rafmacalaba/datause-encoder-data huggingface dataset:
Panel data information at the municipal level allows controlling for individual-specific, timeinvariant, and unobserved heterogeneity (which can make the Ordinary Least Squares estimator both inconsistent and inefficient). Unobserved factors that affect a municipality’s crime rate in 2007 would also affect the municipality’s crime rate in 2014. Fixed effect estimations are thus important for controlling for those unobservable characteristics. In particular, the following municipal-level fixed-effects equation is estimated: where ������,�denotes the crime rate of type _i_ in municipality _s_ and time _t_ ; �������,� is a dummy variable that takes the value of 1 if the municipality _s_ participated in the program in time _t_ ; and �� and �� are municipalities and time fixed effects. The unobserved municipality effect �� represents all factors affecting municipal crime rates that do not change over the time period of analysis. These might include certain demographic features of the population (age, education), geographic location, historical factors, and different attitudes toward crime that are typically slow to change. In addition, time fixed effects �� is included to control for temporal variation or special events that may affect the crime rates in all municipalities. Our estimator of interest, ��, provides the effect of the program on the different types crime rates.

The gradual introduction of the program also allows us to identify the effects of PATI at different months of exposure of the selected municipalities. We thus identify a similar estimator that detects changes in crime rates up to five months after the implementation of the program in each municipality.

where ��� is the dummy variable denoting the treatment status of each municipality after the intervention started. The significance of ���implies that the program generated effects on crime rates months after the implementation.

As a complementary exercise, we also check the presence of spillover effects from treated municipalities to those where the program was not introduced. We thus carried out a single test that consists of accounting for the number of treated municipalities within a radius of 50 kilometers 9
Curtis, V., S. Cairncross, and R. Yonli. 2000. _Tropical Medicine and International Health_ 5 (1), 22–32, January.

Curtis, V., and S. Cairncross. 2003. Effect of washing hands with soap on diarrhea risk in the community: a systematic review. _The Lancet Infectious Diseases_ 3 (5), 275–281, May.

Engle, P., M. Black, J. Behrman, M. Cabral de Mello, P. Gertler, L. Kapiriri, R. Martorell, and M. Young. 2007. Strategies to avoid the loss of developmental potential among 200 million children in the developing world. _The Lancet_ 329, 229–240.

Esrey, S., J. Potash, L. Roberts, and C. Shiff. 1991. Effects of improved water supply and sanitation on ascariasis, diarrhea, dracunculiasis, hookworm infection, schistosomiasis and trachoma. _Bulletin of the World Health Organization_ 69, 609–621.

Faix, R. 1987. Comparative efficacy of handwashing agents against cytomegalovirus. _Infection Control_ 8, 158–162.

Favin, M. 2011. _Endline Assessment of the Enabling Environment in Peru_ . Water and Sanitation Program, World Bank, August 2010.

Galiani, S., P. Gertler, and E. Schargrodsky. 2005. Water for life: The impact of the privatization of water services on child mortality. _Journal of Political Economy_ 113(1), 83– 120, February.

Galiani, S., and A. Orsola-Vidal. 2010. _Scaling Up Handwashing Behavior: Findings from the Impact Evaluation Baseline Survey in Peru_ . Water and Sanitation Program, World Bank, August 2010.

Gibson, L., J. Rose, C. Haas, C. Gerba, and P. Rusin. 2002. Quantitative assessment of risk reduction from hand washing with antibacterial soaps. _Journal of Applied Microbiology_ 92, 136S–143S.

Hendley, J., R. Wenzel, and J. Gwaltney. 1973. Transmission of rhinovirus colds by selfinoculation. _The New England Journal of Medicine_ 288, 1361–1364.

Hesham, M., A. Edariah, and M. Norhayati. 2004. Intestinal parasitic infections and micronutrients deficiency: A review. _Medical Journal of Malaysia_ 59(2): 284–293.

Instituto Nacional de Estadísticas e Informática (INEI). 2012. _Condiciones de Vida en Peru_ , Octubre-Noviembre-Diciembre 2011.

International Monetary Fund (IMF). 2011. _World Economic Outlook_ .

Luby, S., M. Agboatwalla, J. Paynter, A. Altaf, W. Billhimer, and R. Hoekstra. 2001. Effect of intensive handwashing promotion on childhood diarrhea in high-risk communities in Pakistan. _JAMA, Journal of the American Medical Association_ 291(21), 2547–2554.

34
|Variable|Fixed|Efects Po|isson||Fixed Efects||
|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|
|Peiex|0.0390|0.0376|0.0325|0.0463|0.0448|0.0405|
||(0.0051)|(0.0051)|(0.0050)|(0.0059)|(0.0059)|(0.0058)|
|Apex(other programs)|0.0229|0.0202|0.0116|0.0278|0.0245|0.0142|
||(0.0067)|(0.0066)|(0.0061)|(0.0085)|(0.0084)|(0.0078)|
|Firms’ age|0.0118<br>|0.0122<br>|0.0100<br>|0.0155<br>|0.0156<br>|0.0134<br>|
||(0.0013)|(0.0013)|(0.0012)|(0.0017)|(0.0017)|(0.0016)|
|Firms’ age<sup>2</sup>|-0.00021|-0.00022|-0.00018|-0.00025|-0.00027|-0.00023|
||(0.0000)|(0.0000)|(0.0000)|(0.00002)|(0.00002)|(0.00002)|
|log(number employees)_t−_2|0.0183|0.0174|0.0130|0.0170|0.0161|0.0124|
||(0.0008)|(0.0008)|(0.0008)|(0.0008)|(0.0008)|(0.0008)|
|log(wage)_t−_2||0.0034|0.0042||0.0013|0.0019|
|||(0.0016)|(0.0015)||(0.0013)|(0.0012)|
|log(schooling)_t−_2||-0.0108|-0.0115||-0.0117|-0.0121|
|||(0.0037)|(0.0035)||(0.0036)|(0.0035)|
|log(nbr exporters - region)||0.0006|0.0008||0.0016|0.0018|
|||(0.0015)|(0.0014)||(0.0016)|(0.0016)|
|share(engenieer and R&D)_t−_2||0.0193|0.0147||0.0241|0.0189|
|||(0.0062)|(0.0051)||(0.0073)|(0.0063)|
|average experience _t−_2||-0.0045|-0.0043||-0.0039|-0.0038|
|||(0.0001)|(0.0001)||(0.0001)|(0.0001)|
|number of subsidiaries _t−_2||0.0056<br>|0.0049<br>||0.0109<br>|0.0095<br>|
|||(0.0032)|(0.0030)||(0.0057)|(0.0053)|
|Manager exp _M_1|||0.0156|||0.0239|
||||(0.0049)|||(0.0067)|
|Manager exp _M_1|||0.0189|||0.0259|
||||(0.0047)|||(0.0066)|
|Manager exp _M_3|||0.0193|||0.0248|
||||(0.0034)|||(0.0045)|
|Manager _M_1 N-_Q_3|||0.1595|||0.1717|
||||(0.0080)|||(0.0112)|
|Manager _M_1 F-_Q_3|||0.0335<br>|||0.0621<br>|
||||(0.0180)|||(0.0284)|
|Manager _M_2 N-_Q_3|||0.1224|||0.1201|
||||(0.0148)|||(0.0221)|
|Manager _M_2 N-_Q_3|||0.0828|||0.1245|
||||(0.0333)|||(0.0557)|
|Manager _M_3 N-_Q_3|||0.2021<br>|||0.2582<br>|
||||(0.0073)|||(0.0097)|
|Manager _M_3 N-_Q_3|||0.0135|||0.0089|
||||(0.0160)|||(0.0272)|
|Additional Manager’s control*|||||||
|Managers QII-QIII|NO|NO|YES|NO|NO|YES|
|FIXED EFFECT|||||||
|FIRMS|YES|YES|YES|YES|YES|YES|
|Sector-Year|YES|YES|YES|YES|YES|YES|
|Region(State)-Year|YES|YES|YES|YES|YES|YES|
|N|731,265|731,265|731,265|779,356|779,278|779,278|
|Firms (cluster)|207,415|207,415|207,415|255,428|255,428|255,428|

Table 10: Dependent variable: Number of layers. Poisson panel fixed effects Note: Standard errors clustered at firm level are reported in parentheses. Managers are classified by occupations equivalent to CEOs and Directors (M1), Senior staff (M2) and Supervisors (M3). N and F refer to their nationalities, where N(Nationals) and F(Foreigners). Additional Manager’s control (Managers QII-QIII) refers to dummy variables for managers in the second and third quartiles of wage distribution. Sector (CNAE 2 digits).

are we assuming that the more managers, the better. Instead, increasing the number of layers in this case means that a firm is incorporating workers with different levels of competency and specialized knowledge in solving different problems at the firm. For example, if a firm already has 3 layers and decides to 21
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pandemic, postponing the timing of the vocational training sessions as a result. Clearance to conduct an in-person follow-up survey was provided by the Ministry of Health, and the survey team followed masking, social distancing, and outdoor interviewing protocols.

# **3.3 Take-up rates and implementation**

The information interventions were carried out at the same time as the baseline survey, and had 100 percent compliance: all individuals were shown the videos corresponding to their respective treatments.

Facilitation of migration to Dakar, Senegal, experienced several logistical problems and low takeup. During the baseline, respondents in this treatment group were asked whether they were interested in migrating to Dakar. Of the 907 respondents in this group, 589 (65 percent) were interested in taking up the bus service to Dakar and were asked to indicate when they wished to travel. An agreement was signed with The Gambia Transport Service Corporation to have buses made available to meet the specific daily demand coming from these young people. Due to failure of GTSC in providing these buses on time, we opted to rent vans as a substitute. After a series of trials in transporting respondents where we learned, for example, that individual migrants disliked taking a publicly visible bus to Dakar, take-up interest was low. This was also explained by timing reasons, with respondents expressing a desire to migrate during the dry season. We therefore altered the design to instead give respondents labeled transport fees that could be used to pay for the cost of the migration. We revisited the Senegal treatment settlements, and gave respondents a cash transfer voucher of 1,200 GMD (about 20 euros). Respondents were informed that they should visit the nearest JFIN Money Transfer Agency<sup>18</sup> and exchange this voucher for cash. Of the 907 respondents in this group, 316 (about 35 percent) were successfully able to redeem their vouchers for cash. According to the follow up data, about 110 (16 percent of 691 respondents) reported contacting our Dakar representative. However, only 12 beneficiaries migrated to Dakar using this offer. Furthermore, we registered 4 respondents who attempted to migrate but were intercepted by immigration officers due to improper documentation (lack of valid photo identification). The authorities raised questions about the objective of the travel and requested further clarifications to rule out smuggling intent. Respondents explained the objective of the project and gave the contact information of the project. We provided clarification with a supporting

> 18 JFIN has 15 money transfer payment points across the two regions, 8 in CRR and 7 in URR.

19
employed in NCR earn the highest wage followed by those in Luzon. For example, in average, wage in Visayas is 40 percent lower than that in NCR, others being equal. Over time, wage gaps widened. The large wage gap may be related to the unique production structure of the primary city NCR where high-paid jobs in high-value-added services industries agglomerate.<sup>9</sup> Table 8 shows relative contribution of personal attributes, sectoral and occupational factors to wage differentials, while regional and year effects are controlled for. For both genders, personal attributes account for approximately one-third of wage inequality, of which education accounts for 30 percent. Occupation accounts for 12 percent of wage differentials.<sup>10</sup> Personal attributes, in particular education level, play a more important role in wage differentials for female workers than for male workers. Interestingly, the sector of employment plays a more important role for male workers than for female workers, while the type of occupation plays a more important role for female workers than for male workers. This is likely related to the wide spectrum of jobs that female workers have within a sector (especially the services sector). Comparing the results in 2003 and 2007, the contribution of personal attributes, including education level, is stable over time; while the contribution of sector and occupation slightly declines and that of region increases. This is consistent with the findings of the slightly declining wage inequality within sectors and the widening gap between the NCR and other regions.

**Table 8: Contribution of Each Explanatory Factor to Wage Differentials (2003-2007)**

||**#1**<br>**(Both sexes) **|**#2**<br> <sup>**(Both sexes,**</sup><br>**2003)**|**#3**<br>**(Both sexes,**<br>**2007)**|**#4**<br>**(All men)**|**#5**<br>**(All women)**|
|---|---|---|---|---|---|
|**Residual**|**45.7**|**44.0**|**47.2**|**47.7**|**42.0**|
|**Personal Attributes**|**32.0**|**30.4**|**31.9**|**25.8**|**37.2**|
|_Gender_|_1.3_|_1.2_|_1.4_|_--_|_--_|
|_Experience_|_1.6_|_1.4_|_1.4_|_2.3_|_0.7_|
|_Dummies for Highest Degree_<br>_Completed_|_29.1_|_27.9_|_29.0_|_23.5_|_36.5_|
|**Sector**|**4.6**|**6.3**|**4.1**|**10.1**|**1.2**|
|**Occupation**|**11.6**|**14.3**|**9.8**|**8.8**|**14.8**|
|**Region**|**5.9**|**5.1**|**7.1**|**7.5**|**4.6**|
|**Year**|**0.2**|**--**|**--**|**0.1**|**0.3**|

> 9 The services sector in NCR employs 78 percent of labor force, accounts for approximately 68 percent of regional GDP.

> 10 As occupation is closely associated with education, the total contribution of education to wage differential could be even higher than 30 percent, as part of it is captured by occupation.

12
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-42-

ANNX 1 continued -

|Country/Date|Survey Name|Execouting<br>Agency|Goographical<br>Coverage|Number of<br>Houscholds|
|---|---|---|---|---|
|El Salvador<br>October 1990|Encuesta de Hogamos<br>de Proposizos<br>Multiples (EHPM)|Ministerio do Planificacion<br>y Coordinacion<br>del<br>Desarrolo Eoonomico<br>y Social|Urban|23,773|
|Guatemala<br>Oct 1986-Aug 1987|Encuesta Nacional<br>Soci-Detmografica<br>(ENSD)|Instituto Nacional de<br>Estadistica|National|9,660|
|Guatemala<br>Apr 4-July 24, 1989|Encuesta Nacional<br>Socio-Demografica<br>(ENSD)|Instituto Nacional do<br>Estadistica|National|10,934|
|Honduras<br>September 1986|Encuesta Continua<br>Sobre Fucrza<br>do Trbajo (ECSFT)|Direccion General do<br>Estadstica y Censos|16 major cities|8,650|
|Honduras<br>September 1989|Encues Pormanente<br>do<br>Hogares do Propositos<br>Multipls (EPHPM)|Direccion General de<br>Estadistica y Ccnsos|National|8,648|
|Jamaica<br>July 1989|Jamlaica Survey of<br>Living Conditions|Statistidcal<br>htitute<br>of Jamaica|National|2,725|
|Mexico<br>Third quartwr 1984|Encuesta Nacional do<br>Ingreso- Gasto de<br>los Hogarps (ENIGH)|Diroccion de Estadistica<br>de Corto Plaza|National|4,708|
|Panama<br>Sep 19-Oct 28. 1979|Encueta do Hogares,<br>Mano de Obra (EMO)|Direcocion<br>de EstAdistica<br>y Conso|National|8,593|
|P4nama<br>August 1989|Encuesta de Hogares,<br>Mano de Obra (EMO)|Diroocion de Estadistica<br>y Censos|National|8,817|
|Parguay<br>June-August 1983|Encuesta do Hogaes,<br>Mano de Obra (EMO)|Direcion General de<br>Estadistica y Coms|Metropolitan Area|1,002|
|Paraguay<br>June-August 1990|Encuesta d Hoepros,<br>Mane do Obra (EMO)|Direccion General do<br>Estadistica y Censos|Metopoltan Ar|1,000|
|Peru<br>July 1985-July 1986|Peru LSMS|Instituto Nacional de<br>_Estica_<br>Informatica|National|4,981|
|Peru<br>June-July 1990|Peru LSMS|Insdtuto Cuanto|Lima|1,385|
|Uruguay<br>Second half 1981|Encuest Nacional de<br>Hogas (ENH)|Direccion Generald<br>Estadistica y Caaos|Urban|9,506|

Continued -
- Derek Leslie and John Wise. The productivity of hours in u.k. manufacturing and production industries. _The Economic Journal_ , 90(357):74–84, March 1980.

- Robert E. Lucas. On the mechanics of economic development. _Journal of Monetary Economics_ , 22:3–42, 1988.

- Robert E. Lucas. Making a miracle. _Econometrica_ , 61(2):251–272, March 1993.

- Domenico J. Marchetti and Francesco Nucci. Labor effort over the business cycle. Temi di discussione (Economic working papers) 424, Bank of Italy, Economic Research and International Relations Area, 2001.

- Rachel M. McCleary and Robert J. Barro. Religion and economy. _The Journal of Economic Perspectives_ , 20(2):49–72, Spring 2005.

- Jacob Mincer and Haim Ofek. Interrupted work careers: Depreciation and restoration of human capital. _The Journal of Human Resources_ , 17(1): 3–24, Winter 1982.

- Nidhi Pande and Juan Pantano. Using a model of location around the clock to recover the amenity value of daylight. 2011.

- Nicola Pavoni. Optimal unemployment insurance, with human capital depreciation, and duration dependence. _International Economic Review_ , 50 (2):323–362, May 2009.

- John Pencavel. The productivity of working hours. _The Economic Journal_ , doi: 10.1111/ecoj.12166, 2014.

- Matthew D. Shapiro. The dynamic demand for capital and labor. _The Quarterly Journal of Economics_ , 101(3):513–542, August 1986.

- Eviatar Zerubavel. _The seven day circle: The history and meaning of the week_ . University of Chicago Press, 1989.

40
**Table 5.** Technical quality of infrastructure services, by country income group

||Electricity<br>(1999)|Water<br>(1997-01)|Telecoms<br>(1997-00)|Transport<br>(1997-00)|Housing<br>(1994-00)|
|---|---|---|---|---|---|
|Income group|_Transmission and_<br>_distribution losses_<br>_as percentage of_<br>_total output_|_Percentage of urban_<br>_households with water_<br>_access that get water from_<br>_piped or well water source_|_Reported_<br>_phone faults_<br>_per 100 main_<br>_lines_|_Percentag_<br>_e of total_<br>_roads_<br>_paved_|_Percentage of_<br>_structures built to_<br>_last 20 years_|
|Low|24.1<br>(33)|89.4<br>(48)|77<br>(48)|28.6<br>(59)|76|
|Lower middle|16.2<br>(31)|84.5<br>(18)|42.9<br>(38)|46.9<br>(46)|94|
|Upper middle|13.6<br>(23)|n.a.|25.3<br>(27)|55.1<br>(33)|97|
|Best<br>practice<br>in OECD|8–12|100|<5|>80|100|

Note : Figures in parentheses indicate the number of countries for which data are available. Source: USAID Demographic and Health Surveys; World Bank, _World Development Indicators_ , various years; International Telecommunication Union; Angel, 2000.

New investment also requires policy changes to ensure that service quality is improved and maintained at a reasonable level, and that assets are effectively operated and systematically maintained. Preventive maintenance lowers operating costs, reduces adverse external impacts, and extends asset life—savings that are generally underestimated when maintenance budgets are cut to meet fiscal targets. There is evidence that shifting resources toward maintenance can have positive consequences for GDP and welfare. For example, Rioja (1999), using data for seven Latin American countries, found that they allocate two-thirds over their resources to new infrastructure investment and only one-third to maintenance, a pattern that was exacerbated by international donors. Shifting just 20% of donor aid away from investment and toward maintenance would raise GDP and welfare by about 15% in the long run.

The only data available on the perceived quality of infrastructure services reflect commercial and industrial perspectives (table 6). No systematic effort has been made to collect comparable data from residential users—a clear information gap.

15
# **5. Poverty Correlates**

After analyzing the poverty profile, this section turns to the factors associated with poverty in rural Mexico (areas with less than 15,000 inhabitants). Many individual characteristics such as labor market association and human capital are important correlates of poverty and the dynamics thereof. This section investigates the marginal impact of each individual attribute on the likelihood that a household falls below the poverty line. The analysis is undertaken applying probit regression techniques.

Other researchers have addressed the determinates of poverty, for example, Ferreira, Lanjouw and Neri (1999) for Brazil, and these authors perform the study for one year, which gives a good but static picture of the situation in a country. Here the analysis is more dynamic in nature as it is based on three ENIGH data from the years 1992, 1996, and 2002. This allows for an evaluation of the evolution of poverty over time and the most important variables determining poverty. Hence, three poverty analyses are performed for rural Mexico as a whole using data from localities with less than 15,000 inhabitants. This analysis reveals: (i) conditional correlation between poverty and characteristics of household heads; (ii) information about the volatility of the impact of the attributes on the likelihood that a household experiences poverty during the beginning and mid-1990s and beginning of the 2000s; and (iii) information about groups that are particularly vulnerable and changes thereof over the past decade.

The status of the household—poor or nonpoor—is regressed on relevant individual and household characteristics. The dependent variable takes the value of 1 if the average percapita income is below the SEDESOL capacity poverty line and 0 otherwise. The dependent binary variable takes the value of one when income is below the indigence line and zero otherwise. The vector of five sets of independent variables includes: (1) attributes of household head: gender, education, experience and labor market connection, whether the household head works, the type of relation with the labor market, sector of employment; (2) family variables: size and age of its members; (3) spouse characteristics: education and labor market connection; (4) geographical characteristics; and (5) dispersed rural areas.

Probit coefficients are not easy to interpret, since they do not represent the standard marginal effects represented by linear regression coefficients. Therefore, rather than probit coefficients we chose to present marginal effects that have a straightforward interpretation.<sup>7</sup>

> 7 The marginal effects for a household head _i_ in the Probit model are simply given by: _mi_ = _d_ Pr _dx_ <u>(</u> _<u>y</u> i_ = 1) =φ( _xi_ β)β

> _i_

> This represents the marginal changes in probability that a household head _i_ is poor due to changes in the underlying regressors. In order to summarize representative marginal effects, the changes are evaluated at the mean of the data.  Since similar conditions apply for marginal effects as for probit coefficients, the same tests for the positively, negativity or significance can be applied.

23