rafmacalaba/datause-encoder-data huggingface dataset:
# **Table 1: Constituency and Project Characteristics Means and standard deviations**

|**Number of constituencies**<br>_Panel A: Constituencies_|345<br>-|
|---|---|
|**Number of local governments in a constituency**|2.03|
||(0.79)|
|**Population (2006)**|370,000|
||(130,000)|
|**Proportion of constituency population in extreme poverty**|0.23<br>|
||(0.15)|
|**Proportion of constituents with access to potable water**|0.48<br>|
||(0.27)|
|**Average hours of electricity in a day**|4.46|
||(3.89)|
|**Time in minutes to nearest primary school**|20.33|
||(7.27)|
|**Time in minutes to nearest secondary school**|33.43|
||(10.04)|
|**Winning vote share (2003 elections)**|0.62|
||(0.15)|
|**Runner's up vote share (2003 elections)**|0.29|
||(0.12)|
|**Proportion of constituencies run by ruling party**|0.63<br>-|
|**Proportion of constituencies ruling party is runner up**|0.34<br>-|
|_Panel B: OPEN projects_||
|**OPEN funds per constituency (US$)**|1,100,000|
||(1,900,000)|
|**Number of OPEN projects by constituency**|9|
||(7.1)|
|**Number of OPEN project types by constituency**|3|
||(1.5)|
|**Number of sectors in constituency**|3|
||(0.9)|
|**Average project budget (US$)**|130,000|
||(150,000)|
|**Average project complexity (proportion)**|0.29|
||(0.10)|
|**Proportion of constituency projects never started**|0.41|
||(0.25)|
|**Average level of progress of constituency projects**|0.46|
||(0.25)|
|**Level of progress conditional on being started**|0.76|
||(0.22)|
|**Proportion of constituency projects fully completed**|0.32|
||(0.28)|
|**Proportion started with satisfactory quality**|0.78|
||(0.31)|

**Notes** : Standard deviations are in parentheses.  The OPEN project data does not include projects in 15 of Nigeria's 360 federal constituencies, so the descriptives provided here are for the restricted set of 345 constituencies only. Constituencies are weighted equally in creating descriptives.  Population data is from the 2006 Census.  Election data is from the Independent National Electoral Commission official record for the 2003 election.  Budget figures originally in Nigerian Naira are converted to US dollars at a rate of US$1:N150.  Figures are rounded to two decimal places where relevant.
(National Research Council, 2009). First, it committed its technical staff to be a core part of the analytical team. They brought their expertise on Lima’s water needs and resources, SEDAPAL’s system, the regulatory and policy context in which they make decisions, and an understanding of the goals and aims of their Master Plan. The analytical team met weekly – sometimes daily – by phone. SEDAPAL’s staff validated the data, models, and findings at each step of the analysis. Their involvement has ensured that the analysis asks and answers the right questions, that the results are important and practical for SEDAPAL, and that SEDAPAL seeks to take ownership of the methods and tools developed in the analysis and employ them in their future planning activities.

Second, and perhaps even more importantly, SEDAPAL was willing to experiment with a new approach to long-term planning and embark on a complex analysis that differed greatly from approaches it used in the past. In embracing innovation, SEDAPAL also embraced the possibility that the analysis would produce complex or controversial findings that it would have to explain to stakeholders, including regulators and national authorities. Because of SEDAPAL’s boldness, we were able to ask and answer difficult questions about its Master Plan in the pursuit of long-term water reliability for Lima.

## 8.5 Conclusions

This project helps SEDAPAL understand its Master Plan more fully. It enables SEDAPAL to assess climate change threats without first needing to predict the future climate. It helps SEDAPAL identify projects that are particularly important for achieving water reliability. And, it reveals the strengths and vulnerabilities of its Master Plan concisely—as a specific set of conditions in which the Master Plan and related projects can achieve water reliability and in which additional actions may be necessary. It also helps SEDAPAL implement its Master Plan robustly by identifying near-term, no-regret projects that it can embark upon now, while pursuing additional actions adaptively as future conditions evolve.

# 9 References

Anandhi A, Frei A, Pierson DC, Schneiderman EM, Zion MS, Lounsbury D, Matonse AH. 2011. Examination of change factor methodologies for climate change impact assessment. _Water Resources Research_ **47** (3): n/a–n/a. DOI: 10.1029/2010WR009104.

Brown C. 2010. The end of reliability. _Journal of Water Resources Planning and Management_ **136** (2): 143–145.

Brown C. 2011. Decision-Scaling for Robust Planning and Policy under Climate Uncertainty. _World Resources Report Uncertainty Series_ . Washington DC.

Brown C, Ghile Y, Laverty M, Li K. 2012. Decision scaling: Linking bottom-up vulnerability analysis with climate projections in the water sector. _Water Resources Research_ **48** (9): n/a–n/a. DOI: 10.1029/2011WR011212.

37
- Gaviria, A. & Raphael, S. (2001). School-based peer effects and juvenile behavior, _Review of Economics and Statistics_ **83** (2): 257268.

- Ginther, D., Haveman, R. & Wolfe, B. (2000). Neighbiurhood attributes as determinants of children’s outcome: How robust are the relationships?, _Journal of Human Resources_ **35** (4): 603–42.

- Gradstein, M. & Justman, M. (2000). Human capital, social capital, and public schooling, _European Economic Review_ **44** (879-890): 4–6.

- Gradstein, M. & Justman, M. (2002). Education, social cohesion and economic growth, _American Economic Review_ **92** (1192-1204): 4.

- Hanushek, E. A., Kain, J. F., Markman, J. M. & Rivkin, S. G. (2003). Does peer ability affect student achievement?, _Journal of Applied Econometrics_ **18** (5): 527–44.

- Hanushek, E. A. & Woessmann, L. (2008). The role of cognitive skills in economic development, _Journal of Economic Literature_ **46** (3): 607–68.

- Hoxby, C. (2000). Peer effects in the classroom: learning from gender and race variation. NBER Working Paper No. 7867.

- INDEC (2001). _Censo Nacional de Poblaci ́on y Vivienda 2001_ , Instituto Nacional de Estad ́ısticas y Censos, Buenos Aires, Argentina.

- Ioannides, Y. M. & Zabel, J. E. (2003). Neighborhood effects and housing demand, _Journal of Applied Econometrics_ **18** (1): 563584.

- Jencks, C. & Mayer, S. (1990). _Inner-city poverty in the united states_ , National Academy Press, Washington, D.C., chapter The social consequences of growing up in a poor neighborhood, pp. 111–186.

- Jenkins, S., Micklewright, J. & Schnepf, S. (2008). Social segregation in secondary schools: How does England compare with other countries?, _Oxford Review of Education_ **34** (1): 21–38.

- Judson, R. (1998). Economic growth and investment in education: How allocation matters, _Journal of Economic Growth_ **3** (4): 337–59.

- Katz, L., Kling, J. & Liebman, J. (2001). Moving to opportunity in Boston: Early results of a randomized mobility experiment, _Quarterly Journal of Economics_ **116** (2): 607–654.

31
presented in Table 9 along with results of an endogeneity test.<sup>14</sup> The results of the Hausman test for endogeneity reject the null hypothesis in 5 out of 8 outcomes at 5 percent level, confirming that IV estimation is the better method for this situation. We report the OLS results for comparison nevertheless. Most of our earlier findings still hold, and it is mainly the magnitude of the impact that changes. Household per capita expenditure increases by 8.2 percent and overall total income goes up by 12.2 percent due to electrification. Examining the components of income, it appears that having electricity improves both farm and nonfarm income. In addition, the impact of household electrification on educational outcomes is quite positive, improving school completion rates as well as study time of both boys and girls. So we can summarize our findings by saying that electrification has a significant positive impact on household’s socio-economic welfare, and various impact assessment techniques yield very similar results.

# **6. The growth and persistence of the benefits of household electricity**

The welfare impacts estimated so far report the average value of benefits accrued to all households that have electricity. However, another important issue to investigate is how these benefits grow over time as the households add electricity appliances and increase the use of electricity. It is quite likely that the benefits vary according to the length of time that a household has electricity. For example, a household with electricity service for 5 years would be expected to benefit more than the one having it for just a few months. Over time, households are expected to consume more electricity, diversify its use through accumulation of various appliances, and make more productive use of electricity.

To examine the relationship between the benefits of electricity and household’s exposure to electricity, households are grouped according to the length of time they have had electricity. Form Table 10, a trend of increased use of electricity from prolonged exposure to electrification is obvious. Because the rural electrification program in Bangladesh has been growing rapidly during the last five years, households that have grid for 5 years or less are the most common, accounting for about 40 percent of all households with electricity. Also, as expected, the demand for electricity goes up with the length of time that

> 14 Here the null hypothesis is that both OLS and IV estimates are consistent, and the alternate hypothesis is that only the IV estimate is consistent. If the null hypothesis is true, OLS model should be used because it is more efficient. Otherwise, IV model should be used.

18
# **I. Introduction**

Earlier work has shown that delays in getting goods from the factory gate onto the ship hinder exports more than foreign tariffs do (Hummels (2001), Djankov, Freund and Pham (2010), and Portugal and Wilson (2009)). This is especially debilitating for Africa’s exports because of extreme delays. This suggests that improving trade facilitation in Africa would significantly boost exports. But there are different ways to accomplish this, as the time delay has three distinct components: documentation, transit time, and port handling and customs clearance. In this paper, we explore whether these delays are equally burdensome or whether one of these binds relatively more, using detailed data on average trade times from the World Bank’s _Doing Business_ report.

Bureaucratic delays are the longest, taking 19 days on average. There is a lot of variation across countries. For example, it takes 36 days to process export documents in countries such as Angola, Zambia and Niger. In contrast, in Swaziland, it takes only 5 days to produce all necessary export documents. Bureaucratic delays may be especially burdensome if they change often, making them difficult to predict, or if officials use them as a means to extract rents. In contrast, documentation procedures may be less problematic if they are predictable and can be done in advance or if there is learning by doing.

Customs and ports delays are the second longest, taking on average 9 days. They are less variable than documents. Customs and ports could be especially restrictive if there is a hold-up problem. Once the goods arrive, customs and port authorities could extract high rents by delaying goods. In contrast, if customs and ports are reliable (but slow) or if exporters can pay for faster service they may cause fewer problems.

Transit costs are on average the shortest, taking 7 days. But, again, there is a lot of variation. For example, it takes 37 days for the goods to be shipped from Bujumbura (Burundi) to Dar Es Salaam port (Tanzania) and only one day within Gabon. Transit costs may be less burdensome if economic activity has developed endogenously, close to ports and borders when transit costs are large. However, they may be more constraining if there is a lot of uncertainty that cannot be avoided.

The main contribution of our paper is to understand whether different types of export costs affect trade differently. We use a modified gravity equation that controls for importer fixed effects and exporter remoteness. An important concern with this approach is that the volume of trade may directly affect trade costs. The marginal value of 2
The anal,ytical approach in the existing literature to assess the impacts of exogenous changes in these outside chores on time allocation is to use a variable that captures the "productivity of collection".13 The data on Peru do not include this level of detail. All that can be constructed are dummy variables that indicate whether the household has access to in-house water or if it uses gas/electricity, firewood, coal or something else for energy. However, the crudeness of these variables is somewhat overcome by the fact that unlike previous work we are dealing with panel data where the results would control for unobserved heterogeneity.'<sup>4</sup>

# _Demography and Life cycle_

An analysis of the determinants of intra-household allocation of time in developing countries is incomplete without incorporating demographic and life-cycle explanatory variables. Household time burdens are not equally distributed across age (see Ilahi and Grimard, 1998; Ilahi and Jafarey, 1998). It appears that the expectations of work and its composition for an individual change over the life cycle. We include age and its square to capture these effects. Moreover, the age composition of the female members may also be indicative of the possibilities that are open to other members (men and women) for having their work substituted by others. For instance, the presence of adolescent children in the household may allow adults to pursue income-generating work. We include the number of children under 4 and the number of females in the following age groups: 5 through 14, 15 through 60 and above 60. Marital status may also influence time allocation-we include a dummy for if the person was single.

We capture gender and vulnerability indicators by using a dummy for female headship. While the concept of female headship has come under a lot of criticism for not adequately identifying gender vulnerability, it remains the most useful single indicator in the absence of

> **13** This is typically kilograms collected per hour.

**14** This paper does not address the even more complicated issue of correcting for the endogenous placement of basic services. This is due to a lack of data.

10
38 None of this means that poor consumers would not have been willing to pay more for a connection; a SEDAPAL survey in 1993 found that the average property value of houses in the _pueblos j6venes_ quadrupled after being connected to piped water (from US$1,542 to US$6,375). Moreover, poor consumers already paid high prices for water from vendors; a survey in December 1993 found that the average price was US$2.50 per M<sup>3</sup> .

The cost to the poor could have been made affordable through subsidies, by including the connection costs in the variable water tariff charged to all consumers, or by extending longer term, lower interest loans for connections.<sup>31</sup> However, none of these arrangements were contemplated in the concession agreement, and would not have been easily added. Any efficient provider, public or private would have wanted to reduce the already large cross subsidies in Lima's tariffs. Industrial tariffs were 130 percent larger than residential. An efficient operator would have also wanted to raise metering and change per meter prices closer to marginal cost, not only to assure a reasonable return on investment, but also to keep the demand for water down so as to reduce the need for further costly expansion. It was therefore inevitable that poorer consumers would face substantially higher prices, as indeed occurred under public ownership later, reducing the political benefits of the concession.

Not only would price increases reduce the net benefits from the concession for lower income consumers, the size of the projected benefits from investment was open to

> 3 Loans were in fact offered to water companies and consumers to finance construction of primary networks and to cover investments in secondary networks and costs of connection through a government fund financed by a Social Tax, FONAVI. FONAVI channeled about US$1,000 million to the water sector at moderately favorable interest rates, out of which 75% went to customers. However, customers failed to repay the loans. By the end of 1998, the government recognized the big problem and lunched a plan to "recovere the FONAVI debts offering the water companies the option to issue shares in favor of the Central Government.
Végh and Vuletin (2013) show that institutional frameworks characterized by property rights protection, control of corruption, higher bureaucratic quality, and a strong law and order tradition, have enabled some developing countries to break free from procyclicality in the previous decade. Céspedes and Velasco (2014), using alternative proxies for institutional quality, find consistent evidence in a sample of 60 resource-rich countries. Additionally, Alesina, Campante and Tabellini (2008) establish a positive correlation between measures of corruption and procyclical fiscal policy.

Procyclical public spending is a significant factor contributing to the excess economic volatility reported in emerging markets (Végh et al. 2018). Whereas too much economic volatility may foster economic uncertainty, hamper credit market development, and limit policy makers' ability to protect the country against negative shocks or harness positive ones,<sup>2</sup> procyclical behavior, if symmetrical across the cycle, may not necessarily lead to fiscal unsustainability. Symmetric behavior along the business cycle implies that while bonanza cycles could be financed with increased public revenues and/or temporary borrowing, retrenchments would be sizeable enough to either adjust for lower revenue or cover those. Nonetheless, the recent history of emerging markets is plagued with episodes of fiscal turmoil. Much more than advanced economies.<sup>3</sup> It is not just higher prevalence of fiscal trauma; the ensuing fiscal adjustments are also different in emerging markets. Emerging markets tend to address episodes of fiscal stress with desperate attempts at “fiscal reforms”, and disordered adjustment strategies which share little correspondence with those idealized in either the growth or equity literature.<sup>4</sup> What gives in this puzzle? We argue that downwardly rigid spending creates fiscal gaps representing the seeds of later fiscal turmoil.

> 2 See, for example, World Bank (2013) and Baez, de la Fuente and Santos (2017).

> 3 Based on the Vulnerability Exercise for Emerging Market Economies (VEE) conducted by the International Monetary Fund (2010). Defining fiscal crisis periods as episodes of outright fiscal distress (public debt default/restructuring, need to access large-scale official/IMF support, hyperinflation) as well as extreme financing problems (spikes in sovereign bond spreads), Baldacci, Gupta and Mulas-Granados (2011) show how much more prevalent fiscal trauma is in emerging markets relative to advance economies. Indeed, since 1970, the authors estimated 135 episodes of fiscal stress in 52 emerging economies and just 41 in advanced economies.

> 4 While most fiscal stress episodes in advanced economies between 1970 and 2011 resulted in higher financing costs for relatively short periods of time, episodes in emerging markets led to longer and costlier adjustments, including 52 debt defaults or restructuring episodes and 79 IMF programs (Baldacci, Belhocine, et al. 2011).
||(1)|2)|(3)|4)|©)|6)|
|---|---|---|---|---|---|---|
||FinCorp|NonFinCorp|Households|Government|DomSavings|ForSavings|
|LGDPpc|0.34"|-0.13|-1.09*|1820|0.93|-3.04*|
|GDPpcGrowth|0.080***|= 0.25***|0.17|DATES|0755FF|-0.15|
|PopGrowth|OAR|-24TE|0.46|0.28|-213**|-1.15*|
|FuelExp|0.018*|0.16***|-0.31*|4 as|0.074|-0.28***|
|OldDepend|0.065***|-0.075|-0.046|-0.21*|-0.40"**|0.11|
|Constant|0.13|14.6***|A a ca|oe er as|19. 755*|30.3"|
|Observations|263|263|263|263|263|263|
|R-squared|0.108|0.385|0.154|0.293|0.172|0.256|

OK D< 0.01, ** p<0.05, * p<01 Source: UN National Accounts Main Agpregates database. Notes: Output srowth is a very sigmficant determinant of corporate savings and sovernment savings (hence of domestic savings as a whole), yet not of household savings, which suggests that (nonfinancial) corporate and public savings are the main factors underlying the growth elasticity of domestic savings.
# **Table 7A. Loan Size, All Members**

OLS

|||Log of Am|ount Requested|||Log of Amo|unt Approv|ed|
|---|---|---|---|---|---|---|---|---|
|||All Members|Males<br>|Females<br>|All|Members|Males<br>|Females<br>|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
|Female Brochure|0.088|0.085*|0.161***|-0.035|0.0036|-0.006|0.008|-0.098|
||(0.054)|(0.049)|(0.052)|(0.104)|(0.029)|(0.030)|(0.030)|(0.069)|
|Offered Business Training||-0.041|-0.032|-0.092||0.004|-0.01|0.007|
|||(0.042)|(0.046)|(0.104)||(0.029)|(0.031)|(0.068)|
|Female||0.078<br>(0.079)||||-0.054<br>(0.055)|||
|Age||0.010|0.015*|-0.007||0.010**|0.013**|0.002|
|||(0.007)|(0.009)|(0.018)||(0.005)|(0.005)|(0.017)|
|Age^2||0.000|-0.000*|0.000||-0.000*|-0.000**|0.000|
|||(0.000)|(0.000)|(0.000)||(0.000)|(0.000)|(0.000)|
|High Decision-making power (Yes=1)||0.015***|0.087***|0.024||0.008*|0.021|0.1|
|||(0.005)|(0.028)|(0.071)||(0.005)|(0.026)|(0.064)|
|Own Education||0.018***|0.016***|0.016||0.016***|0.014***|0.016^|
|||(0.005)|(0.004)|(0.010)||(0.003)|(0.003)|(0.010)|
|Digit Span Recall||-0.012|-0.018**|0.024||-0.013**|-0.012*|-0.001|
|||(0.009)|(0.008)|(0.024)||(0.006)|(0.006)|(0.016)|
|Risk Tolerance||0.003|0.001|0.005||-0.002|-0.004|-0.001|
|||(0.004)|(0.004)|(0.013)||(0.004)|(0.005)|(0.008)|
|Yrs. Of Education of Spouse||0.002|0.003|0.002||0.004|-0.004|0.018**|
|||(0.004)|(0.005)|(0.010)||(0.005)|(0.005)|(0.008)|
|Number of children under 9||0.004|0.004|0.007||0.004|0.003|-0.017|
|||(0.009)|(0.009)|(0.029)||(0.006)|(0.006)|(0.022)|
|Business Owner (Yes=1)||0.023|-0.006|0.064||0.050|0.058|0.027|
|||(0.032)|(0.036)|(0.069)||(0.035)|(0.042)|(0.061)|
|Land||0.006***|0.005***|0.007||0.001|0.000|0.005**|
|||(0.001)|(0.001)|(0.005)||(0.001)|(0.001)|(0.002)|
|Member of a Mixed Group (Yes=1)||0.096|0.377**|-0.233||0.025|-0.06|0.076|
|||(0.153)|(0.170)|(0.167)||(0.176)|(0.176)|(0.197)|
|Mean Dependent Variable|10.93|10.93|10.92|10.97|10.67|10.67|10.72|10.52|
|N. Observations|664|664|491|173|503|503|372|131|
|R-squared|0.21|0.27|0.32|0.33|0.27|0.34|0.26|0.44|

Note: The dependent variable is log of loan amount requested (columns 1-4) and log of loan amount approved (columns 5-8). The sample includes loan applicants. All regressions include branch fixed effects and are estimated using OLS methods. Standard errors are clustered at the borrower group level.The following symbols *, * * and ** * denote significance at the 10 , 5 , and 1 percent level, respectively. See Appendix B for definition of variables.