rafmacalaba/datause-encoder-data huggingface dataset:
5. Part A shows the results of estimating (4) and part B, of (5). Results are quite consistent with each other, and in this sense they seem robust. In every case, we verify the hypothesis that _higher_ commissions are associated with _lower_ adjusted interest rates. Depending on the specification, an additional percentage point in commissions is related to a lower interestrate cost between 9 and 18 basis points.<sup>16</sup> To further understand the importance of this result, assume for example that the annuitant pays 1, 1-c is received by the LICO and c by the broker. To simplify, let us work with perpetuities and assume that they are a function of this commission, A(c). We find that that the adjusted annuity rate R(c)=A(c)/(1-c) decreases with c (dR(c)/dc<0). Differentiating we find implying that _dA_ ( _c_ ) < − _R_ ( _c_ ) _dc_ (7) Therefore, even if annuitants get all of the higher commissions back as rebates, it is actuarially unfair, since the annuity falls by more than the interest rate times the additional commission. In other words, from the perspective of the pensioner, higher commissions definitely are associated with lower net present values.

# **5.3 Changes through Time**

Considering the possibility that our results are driven only by the significant drop in commissions, we reestimated (2) in successive rolling quarters. Because these are essentially cross-sectional regressions, we did not subtract the PRC20 interest rate, and we used a common constant. Results are presented in <mark>figure 5,</mark> together with the LICO-level commissions. We do find that the significantly negative relationship between commissions and adjusted annuity rates exists only until mid-2001 and then disappears.

# **5.4 Other Evidence**

Additional evidence is consistent with the idea that the reported structural changes are due to changes in competition levels or attributes. Indeed, in November 28 of 2000 begins the official Senate discussion of a new draft pensions law to implement a mandatory system of electronic annuity auctions, which would also impose a cap on commissions. Among other things, this new law would have established that “(pension fund) affiliates may select one of the three best pension offers or any other offer which is at least equal to the average of the top three [annuity rates] minus 2 percent”, which would indirectly limit the maximum pension dilution. In addition, it “forbids insurance companies, brokers, sales agents and other people intervening in the process of selling annuities to offer incentives or benefits other than those established in the law, with the purpose of contracting pensions using this modality”.<sup>17</sup>

> 16 For the specification with the lagged dependent variable, we divided the commission coefficient (–

> 0.0304) by one minus the average coefficient on the lagged dependent variable.

> 17 See Diario de sesiones del senado, publicación oficial legislatura 343a. See references.

11
# **_Table A4.8b Relationship between project design and IEG binary outcome rating,_** **_<u>probit</u>_**

||**Dependen**<br>|**t variable: Bin**<br>|**ary IEG outc**<br>|**ome rating (Sa**<br>|**tisfactory=1**<br>|**, Non-satisfa**<br>|**ctory=0)**<br>|
|---|---|---|---|---|---|---|---|
|**Design index components**|**(1)**|**(2)**|**(3)**|**(4)**|**(5)**|**(6)**|**(7)**|
|Number of project|-0.121|||||||
|components|(0.084)|||||||
|Number of project||-0.026||||||
|sub-components||(0.019)||||||
|Number of PDO indicators|||-0.047|||||
|at entry|||(0.030)|||||
|Number of intermediate||||-0.032***||||
|indicators at entry||||(0.013)||||
|PDO rating|||||-0.191|||
|(1 high - 4 low)|||||(0.179)|||
|Results framework rating||||||-0.373**||
|(1 high – 4 low)||||||(0.154)||
|Extent of changes to|||||||-0.253|
|project components|||||||(0.157)|
|Observations|120|120|120|120|120|120|120|
|Pseudo R-squared|0.073|0.073|0.076|0.099|0.068|0.097|0.077|
||**Dependen**<br>|**t variable: Bin**<br>|**ary IEG outc**<br>|**ome rating (Sa**<br>|**tisfactory=1**|**, Non-satisfa**|**ctory=0)**|
||**(8)**|**(9)**|**(10)**|**(11)**||||
|**Design index**|0.280***<br>(0.091)|||||||
|Project Characteristics||0.082***||||||
|Index for comparison<sup>1</sup>||(0.067)||||||
|CPIA at Approval|||0.395*<br>(0.231)|||||
|Change in CPIA over||||-0.445||||
|Project Lifetime||||(0.312)||||
|Observations|120|120|120|120||||
|Pseudo R-squared|0.121|0.070|0.058|0.053||||

Notes: This table provides estimates of IEG Outcome Ratings regressed on common design features and quality ratings. Using Principal Component Analysis , an Index is created for Design based on these features and ratings. (1) For comparison, an Index is created for Project Characteristics based on the log of original commitment, preparation cost, first and second half supervision cost, the change in project size from approval to closing, and the time it takes projects to move from one key milestone in the project lifecycle to another. The table also provides the estimates of regressing IEG Outcome on two country variables, CPIA score of project country at approval and the change in the score over the project lifetime.

61
and Yu, 2002; Blanes-Cristóbal, 2003; Hatzigeorgiou, 2010). The main exceptions are Felbermayr and Toubal (2008), who perform cross-section analysis, and Felbermayr and Jung (2009), who use panel data. Other papers focus on the trade and migration links between province/regions of a country and its trading partners (Wagner et al, 2002; Co et al., 2004; Bardhan and Guhathakurta, 2005; Herander and Saavedra, 2005; Dunlevy, 2006; Bandyopadhyay et al., 2008; Peri and Requena-Silvente, 2010).

The empirical analysis requires the matching of bilateral trade, migration and FDI data. Migration data are generally obtained from macro sources such as censuses, while trade and FDI data come from relevant customs and financial administrations. Singlecountry studies find a positive relationship, but are likely to suffer from omitted-variable biases as we cannot implement country fixed effects due to the nature of the data, an issue emphasized strongly in the literature on gravity models (Anderson, 1979).

Felbermayr and Jung (2009) control for country-pair unobserved heterogeneity (and multilateral resistances) using a panel for 1990 and 2000, but they can cover only North– South trade. These authors find a significant and positive effect as well. In a more recent paper, Parsons (2012) uses the Özden et al. (2011) database of global bilateral migration for 1960–2000. The long-term panel structure of the data, combined with the fact that they cover all countries, enables Parsons (2012) to control for pairwise heterogeneity as well as to instrument properly for migration patterns by using historical stocks.

The parallel analysis can be extended to linkages between FDI and migration flows. Similar macro sources are used for bilateral migration and FDI data. Among the important examples, Kugler and Rapoport (2005) use a standard gravity model while Javorcik et al. (2010) use the US data with education levels to analyze the role of skilled 39
to the overall population, mitigating general equilibrium concerns.

# **2.2 Sample Population**

Mali Health identified 1804 eligible children in the census. At baseline, 1732 children were included in the study. The roll-out of AfH started three months after the baseline in early 2013. By the second survey round in Fall 2013, there were three refusals and an additional 162 children could not be surveyed, including five who passed away.<sup>10</sup> The remaining sample in 2013 contains 1567 children from 990 households in 642 compounds. Attrition between baseline survey (and treatment assignment) and follow-up is 8-11%; the group differences are not significant. At follow-up, 201 children were newly reported by the caretakers of existing study children. Newborn children in the treatment households were enrolled in AfH either by the healthworkers, or by a program officer who visited study households every three months. Since the caretakers’ treatment choices are our outcome of interest, we consider these children part of the sample. We only included newly reported children 3-12 months old. Table C.2 in the online appendix shows the sample over the study period.

Table 1 shows sample characteristics and balance. We find few significant differences between the treatment groups. There are on average more than six people in a household, due in part to polygamous marriages. About half of the household heads are literate and 73% speak the _lingua franca_ Bambara. Households report on average about US$6600 (median $1070) in assets, with an owner occupancy rate of 41%. Only 13% of household heads earn a regular salary. The closest source of formal health care is on average 546 meters away (less than 0.4 mile). The average child age is 2.7 years. The study children are well below agetypical weight for height (normalized using W.H.O. reference distributions). We control for these covariates in the analyses below, except weight for height, which may be an outcome of parents’ health behaviors.

> 10Our sample population moves a fair amount, but households that relocated inside the program area were followed and remained eligible for Action for Health; only children who left the area entirely or could not be tracked are not included.

12
- 2 -

This paper contributes to the literature in four important ways. First, it addresses the difficult methodological questions that have arisen in other studies. Enrollment in a public or private school is a choice made by the students and parents. If this choice is systematically correlated with personal characteristics, there may be sample selection bias. In this study we use some recent methodological advances to model and correct statistically for this bias. Second, the paper extends the empirical evidence for developing countries by analyzing data from a survey, modeled after the International Association for the Evaluation of Educational Achievement (IEA) Second Mathematics Study, carried out in the Dominican Republic (Luna and Gonzalez 1986). Third, we differentiate between two types of private schcols, which we compare with urban public schools. In contrast, most earlier studies failed to distinguish different types of private schools. In some cases, they treated all private schools similarly, while in other cases they examined only particular types of private schools, for example, Catholic ones (Bryk and Lee 1988). Fourth, we use independently gathered data to compare the unit costs of public and private schools before reaching conclusions regarding the relative efficiency (as opposed to the relative productivity) of public and private schools.

The next section of this paper outlines the basic conceptual model. It is followed by sections on the data, school effects, relative costs, and conclusions.
Deshingkar, P., P. Sharma, S. Kumar, S. Akter, and J. Farrington (2008). Circular migration in Madhya Pradesh: Changing patterns and Social Protection Needs. _The European Journal of Development Research_ , vol. 20, no. 4, pp. 612–628.

Deshingkar, P., and E. Anderson (2004) People on the move: new policy challenges for increasingly mobile populations. Overseas Development Institute. Natural Resource Perspectives. Number 92, June.

Dreze, J. and R. Khera (2013). Rural Poverty and the Public Distribution System. _Economic and Political Weekly_ , 58: 55-60.

Dubey, A., R.P. Jone and K. Sen (2004). Surplus Labour, Social Structure and Rural to Urban Migration: Evidence from Indian Data. Paper presented at the Conference on the 50<sup>th</sup> anniversary of the Lewis Model, July 6-7.

Duranton, G., E. Ghani, A.G. Goswami, and W. Kerr (2015). The Misallocation of Land and Other Factors of Production in India. World Bank Policy Research Working Paper 7221.

Fernandes, A. and G. Sharma (2012). “Together We Stand? Agglomeration in Indian Manufacturing,” World Bank Policy Research Working Paper 6062.

Hay, M (1980). A Structural Equations Model of Migration in Tunisia. _Economic Development and Cultural Change_ , 28: 345-58.

Helliwell, J. F. (1997). National borders, trade and migration. _Pacific Economic Review_ 2 (3), 165–185.

Hnatkovska, V. and A. Lahiri (2015). Rural and urban migrants in India: 1983–2008. The _World Bank Economic Review_ 29 (suppl 1), S257–S270.

Howard, L. L. and N. Prakash (2012). Do employment quotas explain the occupational choices of disadvantaged minorities in India? _International Review of Applied Economics_ 26 (4), 489–513.

Kone, Z.L., M.Y. Liu, A. Mattoo, C. Ozden and S. Sharma (2016). Internal Borders and Migration in India, mimeo, The World Bank, Washington D.C.

Lucas, R.E.B. (2015). Internal Migration in Developing Economies: An Overview. Knomad Working Paper 6, World Bank. Washington D.C.

Lusome, R. and R.B. Bhagat (2006). Trends and Patterns of Internal Migration in India, 19712001. Paper presented at the Annual Conference of Indian Association for the Study of Population (IASP) during 7-9 June, 2006, Thiruvananthapuram.

24
Sánchez-Martín, Escribano and de Arce, 2014. “How regional integration and transnational energy networks have boosted FDI in Turkey” Economic liberalization and the previously mentioned market-orientated measures, promoted by Turgut Özal’s government, did not result in significant foreign investment attraction. Önis (1994) argues that the orthodox approach of shifting incentives to foster FDI is insufficient if investors do not rely on the sustainability of the new regime. A large foreign debt stock, lack of confidence between foreign companies and the government, and the persistence of subsidies and protectionist measures in certain sectors were probably some of the reasons why inward FDI continued to be low during the 1980s.

**Figure 1. Capital flows and sudden stops in the Turkish economy**

<!-- Start of picture text --> 6.00% 12<br>5.00% 10<br>8<br>4.00%<br>6<br>3.00%<br>4<br>2.00%<br>2<br>1.00%<br>0<br>0.00%<br>-2<br>-1.00%<br>-4<br>-2.00% -6<br>-3.00% -8<br>Net FDI to GDP Net Portfolio to GDP GDP growth rate<br>GDP growth rate<br>1984 1986 1988 1990 1992 1994 1996 1998 2000 2002 2004 2006 2008 2010 2012<br>Net FDI and portfolio flows to GDP<br><!-- End of picture text --> Source: Central Bank of Turkey and IMF Article IV estimates (December 2013) In 1989 Turkey took a step ahead in its liberalization process by fully opening its capital account to foreign flows (Önder, 2007). This measure has been a subject of controversy, as macroeconomic instability increased and the country was exposed to several sudden stops in the following decade (1994, 1998 and 2000-2001). Moreover, the country was not able to significantly increase FDI attraction, as most of the inward capital flows were driven by short-term speculative purposes.

Demir (2004) summarizes the different explanations given to the unsatisfactory performance of Turkey during the 1990s. On the one hand, existing bureaucracy and domestic institutional inefficiencies could have had a negative impact on the outcome of the reforms (see, for instance, Celasun et al., 1999). On the other hand, while recognizing disturbances introduced by the government, most of the Turkish economists (Önis, 1998; Botarav and Yeldan, 2001) attribute the problems to deficiencies in the 4
<!-- Start of picture text --> Policy, Research, Pnd External Affaiis<br><!-- End of picture text -->

**Macroeconomic Adjustment and Growth**

- This paper a product of the Macroeconomic Adjustment and Growth Division, Country Econoioics Department -- is part of a larger effor' in PRE to develop applied macroeconomic models to support the design of adjustment programs. Copics arc available free from the World Bank, 1818 H1 Street NW, Washington DC 20433. Pleasc contact Emily Khine, room Nl 1-062, extension 39361 (43 pagCs with figures and tablcs).

Solimano uses this model to examine policies geared to reducing poverty' and improving income distribution **pattern.s** in terms of their macroeconomic impact on the Clhilean economy. Hc concludes that in a capacity-constrained environment:

* An unbalanced increase in government spending (in the social sectors) of 3 percent of potential GDP will slow down thc rate of GDP growth **by 1** percent.

* Rcducing interest paymcnis abroad by 3 percent of GDP would accelcraitc thc rate of GDP growth by 1.7 perceint, reduce thc rea! exchange rate by 8. 1 percent, and increase rcal wages 6.6 perceint.

* A cut in the mark-up rate of 4 percent would increase both external competitivencss

and real wages, allow ng the rate of capacity utilization to increase 1.4 percent. InfiTtion would be reduced 4 oercerit on impact.

> * The balance of payments and fiscal hudgct can be considered binding if a turnaround in copper prices takes place, as mani obscrvcrs predict.

> * The lcvel of productivc capalcity seemlis to be a main macrocconomilic constralint lor expansionary demand policies, at least in thc short to mcdium run. An incre.sc in savings and investnient is crucial to sustained gmwwth.

> * Infnation is currentlv moderately low in Chile, but as the cconomy hovers around flull capacity utilization and grovvth rema:ins high. inflationary pressurcs may be mounting.

The PRE Working Paper Serics disseminates the findings of work under way in the Bank's Policy. Research, and External Affairs Complex. An objective of the series is to get these findings out quickly, even if presentations are less than fully polished. The findings, interpretations, and cenclusions in these papers do not necessarily represent official Bank policy.

lrocduced at the PRE Dissemination Center
_Property rights:_ The importance of property rights has been tested for in studies of firm growth (Beck et al. (2005)) as well as on firm investment decisions (Johnson et al. (2002)). In the former case, property rights are not seen as important as financial constraints, particularly for small firms. In the latter case, property rights were more significant in explaining a firm’s willingness to invest. It should be noted that their strongest results, however, came from measures of corruption rather than confidence in courts themselves. As corruption is examined separately, this section looks at issues of whether courts are believed to uphold property rights and on measures of the rule of law. Stronger court systems and the rule of law are associated with the ability to conduct arms-length transactions and to punish contract violations. As such, they broaden the set of market interactions likely to be undertaken, broader competition and discipline those that violate the rules. Together, these are associated with expanding market discipline, which would likely raise exit rates, particularly of less productive firms.

# **Who is relatively more affected by a weak investment climate?**

_Firm Productivity_ Theoretical models emphasize the role of negative productivity shocks in explaining exit. To the extent investment climate weaknesses raise the costs of doing business, these are likely to raise the productivity threshold associated with firm exit. Firms would have to be otherwise that much more profitable to meet these higher costs and survive. On the other hand, weaknesses in the investment climate that undermines market mechanisms could serve to shelter firms, particularly less efficient ones, from having to exit. Thus the interactions of productivity with measures of broader access to finance, court strength and competition are 11
(1995) points out that the initial transformation conditions in Bulgaria were more similar to FSU countries than to CEE countries. Bulgaria had a strong decline in revenues in the first years after the collapse of traditional tax bases, similar to the countries Albania, Moldova, Lithuania, Armenia, Georgia. However, at the end of 1994 considerable efforts have been made to liberalize the economy enabling output to commence growing anew. But Bogetić and Hassan (1997) criticize the income tax development in Bulgaria. The 1993 amendments have complicated the tax system and increased the marginal tax rates from 40 to 52 %. On the other hand, Martinez-Vazquez (1995) states that in 1994 Bulgaria has already established good revenue assignment systems as, e.g., local ownsource revenues. Looking at the 1999-2000 data for CEE countries we observe low values from Romania. Uslaner (2007, p. 36) stresses that it ―may not make much sense to have much faith in the legal system where the courts and the police cannot – or will not – control corruption or where citizens cannot see that their contribution to the public weal actually makes the public more wealthy. In this sense, Romania is not exceptional: There may be less reason for Romanians to have faith in their leaders, who have been unable to control corruption or to bring its citizens prosperity.‖ Frey and Torgler (2007) use a multivariate approach to explore country differences working with the EVS 1999-2000. The estimated coefficient for the Western Europe dummy, suggests that the institutional crisis in many transition countries in Eastern Europe after the collapse of communism, tended to affect negatively the tax morale of citizens. The marginal effects indicate that being a citizen of a Western European country, rather than an Eastern European country, increases the probability of responding that tax evasion is never justified by more than 3 percentage points. Frey and Torgler (2007) also explored single country differences with _Table A7_ in the Appendix presenting the results. Country dummies are included in the estimation equation, using GERMANY as a reference. It is interesting to note that the Central Eastern European (CEE) countries, Hungary, Czech Republic, Slovak Republic, Bulgaria, Croatia, and Poland exhibit _higher_ tax morale than Germany. The coefficient of the first four countries is statistically significant. _Table A7_ also reveals that Former Soviet Union (FSU) countries, such as Russia, Belarus, Ukraine, Lithuania, Estonia or Latvia have lower tax morale than Central Eastern European (CEE) countries. It seems that CEE countries have 48
**Table 7. Projections for all countries from 2010 to 2030 under baseline scenario and persistent informality parameter options, i.e. annual growth rates of μ = 1%,** **_b_ = 0.25%,** **_a_ = 0.25%, and** � **= ‐ 0.25%.**

|**Country**|**Fo**|**rmal la**|**bor**|**Mod**|**ern Info**<br>**Labor**|**rmal**|**Ru**<br>**Inf**|**diment**<br>**ormal L**|**ary**<br>**abor**|
|---|---|---|---|---|---|---|---|---|---|
||**2010**|**2020**|**2030**|**2010**|**2020**|**2030**|**2010**|**2020**|**2030**|
|Albania|39%|46%|64%|10%|15%|25%|52%|40%|12%|
|Argentina|46%|41%|36%|36%|32%|32%|18%|27%|32%|
|Australia|87%|90%|86%|5%|5%|5%|8%|5%|9%|
|Austria|87%|80%|69%|5%|5%|5%|8%|15%|26%|
|Burundi|7%|6%|5%|7%|7%|6%|86%|87%|89%|
|Belgium|86%|82%|71%|5%|5%|5%|9%|13%|24%|
|Benin|6%|5%|4%|7%|6%|5%|87%|89%|91%|
|Bangladesh|5%|3%|0%|38%|50%|67%|57%|47%|32%|
|Bulgaria|75%|77%|76%|18%|18%|19%|8%|5%|5%|
|Bolivia|13%|10%|8%|33%|29%|27%|54%|61%|66%|
|Brazil|58%|57%|55%|20%|19%|19%|23%|24%|27%|
|Botswana|17%|14%|10%|73%|81%|85%|10%|5%|5%|
|Central African||||||||||
|Republic|5%|3%|0%|8%|5%|5%|87%|92%|95%|
|Canada|82%|84%|74%|5%|5%|5%|13%|11%|21%|
|Switzerland|86%|71%|59%|5%|5%|5%|9%|24%|36%|
|Chile|56%|62%|66%|21%|24%|26%|23%|14%|8%|
|China|34%|54%|57%|19%|41%|38%|48%|5%|5%|
|Côte d'Ivoire|14%|6%|0%|10%|5%|5%|76%|89%|95%|
|Cameroon|17%|14%|10%|12%|9%|7%|71%|78%|84%|
|Congo, Rep.|12%|9%|7%|17%|14%|11%|71%|77%|82%|
|Colombia|30%|29%|28%|28%|28%|30%|43%|44%|42%|
|Costa Rica|57%|61%|63%|24%|27%|28%|19%|12%|9%|
|Germany|83%|74%|64%|11%|8%|6%|6%|18%|30%|
|Denmark|88%|85%|71%|7%|5%|5%|5%|10%|24%|
|Dominican Republic|30%|29%|30%|34%|37%|42%|37%|34%|28%|
|Ecuador|27%|25%|22%|31%|30%|29%|42%|46%|49%|
|Egypt, Arab Rep.|54%|52%|46%|26%|25%|23%|21%|24%|30%|
|Spain|65%|69%|68%|26%|26%|27%|9%|5%|5%|
|Finland|85%|85%|76%|6%|5%|5%|9%|10%|19%|
|France|82%|79%|66%|11%|9%|7%|6%|12%|28%|
|United Kingdom|85%|77%|61%|5%|5%|5%|10%|18%|34%|
|<br>Ghana|10%|9%|8%|16%|16%|18%|74%|76%|74%|
|Guinea|7%|6%|6%|5%|5%|5%|88%|89%|89%|
|Greece|71%|72%|62%|5%|5%|5%|24%|23%|33%|
|Guatemala|21%|17%|11%|38%|31%|23%|41%|52%|65%|

46