rafmacalaba/datause-encoder-data huggingface dataset:
urban populations. This distinction is of interested, as there are significant gaps between urban and rural areas in El Salvador (Robayo-Abril and Barroso, 2022). In addition, we estimate vulnerability to poverty for all of the 12 departments included in the household survey. This analysis might generate important insights on where investments in resilience and adaptive responses to shocks are most needed.

**As the model relies on several assumptions, our findings are subject to a number of caveats.** First, the model assumes that household consumption/income variation over time can be proxied by the variation in household consumption/income observed for households with the same characteristics. Moreover, it assumes that there is no measurement error in the measurement of consumption/income.<sup>13</sup> The downside of the approach is that it cannot identify the effects of specific shocks, like a drought or flood. Moreover, the vulnerability tool applied in this paper falls short of identifying which exact shocks are the specific drivers behind vulnerability in the country.

# **5. Results**

**Table 2 shows the results of the multilevel mixed-effects estimation.**<sup>**14**</sup> Nearly all variables besides the occupation rate, share of employed in the public sector, and child assistance rate at the municipality level are significant at the 1-percent level. The unemployment rate, being female, self-employment, the number of children, informality, the number of household members out of the labor force, and perceived security, as well as environmental damages, have a negative correlation with per capita household income. The share of self-employed entrepreneurs, human capital accumulation, working in the public sector, and having a financial account positively influence the per capita household income. The number of children attending school is also positively correlated with per capita household income. Higher exposure to crime goes hand in hand with higher per capita household income. This could be due to those with more income being more exposed to crime.

_Table 2: Multilevel Mixed Effects Estimation Results_

|**Variable**|**Per Capita Household**<br>**Income**|
|---|---|
|<br>**Occupation rate**|0.372|
||(0.237)|
|**Unemployment Rate**|-2.321***|
||(0.480)|
|**Share(Self-employed)**|0.696***|
||(0.145)|
|**Share(Entrepreneurs)**|2.457***|
||(0.352)|
|**Share(Public Sector)**|0.299|
||(0.260)|
|**Child Assistance Rate**|0.179|
||(0.123)|
|**Secondary Schooling **|1.008***|
||(0.151)|
|**Dummy for residency area**|0.513***|
||(0.118)|
|**Sex**|-0.057***|
||(0.006)|
|**Single**|0.253***|
||(0.061)|
|**Age**|0.011***|
||(0.002)|

13 Due to consumption smoothing, measurement error is generally more prevalent and varies more across households in income than in consumption. We test our findings to the inclusion of measurement errors. Our main results are robust to measurement errors. While the vulnerability rate decreases when accounting for measurement errors, our main messages persist across the different specifications. 14 We do not report the respective interaction terms.

10
26 In column (5) we check our results by including retrospective information on the proportion participating in the labor market and in education as of 1986. In particular, if the lower mortality detected among cohorts exposed to the exemption is a mechanical artifact of a longer stay in school and consequent lower exposure to labor related risk factors we should expect the estimated causal effects to weaken. However, the results of column (5) are remarkably in line with those of the basic specification of column (1), also based obtained on the sample of 1956-65 born in treated and control towns. In column (6) we further augment the control set with the proportion in the labor market in 1991. Similarly to the one above, this exercise accounts for the possibility that education affects mortality rates indirectly, through the better working opportunities it commands, rather than directly, for example through cognitive or information processing abilities. Again, we find that point estimates are unchanged although estimated somewhat less precisely.

Finally, in columns (7) and (8) we replicate two placebo experiments. In column (7) we impute exemption status to youths born in 1963-65 in control towns and verify whether 2SLS estimates are significant in a sample of 1956-65 born in control and outer control towns, none affected by the exemption. Consistently with our expectations, the estimated effect is highly non significant. In column (8) we perform a similar experiment on cohorts born in 1953-61 from treated and control towns imputing exemption status to youths born in 1959-61 in control towns, exempt when beyond high school. Again, the 2SLS turns out to be non significant even if, as shown above, about one fourth of the 1959-61 cohort from exempt towns did not serve in the army because of the intervention.

# 5.3 Discussion

The results suggest that high school completion lowers subsequent mortality rates by a significant and quantitatively relevant amount: increasing by 1 percentage point the proportion of graduates reduces subsequent 10-year mortality rates by 0.1-0.2 percentage points. Three facts stand out from
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|APPENDIXt<br>Tra<br>|de<br>Expansion<br>Aspects<br>of<br>Structural<br>**Adjustment**<br>**.....................................**|**.....**<br>67|
|---|---|---|
|(a)|Important Substitution vs. Export<br>Promotion<br>..................................|......<br>67|
|(b)|The Effect of Export Subsidies<br>...........|69|
|(c)|Expanding Exports Without Allowing<br>Imports to Increase<br>....................|71|
# **Information, Knowledge and Behavior: Evaluating Alternative Methods of Delivering School Information to Parents**

Pedro Cerdan-Infantes and Deon Filmer<sup>1</sup> _The World Bank_

**JEL Classification** : I21, I28, H52, O15

**Keywords** : Economics of Education; Accountability; School Based Management; Information and Communication Technology

> 1 We thank Santoso and Mayla Lestari for excellent research assistance, as well as Dina Abu Ghaida, Nazmul Chaudhury, and Futoshi Yamauchi who provided valuable comments on an earlier version of this paper.  All errors and omissions are, of course, our own.  Funding for this work was provided by The World Bank and the Dutch Government.
# RUAM fl)PERTY TAXATION LESSONS FROM BRAZIL

# EXEC%TIVE SUMMARY

i. Efforts to increase the responsiveness and accountability of local governments--as well as fiscal constraints at the central level--have prompted an interest in reviving the urban property tax as a major source of municipal revenue. At present, the urban property tax is the most widely used municipal tax instrument in the developing world, but typically accounts for less than half of municipal recurrent revenues.

# A. Why Focus on Property Tax Administration?

ii. As is true of any tax, property tax yields can be increased by either increasing tax rates or reducing losses arising from evasion and maladministration. In the case of the property tax, there is a strong argument for beginning with the latter. Efforts to increase rates alone would exaggerate the inequities arising from existing maladministration, placing the burden of the increese on those who already pay their fair share. Improvements in administration place the burden or those who evade. A focus on administration thus achieves two objectives: increasing revenue while improving fairness.

iii. Brazil's experience with urban property taxation yields several generalizable lessons on both the practice of property tax administration and the process of implementing administrative reforms.

# B. Improving Administrative Procedures

iv. Brazilian municipalities have found solutions--wholly or partly successful--to several of the common problems confronting property taxation in developing countries.

Problem: Rapid Urban Growth Solution: Cross -Referencing v. In a rapidly growing city, a one-time survey of parcels and property characteristics is obsolete as soon as it is completed. Property tax procedures must be capable of discovering and incorporating new construction onto the tax rolls on an ongoing basis. In Brazil, municipalities rely primarily on ongoing field surveys to update property data. Some success has also been achieved with cross-referencing: the use of data provided by other agencies of government to flag changes in the tax base. One form of cross-referencing, the tax clearance, is particularly successful: registrars of deeds will not recognize a transaction unless it is accompanied by a certificate from the assessor, certifying that he has been notified of it.
- 19  Fathelrahman AI, Omar M, Awang R, _et al._ Smokers’ responses toward cigarette pack warning labels in predicting quit intention, stage of change, and self-efficacy. _Nicotine & Tobacco Research_ 2009; **11** :248–253.

- 20  Levy D, de Almeida LM, Szklo A. The Brazil SimSmoke policy simulation model: the effect of strong tobacco control policies on smoking prevalence and smoking-attributable deaths in a middle income nation. _PLoS medicine_ 2012; **9** :e1001336.

- 21  Durkin S, Brennan E, Wakefield M. Mass media campaigns to promote smoking cessation among adults: an integrative review. _Tobacco control_ 2012; **21** :127–138.

- 22  World Health Organization, Ministry of Health in Ukraine. Tobacco Control in Ukraine. 2009.

- 23  Krasovsky K. Sharp changes in tobacco products affordability and the dynamics of smoking prevalence in various social and income groups in Ukraine in 2008–2012. _Tobacco induced diseases_ 2013; **11** :21.

- 24  Konstantin Krasovsky, Tatiana  Andreeva, Alla Grygorenko, _et al._ Tobacco Control in Ukraine. Second National Report.

- 25  U.S. Department of Health and Human Services, National Institutes of Health, National Cancer Institute, _et al._ The Economics of Tobacco Control. 2016.

- 26  Ramos A. Economía del control del tabaco en los países del Mercosur y Estados Asociados: Uruguay. Published Online First: 2006).

- 27  World Health Organization, others. WHO report on the global tobacco epidemic, 2008: the MPOWER package. 2008.

- 28  International Agency for Research on Cancer, World Health Organization. _Evaluating the effectiveness of smoke-free policies_ . IARC Press, International Agency for Research on Cancer, 2009. 2009.

- 29  Levy D, Rodríguez-Buño RL, Hu T-W, _et al._ The potential effects of tobacco control in China: projections from the China SimSmoke simulation model. _Bmj_ 2014; **348** :g1134.

- 30  World Health Organization. Guidelines for the Implementation of Article 6 of the WHO FCTC. 2013.

- 31  Guindon GE. The impact of tobacco prices on smoking onset: a methodological review. _Tobacco control_ 2013.

- 32  Chaloupka FJ, Grossman M. Price, tobacco control policies and youth smoking. National Bureau of Economic Research 1996.

- 33  Lewit EM, Coate D. The potential for using excise taxes to reduce smoking. _Journal of health economics_ 1982; **1** :121–145.

- 34  Chaloupka F. Rational addictive behavior and cigarette smoking. _Journal of political Economy_ 1991; **99** :722– 742.

- 35  Gallus S, Schiaffino A, La Vecchia C, _et al._ Price and cigarette consumption in Europe. _Tobacco control_ 2006; **15** :114–119.

- 36  Townsend J, Roderick P, Cooper J. Cigarette smoking by socioeconomic group, sex, and age: effects of price, income, and health publicity. _Bmj_ 1994; **309** :923–927.

22
- Vieider, F. M., A. Beyene, R. Bluffstone, S. Dissanayake, Z. Gebreegziabher, P. Martinsson, and A. Mekonnen. 2014. “Measuring Risk Preferences in Rural Ethiopia: Risk Tolerance and Exogenous Income Proxies. World Bank Policy Working Paper WPS 7137, December.

- Wrangham, R.W., J.H. Jones, G. Laden, D. Pilbeam and N.L. Conklin-Brittain. 1999. “The Raw and the Stolen: Cooking and the Ecology of Human Origins.” _Current Anthropology_ 40: 567–594.

- World Bank. 2009. _Forests Sourcebook: Practical Guide for Sustaining Forests in International Cooperation._ World Bank: Washington, D.C.

**31 |** P a g e
- 5 -

Notably, even in a small PPP water transaction, competition may also be limited. In the Conakry water lease contract concluded in 1989, six international water companies expressed interest, and only two consortia, which were composed of four initial applicants, submitted their bids. At that time, the city’s population amounted to about one million. However, the water system coverage was exceptionally low at less than 40 percent (Ménard and Clarke, 2002). Hence, along with the competition and scale issues, other factors must affect the optimality of the contract size. In the Conakry case, both diseconomies of scale and diseconomies of customer density seem to have mattered.

In the existing literature, there are few studies addressing a question about how to decide the optimal size of the PPP contracts. Under what circumstances should the auctioneers downscale or upscale the contract size in order to attract more potential bidders? The paper aims at providing certain qualitative answers to those questions.

The following section is organized as follows. Section II provides an overview of the degree of competition in the water-sector PPP transactions, using the two comprehensive databases on PPP transactions in the world. Section III estimates a cost structure of water and sewage service operations where the private sector is partially or fully involved. Finally, Section IV discusses some tentative policy implications for designing efficient PPP auctions in the water sector.

# **II. OVERVIEW OF COMPETITION IN PPP TRANSACTIONS FOR WATER SERVICES**

Regardless the way in which the private sector is involved—i.e., traditional construction procurement or more comprehensive PPP contracts—the degree of competition generally tends to be low in the infrastructure industry. There are a very small number of active players in each infrastructure sub-sector, presumably because of the significant complexity and value involved in the infrastructure provision.
Figure 1. Indexed wage bill by treatment group over time <!-- Start of picture text --> 110<br>105<br>100<br>95<br>90<br>85<br>80<br>75<br>70<br>No letter Soft letter Strong letter<br>Index (January=100)<br>January February March April May June July August September October November December<br><!-- End of picture text --> Source: Authors’ calculations. Figure plots the aggregate payroll declared by the firm given random assignment to the employer letter. Values are indexed relative to January wage levels (wage in January=100 for all treatment groups) _i. Cross sectional impacts of letters on declaration behavior_ Table 1 below reports the difference in difference estimators on the treatment effect between the baseline (January) and each subsequent period, ignoring the time trend in payroll declarations over the 12-month period (which is incredibly noisy, as shown above). The results highlight the differential impact of the soft letter on the payroll reported by the firm, and no impact of the strong letter (and in fact an immediate negative, albeit marginally significant impact) relative to the group assigned to receive no letter. The important findings from this estimation are the strong negative time effect in the first few months after the onset of the pandemic (reflecting a drop in economic activity, and thus wages paid out) and the isolated effect of the letters. Payroll declarations dropped between 4 and 30 percent between March and July (significant at the 1% level except for the July declaration month), returning to a statistically identical amount beginning in August and even increasing by 3% in December (albeit only significant at the 10% level). The first post-implementation filing period (February, where the deadline for reporting was March 20<sup>th</sup> , about one week after the onset of the pandemic) in theory would not have been influenced by the pandemic (given that February activity was not affected by the pandemic) but could have influenced the behavior of firms that at the time the letter was received had not yet submitted their February declaration. In fact, we find a small, marginally significant negative impact of the strong tone letter (3% reduction in payroll reported, significant at the 10% level). Given that treatment groups were balanced across those who had and who had not submitted a declaration for February by the time the letters were delivered around March 5<sup>th</sup> , this result is interesting as it might point to backfire effects of strong language at times of elevated uncertainty among taxpayers. We will discuss more of this in section VI.

11
suggest that a half million Haitians live in the Dominican Republic. Given a total population for Haiti of 9.5 million in 2005 (based on an adjusted figure from the country’s 2003 National Census), this means that roughly 10 percent of the Haitian-born population lives abroad. It is unclear, however, in what direction this biases the figures presented here.

# **4. Determinants of School Attendance**

In this section we switch from examining the long-run historical evolution of school attainment to consider the more recent story of school attendance. Figure 5 summarizes attendance rates by age, based on children observed in the 1994, 2000, and 2005 DHS. Substantial increases in attendance rates between 1994 and 2000 are evident at every age, and overall the primary attendance rate (for children 6-14) increased from 73.6 to 86 percent between those two years. The years 2000-05 registered a slight decline in primary school attendance, to 83.4 percent. Table 2 shows attendance rates disaggregated by area of residence and gender. The rural-urban gap in attendance rates diminished greatly between 1994 and 2000 but was still 11 percentage points in 2005. The gender gap in schooling reversed in the most recent survey, so that there were more girls than boys attending school in 2005. This phenomenon follows the recent trends in the Central American and Caribbean region as whole, where girls have higher attendance rates, lower dropout, and better overall performance.

The large expansion of school supply during the late 1990s appears to have narrowed the gap between rich and poor in school attendance. Figure 6 shows the attendance rates for the years 1994, 2000 and 2005, by quintiles of a wealth index. From 1994 to 2000 the attendance rates for the poorest quintile increased from 51 percent to 71 percent, while the attendance rates increased only slightly for the richest quintile. Gaps, however, persist: in 2005 67 percent of children in the poorest quintile attended school compared to 95 percent of the children in the top quintile.

In Figure 8, we disaggregate the attendance figures by preschool, primary, and secondary for 2000 and 2005.<sup>11</sup> This figure reveals an important aspect of Haitian education: a very large number of students are “over-age” for their school level. In 2005, a large proportion of older children were attending preschool: 33 percent of children age 8, 17 percent age 10, and 8 percent

> 11 The 1994 data is excluded from this analysis because the 1994 survey did not capture complete information on the level and grade of students attending school.

8
their employers from the Longitudinal Research Database (LRD). Since they have plantlevel information on costs, they can estimate plant-level earnings equations, which represent the aggregation of individual-level wage equations over workers employed in a plant. It is worth pointing out that Hellerstein, Neumark, and Troske (1999) estimate the wage regression at the worker and firm levels to test the robustness of the firm-level estimations and do not find significant differences. They proposed an innovative model discussed in Section IV to estimate firm level equations since productivity is available at the firm level only. The model, based on traditional economic theory, includes an extended Cobb-Douglas production function and a standard wage determination model. By simultaneously estimating production functions and wage equations at the plant level, they compare relative marginal products to relative wages of different types of workers, leading to new evidence on productivity-based and nonproductivity-based explanations of the determination of wages. This represents an improvement over Verner (1999) since it allows for variation in human capital characteristics to have a differentiated impact in worker productivity. They find that the higher wages of medium-aged and older workers is justified by their higher relative productivity. On the other hand, even though women earn lower wages, they are not significantly less productive, indicating the existence of discrimination.<sup>2</sup> This paper follows Hellerstein, Neumark and Troske’s (1999) approach to the study of wage regressions by linking them to productivity functions. This paper uses a unique Mexican data set that combines data on manufacturing firms and their workers to estimate relative marginal products and compare them with relative wages. The paper is organized as follows: section II introduces the data, section III presents the variables used in this work along with some descriptive statistics, section IV presents the framework and explains the methodology, section V analyzes the results, in general, by firm size and examines training by gender, and section VI offers conclusions.

> 2 Hellerstein and Neumark (1999) use the same methodology to test for gender discrimination in Israel. Hellerstein and Neumark (2004) use the 1990 Decennial Employer-Employee Dataset (DEED), a more recently constructed matched employer/employee data set for the United States that contains detailed demographic information on workers (most notably, information on education). Using the same methodology, they use the new to update and expand on previous findings.

3
CLEMENS & TIONGSON Table 3: **Checking discontinuity for 899 households in survey sample**

|Outcome|_μs↑_0|_μs↓_0_−μs↑_0|s.e.|_p_-val.|band-<br>width|
|---|---|---|---|---|---|
|_Migration behavior after a_|_pplication_|||||
|Applicant deployed?|0_._0172|0_._683|0_._0407|_<_0_._001|1_._157|
|Anyone now in Korea?|0_._155|0_._402|0_._0540|_<_0_._001|1_._379|
|Anyone ever in Korea?|0_._241|0_._480|0_._0551|_<_0_._001|1_._307|
|Anyone now abroad?|0_._422|0_._256|0_._0608|_<_0_._001|1_._560|
|Anyone ever abroad?|0_._672|0_._199|0_._0522|_<_0_._001|1_._801|
|_Traits of applicant at the t_<br>|_ime of appl_<br>|_ication_<br>||||
|Age|30_._16|0_._450|0_._556|0_._418|1_._768|
|Female|0_._216|0_._00591|0_._0521|0_._910|1_._941|
|College grad.|0_._345|_−_0_._0305|0_._0593|0_._607|1_._716|
|Months experience|64_._27|10_._93|6_._808|0_._108|1_._472|
|Employed|0_._216|0_._0416|0_._0533|0_._435|1_._956|
|Married|0_._460|0_._0246|0_._110|0_._823|2_._373|
|Region: NCR|0_._198|_−_0_._0340|0_._0487|0_._485|1_._834|
|Region: Luzon|0_._681|0_._0118|0_._0585|0_._840|1_._857|
|Region: Visayas|0_._0862|0_._0138|0_._0365|0_._706|1_._844|
|Region: Mindanao|0_._0345|0_._00837|0_._0242|0_._729|1_._825|
|Test batch 1|0_._0259|_−_0_._0187|0_._0164|0_._255|1_._819|
|Test batch 2|0_._144|_−_0_._0755|0_._0775|0_._329|2_._268|
|Test batch 3|0_._276|0_._0710|0_._0991|0_._474|2_._245|
|Test batch 4|0_._366|0_._00947|0_._107|0_._929|2_._413|
|Test batch 5|0_._181|_−_0_._00961|0_._0481|0_._842|1_._911|

Data for households in survey sample. _N_ fail( _s<_ 0) = 460, _N_ pass( _s_ ⩾0) = 439. _μs↑_ 0 is the mean of the local regression using data for test-failers only, evaluated at _s_ = 0. NCR = National Capital Region. “Luzon” omits NCR. “Anyone” means any household member. Optimal bandwidth selected following Imbens and Kalyanaraman (2012). Triangular kernel.

36