rafmacalaba/datause-encoder-data huggingface dataset:
intra-industry spillovers conclude that the existing results are mixed (Görg and Strobl 2001; Saggi 2001; Görg and Greenaway 2004; Smeets 2008). However, some progress has been made on this front. A novel and interesting approach to examining intra-industry spillovers is adopted by Kee (2010) who is able to identify business relationships between Malaysian garment producers and their suppliers of intermediate inputs. Her results are consistent with Malaysian firms becoming more productive as a result of sharing suppliers with foreign affiliates.

Three studies explicitly focus on spillovers that occur through the movement of labor. Görg and Strobl (2005) employ Ghanaian data that indicate whether the owner of a domestic firm has previous experience working for a foreign affiliate and relate this information to firm-level productivity. Their results suggest that firms that are run by owners who worked for multinationals in the same industry immediately prior to opening their own firm are more productive than other domestic firms. Balsvik (2011) documents extensive labor mobility flows from multinationals to non-multinationals in Norwegian manufacturing during the 1990s. During this period, 14,400 workers moved from multinationals to nonmultinationals. By the year 2000, 28 percent of workers employed in nonmultinationals had previously been employed in multinationals. Balsvik shows a robust and significant positive correlation between the share of workers with multinational experience and the productivity of non-multinationals. This finding is consistent with spillovers through labor mobility. Workers with multinational 21
# KENYA: FRAMEWORK FOR MACROECONOMIC ANALYSIS External Gap and Residual PSBR

Value <!-- Start of picture text --> 600<br>375-<br>150<br>-75-<br>-300 1991 1992 1993 1994 1995 1996<br><!-- End of picture text --> Years Base projections: ext. gap (US$m) gRR Base projections: residual PSBR (KLm) Residual PSBR after exchange rate action (KLm) Ext. gap after interest rate action (US$m) Figure One
We plot variation in mask manufacturing exposure across sub-Districts in Figure 2a, and for comparison plot variation in mask manufacturing intensity in Figure 2b. Since mask manufacturing intensity measures exposure to certified masks, we note that our exposure measure should also be interpreted as exposure to high-grade masks. In Section 3.1.2, we argue variation in mask manufacturing exposure is exogenous.

**Construction** We exclude sales to destination sub-District _d_ when constructing mask manufacturing intensity of each origin sub-District _o_ to mitigate the concern that shocks to demand for manufactured masks in destination sub-District _d_ could increase the mask manufacturing intensity in closely connected origin sub-Districts, increasing _d_ ’s mask manufacturing exposure and generating simultaneity bias; in Appendix F.1, we show that our results are unaffected if we do not exclude sales to destination sub-District _d_ . In addition, we note that mask manufacturing exposure is only defined for sub-Districts with non-mask textile purchases, and that we define mask manufacturing intensity to be 0 in origin sub-Districts with no mask sales to other destinations.

**Interpretation** We note that mask manufacturing exposure can be interpreted as destination sub-District _d_ ’s predicted purchases of domestically manufactured masks as a share of total purchases of masks. Mask manufacturing exposure is not equal to actual purchases of domestically manufactured masks as a share of total purchases of masks for two reasons—leave-out mask manufacturing intensity of origin _o_ may not equal the mask manufacturing intensity of origin _o_ ’s mask sales to destination _d_ , and destination _d_ ’s non-mask textile purchase share from origin _o_ may differ from its mask purchase share from origin _o_ .<sup>3</sup> Importantly, these differences exclude idiosyn-

> 3Formally, <u>�</u> _d_<sup>_′̸_</sup> <u>�=</u> _dd_<sup>_′̸M_</sup> = _d_<sup>mnf</sup> _od_<sup>_M′_mnf</sup> _<u>od</u>_<sup>+</sup><sup>_M′_oth</sup> _od_<sup>_′_=</sup> _Mod_<sup>mnf</sup> _Mod_<sup>+mnf</sup><sup>_M_oth</sup> _od_ and <u>�</u> _<u>To</u>_<sup>_′_</sup> _<u>od</u>_<sup>_Tod_=</sup> <u>�</u> _Mo_<sup>_′_</sup> _<u>od</u>_<sup>_M_mnf mnf</sup> _o_<sup>_′_+</sup> _d_<sup>_M_+ oth</sup> _<u>od</u>_<sup>_M_oth</sup> _o_<sup>_′_</sup> _d_<sup>,respectively.InAp-</sup> pendix I, we show that the mask manufacturing intensity of origin sub-District mask sales is corre-

13
||(0.0204)|(0.0138)|
|---|---|---|
|Dwelling connected to trench/private network|0.0390***|-0.00679|
||(0.0151)|(0.00976)|
|Dwelling has brick/cement walls|0.0669***|0.0264*|
||(0.0200)|(0.0153)|
|Dwelling has steel ceiling|0.130***|0.163***|
||(0.0166)|(0.0119)|
|Dwelling has ordinary tile floor|-0.345***|-0.239***|
||(0.0587)|(0.0423)|
|Dwelling has ceramic tile floor|-0.182***|-0.0491|
||(0.0609)|(0.0427)|
|Dwelling has concrete floor|-0.442***|-0.330***|
||(0.0582)|(0.0425)|
|Dwelling has soil floor|-0.546***|-0.342***|
||(0.0606)|(0.0437)|
|Lower Urban|0.106|-0.105*|
||(0.0872)|(0.0627)|
|Lower Rural|-0.172*|-0.322***|
||(0.0899)|(0.0577)|
|Upper Urban|0.134|-0.0746|
||(0.0938)|(0.0601)|
|Upper Rural|0.0632|-0.301***|
||(0.105)|(0.0748)|
|Borders Urban|0.488***|-0.0291|
||(0.0808)|(0.0910)|
|Borders Rural|0.683***|0.433***|
||(0.0692)|(0.0499)|
|Region*Owner|Yes|Yes|
|Region*Not-owner|Yes|Yes|
|Constant|7.122***|7.653***|
||(0.101)|(0.0737)|
|Observations|11,841|23,970|
|R-squared|0.505|0.411|
|Robust standard errors in parentheses.|||
|*** p<0.01, ** p<0.05, * p<0.1|||
|**Source**: Authors’ calculations based on HIECS 20|15 and 2017/18.||
|**Note**: Regression run using total expenditures.|||

23
Table A.10: Robustness checks using diplomatic exchange at both sending and receiving countries. Estimating the effect of migrants’ legal status on hourly wages after return, unconditional on return migration

||(1)<br>Migrate|(2)<br>Illegal|(3)<br>Return|(4)<br>Log of<br>hourly wage|
|---|---|---|---|---|
|Age|0.012***<br>(0.001)|-0.100***<br>(0.034)|-0.018***<br>(0.007)|0.000<br>(0.005)|
|Age squared|-0.000***|0.001***|0.000***|0.000**|
||(0.000)|(0.000)|(0.000)|(0.000)|
|No education|0.021***|0.659***|-0.031|-0.445***|
||(0.004)|(0.109)|(0.021)|(0.026)|
|Primary or preparatory|0.031***|0.517***|0.041*|-0.419***|
||(0.004)|(0.122)|(0.022)|(0.025)|
|Secondary|0.017***|0.393***|0.029|-0.286***|
||(0.003)|(0.099)|(0.018)|(0.020)|
|Rural|0.013***|0.172**|-0.005|-0.029*|
||(0.003)|(0.074)|(0.014)|(0.016)|
|Oil prices|0.013***<br>(0.000)||||
|Diplomatic exchange||-0.152*<br>(0.091)|||
|Confict|||0.059***<br>(0.014)||
|Illegal status||||-0.197***<br>(0.075)|
|Legal status||||0.149***<br>(0.049)|
|Observations|14,879|1,960|2,071|8,054|
|Parental controls|Yes|Yes|Yes|Yes|
|Decades of migration dummies|Yes|Yes|Yes|No|
|Labor market controls in Egypt|No|No|No|Yes|
|Predicted log wage: Non-mig.=1.|515; Illegal mig|.=1.354; Legal|mig.=1.742||

Robust standard errors in parentheses. *** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1 _Notes_ : Equation 1 is the probability of temporary migration. Equation 2 is the probability of undocumented migration. Equation 3 is the probability of return migration. Equation 4 models the logarithm of hourly wages upon return. For identification of equation (1), we use the inflation adjusted historical oil prices (in US dollars). For identification of equation (2), we use diplomatic exchange between Egypt and the countries of destination of Egyptian migrants at time of migration. It is a dummy variable equal zero if there is no evidence of diplomatic exchange neither in Egypt, nor in the country of destination and is equal one if there is evidence of diplomatic exchange either in Egypt or in the country of destination at the level of charg ́e d’affaires, minister, ambassador or other. For identification of equation (3), we use the number of active armed conflicts that is both country and year specific from the Uppsala Conflict Data Project (UCDP). Equation (4) estimates the effect of illegality on the logarithm of hourly wages, conditional on return migration. The predicted logarithm of hourly wages for non-migrants, illegal migrants and legal migrants are reported in the last row.

57
_Sanitation Crisis_ , London: Earthscan.

- **Bleakley, Hoyt.** 2007. “Disease and Development: Evidence from Hookworm Eradication in the American South.” _Quarterly Journal of Economics_ , 122(1): 73–117.

- **Brooks-Gunn, Jeanne, Flavio Cunha, Greg Duncan, James J. Heckman, and Aaron Sojourner.** 2006. “A Reanalysis of the IHDP Program.” workng paper, Northwestern University.

- **Cameron, A. Colin, Jonah B. Gelbach, and Douglas L. Miller.** 2008. “BootstrapBased Improvements for Inference with Clustered Errors.” _The Review of Economics and Statistics_ , 90(3): 414–427.

- **Case, Anne and Christina Paxson.** 2008. “Stature and Status: Height, Ability, and Labor Market Outcomes.” _Journal of Political Economy_ , 116(3): 499–532. 2010. “Causes and Consequences of Early-Life Health.” _Demography_ , 47: S65–S85.

- **Checkley, William, Gillian Buckley, Robert H Gilman, Ana MO Assis, Richard L Guerrant, Saul S Morris, K ̊are Mølbak, Palle Valentiner-Branth, Claudio F Lanata, Robert E Black, and The Childhood Malnutrition and Infection Network.** 2008. “Multi-country analysis of the effects of diarrhoea on childhood stunting.” _International Journal of Epidemiology_ , 37: 816–830.

- **Cunha, Flavio, James J. Heckman, and Susanne M. Schennach.** 2010. “Estimating the Technology of Cognitive and Noncognitive Skill Formation.” _Econometrica_ , 78(3): 883–931.

- **Currie, Janet.** 2009. “Healthy, Wealthy, and Wise: Socioeconomic Status, Poor Health in Childhood, and Human Capital Development.” _Journal of Economic Literature_ , 47(1): 87–122.

- **Cutler, David and Grant Miller.** 2005. “The role of public health improvements in health advances: The twentieth-century United States.” _Demography_ , 42(1): 1–22.

- **Doyle, Orla, Colm P. Harmon, James J. Heckman, and Richard E. Tremblay.** 2009. “Investing in Early Human Development: Timing and Economic Efficiency.” _Eco-_

30
the estimation of sibling correlation in equation (6), the family and individual components need to be estimated. The available literature on sibling correlations relies on two alternative estimation methods. Mazumder (2006, 2011) uses the Restricted Maximum Likelihood (REML) method which has better small sample properties under the normality assumption. Bjorklund et al. (2010) instead utilize a mixed effects model to estimate the family and individual components. The procedure in Bjorklund et al. (2010) can be implemented as a two-step procedure similar to the method employed also by Solon et al (1991) and Bjorklund et al (2002). The weakness of this procedure is that its small sample properties are not well understood. We implemented both procedures. Given the large size of the samples used in this paper (more than 34,000 in 1993 and 38,000 in 2006), both procedures produce nearly identical parameter estimates, and for the sake of brevity, we report estimates from the procedure suggested by Bjorklund et al. (2010). The estimates using Restricted Maximum Likelihood are available from the authors. The estimates of sibling correlations presented in this paper are from the mixed effects model using Stata GLLAMM procedure. As noted before, all standard errors are corrected for clustering at the family level.<sup>22</sup>

# **(3) Data and Empirical Issues**

The data for our analysis come from the National Family Health Survey (NFHS), 1992/93 and 2006. The NFHS is a large-scale and nationally representative survey of nearly all of Indian states.<sup>23</sup> The main target group for this survey is women in their reproductive years. While both surveys followed similar sampling methodology, the surveys differ somewhat in terms of sample size and questionnaires. The NFHS 2006 used three separate questionnaires to interview 109,041 households, 124,385 unmarried and ever married women between 15 to 49 years of age and 74,369 unmarried and ever married men in the age group 15-54 years. The NFHS 1992/93 on the other hand collected information from 88,562 households and 89,777 ever-married women in the age group 1349 years. Data for our analysis are drawn from the household and women‘s questionnaires which are common to both surveys.

While sample sizes of the NFHS are comparable to that of National Sample Surveys (NSS) in India, the data from NFHS offer two distinct advantages for our analysis. First, all children up to 17 years of age in the NFHS are matched to their co-resident parents (not just to the household head). This is particularly important in the Indian context where joint families are still common. This

> 22 For details of the estimation method, please see Rabe-Hesketh et al (2002).

> 23 The NFHS 1992/93 covered 24 states and Delhi (the Capital city) whereas 2006 survey covered all of the 29 states. The sample sizes in the NFHS are comparable to more widely used National Sample Surveys.

11
lack of infrastructure, and ethnic diversions) are also time-invariant or change only very slowly over time and may thus not easily be distinguished from country level fixed effects. Secondly, even then, crosscountry time series data are still relatively rare, especially for (African) countries and for "non-standard" factors such as violence. In fact, countries with high incidence of civil strife are normally also characterized by lack of data or by notoriously bad quality of whatever data might be available.

A second issue that is of great importance, especially if the goal is to ascertain the impacts of civil strife, is the potential for reverse causation. To take one example, low secondary enrollment is at least as likely to be a cause as it is a consequence of civil strife (e.g. because schools have been destroyed, teachers are absent or have been prohibited to teach, and the expected returns to acquiring education in a war-like envirom-nent are low). The same is true for low investment and GDP growth, and, to a lesser degree, even an economy's dependence on primary exports. While time series data of sufficiently high frequency would allow to distinguish between the two (as in Asteriou and Siriopoulos, 2000), these are rarely available in practice. It is of interest that, in cross-country regressions dealing with this question, deleting one or two influential observations can completely change the nature of the results obtained, especially insofar as Africa is concerned (Arcand et al, 2000). This has led scholars to exhibit considerable skepticism with regard to results from cross-country regressions. Case studies can normally provide an adequate _description_ of the main issues, but are often not sufficiently detailed to allow generalization of the conclusions.

Both of these problems have not only limited the policy relevance of existing research but also given rise to the argument that existing data sets are insufficient to facilitate the in-depth analysis that would be needed to support more far-reaching policy conclusions, especially for Africa (Herbst 2000; Ali 2000). It is therefore of interest to assess the extent to which existing results from cross-country analysis hold up to the more rigorous scrutiny that is possible within individual countries and there is general agreement that data at a lower level of aggregation would be desirable (e.g. Dreze and Reetika 2000). We use a similar

> 3 In existing models, potential non-linearities, which may arise from increasing retums to military activity (e.g. Gaviria, 2000) or from

7
# **1. Introduction**

With more than 140,000 homicide deaths per year, Latin America’s homicide rate is twice the world’s average, making it the most violent region in the world after SubSaharan Africa.<sup>2</sup> Selected indicators such as homicide rates, injury rates, and the size of the private security sector would suggest that some countries in the region exhibit warlike symptoms, despite being formally at peace. Alarmingly, crime and violence levels have been on the rise since the 1970s. The Pan-American Health Organization has described violence as the regional pandemic. Every year, Latin Americans lose an average of 3 days of good health due to violence and 28 million families are subject to robbery and theft.<sup>3</sup> In Latin America, violence disproportionately affects the poor, eroding their assets and livelihoods. The abnormally high levels of crime and violence constitute a key obstacle to the development of the region. The cost associated with these levels of crime and violence is astounding: it is estimated at 14.2% of the regional GDP.<sup>4</sup> In terms of human capital, 1.9% of GDP is lost annually, which is equivalent to the region’s spending on primary education. Over the past 15 years, the net accumulation of human capital has been cut in half due to the increase in crime and violence.<sup>5</sup> According to the Colombian National Planning Department, the cost of violence (including urban violence and armed conflict) amounted to 18.5% of GDP between 1991 and 1996.<sup>6</sup> The cumulative effect of “lost growth” as a result of crime and violence is such that Colombia would today have a per capita income on the order of 32% higher than at present.<sup>7</sup> This paper surveys the recent literature on crime and violence and provides a broad overview of the main ideas and empirical findings. It does not attempt to be comprehensive, but it rather seeks to provide an introduction. Section 2 provides an overview of the magnitude, trends, and manifestations of crime and violence in Latin America. Section 3 discusses the ways in which violence impacts on development and offers some cost estimates. Section 4 surveys the literature on the root causes of violence and the empirical evidence on the determinants of crime. Finally, Section 5 concludes. Additionally, the paper presents three annexes: (1) Data Sources on Crime and Violence for LAC, (2) Overview of Selected Papers on Crime and Violence in LAC, and (3) Overview of Selected Empirical Papers on Crime and Violence in LAC.

> 2 The World Health Organitzation’s “World Report on Violence and Health _”_ (2002) estimates the homicide rate per 100,000 population at 19.3 for the Region of the Americas, including the United States and Canada.

> 3 Londoño and Guerrero (2000).

> 4 Ibid.

> 5 Londoño (1996) quoted in Ayres (1998).

> 6 Departamento Nacional de Planeación (1998) quoted in Buvinic, Morrison and Orlando (2002). 7 Rubio (2000).

2
# Figure 1: Share of manufacturing <u>plants with financial loans</u>

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<mark>survey of public servants. What then are the key concepts for measurement in a survey of public servants that derive from patrimonial rule? In terms of management practices, politicization and ‘personalization’ (nepotism) are central: personnel decisions – such as recruitment or promotions – are decided on political and personal (family and friends) criteria. In terms of employee attitudes and behaviors, corruption (the abuse of public office for private gain) and clientelism (the exchange of state resources for electoral support) stand out – so much so that patrimonialism (or patronage) is often equated with both (Rothstein and Varraich 2017).</mark> <mark>Weber’s (1978) bureaucratic ideal-type was designed as an antidote to neopatrimonialism: bureaucrats were to become autonomous from political (or other particularistic) influences through merit examinations, job stability, seniority-based promotions, and sufficient pay to reduce corruption to supplement incomes. This was complemented by hierarchy and detailed rules, in line with a Rechtsstaat ideal (cf. Olsen 2006). This set of administrative characteristics was intended to foster key (Weberian) employee attitudes: an</mark> _<mark>esprit de corps</mark>_ <mark>, impartiality, political neutrality, integrity, rule following, and expertise. Our core questionnaire thus includes their measurement.</mark> <mark>While Weberian bureaucracy was an antidote to patrimonialism, it created problems of its own; in particular, a lack of incentives to perform and innovate (Osborne and Gaebler 1992). New Public Management (NPM) sought to address these (Hood 1991). While the term has competing conceptualizations (Dunleavy et al. 2006), at its core it is a “doctrine that the public sector can be improved [through] business concepts, techniques and values” (Pollitt and Bouckaert 2012). For personnel management, this implied a shift to performance: the setting of targets, the measurement of their achievement, and the incentivization of public servants to achieve those targets, for instance through performance pay, temporary job contracts and autonomy to innovate (</mark> Osborn <mark>e and Gaebler 1992). These management</mark> 6
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