households on consumption, income, depression (via a version of the Center for Epidemiological Studies-Depression or CES-D test), family history of conflicts, deaths, weather and economic shocks.<sup>3</sup> In addition, the survey randomized the order of modules in a priming survey experiment:

> 1 http://www.who.int/news-room/detail/30-03-2017--depression-let-s-talk-says-who-as-depression-tops-list-ofcauses-of-ill-health

> 2 https://www.bbc.com/news/world-africa-42735414

> 3 The survey questionnaire also included questions on basic preferences (e.g., risk, time discounting, and trust).

2