rafmacalaba/data-use-mentions-extended huggingface dataset:
laundering comes mainly from drug tra¢ cking, then it may have a positive e¤ect on aggregate savings and social welfare in the economy, while if it comes mainly form common crime then it has a clear negative e¤ect on aggregate savings and social welfare. Hence, the common belief of some economists that asset laundering activities are always good for economic growth does not hold. On the other hand the opposite belief of some regulators that consider asset laundering activities to be always harmful for social welfare is also not always the case.

On the empirical contribution of the paper, we generate estimates of the size of illicit income for cocaine exports and common crime and provide simulated and econometric estimates of asset laundering in the Colombian economy. For this purpose, a macroeconometric speci...cation is derived from the theoretical model described above.

An important contribution is the development of a data set for this purpose, in which the key components are estimates of illicit income from drug tra¢ cking and common crime. Illicit income from drug tra¢ cking is calculated as the volume of cocaine production (from UNODC) times the average of the point-of-export and US point-of-import price of the drug, minus the portion con...scated by the authorities. Income from common crime is computed as the value of reported property crime (from police sources), adjusting by the rates of o¢ cial underreporting.

Estimates of the volume of laundered assets in the Colombian economy in the period 1985 to 2013 are developed using two empirical methodologies. These methodologies are based on a macroeconomic system of equations that is derived directly from the theoretical model. The system includes two equations. The ...rst is a “transition” equation that speci...es the dynamic nature of asset laundering in the economy in terms of its determinants and the second is a “measurement”equation that represents the mechanism through which asset laundering a¤ects the capital stock of the economy. The methodologies used are: i) a calibration based
two lines. First, we conduct standard two-group comparison tests and assess whether statistically significant differences exist between the relevant bank groups (e.g. state versus private, domestic versus foreign, listed versus non-listed). These statistical tests provide useful initial insights into how ownership and bank performance are associated, although they have to be interpreted with care, because bank performance is determined by many factors in addition to ownership.

Therefore, in a second step we turn to bank-level multivariate panel regression analysis in which we analyze ownership while simultaneously controlling for various bank characteristics. For our regressions we employ simple Ordinary Least Squares (OLS) on pooled annual bank data for the period 2001-08. Throughout our regressions we also include country- and time-fixed effects to mitigate omitted variable bias. By introducing these fixed effects we aim to control for general country conditions to which the banks are exposed throughout the sample years.

We also account for the possibility that the results could be driven by countries that have a larger number of banks. Therefore, we also conduct regressions where each bank-year observation carries a weight that is inversely proportional to the number of banks in its banking system in that particular year. We also relax the independency and homoskedasticity assumptions that are required by OLS by reporting three standard errors variations: 1) Huber/White robust standard errors and robust standard errors corrected for possible intra-group correlation for which we consider 2) a bank- or 3) a country-level grouping of bank-year observations<sup>7</sup> .

Most of our data are taken from Fitch’s Bankscope database and include unconsolidated statements of commercial banks in MENA. The sample roughly comprises 600 bank-year observations of about 120 banks in 9 countries for the period 2001-08. As noted before, our sample consists of banks in Algeria, Egypt, Jordan, Lebanon, Libya, Morocco, Tunisia, Syria, and Yemen. Table 3 provides an overview of variable definitions and their sources. Table 4 shows pairwise correlations. Appendix A shows the number of banks for each country
them are penalized more per mile.

However, without a mostly paved road to the primate, firms in a city may seek alternate trading connections, relying on intermediate cities instead. Column 2 adds the distance to the nearest city in the top population quintile if that city is not the primate, separately based on the paving status (high or low) of the route to the primate. As in Table 6, higher transport cost to a top quintile city decreases output. However, this effect is limited to cities with relatively unpaved routes to the primate. This suggests that these cities, relatively unconnected to the primate, are essentially consumer cities as in the formulation of Jedwab (2011). In essence, their trade is funneled through a regional hub, not the primate. Conversely, among cities that are relatively wellconnected to the primate, it is the primate distance that matters, not the top quintile

> 48Produced by Ghana Statistical Services (2000), National Statistics Directorate (Guinea; 1983), National Agency of Statistics and Demography (Senegal; 1988) and National Bureau of Statistics (Tanzania; 2002), respectively, and distributed as: Minnesota Population Center. Integrated Public Use Microdata Series, International: Version 6.1 [Machine-readable database]. Minneapolis: University of Minnesota, 2011. https://international.ipums.org/. Accessed 28 April 2011.

22
We aim to answer three main questions. First, is there a gap between students’ aspirations and expectations, which would indicate that they feel constrained when imagining their future education? If yes, how large is it and how does it differ by gender? Second, how do aspirations, expectations, and having a gap between the two correlate with schooling outcomes such as grades and school attendance, and are these relationships different for girls and boys? Such correlations can suggest the extent to which aspirations and expectations relate to students’ current effort in school. Third, what factors do students perceive as their main barriers to fulfilling their aspirations, and do these factors vary by gender?

Our study has at least three main contributions. First, we compare the relationship between aspirations and outcomes with the relationship between expectations and outcomes from one of the few surveys of students measuring both concepts. Second, this is one of the first studies to discuss students’ constraints related to their aspirations. Third, our analysis systematically assesses gender differences, a relatively understudied topic, and our results are based on a large-scale survey from a middle-income country.

We find that most Indonesian students aspire to high levels of education but only half of the students expect to complete their aspired level. Three-quarters of the students aspire to postsecondary education, dominantly university education. However, on average, students expect educational levels that represent three years less of education than their aspired level. A non-negligible 15 percent of students have a 9-year aspirations-expectations gap, which represents aspiring for an 8-year university degree and expecting to complete the equivalent of high school. While boys tend to aspire to lower levels of education, girls aspire higher but expect much less than they aspire to: while 86 percent of girls aspire to a postsecondary degree (compared to 67 percent of boys), only 55 percent expect it (compared to 42 percent of boys).

Higher aspirations correlate with better grades and, up to a point, lower absenteeism. Aspirations are positively correlated with grades across the distribution
obtained by students similar to them (in terms of the variables in _X_ ). If they are, in fact, likely to perform somewhat less well because of unobserved differences, the procedure overstates their true performance.

By its very nature, of course, selection on unobservables is harder to account for. The ancillary household surveys used to construct the reweighting function do not contain information on test scores. To provide another sensitivity test for the possible magnitude of sample selection bias driven by unobservables, we consider the (rather extreme) assumption that all those students who are counterfactually “re-introduced” into the PISA sample by the above procedure – a proportion given by , for each _X_ – do no better than those who are actually in the sample. In practice, we ascribe to them the lowest observed score for their cell in the partition. As an illustration of the effects of these two re-weighting procedures on the distribution, Figure 3 shows the histograms and kernel density estimates of the distribution of mathematics test scores in Turkey, under each alternative sample selection correction scenario: no correction, correction for selection on observables, and correction for selection on observables and unobservables, under the assumption of no common support.

In order to provide a sense of how sensitive our estimates of educational inequality (reported in Table 1) might be to sample selection, Table 3 reports the results of both of the above scenarios for the four countries with the lowest PISA coverage ratios in Table 1.<sup>15</sup> To economize on space, Table 3 reports the effects of these ‘selection correction’ procedures both on the standard deviation of test scores and on our measure of inequality of educational opportunity, which is introduced in the next section. The first three columns report these measures (and standard errors) for the uncorrected, original PISA sample, for reading, math and science respectively. The next three report estimates for the correction that assumes selection on observables only (equation 8), and the final three for the correction that
information on inputs expenditure shares is readily computed from firm-level production data, the production function needs to be estimated in order to recover output elasticities.

# **3.3 Estimation method**

To obtain _θit_<sup>_v_=</sup><sup>_{θ_</sup> _it_<sup>_m, θ_</sup> _it_<sup>_l}_,rewriteequation(A.1)intermsoflogarithmsandallowfor</sup> log-additive measurement error and/or unanticipated shocks as where _qit_ is the production level for firm _i_ at time _t_ , **x** _it_ is a vector of inputs, specifically, of labor, materials, and capital; **_β_** is the vector of production function coefficients to be estimated; _ωit_ is firm-specific productivity; and _εit_ is an idiosyncratic error term.

Ignoring input price differences in the production function estimation can lead to biased estimates of the output elasticities. Taking advantage of firm-specific input and output prices, this article adopts a recent approach developed by De Loecker et al. (2016) to estimate the production function. Equation (A.2) becomes: where ** ̃x** _it_ denotes the vector deflated (log) inputs and **w** _it_ is a vector of firm-specific prices. To control for input price variation, the following observables were used: firmspecific prices, market share, location, and export status. The input price vector becomes where ** ̃x**<sup>_c_</sup> _it_<sup>=</sup><sup>_{_1</sup><sup>_,_</sup><sup>** ̃x**</sup><sup>_it}_;and</sup><sup>**_δ_**isanadditionalparametertobeestimatedtogetherwiththe</sup> production function parameters **_β_** . Next, the selection bias in equation 9 is corrected for by defining
, FEAs are not focused on the rural-urban continuum _per se_ ; FEAs truncate the urban–rural continuum at the point where agglomeration economies become too weak and tend to exclude smaller urban centers.

URCAs, on the other hand, are focused on the full rural–urban continuum, linking even distant rural locations that may not have a strong social and economic integration with their urban center of reference.<sup>4</sup> This is facilitated by the use of an urban hierarchy and a travel-time gradient as opposed to a commuting time threshold.<sup>5</sup> URCAs also use 20,000 people as lower bound on population of an urban center to capture also towns without strong agglomeration economies. These differences are exemplified by Moreno-Monroy and co-authors (2020) identifying 8,790 FEAs globally that have agglomeration economies as a guiding principle, whereas Cattaneo and co-authors (2021) identify 30,709 URCAs globally that are more oriented to representing heterogeneity along the urban-rural continuum. These differences translate into FEAs representing just about half of the world population through its focus on agglomeration economies, compared to 99 percent for URCAs focusing on the whole continuum.

In summary, FEAs will be more appropriate when one needs revealed economic interdependence with sufficient levels of economic spillovers, whereas URCAs will be more useful if there is a need to be exhaustive in accounting for the whole population in a region or country. Information used in the

> 4 The rural-urban continuum approach is not included as it can be obtained as a special case of the URCA approach by assigning the administrative unit to one of its categories.

> 5 Using travel-time gradients also greatly simplifies the need for commuting data that are often unavailable, especially in developing countries.

10
<sup>27</sup> Specifically, in Table 2, we examine effects on inputs and resources available at clinics (clinic quality), as well as the types and quantity of services provided (health service delivery). We also examine community support for the clinic and community development and political engagement (CDPE). A supply-side response — more resources at clinics or a larger menu of services — or groundswell of community support could also draw would-be patients into clinics and improve their outcomes.

Yet, we do not observe effects on the resources or services available at the clinic: the clinic quality and health service delivery families do not change significantly in response to either treatment (or jointly). This is perhaps unsurprising given that the CM and NFA interventions do not facilitate clinics’ access to supplies or training; the Government’s interest in the evaluation is understanding how to extract greater effort from health workers under existing budget and logistical constraints. None of the sub-components of clinic quality significantly improve (see Table D.5). And the coefficients are essentially zero or negative for those sub-

> 26 Patient-provider interactions are rarely directly observed; for a rare exception see (Das et al. 2016).

> 27 Spillovers do not provide a plausible explanation: our utilization measure is based on household surveys, and not clinic registers. If our endline respondents traveled to treated clinics for care this would attenuate our medium-run results, as it would appear to increase utilization among households living in the catchment areas of control clinics.

23
less efficient judiciaries, greater corruption, lower quality bureaucracies, higher rates of tax evasion, lower rates of participation in civic activities, lower importance of large firms in the economy, inferior infrastructure and higher inflation. The results remain unchanged when hierarchical religion is used as an instrument for trust. Developed industrialized countries are over-represented in the sample.

Finally, Knack and Keefer (1997), also using the same World Values Data set from 29 market economies, conclude that trust and civic co-operation are associated with stronger economic performance. However, associational activity is not correlated with economic performance, contrary to Putnam's findings about Italy. They speculate that any positive effects of groups may be balanced by the damage done by distributional coalitions - the Olson (1982) thesis. This speculation is not borne out by their analysis, however, since the measure of associations is weak and does not include intensity of participation in groups or the nature of 22
a qualitative study is a limitation in validating this finding. However, the existing literature does not provide substantial contradictory evidence to refute this finding. While studies do reflect the promising role of CHWs in FP utilization, there is limited evidence of influence on other utilization indicators.<sup>37</sup> Nonetheless, this limited role is not promising, especially when compared with other countries in the South Asia region that have shown the impressive role of CHWs in driving maternal and child service uptake. The Accredited Social Health Activist (ASHA) program in India, for example, has emerged as one of the largest and most effective CHW programs globally for RMNCH-N services.<sup>38</sup> It is worth noting that the Bangladesh National Community Health Worker Strategy 2019 also calls for reinforcing the scope of CHWs in community health through multi-faceted approaches.<sup>37</sup>

# **Study Limitations**

This study has certain limitations. First, the selection of determinants and outcome indicators was limited by both data availability and compatibility for a comparative analysis across two time points. Therefore, certain crucial health outcomes (e.g., maternal and under-five mortalities) and child feeding practices could not be included when considering the nutrition outcomes. Also, data for Mymensingh was not separately included in DHS 2014 and therefore this division was excluded from division-specific analyses. Second, this study inherits the data quality issues and biases of both DHS and BHFS. Utilization and quality of care data were self-reported without verification, with a chance of recall bias, information bias and social desirability bias. BHFS did not physically verify the availability of services in facilities, as it collected information from the facility records. Validation studies in LMICs, however, have found a <mark>moderate to high sensitivity, and a m</mark> oderate validity for self-reported coverage of RMNCH-N service utilization and quality of care in household surveys.<sup>39,40</sup> For quality of care, DHS
closer to or further from the camps. The sample includes the 25% of grids that are closest to the camps and the 25% that are furthest from the camps. I(SID) represents an indicator variable equal to 1 if the grid is closer to the camp, and 0 if it is furthest. I(Post2017) is an indicator variable with a value of 1 for post-2017, marking the onset of substantial Rohingya inflows to Bangladesh. Dependent variables include: (i) Night light density [0 to 63]: This variable represents the average light density at the grid level, obtained by averaging across pixels at the desired level of aggregation. Values range from 0 (no light) to 63 (maximum light density). (ii) Night light density Sin*: This variable is the inverse hyperbolic sine transformation of the original night light density variable, interpretable as a logarithm transformation. (iii) Deforestation _Ha/Km_<sup>2</sup> : This variable corresponds to landsat images from the Hansen Global Forest Change, focusing on identifying forest loss defined as the transformation of forested areas into non-forested ones due to stand-replacement disturbances. The measure is expressed in hectares per square kilometer. (iv) Deforestation (Sin*): This variable represents the inverse hyperbolic sine transformation of the original deforestation variable, also interpretable as a logarithm transformation. The estimates account for grid and year fixed effects, with clustered standard errors reported at the grid level in parentheses. False Discovery Rate (FDR) q-values are reported in brackets. Significance levels are denoted as follows:<sup>_∗∗∗_</sup> significant at the 1%,<sup>_∗∗_</sup> significant at the 5%,<sup>_∗_</sup> significant at the 10%.

61
**Figure 2.30 MENA Region: Internet Penetration, Recent Years in percent**

<!-- Start of picture text --> 90 Ookla SpeedTest (Download Link, in Mbps) MB Mobile Broadband Penetration<br>80 80 82 80 80<br>70 71 69<br>63<br>60<br>56 57 56.95<br>50 49 52 52.99<br>44<br>40<br>33.54<br>30 30 Global Average Speed<br>20 15.56 16.97 17.83 18.61 20.91 22.24<br>12.36<br>10<br>8.16<br>3.94 4.52<br>0<br>Sources: GSMA, Ookla<br>Iraq Libya Algeria Syria Egypt Jordan Morocco Tunisia Saudi Arabia Iran Lebanon UAE Qatar<br><!-- End of picture text --> Thus, in several ways, and despite the country’s achievements, Morocco exhibits _some of the symptoms_ of a middle‐income trap. Nevertheless, the view taken in this paper is that Morocco has _not yet_ fallen into a middle‐income trap. At the same time, if appropriate reforms are not implemented in a timely fashion, there is a _risk_ that the country may fall into one. There is therefore a _window of opportunity_ for policy makers, roughly the next 6‐8 years or so, to implement the much‐needed reforms that would help the country avoid falling into a middle‐income trap.

33
available may uncover evidence of the effects of monetary policy that are obscured by the low frequency and absence of dynamics in the present paper.

Second, in this paper I have relied on the very crude indicators of monetary policy that can readily be constructed from available monthly data. However, as noted earlier, monetary authorities have a wide variety of instruments at their disposal, including open market operations, direct interventions in foreign exchange markets, imposition of credit ceilings, etc. Disentangling these interventions from the observed fluctuations in observable high-frequency data, and modeling the choice between instruments over time and across episodes, is essential to obtaining a better understanding of the role of monetary policy during speculative attacks.

Third, in this paper I have relied on what turn out to be rather weak instruments to extract the exogenous component monetary policy. While it is theoretically unclear how this will systematically bias the results -- given that the direction of the endogeneity bias is ambiguous -- it is nevertheless unsatisfying if one is interested in understanding the effects of monetary policy during speculative attacks. One possibility for progress on this front is that by switching to higher frequency data, the more pronounced informational asymmetries between speculators and the monetary authority will result in more robust instruments. Another is to carefully investigate the institutional peculiarities of the monetary authority during individual speculative attack episodes in the hopes of identifying changes in these institutions which might serve as valid instruments.

Implementing these improvements for a sufficiently large set of speculative attack episodes that span the relevant range of country experiences will take time. Until then, however, it seems that the burden of proof for both the conventional wisdom that raising interest rates strengthens currencies under speculative attack, and also the contrarian view that it weakens them, lies with the proponents of these views.

27