rafmacalaba/datause-encoder-data huggingface dataset:
# **VII. BIBLIOGRAPHY**

- Adelmam, Irma and Sherman Robinson (1988). "Macroeconomic Adjustment and Income Distribution." _Journal of Development Economics_ 29: 23-44.

- Anand, Sudhir and Ravi Kanbur (1987). "Public Policy and Basic Needs Provision: Intervention and Achievement in Sri Lanka. " Development Economics Research Centre, Working Paper No. 74, University of Warwick, Coventry, U.K.

- Bahl, Roy and Johannes Linn (1985). _Urban Public Finance and Administration in Developing Countries._ Oxford University Press, New York.

- Ballard, C.L., Fullerton, D., Shoven, J.B. and Whalley, J. _(1985). A General Equilibrium Model for Tax Policy Evaluation._ Chicago: The University of Chicago Press.

- Benjamin, N. and Devarajan, S. (1985). _"Oil Revenues and Economic Policy on Cameroon," World Bank Staff Working Paper,_ No. 745.

- Besley, T.J. and S.M.R. Kanbur (1988). "Food Subsidies and Poverty Alleviation." _The Economic Journal,_ Vol. 98: 701-719.

- Bird, Richard M. and Barbara D. Miller (1989). "Taxation, Pricing, and the Poor" in Richard M. Bird and Susan Horton (eds.), _Government Policy and the Poor in Developing Countries._ University of Toronto Press.

- Bourguignon, Frangois, Jaime de Melo, and Akiko Suwa (1991). "Distributional Effects of Adjustment Policies: Simulations for Archetype Economies in Africa and Latin America." _World Bank Economic Review, Vol. 5,_ No. 2.

- Bourguignon, Franqois, William H. Branson, and Jaime de Melo (1989). "Macroeconomic Adjustment and Income Distribution: A Macro-Micro Simulation Model." Technical Papers No. 1, OECD Development Centre.

- Bovenberg, A.L. (1987). "Indirect Taxation in Developing Countries: A General Equilibrium Approach." _IMF Staff Papers_ 34 (2, June): 333-73.

- Browning, Edgar K. (1978). "The Burden of Taxation," _Journal of Political Economy 86:_ 649-71.

- Browning, Edgar K. and William R. Johnson (1979). _The Distribution of the Tax Burden._ American Enterprise Institute for Public Policy Research, Washington, D.C.

- Chamley, Christophe (1991). "C6te d'lvoire: The Failure of Structural Adjustment" in _Restructuring Economies in Distress. Policy Reforms and the World Bank,_ Vinod Thomas et al. (eds.). Oxford University Press, Oxford.
**<u>Table 5: Determinants of compensation received by households subject to land taking</u>**

||**To**|**bit**|**TobitIV**|
|---|---|---|---|
|Reform dummy (β)|0.680**|1.016|-0.017|
||(2.16)|(1.62)|(0.01)|
|Leaders elected (γ)||1.433***<br>|4.279<br>|
|||(2.86)|(1.19)|
|Election *Reform dummy (δ)||0.130|1.939|
|||(0.19)|(0.52)|
|Value of production before taking (Y/mu)|0.609***<br>(4.06)|0.639***<br>(4.28)|0.711***<br>(4.20)|
|Land taken irrigable|0.123|0.339|0.978**|
||(0.33)|(0.90)|(1.98)|
|Distance to county (km)|-0.003|-0.002|-0.002|
||(0.34)|(0.29)|(0.25)|
|Land next to national/provincial road|0.898**|1.127***|1.556**|
||(2.11)|(2.63)|(2.27)|
|Share of land received|-2.211***|-2.329***|-2.562***|
||(5.88)|(6.27)|(5.72)|
|Village income (log)|2.102***|1.916***|1.806***|
||(5.34)|(4.80)|(2.77)|
|Public land use dummy (ν)|-0.791**|-1.781***|-1.305*|
||(2.12)|(2.98)|(1.91)|
|Public land use*Reform dummy (φ)||1.509**|0.662|
|||(1.99)|(0.76)|
|Household has certificate|0.782*|0.810*|1.179**|
||(1.71)|(1.78)|(2.02)|
|Leaders’ knowledge of the law|0.421|0.443|0.629|
||(0.91)|(0.97)|(1.24)|
|β + δ||1.146***|1.922**|
|ν+φ||0.272|-0.643|
|Wald Test of Exogeneity, χ<sup>2</sup>(2)|||1.61|
|Observations|608|607|595|

Absolute value of t statistics in parentheses *, **, and *** denote significance at 10, 5, and 1%, respectively. Province dummies and constant included throughout but not reported.

27
Table 4: The effect of orphanhood on probability of marriage by 2004 with interactions, men (Dependent variable: _ever married)_

||(1)|(2)|(3)|
|---|---|---|---|
|Male prime-age adult died|0.059|0.074|0.063|
||(0.122)|(0.138)|(0.150)|
|Female prime-age adult died|-0.307***<br>(0.088)|-0.369***<br>(0.078)|-0.344***<br>(0.099)|
|Father died|-0.052|0.111|0.040|
||(0.170)|(0.136)|(0.146)|
|Mother died|-0.097|0.103|-0.105|
||(0.293)|(0.191)|(0.166)|
|_Interactions_||||
|Father died & in school|0.075<br>(0.220)|||
|Mother died & in school|0.101<br>(0.352)|||
|Father died & non-farm household||-0.389***<br>(0.067)||
|Mother died & non-farm household||-0.275<br>(0.170)||
|Male adult died & non-farm household||-0.126<br>(0.253)||
|Female adult died & non-farm household||0.348<br>(0.280)||
|Father died & rich household|||-0.122<br>(0.195)|
|Mother died & rich household|||0.165<br>(0.264)|
|Male adult died & rich household|||-0.030<br>(0.231)|
|Female adult died & rich household|||0.213<br>(0.294)|
|Observations|363|363|363|
|R-squared|0.209|0.221|0.208|

Notes: Results reported are marginal effects estimated using a Probit. Robust standard errors in brackets. * significant at 10%; ** significant at 5%; *** significant at 1%. Includes controls for age and age squared, district fixed effects, whether child was in school at baseline and baseline characteristics in Table 2 (head’s education, sex, and age; log PCE; flooring material; non-farm status of household; father/mother living in household.). Column 1 includes indicator for being in school at baseline 16
rupted interbank credit markets, leading to sharp spikes in the spread between the interbank lending rate and the overnight index swap (Libor-OIS), especially in the aftermath of the Lehman collapse in September 2008 (Figure 1(a)). This led to widespread difficulties in obtaining basic rollover credit, and likely played a significant role in the contraction of credit. As the crisis wore on, moreover, spreads on credit default swaps (CDS) for bank bonds began to widen considerably, suggesting increasing concerns over (bank) credit impairment as a result of worsening balance sheets (Figure 1(b)). The degraded balance sheets, in turn, would likely have led banks to limit their lending to only the most creditworthy borrowers. Finally, the volatility of asset returns increased substantially during the crisis period; the implied volatility of the S&P 500, for example, rose in the first phase of the crisis, and jumped sharply in the second (Figure 1(c)).

<!-- Start of picture text --> bps bps<br>600<br>350 U.S. banks 5-year<br>300 500 CDS spread<br>250 400<br>200<br>300<br>150<br>200<br>100<br>100<br>50<br>LIBOR-OIS spread<br>0 0<br>Jan-07 Jul-07 Jan-08 Jul-08 Jan-09 Jul-09 Jan-07 Jul-07 Jan-08 Jul-08 Jan-09 Jul-09<br>Source: Bloomberg Source: Datastream<br>(a) LIBOR-OIS spread, 2007–10 (b) CDS spreads on credit sector, 2007–10<br>90<br>80<br>70<br>60<br>50<br>VIX implied volatility<br>40<br>30<br>20<br>10<br>0<br>Jan-07 Jul-07 Jan-08 Jul-08 Jan-09 Jul-09<br>Source: Datastream<br>(c) VIX market volatility index, 2007–10<br><!-- End of picture text --> Figure 1: Three-month LIBOR-OIS and TED spreads (top panel), five-year CDS spreads on U.S. banking sector index (middle panel), and VIX implied volatility of S&P 500 index options (bottom panel), January 2007 through December 2010. The charts show, for both the initial (light shaded area) and acute (dark shaded area) phases of the crisis, that there was severe difficulty in obtaining liquidity (interest rate spreads), significant concerns over bank solvency (credit default swap spreads), and that the volatility of returns on asset markets was elevated relative to non-crisis periods (market volatility index).

7
The _micro-credit program_ seeks to encourage the supply of loans to low-income households. It provides working capital to some financial intermediaries, such as Community Development Associations, Productive Family Associations, and the Principal Bank for Development and Agricultural Credit; in other cases it gives grants to financial institutions, on condition that they commit to more microlending.

The government has been increasing the pressure on the SFD to operate in a way that is financially selfsustaining – for instance, by charging fees, or putting more emphasis on microlending at appropriate interest rates. It recently refused a loan from the World Bank that would have provided funds to the SFD, because it was concerned that the return on the spending would not be sufficient to justify the cost of repaying the loan. On the other hand, the government might be more inclined to help the SFD obtain such funding if it found that SFD projects have a strong impact, which returns us again to the importance of evaluating the effects of SFD spending.

# **Measuring Impacts**

In seeking to measure the impact of SFD programs, we first need to clarify what impacts need to be measured, and then set out an appropriate methodology whereby these impacts may be identified and quantified.

There are two broad types of program effect that are of interest to us: short-term outcomes, and long-term impacts. The variables that we use to measure these effects are set out in more detail in Table 2, for each of the six sectors of interest. So, for instance, in evaluating projects under the education program, we might be interested in the proportion of participants in literacy classes who obtained diplomas (an outcome), and the effect on the illiteracy rate or employment rate (impacts).

We would like to think that SFD interventions would have unambiguously positive effects: more education would reduce the illiteracy rate; health projects should reduce mortality rates and poverty, as should more potable water and improved sanitation; better roads, and more microcredit, should increase incomes and lower poverty.

However, experience from other evaluations of social funds suggests that improvements such as these cannot be taken for granted. For instance, Rawlings et al. (2004), in a comparative study of social funds in six countries, found that while interventions led to greater access to and use of basic services, tangible improvements in welfare were sometimes elusive. For instance, educational projects did not raise test _Impact Evaluation: Egyptian Social Fund for Development_ _Version of June 29, 2009_ _Page 5 of 36_
# **6. ESTIMATION RESULTS AND POLICY IMPLICATIONS**

Four models are performed by the two-stage least-squares method; the first stage probit results are shown in Table 10. This essentially reveals why the governments decided to use the multidimensional auction format in their PPP transactions. As expected, higher unemployment is strongly associated with the decision to adopt multiple award criteria. It means that auctioneers are using the multidimensional format to respond to the need for social considerations. This evidence can be interpreted as a risk of jeopardizing auction efficiency due to political compromise.

On the other hand, there is no evidence that multidimensional auctions would be preferred under more corrupt circumstances. Such a worst-case scenario is not true in our estimation results. Rather, the governments are more likely to take advantage of multiple criteria when the government effectiveness and the quality of regulation are high. This may reflect the fact that without effective and sound government systems it would be impossible to manage multidimensional auctions for contracting out PPP infrastructure transactions.

Table 10. First Stage Probit of Multidimensional Auction Selection Equation

||(1)|(2)|(3)|(4)|
|---|---|---|---|---|
|Road length|-0.0016|-0.0016|-0.0019|-0.0016|
||(0.0011)|(0.0012)|(0.0012)|(0.0011)|
|Track length|0.0005 <sup>**</sup>|0.0004 <sup>**</sup>|0.0004 <sup>**</sup>|0.0005 <sup>**</sup>|
||(0.0002)|(0.0002)|(0.0001)|(0.0002)|
|Contract duration|-0.0793 <sup>**</sup>|-0.0773 <sup>**</sup>|-0.0699 <sup>**</sup>|-0.0806 <sup>**</sup>|
||(0.0311)|(0.0318)|(0.0303)|(0.0342)|
|Unemployment|0.1031 <sup>**</sup>|0.1027 <sup>**</sup>|0.0573|0.1095 <sup>**</sup>|
||(0.0483)|(0.0456)|(0.0394)|(0.0498)|
|Govt effectiveness|0.3333|0.8342|2.8768 <sup>**</sup>||
||(1.7082)|(1.9680)|(1.3649)||
|Regulatory quality|6.4568 <sup>**</sup>|3.5310||7.1031 <sup>**</sup>|
||(2.2444)|(2.4543)||(3.0013)|
|Control of corruption|-0.8357||0.0825|-0.8831|
||(0.6091)||(0.5711)|(0.7715)|
|Constant|-5.6175 <sup>***</sup>|-3.3651 <sup>*</sup>|-0.7376|-6.0955 <sup>**</sup>|
||(1.9660)|(1.9913)|(0.7891)|(2.6609)|
|Obs.|106|106|106|106|
|Wald test statistics|29.17|33.39|25.69|22.99|
|PseudoR-squared|0.4972|0.4893|0.4764|0.4969|

Note: The dependent variable is the binary variable for the multidimensional auction selection. The robust standard errors are shown in parentheses. *, ** and *** indicate the 10%, 5% and 1% significance levels, respectively.

In terms of project characteristics, the total length of tracks is among the significant determinant of the award design decision (in the railway sector). The 20
were civil servants compared with 14% of the returnees and 9% of the IDPs. Agriculture was the main occupation of 5% of IDPs, 4% of refugees and 1% of returnees. 9% of the refugees were pastoralists. The dominance of people who used to work in commerce among the displaced may be a reflection of the fact that the rebel groups targeted the cities –places of commerce by definition. It may also be that for people who work in commerce it was easier to move, possibly because their social networks expanded over larger areas or because certain types of commerce can be done even while displaced. Farmers and pastoralists, on the other hand, may have more locally confined social networks and they may have greater incentives to stay put to take care of their herds and to tend their fields. In addition unlike civil servants, who were a direct target of the rebels, farmers and pastoralists may have had less to fear from the rebels.

**Table 5: Employment before the crisis**

|**Occupation**|**IDPs (%)**|**Refugees (%)**|**Returnees (%)**|
|---|---|---|---|
|Commerce|51|37|34|
|Arts & Craft|10|9|10|
|Agriculture|5|4|1|
|Civil Servant|9|18|14|
|Pastoralism|2|9|-|
|No Employment|11|10|31|
|Other|12|13|10|
|**TOTAL (%)**|**100**|**100**|**100**|

Source: Listening to Displaced People Survey, 2014.

The vast majority of IDPs and refugees indicated to want to return home (81% and 91% respectively) but the majority of IDPs (82%) and refugees (97%) who stated this have not yet started planning their return. The primary condition for return for IDPs and refugees is a viable peace accord (35% and 59% respectively); for 27% of the IDPs it is better financial means; for 20% of the refugees the independence of Azawad is critical. Of those refugees who do not intend to return (3%), insecurity in the North makes them stay away; for IDPs in Bamako better job prospects are the main reason for not returning.

11
# I. INTRODUCTION

Argentina has one of the most developed education systems in the Americas. Indicators show that despite the recent economic crisis, school enrollment rates are high (Parandekar, España and Savanti 2003). Educational advances began early in Argentina following the Constitution of 1853, especially due to the efforts of Domingo Sarmiento, the fourth president of Argentina. Sarmiento set the guidelines for the modern education system in the latter half of the nineteenth century, pushing through reforms that supported school expansion for all citizens. The literacy rate rose from 33 percent in 1869 to nearly 50 percent by the turn of the century.

Argentina has a solid base from which an efficient and modern education and training system can be built, helping the country to compete in the global economy. Average years of schooling of the population are 8.5, significantly higher than the regional average of 5.9 years (Table 1). Argentina also compares well with East and Central Europe and East Asia, where average educational attainment is 8.4 years and 7.6 years (Barro and Lee 2000).

Table 1: Educational Attainment of the Total Population Aged 15 and Over: Average years of schooling

||1960|1965|1970|1975|1980|1985|1990|1995|2000|
|---|---|---|---|---|---|---|---|---|---|
|Argentina|5.3|5.5|6.2|6.3|7|7.1|8.1|8.5|8.8|
|Brazil|2.9|3|3.3|3|3.1|3.5|4|4.5|4.9|
|Canada|9.1|8.8|9.1|9.8|10.3|10.8|11|11.4|11.6|
|Chile|5.2|5|5.7|5.6|6.4|6.7|7|7.3|7.6|
|Greece|4.8|5.1|5.4|5.9|7|7.3|8|8.3|8.7|
|Italy|4.7|5|5.5|5.7|5.9|6.2|6.5|6.9|7.2|
|Korea|4.3|5.4|4.9|6.6|7.9|8.7|9.9|10.6|10.8|
|Mexico|2.8|2.9|3.7|3.9|4.8|5.2|6.7|7|7.2|
|Portugal|1.9|2.4|2.6|2.8|3.8|3.9|4.9|5.5|5.9|
|Spain|3.7|3.8|4.8|4.7|6|5.8|6.4|6.8|7.3|
|Uruguay|5.4|5.1|5.7|6.2|6.2|6.9|7.1|7.3|7.6|
|USA|8.5|9.1|9.5|9.7|11.9|11.6|11.7|11.9|12.1|

Source: Barro and Lee 2000
<!-- Start of picture text --> Figure 11. Declining MPK ... Figure 12. ... little appetite for investment<br>Tobin's q Domestic credit to private sector<br>6<br>70<br>5 60 Asian crisis<br>Aquino's assassination<br>Marcos leaves<br>50<br>4<br>Coup attempts<br>40<br>3<br>30<br>2<br>20<br>Economic reforms<br>1 10<br>Average (China, Indonesia, Malaysia, Thailand)<br>Philippines<br>0 0<br>198819891990199119921993199419951996199719981999200020012002200320042005 1980 19821984 19861988 19901992 19941996 19982000 20022004 2006<br>% GDP<br><!-- End of picture text --> _Source:_ S&PIFC EMDB - Price to Book Value Ratios _Source:_ Central Bank of the Philippines, 2007.

**Why a low productivity of capital?** In the traditional sectors of the economy, several rentseeking corporate conglomerates, controlled by the local _élite_ , use their political connections to: a) hinder tax collection, hence hampering public capital spending (which is a necessary condition for private investment: for example, the availability of public infrastructure is essential to stimulate the private sector’s willingness to invest); and b) limit economic entry, drive potential investors out, discourage smaller firms to grow bigger, produce expensive inputs, and enjoy market power and oligopolistic rents. As a result, MPK is low because of: i) insufficient public investment; and ii) a high cost of inputs, due to _élite_ capture (Figure 13).

**Oligopolies further reduce the investment appetite.** Operating as monopolies and oligopolies, the corporate conglomerates find convenient to restrict production – and investment - below the competitive level.<sup>22</sup> Also, their willingness to invest is inhibited by their concentrated ownership structure, and their uncertainties about the stability and duration of government favoritism.<sup>23</sup>

> 22 Monopolists produce where the marginal revenue equals its marginal cost, and not where the average revenue the marginal cost. Oligopolists are aware of the actions of the others and always take into account the likely responses of the other market participants.

> 23 Under Marcos, conglomerate owners who had close ties to the government enjoyed lasting favoritism, which guaranteed a predictable business environment and a longer term investment horizon. This “organized corruption” was not replaced by a predictable system of rules, regulations, and contract enforcement. In other words, the country moved from “organized” to “disorganized corruption”.

17
enjoys the benefits of rule. Much of the politics of the Third World is concerned with the ebb and flow of thew concessions and retractions to various segments of the society. (iv) Despite its appeal as a charcteization of Third World states the neopatrimonial concept needs to be supplemented by some typology or classification of the scores of actual states that at present exist in Asia, Africa and Latin America, among which there is enormous variation in size, ideology and political organization, even if they all share the feature of neo-patrimonialism in some degree or other. Democracy and dictatorship is one axis along which they might be situated, and "market-oriented" versus "centrally planned" another, giving us four catergories if each of these distinctions is applied dichotomously. I had hoped that the liteature on comparative polidcs would have produced some usable classification but have not yet come across ore. What follows is therefore a very rough and ready one of my own devising, which I will be very happy to discard if a superior alternative can be found.

# 1. Traditional Monarchies

There are a number of significant and interestiTg cases that can be placed in this category. One thinks readily of Saudi Arabia and the Gulf oil sheikdoms, Morocco and Jordan, as well as of Ethiopia under Haile Selassie. In each of these cases the ruler could claim legitimacy on the basis of the traditional authority of the institudon of the monarchy and of the dynasty in particular. In Weber's terms they correspond more to his "patrimonial" category then to the modem mixture of this with rational-legal norms that Clapham stresses. Qperationally the difference is that the extracdon and redistribution of rents from the economy and society can be made more openly, with the sanction of royal authority, than under regimes in which this is done behind a facade of impersonal and objective rules. In other words the distinction between the public treasury and the private purse of the monarch is blurred.

10
**Table 9: Instrumental variable regression results of project impact on playgroup and kindergarten enrolment rates: difference between Batch 1 and Batch 3. Dependent variable equals 1 if child ever enrolled in specified service.**

||Project p|laygroup|Non-project|playgroup|Kindergart|en, MoEC|Kindergart|en, MoRA|
|---|---|---|---|---|---|---|---|---|
||Unadjusted|Adjusted|Unadjusted|Adjusted|Unadjusted|Adjusted|Unadjusted|Adjusted|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
|Child’s village is|||||||||
|Batch 1 (1=Yes)|0.0302|0.0253|-0.0180|-0.0256|-0.0446|-0.0256|-0.0358|-0.0365|
||(0.0418)|(0.0422)|(0.0262)|(0.0286)|(0.0369)|(0.0366)|(0.0251)|(0.0261)|
|Constant|0.625***|0.542**|0.0750*|0.176|0.177***|-0.158|0.0489*|-0.0513|
||(0.0678)|(0.191)|(0.0365)|(0.124)|(0.0449)|(0.169)|(0.0224)|(0.115)|
|Observations|1989|1717|1989|1717|1989|1717|1989|1717|
|R-squared|0.166|0.173|0.078|0.077|0.230|0.275|0.032|0.041|
|**Controls Included**<br>Child|||||||||
|Characteristics|N|Y|N|Y|N|Y|N|Y|
|Household|||||||||
|Characteristics|N|Y|N|Y|N|Y|N|Y|
|Height and Weight|N|Y|N|Y|N|Y|N|Y|
|Parenting practices<br>at baseline|N|Y|N|Y|N|Y|N|Y|
|District dummies|Y|Y|Y|Y|Y|Y|Y|Y|
|First stage F-|||||||||
|Statistic||97.630||97.630||97.630||97.630|
|First stage R-<br>squared||0.775||0.775||0.775||0.775|

_Note:_ Robust standard errors clustered at the village level in parentheses. Child characteristics include: child’s age and gender. Household characteristics include household size, household wealth and mother’s education. Height and weight include stunting and wasting status at baseline. Parenting practices are self-reported by caregiver at baseline. District dummies are controlled for in both adjusted and unadjusted models. *** p<0.01, ** p<0.05, * p<0.1 43