rafmacalaba/datause-encoder-data huggingface dataset:
with age 65 and above are from the World Development Indicators database of the World Bank.

In a robustness test, we also control for the ability of banks to engage in insurance-related activities. Although insurance companies are not directly subject to deposit insurance regulation, they are indirectly affected if banks can sell insurance products because they produce substitute products that directly compete with the regulated banking industry. A number of countries permit commercial banks to sell insurance. In those countries, private interest theory predicts that the insurance industry lobby would be particularly concerned about deposit insurance because it may give banks a competitive edge in the provision of certain financial services. Thus, private interest theory predicts that coverage levels of deposit insurance would be smaller in countries where banks may sell insurance. The public interest view suggests that activity restrictions prevent the exploitation of scope economies and, therefore, have negative wealth effects. Deposit insurance could compensate banks for this opportunity cost. Public interest theory thus also predicts that deposit insurance coverage is lower in countries where banks can sell insurance. Alternatively, if it is in the public interest to protect the franchise value of banks from competing industries, we may find the opposite link. Particularly in countries with weak banking systems, it could be in the interest of the public to restrict competition among banks. To measure the effects of the rival insurance industry, we use a variable RESTRICT that captures the degree to which the country restricts banks to sell insurance. RESTRICT can take values between 0 and 3, with a higher score indicating more restrictions. A score of 3 indicates that banks are prohibited to sell insurance, and a score of 0 indicates that there are no restrictions on banks to sell 14
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survey where this tendency was maximal, measurement error would offset transitory components completely and we would observe no change, i.e. the opposite of what the CILSS results indicate.

While we certainly think that the data do include a measurement error component, we think that the first differencing procedure has largely eliminated it, and survey experience does not support that households tend to flip-flop from year to year in the magnitude and direction of error. Thus we think that the results in Table 2 measure in the first place genuine changes in transitory welfare.

Of course, perhaps an even more convincing argument is whether economic and social characteristics are systematically related to the observed changes, and for this we turn to the rest of the results. If measurement error were to dominate, we would expect to find no or low association between measured change and economic and social characteristics and no significant increase in explanatory power of the regression from adding such variables. The results in Table 2 show that there are systematic patterns in the observed welfare changes. Adding the base-year variables increases corrected R<sup>2</sup> from 0.41 to 0.47, and adding the first difference variables further raises it to 0.60. This also suggests that both initial conditions and the pattern of changes in endowment affect changes in welfare.<sup>5</sup> Among the human capital variables, both household composition and level and type of education are strong predictors of change in welfare. Larger families suffer larger welfare losses. Especially the presence of adult males contributes to declines in welfare. This indicates that the earnings potential of males is on average lower than the claim they make on household spending.

Years of education has a strong positive effect on welfare change. Each

> 5/ We also estimated the model without base-year expenditure per capita as regressor. This proved to be an inferior specification and the results are not shown here.
# 6. Results

## _Learning and Enrollment Outcomes_

We begin with a simple comparison of the mean drop-out rates, failure rates and repetition rates using the two alternative definitions of treatment or coverage by PEC, and without controlling for any background factors (see table 4). Put in another way, these preliminary estimates do not make any effort to control for the fact that selection into the program is voluntary. Such a comparison of the key outcome variables would be more appropriate if participation or no-participation into PEC was determined through a national lottery, where each school had an equal chance of participating into PEC. Given that in reality, participation into PEC is determined on a voluntary basis, it is important to keep in mind that such a comparison is more useful for seeing differences and trends between PEC and non-PEC schools rather than for determining the causality of outcomes.

The CS estimates show that PEC schools have half a percentage point higher dropout but two percentage points lower failure and repetition rates than non-PEC schools. The BA estimates, on the other hand, show that between 2000 and 2003, the mean dropout rate in PEC’s schools decreased by 0.41 percentage points and mean failure and repetition rates decreased by 0.87 and 1.06 percentage points.

A simple differences-in-differences comparison between PEC and non-PEC schools shows that dropout rates decreased by 0.20 percentage points faster in PEC schools while failure rates decreased by 0.18 percentage points faster in non-PEC schools. Since these estimates do not control for any background factors, they may not reflect PEC’s true impact.

In table 5 we present estimates of program impact by taking into account the role of observable characteristics likely to influence participation into the program.<sup>11</sup> Our discussion focuses on comparing and contrasting the various estimates of program impact. We first 22
62 Chernichovsky, D., and Coate, Ds. "An Economic Analysis of the Diet, Growth, and Health of Young Children in the U. S. A." in Salkever, D., Serageldin, I. and Sorkin A. (eds) Research in Human Capital and Develooment. Vol. 3 JAI Press. Greenwich., 1983.

Chernichovsky, D., "The Economic Theory of the Household and Impact Measurement of Nutrltion and Related Health Programs" in Klein, R.E., et al (eds) Evaluating the Impact of Nutrition and Hcalth Programs, New York, Plenum Publishing corporation.1979.

Chernichovsky, D. and Meesook, O.A. "Urban-Rural Food and Nutrition Consumption Patterns in Indonesia." in Serageldin, I. and Sorkin, A. (eds.) Research in Human Resources (Forthcoming) Chernichovsky, D. and Meesook, 0. A. "Food and Nutrition Consumption in Indonesia" World Bank Working Series Papers no. 670 Washington D.C., 1984.

Chernichovsky, D. and Meesook, O.A. "Utilization of Health Services in Indonesia". Social Science and Medecine. 23:611-620, 1986.

Clay, E., Chambers, R., Singer, H., Lipton, M."Food Policy Issues in Low-Income Countries". World Bank Staff Working Paper No.473, 1981.

Deaton, A. "Quality, quantity, and spatial variation of price". World Bank. Mimeo. Washington D.C. 1986.

Food and Agriculture Organization. Food and nutrition education in the primary school. FAO Nutritional Studies, NO. 25.

Food and Nutrition Board, Commission on Life Sciences and National Research Councils., Nutrition Issues in Developing Countries for the 1980's and 1990's - Proceing asympos3um, 0asiington D.C.: National Academy Press, 1986.

Gittinger, J.P. Leslie, J. and Hoisington, L. (eds). Food Policy Integrating Supply, Distribution and Consumption. Baltimore, John Hopkins University Press. 1987.

Grossman, M. The Demand for Health - A Theoretical and Empirical Investigation, New York: Colombia University Press, 1972.

Helled, P.S. and Drake, W.D. "Malnutrition, Child Morbidity and the Family Decision Process". Journal of Development Economics 6;203-235. 1979.

Hornik, R.C. "Nutrition Education: A State-of-the-Art-Review". ACC/SCN State-of-the-ARt-SeriesNutrition Policy Discussion Paper No. 1. New York: United Nations, January, 1985.

Horton, S. and King, T. "Labor Productivity: Un Tour D'Horizon." Vorld Bank Staff Working Paper No.497, 1981.
**Figure 17: Perceptions of Quality of SUS Services in Northeastern States (percentage), 2012**

<!-- Start of picture text --> Sergipe<br>Rio Grande do Norte<br>Piaui<br>Pernambuco<br>Paraíba<br>Maranhão<br>Ceará<br>Bahía<br>Alagoas<br>0 20 40 60 80 100<br>percentage<br>Good Intermediate Poor<br><!-- End of picture text --> _Source_ : System of Social Perception Indicators (SIPS), IPEA, 2012.

**Figure 18: Perceptions of Quality of SUS Services in Southeastern States (percentage), 2010**

<!-- Start of picture text --> São Paulo<br>Rio de Janeiro<br>Minas Gerais<br>Espírito Santo<br>0 20 40 60 80 100<br>percentage<br>Good Intermediate Poor<br><!-- End of picture text --> _Source_ : System of Social Perception Indicators (SIPS), IPEA, 2012.

# **VII. CONCLUSION AND OUTLINE OF A PROPOSED RESEARCH AGENDA**

The paper explores the relationship between performance management and changes in sectoral outcomes using a variety of methods. The results of the empirical analysis support the expectation that the implementation of team-level results agreements and performance-related pay is associated with significant and positive changes in outcomes in the security and education sectors, at least in the short and medium term.

Minas Gerais registered positive results in the education sector, surpassing the average of the southeastern region. This state also has a lower homicide rate than other states with the same GDP per capita. Similarly, Pernambuco has reversed a rising homicide rate and recorded considerable gains in the education sector through the implementation of performance agreements. In the case of Rio de Janeiro, the introduction of performance agreements and a teacher bonus corresponded with the first positive change recorded in educational achievements since 2005. Although Ceará is not using performance-related pay yet, its monitoring system and results-based management model has translated into sizable and consistent gains in the education and health sectors.

It is important to underscore that the paper does not analyze unit or team level data, which would be necessary to draw more rigorous conclusions about how results-based interventions affect the behavior of civil servants and outcomes over time. Therefore, the results should be interpreted with appropriate caution.

However, from an empirical perspective, the relationship between performance management and outcomes in service delivery remain largely understudied, in particular in the context of developing countries. Having reviewed the existing evidence, this paper highlights the pressing need to expand data collection and analysis efforts at the subnational level. Many of the 28
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Malaysia and Mexico are extending already sophisticated IT systems, and in the Philippines, the supervisory authority has identified the expanded use of IT in the stock exchanges and stock brokers' offie s as a key tool in detecting and reducing market manipulation, insiler trading an; other breaches of the law.

Increasing automation of securities market functions is a trend which will increase in the 1990s, perhaps particularly in emerging markets. This is likely because competition between software and hardware providers will continue to bring costs down and because the increasingly international nature of securities market trading will encourage a convergence of standards of efficiency, fairness and stability towards those offered in highly computerized markets. An examnple of the first is the Reuters Instinet system which is less expensive now than it was five years ago and yet offers enhanced services. An example of the second is the internationally agreed standards for efficient clearance and settlement proposed by the Group of Thirty which imply the use of computerization.' On nhe other hand, it should not be assumed that a high level of computerization is necessary or desirable in all situations. For example, in small but growing securities markets, upgrading manual systems and gradually increasing the use of computer support is a viable approRch. Deciding on the appropriate pace and sequence for modernization requires a balanced consideration of Lar factors: the scope and scale of changes needed to accommodate the volume of transactions conducted on the market; the capacity of the local infrastructure, institutions and personnel to assimilate the changes; supervisory imperatives such as the need for effective market monitoring, enforcement of trading rules, and investigation of unlawful transactions; and the cap+al and recurrent costs of the modernization program in relation to the size of the market and the way the costs will be met. No single factor dominates and no simple formula applies in all situations, so striking the right balance between the four factors is a matter of country-specific analysis. !t follows that no single prescription will arise from the analysis. The range of outcomes will cover the spectrum from improved manual record keeping and paper handling, through small-scale computer assistance for these functions, to sophisticated fullycomputerized trading and clearance systems.

Nor should it be assumed that iT is the solution to securities market development in emerging market countries. In fact, IT is but one element of the institutional arrangements making up a securities m,larket. Development of the market is facilitated by improving such institutional arrangements but derives its impetus from other factors. Chief among these is the creation of a conducive

**The** G30 **proposals are** a ssed in **detail** in section IV.
interventions, has observed.<sup>26</sup> But the effects of any future expansion of the project can be rigorously evaluated—at relatively low cost.

_One Way to Rigorously Measure the Impacts of the MVP_ A rigorous impact evaluation of the Millennium Villages can be conducted in the future. One good way to do this is to randomize treatment between members of several matched pairs of village clusters. First, several pairs of village clusters would be chosen using a systematic matching procedure to resemble each other on a number of observable traits—such as population, average education, and a wealth index. This could be based on existing climate and population data, without additional surveys.<sup>27</sup> Second, only one cluster per pair of clusters would be selected—at random—to receive the package intervention. This eliminates the possibility of subjectivity in the choice of untreated comparison villages. Third, surveys of various development indicators would be performed for all the treated and untreated clusters, at multiple time points such as 0, 5, 10, and 15 years after the intervention began. Differences between the treated and untreated clusters would be scientifically attributable to the intervention. These differences could be measured for all of the indicators in Section 4 above, plus household income, consumption, and assets.

This design would address the weaknesses of the current evaluation protocol we discussed above. It would allow measurement of short- and medium-run indicators of various kinds, make the choice of treated villages clear and objective, make the choice of comparison villages clear and objective, and allow for complete baseline data on both groups. This would allow a rigorous measurement of the overall impact of the entire package, across all treatment sites, relative to no intervention.

> 26 Esther Duflo is a professor of economics at the Massachusetts Institute of Technology. Parker (2010) paraphrases an email sent by Duflo to MVP creator and Columbia University professor Jeffrey Sachs in 2009 stating that in her opinion it was “too late” to use rigorous evaluation methods on the existing program, though “the methods could be used in any later expansion.”

> 27 This could be done using widely-available rainfall data plus rich information contained in the most recent census. Recent censuses are available for many countries, including Ethiopia (1994, 2007), Ghana (2000), Kenya (1999, 2009), Malawi (1998, 2008), Mali (1998, 2008), Nigeria (1991, 2006), Rwanda (2002), Senegal (2002), Tanzania (2002), and Uganda (2002). Access policies for the census microdata vary by country. For many countries that do not make their microdata publicly available, given the extremely high profile of the MVP, we believe that it is likely that access to the data sufficient to conduct the procedure we describe could be negotiated.

25
promote housing finance systems. Wolswijk (2006) analyzes mortgage debt holding among households in the older member states of the EU and finds that financial deregulation, among others, have been important drivers of mortgage debt growth.

Regression analyses of tenure status, i.e. the determinants of being a mortgage holder, show that there are still significant structural differences between old and new EU countries. Specifically, the probability of being a mortgage holder is _less_ sensitive to income and age in the new than in the old EU countries. In general, it is more difficult to explain variations in tenure status in the new than in the old EU countries. More important, we find no evidence that prospective, rather than current, income determines the use of mortgage finance in any of the new EU countries, though this seems generally true for the older EU members as well. Our empirical analysis also suggests that in spite of the rapid aggregate increase in mortgage finance, mortgage holders are not more likely to report financial burden or incur arrears than renters or outright owners. In fact, the likelihood of financial distress among mortgage holders in the new EU member countries fell between 2005 and 2007, possibly reflecting the generally strong income growth experience by these countries over this period.

While this paper represents significant progress in understanding the benefits and costs of increasing access to household and more specifically mortgage credit, some caution is warranted in interpreting the results. First, given the nature of the SILC surveys as instruments to measure living standards and poverty incidence, we do not have as much information on households’ financial assets, liabilities and flows, as we would like. Second, although we have three years of data, these are repeated cross-sections and we do not have the longitudinal dimension available. Finally, our sample ends in 2007, so the results reported here refer the period before the onset of the global financial crisis.

The remainder of the paper is structured as follows. Section 2 discusses the data we use. Section 3 provides an aggregate analysis of recent trends in mortgage lending across transition 6
characteristics, shipping technology, fuel costs, and economies of scale at the vessel level. We discuss these components in Sections 4.1–4.3. The fixed cost component is a function of the vessel prices and port infrastructure. We discuss the port infrastructure in Section 4.4.<sup>24</sup>

# **4.1 Market Structure: Network Organization, Industry Concentration, AnnualContract versus Spot Markets**

Containership transportation is organized similar to bus services, with the routes labeled as rotations and ports serving as bus stops. Major rotations include multiple ports, with the total duration of a rotation often exceeding 60 days. Both ports and ships vary in their capacity and cost of transportation. As discussed above, the average containership has become much larger over the last decades. These larger ships require automatic and modern equipment such as engines and cranes to load and unload them. The developments in shipping technology have created the need for ports to develop their infrastructure to handle these larger ships, such as special berths, bigger cranes, more storage space for the increased cargo size, and more developed and efficient port logistics. Since the investment in port infrastructure requires a large amount of capital and human resources, the largest container ports handled a disproportionate volume and value of world trade. Notteboom (2018) calculated that in 2015, the 20 largest container ports handled 312.5 million TEU, which is 45% of world trade. The network structure of the containerized shipping was explored in detail by Heiland et al. (2019) and Ganapati et al. (2020). The latter also showed that a handful of ports play a disproportionately significant role:<sup>25</sup>

**Stylized Fact 18.** _Larger ports serve as hubs accommodating larger ships, making hub-to-hub transportation significantly cheaper and hubs essential for maritime trade._

Most networks in liner focus on cross-sectional data The papers analyzing shipping analysis. literature is scant when it comes to documenting the network evolution of containerized shipping 24The discussion on (i) crewing and registration and (ii) trade and transport facilitation is beyond the scope of this review.

25They also document that, for a given leg, a 1% increase in traffic reduces transportation costs by over 0.05%.

27
than the truth. As discussed above, the data matches both the over-inference result as well as the dependence of updating behavior on the sequence of signals.

This paper finds that the belief on mortality rate expressed by inexperienced potential migrants in Shrestha (2017) is consistent with their experience and their updating behavior. A suggested explanation for the high expected mortality is that the potential migrants observe migrant deaths in their own district for a few months to form their priors, but at the same time they commit the fallacy of the law of ‘small’ numbers which leads them to over-infer from the migrant deaths that they see.

Finally, this paper presents a real world setting in which a learning fallacy can have large welfare consequences for potential migrants. Incidences of deaths, which should convey zero information to a fully informed potential migrant, seems to thwart migration substantially. Policies designed to make individuals less ‘scared’ of migrant deaths abroad could potentially have large welfare effects.

17
Table 8: Falsification tests – placebo regressions

||Nati|ves’|Nat|ives’|
|---|---|---|---|---|
||languag<br>|e score<br>|math<br>|score<br>|
||(1)|(2)|(3)|(4)|
|Treatment variable:|||||
|Actual treatment: Share of recent immigrants in grade|-3.08***|-2.88***|-1.55|-1.49|
||(0.981)|(0.977)|(0.913)|(0.906)|
|Placebo 1: Share of recent immigrants in next grade|-0.361|0.120|0.038|0.179|
||(1.241)|(0.112)|(1.201)|(1.192)|
|Placebo 2: Share of recent immigrants in previous grade|0.526|0.534|-0.472|-0.294|
||(1.189)|(1.181)|(1.092)|(1.071)|
|Grade-level controls||✓||✓|
|Grade effects|✓|✓|✓|✓|
|School-by-year effects|✓|✓|✓|✓|
|Number ofgrade-level observations|12,053|12,053|12,053|12,053|

_Notes._ ***: significant at the 1% level, **: significant at the 5% level, *: significant at the 10% level. Robust standard errors clustered at the school level are reported in parentheses. Each row reports the coefficients from separate regressions of the effect of the corresponding treatment on natives’ average test scores. Controls for mean characteristics at the grade level include: the share of students by level of parental education, the share of female students in the grade, the share of disadvantaged students according to the Dutch weighting system, the share of students that repeated a grade, the average class size in the grade, teacher’s years of experience, the total number of students in the grade and its square. Controls for linear school trends are also included. Estimates that do not control for linear school trends are quantitatively very similar and available upon request.

55