rafmacalaba/datause-encoder-data huggingface dataset:
We complement this body of evidence by studying the effects of violence in human capital accumulation. This is important in order to understand whether the channel that explains these findings on the negative effects of violence on economic activity might be linked to education; and also since it could preview longer term impacts of the crisis.

# **3. Recent Spike in Violent Crime in Mexico**

After 2007, there has been a dramatic increase in the level of violence in Mexico. The number of homicides per 100,000 inhabitants almost tripled between 2005 and 2011 (from 9,921 to 27,199 homicides). The sharp increase in homicides experienced in Mexico after 2006 began right after the Federal Government declared a "War on Drugs" and launched of a military offensive against DTOs drug trafficking organizations. Figure 1 shows the monthly number of homicides as reported by INEGI since 1990 until 2011.<sup>2</sup>

**Figure 1. Total Number of Homicides. Monthly Data, 1990-2010**

<!-- Start of picture text --> February 2007<br>1990/January 1995/January 2000/January 2005/January 2010/January<br>Homicides Drug-Related<br>500<br>0<br><!-- End of picture text --> Source: INEGI (2014), SNSP, 2011, 2012 and 2014.

> 2 The figure also shows a measure of "drug-related homicides" collected by the National Council of Public Security (Consejo Nacional de Seguridad Pública) of the Federal Government. It includes all deaths by "presumed delinquent rivalry". Homicides are catalogued into "deaths by execution" (violent deaths where the deceased presumably belongs to a DTO, and where no government authority was involved), "deaths from confrontations" (violent deaths resulting from a confrontation between DTOs and official authorities), and "deaths by aggression" (deaths resulting from an assault on official government authorities). These data cover the period from December 2006 until June 2012.

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further investigation would be required of the extent to which the time of year was representative of the whole year. Similar comments apply to OPD and health centre orders.

Capital. Capital costs are likely to be very approximate. The hospital building cost was a Ministry of Works estimate, presumably reasonably accurate and reflecting local conditions. The equipment cost, however, derived from an EEC tender and the equipment specification seems to be to European standards. It is therefore not necessarily a good reflection of the value of most of the equipment currently in district hospitals and historical records of its value were not readily available. Because of these problems, most results are presented for recurrent costs oily.

Activity statistics. The cost anulysis relier oin activity statistics both in the apportionment procedures and in the production of the final unit costs. Unfortunately few hospitals paid much attention to inpatient statistics which were frequently incomplete. The information available, other sources of data (e.g. ward admission books and daily inpatient census records) and advice of district staff were used to estimate missing data, or to compile alternative statistics. On balance, it seems likely that statistics on admissions are more accurate than statistics on inpatient days. Outpatient statistics, when available, recorded only new outpatients not total visits. Where possible, estimates of total visits were also obtained.

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Table 10: Parameters and data used to solve the model

|Parameters||
|---|---|
|Productivity dispersion<br>_θ_<sup>_j_</sup> = 3_._29<br>|Caliendo, Parro (2015)|
|Trade costs by industry<br>_τ _<sup>_j_</sup><br>_ni_|Survey|
|Consumption expenditure shares<br>_α_<sup>_j_</sup>|PCBS (National Accounts)|
|Land input shares<br>_β_<sup>_j_</sup>|Survey|
|Share of value added in gross output<br>_γ_<sup>_j_</sup><br>_n_|EORA (Jordan 2010)|
|Input-output coefcients<br>_γjk_|EORA (Jordan 2010)|
|Data<br>||
|Total employment by industry and location<br>_L_<sup>_j_</sup><br>_i_|PCBS (Census 2007)|
|Wages<br>_wi_|PCBS (Labor Force Survey)|

trading partner in location _d_ . We regress this probability on origin fixed effects, destination fixed effects, and the (log of the) distance between origin and destination. Second, for the estimation of the productivities, we invert an equation linking the demand of traded goods with its supply. We argue that the solution is unique and that we describe an approach to find it numerically. Notice that we only need estimates for productivities for manufacturing in order to recover trade shares. The other sectors are considered non-tradable, and we will not have estimates for industry-specific productivities in that case.

In the survey we ask the following information: From the largest to the smallest commercial partner (buyers or sellers), specify town, district, and average shipping time. This question allows us to compute the probability that a firm in location _i_ has a client or seller in location _n_ . We explicitly asked ICT firms if they were selling physical goods as output. 75% of them replied affirmatively.

We model trade costs as a function of distance. We assume that bilateral trade costs are a constant elasticity function of distance and a stochastic error, _τni_<sup>_j_=time</sup><sup>_t_</sup> _ni_<sup>_j_where</sup><sup>_tj_isa</sup> parameter to be estimated.

Equation 10 can be rewritten as The probability of a firm with productivity _z_ operating in sector _j_ , location _i_ , having trading partners in location _n_ is a function of origin-specific factors, _ζi_ , destination-specific factors, _ζn_ , and the distance between the origin and the destination. _ezjni_ is an error term.

To estimate this elasticity, we use the answers from the survey on the location of trading partners. Observations are the firms in the manufacturing sector with at least two trading partners outside of the town or village where their establishment is located.

Results are reported in table 11. The table reports results for a weighted and an unweighted regressions. Standard errors have been clustered at the firm level. Origin and destination fixed effects are included but not shown in the table. In order to assess the explanatory power of the travel time, we report pseudo- _R_<sup>2</sup> for two competing models, one 26
Second, our paper contributes to evidence on SCP returns. Most of this literature focuses either on a single US state<sup>14</sup> or a single country.<sup>15</sup> Our results extend the current literature by highlighting—within and across countries—the variance of a large set of program quality determinants, the association between these determinants and student outcomes, and the heterogeneity of these associations. We do not estimate SCP returns relative to a high school diploma (as in some of this literature) but rather compare SCPs among themselves.

Finally, this paper also relates to the literature that uses surveys to collect international data on practices to compare across countries. Some recent efforts of this kind include surveys on managerial practices in schools, firms, and health care (Bloom et al., 2015; 2016; 2020) as well as on universities’ practices on governance and performance (Aghion et al., 2010; McCormack et al., 2014). While data from administrative sources and “big data” methods have become increasingly prevalent, survey data remains an attractive option for two reasons. First, granular data on program quality determinants is usually not collected administratively either in developed or developing countries. Even countries that collect general institution or program characteristics do not inquire about issues such as links with the productive sector, faculty, and infrastructure. Second, as Bloom et al. (2016) note, some practices rarely have comparable measures across countries. To our knowledge, this is the first paper to conduct a multi-country data collection initiative on higher education quality determinants and student outcomes for cross-country comparisons.

> 14 See Bahr (2013; 2016) and Carrell and Kurlaender (2016) for California; De Vlieger, Jacob, and Stange (2017) for University of Phoenix; Kane et al. (2021) and Boatman and Long (2018) for Tennessee; Liu et al. (2015) for North Carolina; and Xu & Dadgar (2018) for Virginia.

> 15 See Cellini and Turner (2019), Stange (2012), and Scott-Clayton and Rodriguez (2015) for the United States; Melguizo and Wainer (2016) for Brazil; Dinarte et al. (2022), Shavelson et al. (2016), Melguizo et al. (2016), Saavedra (2009), Barrera-Osorio and Bayona-Rodriguez (2019) for Colombia; and Rodriguez et al. (2016) for Chile.

8
countries. Large retailers, like Walmart, play a much bigger role in the US than in the EU. Firms that are lucky enough to land an order from such a large buyer will look like they had a higher positive demand shock. The higher mean demand shock in the US is also consistent with the fact that the US is the older market for Bangladesh, which ties in with the work of Foster, Haltiwanger and Syverson (2008, 2012). They allow for both TFP and demand shock di¤erences among ...rms and argue that younger ...rms tend to be at least as productive as old ones, but are smaller, i.e., have smaller demand shocks. They interpret this result in terms of ...rms having “market capital”(due to advertising or consumer experience with their goods), which grows slowly over time.<sup>37</sup> Table 3. TFP distribution

||Estimate|Std.|Err.|
|---|---|---|---|
|Shape (�TFP)|0.81|0.|21|
|Scale (�TFP)|0.42|0.|15|
|Implied moment|s of the distrib|ution||
|Implied mean shock<sup>38</sup>|0.|47||
|Coe¢ cient of Variation|1.|24||

Table 4. Distribution of demand shocks

||E|U|U|S|
|---|---|---|---|---|
||Estimate|Std. Err.|Estimate|Std. Err.|
|Shape (�)|0.32|0.008|0.17|0.003|
|Scale (�)|1.39|0.087|0.57|0.020|
|||Implied|moments||
|Implied mean shock|10|.4|42|1.8|
|Coe¢ cient of variation|4|.9|30|.7|

> 37Among other papers that emphasize the dominant role of changes in demand over productivity shocks are Roberts, Xu, Fan and Zhang (2012), who show that demand shocks are the driving force behind ...rms’ turnover, and Eaton, Kortum, Neiman and Romalis (2011), who demonstrate that changes in demand explain more than 80% of the drop in trade/GDP during the Great Recession of 2008-2009.

> 38The mean equals ��(1 + �1<sup>);where�(:)denotesthestandardgammafunction,andthevarianceequals</sup> 2 �<sup>2</sup> �(1 + �<sup><u>2</u>) �</sup> ���(1 + �<sup><u>1</u>)</sup> � .

25
In summary, the quantitative results demonstrate that there was an increase over time in both control and treatment areas in knowledge of and participation in village government, and a concurrent improvement in the delivery of village government services. However, the data do not indicate that the People’s Planning intervention had a significant impact across a wide spectrum of possible outcomes. The results, at best, show very weak evidence of both positive and negative impacts of the intervention on a very small number of outcomes.

# <u>Section 3:  ETHNOGRAPHIC ANALYSIS</u>

The qualitative data is very detailed and extensive and it impossible to do justice to all its nuances in a short paper. Consequently, our goal here is to limit our discussion to those issues that have a bearing on understanding the processes and mechanisms that led to the observed lack of impact in the quantitative data. Like other qualitative studies we emphasize issues that were repeatedly observed by our investigators in several villages, and de-emphasize outliers - events and issues that were unique to a particular village at a particular point of time.

# <u>1. The Context</u>

The average literacy rates in Gulbarga and Raichur districts, according to the 2011 census, are 65.65 and 60.46 respectively, well below the state average of 75.60. Both districts are in the Hyderabad-Karnataka region that for several centuries, prior to 1950, was ruled by the Nizam of Hyderabad under a feudal regime. Vestiges of feudalism like patronage networks, nepotism, and high inequality characterize village life even today with powerful landlords, hereditary nobles (jagirdars) other village elites continuing to exert strong influence. These systems of patronage also manifest themselves in all our villages in the form traditional panchayats 8 that are led by village elites (Ananthpur, 2007). This informal system of government works in parallel with GPs and is the primary local authority for resolving local disputes and organizing village festivals. With the introduction of the constitutionally mandated GPs, traditional panchayats have found new ways to interface with the GP. They not only try to influence who contests GP elections, but also influence the GPs decisions. The following vignettes illustrate the nature of elite rule in these villages:

> 8 Traditional panchayats, in contrast to Gram Panchayats (GPs) are informal village institutions that consist of powerful elites and elders in the village who resolve disputes, act as intermediaries with government bodies including the GP, and are often quite politically powerful.

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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 on a parameterization of the transition equation and ii) a Kalman ...lter method based on both the transition and the measurement equation.

The estimates of illicit income, money laundering, and laundered assets are reasonable according to the history of Colombia and are consistent with the dominant role cocaine tra¢ cking played in the 1990s in the country until the implementation of Plan Colombia in the early years of the new century. Figure 12 presents a summary of the results.

49
Notes Professor of Development Economics, University of Oxford and Chief Economist, Department for International Development, the UK government department involved in aid and development. Paper prepared for the World Bank project on Inclusive Green Growth. I am grateful for helpful comments by Mike Toman and the editor and referees of this journal. All views and errors are mine and should not be attributed to the UK’s Department for International Development.

1 Although always a difficult exercise, some serious attempts have been made to quantify the costs of climate change for the poor, combining microeconometric evidence with modeling. World Bank (2010b) puts the price tag for adaptation at $70-$100 billion per year for the developing world, well outweighing the likely future welfare costs of climate change. 2 We must be careful with the evidence for poverty traps narrowly defined. In fact, it is difficult to find strong evidence. However, there is considerable evidence of poverty persistence in the sense of very slow escape and recovery and actions that lead to a perpetuation of poverty to avoid risk (Dercon 2009).

3 Note, however, that in an endogenous growth framework with growth externalities from the accumulation of certain factors of production, this may not _necessarily_ be the case. For example, in some models, temporary reductions in growth due to increases in costs of production may actually have permanent consequences. For example, increasing environmental costs may reduce short-run resources for human capital investment, and overall growth may slow. Of course, there is no necessity to this either; it depends on the specific endogenous growth model applied (Aghion and Howitt 1998). 4 This same reason could also be the case for _appropriately_ valued output growth if only _some_ environmental regulations are put in without a full accounting of environmental capital such that not all factors are appropriately accounted for. A well-known welfare economics result from the theory of second best is that if only _some_ and not all distortions are removed, then the allocation may actually become _less_ efficient, both statically and dynamically. Applying this result, environmental regulations on some but not all forms of fuel-intensive transport may make the allocation less efficient and slow _appropriately_ valued output growth.

5 The exception would be if the benefits for the poor that result from the new pricing of scarce resources, such as cleaner water or air, have a value to them that is larger than the costs of increased payments for public services (or for private goods whose costs are higher to reflect internalization of externalities outweigh the cost). This is possible, but they are likely to be potential benefits in the long run for higher short-run costs in the form of reduced real income.

18
As background, we begin by noting that while economic growth over the past few decades has been disappointing in Africa, some countries began growing fast at the start of the 21st century, at least until the negative shock of the global crisis in 2007-2009. The GDP growth for the five year period running from 2002 to 2007 averaged over 5% in Africa, outpacing population growth and hence implying an increase in GDP per capita (Allen, Otchere, and Senbet, 2011). However, and as discussed earlier, despite the adoption of extensive economic and financial reforms, most SubSaharan African countries still face a severe gap in the development of their financial sectors.

Figure 5 shows the relationship between demographic bank penetration (measured by the number of bank branches per 100,000 people) and GDP per capita across low income economies in 2003-2004. The figure suggests three stylized facts. First, there is a strong positive relationship between bank penetration and economic development. Second, Sub-Saharan African countries are characterized by both low income per capita and low bank branch penetration. Third, the bank branch network in Sub-Saharan African countries is underdeveloped relative to not only the higher income economies but other peers with similar income levels.

The underdevelopment of the bank branch network in Sub-Saharan Africa has resulted in low access to banking services, as illustrated by our firm-level study (on the bank credit side). While in the early 2000s roughly half of the world’s adult population lacked any form of bank account, in Sub-Saharan Africa over eighty percent of the adult population lacked an account (Chaia et al. (2011) and Honohan (2008)). In line with these facts, Kendall et al. (2010) report that the average number of bank accounts per adult is 3.7 in developed countries, 0.9 in developing countries and less than 0.5 in Sub-Saharan African countries.

The financial access problem in many Sub-Saharan countries and the emergence of new business models that may contribute to solve this problem deserve particular attention. In this respect, Kenya offers an interesting case study for financial access as in recent years it has 33
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