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_— 30 —_
# **Table 12. Indirect effects, with measures on input-output and labor pooling linkages** **<u>(2013, town-sector level)</u>**

||||Log (employ<br>|ment)<br>||||
|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|
||40 kms|50 kms|60 kms|70 kms|80 kms|90 kms|100 kms|
|_Target_|0.3107***<br>(5.47)|0.2325***<br>(4.55)|0.2972***<br>(6.09)|0.2960***<br>(6.53)|0.3089***<br>(7.22)|0.3387***<br>(8.28)|0.3372***<br>(8.47)|
|_Target X Treat_|0.2604<br>(1.56)|0.4262**<br>(2.89)|0.4349**<br>(3.16)|0.4270***<br>(3.38)|0.4519***<br>(3.75)|0.4557***<br>(3.99)|0.4961***<br>(4.49)|
|_Target X IO_<sup>_T_</sup>|0.0022|0.0017|0.0012|0.0017|0.0023|0.0023|0.0014|
||(1.34)|(1.09)|(0.77)|(1.21)|(1.67)|(1.70)|(1.02)|
|_Target X IO_<sup>_NT_</sup>|-0.0007|-0.0004|-0.0005|-0.0007|-0.0009*|-0.0009*|-0.0006|
||(-1.21)|(-0.84)|(-0.97)|(-1.54)|(-2.02)|(-1.97)|(-1.37)|
|_Target X LP_<sup>_T_</sup><br>_Target X LP_<sup>_NT_</sup>|0.0068***<br>(3.35)<br>-0.0041***<br>(-4.65)|0.0055**<br>(2.93)<br>-0.0034***<br>(-4.18)|0.0057**<br>(3.18)<br>-0.0034***<br>(-4.39)|0.0067***<br>(3.89)<br>-0.0036***<br>(-4.90)|0.0069***<br>(4.16)<br>-0.0036***<br>(-4.96)|0.0067***<br>(4.14)<br>-0.0034***<br>(-4.83)|0.0046**<br>(2.87)<br>-0.0027***<br>(-3.94)|
|_No. of_|11980|14244|16218|18209|20290|22350|24539|
|_observations_||||||||
||||Log (total fi|rms)||||
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|
||40 kms|50 kms|60 kms|70 kms|80 kms|90 kms|100 kms|
|_Target_|-0.6762|-1.1039**|-0.8029|-1.2698|-1.4340*|-1.3325*|-1.2290*|
||(-1.70)|(-3.17)|(-0.85)|(-1.56)|(-2.04)|(-2.14)|(-2.17)|
|_Target X Treat_|0.0335|0.0925|0.1124|0.0731|0.1023|0.0934|0.1178|
||(0.32)|(0.99)|(1.26)|(0.91)|(1.34)|(1.29)|(1.70)|
|_Target X IO_<sup>_T_</sup>|-0.0016|-0.0017|-0.0022*|-0.0019|-0.0015|-0.0015|-0.0020*|
||(-1.39)|(-1.66)|(-2.10)|(-1.94)|(-1.66)|(-1.69)|(-2.26)|
|_Target X IO_<sup>_NT_</sup>|0.0007|0.0008*|0.0008**|0.0007*|0.0006*|0.0006*|0.0007**|
||(1.94)|(2.37)|(2.59)|(2.38)|(2.08)|(2.21)|(2.70)|
|_Target X LP_<sup>_T_</sup>|0.0011|0.0003|0.0003|0.0008|0.0008|0.0009|-0.0005|
||(0.87)|(0.25)|(0.23)|(0.72)|(0.81)|(0.94)|(-0.49)|
|_Target X LP_<sup>_NT_</sup>|-0.0013*|-0.0007|-0.0008|-0.0008|-0.0007|-0.0008|-0.0003|
||(-2.14)|(-1.35)|(-1.49)|(-1.58)|(-1.56)|(-1.67)|(-0.74)|
|_No. of_<br>_observations_|11980|14244|16218|18209|20290|22350|24539|

_Note:_ All regressions include a _constant_ , and a linear function of _Distance_ . All standard errors are heteroskedastic robust. _t_ statistics in parentheses.<sup>*</sup> _p_ < 0.05,<sup>**</sup> _p_ < 0.01,<sup>***</sup> _p_ < 0.001.

32
28 paper on primary education (Lockheed, Verspoor and others) which in turn is largely based in UNESCO data.

Tpe trends During the 1960s and the 1970s,<sup>most developing</sup> countries, whether low or middle income, experienced a phenomenal expansion of their primary education capacity. Primary school enrollments went up from 195.5 million in 1965 to 278.4 mn in 1980, a 42<sup>per cent increase</sup> in 15 years, in low income countries; from 47.8 million to 93.7 mn, or<sup>a 96 per cent increase,</sup> over the same period fo lower middle<sup>income countries;</sup> and from 54.7 to 85 million, or a 55.3 per cent increase, for upper middle income countries.24 The expansion of primary school enrollments is as dramatic when looked at region by region. In Africa, enrollment **s** oared between 1970<sup>and 1980 from</sup> 20.3 million to 45.7 million.<sup>In Asia, it went from 239.4 million to 323.7</sup> million, and in Latin America and the Caribbean from 46.6 million to 64.8 million over the same period. These<sup>figures</sup> correspond to increases of 125, 35, and 39 per cent, respectively.5 Africa's increase is sharpest as its coverage was initially lowest. These figures witness the tremendous efforts put by developing countries in their struggle towards universal primary education. Indeed, the total number<sup>of schools</sup> doubled in developing countries between 1965 and 1986.

24Lockheed, Verspoor and others (1990) op.cit. Annexes Table 2. 2<sup>5</sup> UNESCO yearbook, 1988.
that is consistent with the assumptions on the signs of the other parameters.<sup>26</sup> As shown in Figure 8, under this scenario, flex cars owners hardly respond to the change in the price gap. The opposite is true for Scenario 3 (where _B_ = 5), where a small deviation from the current price gap makes many flex cars switch to an alternative fuel.

_Policy Simulations_ We run a battery of simulations to analyze the impacts of individual biofuel policies and market shocks quantitatively. All price changes possess the predicted signs presented in Table 4. The first set of simulations (columns denoted by B = 1.2 in Table 6 and 7) assumes the behavior of owners of flex cars is described by the benchmark logistic curve in Figure 8, whereas the second set (columns denoted by B = 5) uses the Scenario 3 logistic curve to investigate how the market outcome differs if flex cars owners are very sensitive to a change in the fuel price gap. The vector of baseline policies (in effect in the 2010/11 marketing year) comprises the volumetric blend mandate _α_ = 0.246; the tax on gasoline _tG_ = $1.28/liter; the tax on anhydrous ethanol _tA_ = $0.048/liter; the tax on hydrous ethanol _tH_ = $0.262/liter; and the gasoline price _PG_ = $1.05/liter.

|**Table 6. Policy Simulations**|**Results***|||Diffe<br>5 perc|rence rela<br>ntage|tive to base<br>Parity b|line<br>etween|Parity b|etween|||
|---|---|---|---|---|---|---|---|---|---|---|---|
||Baseline|28 ¢ redu<br>gasolin|ction in<br>e tax**|<br>point red<br>man|<br>uction in<br>date|<br>anhydro<br>gasoli|<br>us and<br>ne tax|<br>hydrous<br>tax|<br>and fuel<br>es|No mand<br>all taxes|ate, and<br>at parity|
|||B = 1.2|B = 5|B = 1.2|B = 5|B = 1.2|B = 5|B = 1.2|B = 5|B = 1.2|B = 5|
|Fuel price ($/liter)|2.47|-0.21|-0.23|0.05|0.05|0.20|0.22|-0.02|-0.05|0.27|0.08|
|Market price of anhydrous<br>ethanol ($/liter)|1.18|-0.01|-0.06|-0.03|-0.02|0.01|0.06|-0.07|-0.19|-0.21|-0.18|
|Market price of E100 ($/liter)|0.96|-0.01|-0.06|-0.03|-0.01|0.01|0.06|-0.07|-0.18|-0.20|-0.17|
|Consumer price of E100<br>($/liter)|1.54|-0.01|-0.06|-0.03|-0.01|0.01|0.06|0.38|0.27|0.40|0.42|
|Price of sugarcane ($/tonne)|56.11|-0.59|-4.50|-1.96|-1.12|0.58|4.50|-5.26|-13.56|-14.73|-13.03|
|Price of sugar ($/tonne)|700.93|-3.88|-29.64|-12.93|-7.39|3.83|29.59|-34.60|-89.26|-96.95|-85.78|

* Mandate binding in all simulations except the last one. ** Or the same reduction in gasoline price. B = 1.2 means that flex cars owners are less sensitive, and B = 5 that they are very sensitive to a change in the fuel price gap. Source: own calculations

> 26 If the upper asymptote were higher than 12.2 bil. GEELs, the logistic function would be decreasing, thus contradicting our assumption that the bigger the price gap, the bigger the shift. This happens because by requiring that _X_ (θ 0 ) = 0 , we are imposing structure on the logistic function that does not allow for any choice of the upper

asymptote.

28
_The Co-integration or Long-run Relationship_ We first run the regression of savings rate on all the independent variables. We also include the dummy variable INDCRIS. Its value is equal to 0 before the crisis and equal to one after the crisis hit Armenia in 2009. Only 4 variables are significant in our regression: GDP, Time deposit rate (TDR), Real exchange rate (REER) and the dummy variable INDCRIS. The cointegration relationship is tested by a stationarity test on the residuals of the regression: if the residuals are stationary, there is co-integration. Using the Dickey-Fuller test on the residuals, we reject the null hypothesis, which suggests that the residuals are stationary and there is cointegration. The Johansen test also confirms these results. The results of the regression and previous tests are presented below. The figures in bracket represent the standard errors in the co-integration relationship.

**Table 4: Co-integration relationship and tests**

**_Co-integration Relationship_** sr= - 2.88 (0.27) + 0.37 (0.02)* gdpc + 0.02 (0.006)*TDR – 0.36 (0.066)*reer -0.2 (0.025)*INDCRIS Period: 2002q1-2011q2 ; N° obs=38; R<sup>2</sup> =0.90;   F(4;33)= 77.85   ; Durbin Watson=2.2 _Dickey Fuller on Residuals_ Test statistics= **-6.8** ; Critical value at 1% = -3.67 _Johansen Test_ Trace statistics (for rank=0) = **193.6** ;       Critical value at 1% =  68.5

As expected, an increase in GDP per capita has a positive impact on savings. In our specification, a 10 percent increase in the GDP per capita (i.e. gdpc increase in level by 0.1) leads to a 3.7 percent points increase in the savings rate in the long run (i.e. if the initial savings rate was 5 percent, after a 10 percent increase in the GDP per capita, the savings rate becomes 8.7 percent). Also the impact of the interest rate is positive on savings. Indeed a 1 percentage point increase in the TDR leads to a 0.02 percentage points increase in the total savings rate. This means that in our model, the substitution effect is higher than the income effect. That could be due to the fact that the initial assets are not very high and as a result the income effect is smaller. We notice the important negative impact of the crisis on the savings. However, intuitive determinants such as Openness and public deficit do not appear in the long run relationship. This might be due to the fact that those variables have experienced a dramatic change after the crisis, so that their effect is directly captured by the dummy variable INDCRIS. There is also a negative and significant impact of REER. Indeed, we find that a 10 percent increase in the REER (appreciation) leads a 3.7 percentage point decrease in the savings rate in the long run. Economically an appreciation of the REER is associated with a decrease in net exports, hence a decrease in aggregate output which may lead to a decrease in the savings rate.

8
# **1 Introduction and Reasons**

Infrastructure investment is a fundamental instrument to promote development and enhance capital accumulation. It reinforces network effects (Roller and Waverman 2001), regional comparative advantage (Estache and Fay 1997), education (Brenneman 2002), health (Agenor and Moreno-Dodson 2006) and other growth factors. Its importance is out of question, as the 1994 World Development Report on infrastructure shows (World Bank 1994), and every year more than 200 billion dollars are spent on this sector in developing countries, roughly a fifth of total investment.

As well as public authorities, also Multilateral Development Banks (MDBs) regard infrastructure investment as a priority and promote not only its financing, but also design and regulation. As Table 1 reports, the five largest MDBs lend more than USD 50 billion in infrastructure per year, with this single sector absorbing between 30 and 70 percent of their total lending.

However, pouring money is often insufficient to achieve results and, as this work shows, the governance of projects, their selection and implementation are the main factors on which authorities, both national and international, need to focus.

For this reason, in this paper we study the performance indicators of infrastructure projects and focus on the micro relations involved in them. Through a rigorous empirical analysis, we explain which variables are central in getting successful infrastructure projects and what actions can lead to high performance. The results of this work are mainly directed to Multilateral Development Banks, as we explore how to boost project effectiveness and what can be done toward this aim. We believe that this is an important research field, because in times of high uncertainty in economic prospects, especially for low-income countries, getting infrastructure investment right is central in cutting poverty, sparking growth and consolidating future fiscal positions.

The empirical analysis relies on a new database we assembled, which contains 1912 infrastructure projects financed by the World Bank between 1979 and 2008; these include detailed project performance evaluations on which we build this paper. Anticipating on the results, three main insights emerge from this research: 1) the quality of project implementers (borrowers) is the most important proxy for project success, regardless of most macroeconomic variables; 2) successful project implementation is positively related to the performance of the 2
female heads and urban male heads, underscoring the societal reality that younger widows typically remarry into MHH. The very few younger female heads who are widows may well be a selected group who are between marriages or have chosen not to remarry because they can get along on their own, while older ones end up on their own less by choice than necessity.

A simple regression of whether a household has a female head against a large number of individual, household and location characteristics confirms that in both rural and urban areas female heads are much more likely than male heads to be widowed or divorced (particularly so in urban areas), to have fewer years of education and to head households that have fewer members as well as significantly fewer adult female members (aged 15 and above) (Table 2).<sup>12</sup> They also appear to be concentrated in certain locations as evidenced by significant location fixed effects (not shown to conserve space). Attributes that are significantly correlated with having a female head also include a younger head in rural Mali and less agricultural land in urban Mali. However, controlling for these other covariates, FHH do not appear to be distinguishable on average from households with male heads on the basis of per capita consumption, other wealth and assets. The significant attributes of FHH are clear candidates as determinants of consumption levels which I next investigate.

It might be conjectured that estimated coefficients on having a female or a widowed female head in consumption regressions will be biased because a household may choose to be female headed for example when husbands live elsewhere and send transfers. This argument is less plausible with respect to widowed heads which account for the vast majority of female heads, but it could still be made. However, my interest here is not to identify the causal effect of having a female or widowed female head but rather to identify differences in conditional means between different types of households. The issue I am addressing is whether FHH and in particular, widowed FFH are poorer than other households, and not whether the fact of being a FHH or becoming a widow caused them to be poorer. From a policy point of view this is the relevant question, for example with respect to targeting on the basis of observed characteristics.

The regressions given in Table 3 focus on the determinants of log household consumption per capita for four different samples. In columns (1) and (2) the samples are restricted to FHH and MHH, respectively. The models prove to be quite different. FHH headed

> 12  Unfortunately, the ELIM survey did not collect information on ethnicity.  I am thus unable to test whether these factors vary across different ethnic groups, although much of the variance due to ethnicity will be captured by the geographic fixed effects since different ethnic groups tend to be concentrated in different areas.

11
changing program targeting and payment rates. However, in isolation, offering reduced payments will simply attract more applicants at low risk, resulting in less avoided deforestation. To address adverse selection, it is essential to combine payment adjustments with targeting to higher risk areas. This conclusion is broadly applicable to a variety of cash transfer situations – in the event that a behavior is costly, reducing the compensation for changing that behavior will reduce program effectiveness on aggregate, unless there is also a mechanism encouraging enrollment of those who are likely to engage in behavioral change.

Finally, this paper makes a methodological contribution to the stated preference literature by using information provided by households to support the use of a community leader statement regarding willingness to accept. Although the specifics of Mexico’s communal governance forests are rather unique, a recent global assessment found that at least 18 percent of the world’s total land area is held communally or by indigenous groups (Rights and Resources Initiative 2015). Thus in both land conservation and other realms, situations in which individuals are given voice by a leader are important, and we hope that our theory and analysis linking household stated preference to leader behavior on a referendum question can prove useful in other contexts.

# References

- Alix-Garcia, J., De Janvry, A. & Sadoulet, E. (2008), ‘The role of deforestation risk and calibrated compensation in designing payments for environmental services’, Environment and Development Economics 13(3), 375–394.

- Alix-Garcia, J. M., Shapiro, E. N. & Sims, K. R. (2012), ‘Forest conservation and slippage: Evidence from Mexico’s national payments for ecosystem services program’, Land Economics 88(4), 613–638.

- Alix-Garcia, J. M., Sims, K. R., Orozco-Olvera, V., Costica, L., Medina, J. D. F., Romo-Monroy, S. & Pagiola, S. (2018), ‘Can Environmental Cash Transfers Reduce Deforestation and Improve Social Outcomes? A Regression Discontinuity Analysis of Mexico?s National Program (20112014)’, World Bank Working Paper .

- Alix-Garcia, J. M., Sims, K. R., Orozco-Olvera, V. H., Costica, L., Medina, J. D. F. & Monroy,

43