rafmacalaba/datause-encoder-data huggingface dataset:
leaders after the initial assignment. A small number of households had also migrated out of the communities after baseline. In order to avoid any selection bias, we treat all of these households as _eligible._ Table 1 shows that random assignment effectively equated the characteristics of households randomly assigned to receive _Atención a Crisis_ transfers and households in the control group: of the 34 variables that are summarized in the table, only one (the number of people aged 65 or older in a household) indicates a significant difference between the two groups at baseline. Table 1 also shows that households in our sample are disadvantaged in a number of important ways. The mean years of schooling of mothers are 4 years, and 67 percent have not completed primary school. The mean years of schooling of fathers is even lower, approximately 3.5 years, and 75 percent have not completed primary school. Children in this sample also have substantial health problems—27 percent are stunted (have height for their age that is more than two standard deviations below that of a reference population). Finally, expenditure levels are very low. Turning the local currency units in the table ( _Córdobas_ ) into US $ shows that 82 percent of households in our sample have per capita expenditures that are below one dollar per capita per day.

# _C. Early childhood development indicators_

We focus on eight measures of early childhood development. Social-personal, language, fine motor, and gross motor skills for all children were assessed using the four sub-tests of the Denver Developmental Screening Test (Frankenberg and Dodds 1996). The Denver can be applied to children as young as one month of age. A slightly modified version of the Denver is used for child monitoring by the national early childhood stimulation program in Nicaragua, which suggests that the test is appropriate for the population we study.

For children age 36-83 months or older we applied four additional tests. The first of these is the TVIP, the Spanish-speaking version of the Peabody Picture Vocabulary Test (PPVT), a test of receptive vocabulary that has been widely used in developed and developing countries. We also use a short-term memory test and a leg motor test from the McCarthy test battery. The final test we use is the Behavior Problem Index (BPI), which is based on the caregiver’s report of the frequency that a child displays each of 7
Schultz, T. W. 1961. "Investment in human capital." _American Economic Review_ 51(1): 1−17.

Smith, A. 1776. _The Wealth of Nations_ . Alfred A. Knopf (1991).

Tenjo, J., O. Álvarez, A. Gaviria and M.C. Jiménez. 2015. “Evolution of Returns to Education in Colombia (1976-2014).” Universidad de Bogota Jorge Tadeo Lozano Working Paper N. 2015-9.

Tinbergen, J. 1975. _Income Distribution: Analysis and Policies_ . North- Holland.

World Bank, 2016. <u>http://data.worldbank.org/about/country-and-lending-groups</u> 19
characteristics. However, even if one could perfectly control for selection, the differences in wages might still be a consequence of efficiency wages being paid to elicit higher effort.

A notable exception is Barton et al. (2016) who use a regression discontinuity design that exploits the Kenyan government’s decision to hire roughly 18,000 new teachers in 2010. In each constituency, candidates were ranked according to a simple algorithm that rewarded grades in secondary school and teacher training as well as length since graduation from teacher training. Comparing successful and unsuccessful candidates close to the cut-off, the authors find a return of around 50 percent to employment as a public sector teacher (compared to the outside option). Given that the large majority of unsuccessful candidates also worked as teachers on short-term contracts with lower pay but in identical jobs and the fact that experimental studies show that these types of teachers are no less (if not more) effective than permanently employed public sector teachers (Duflo et al. 2015; Muralidharan and Sundararaman 2013), the authors can rule out (or at least cast doubt on) compensating differentials and efficiency wage explanations of this wage differential. Instead, rent-sharing between politicians and public sector workers seems the most likely explanation.

# **Lower Effort**

Once on the job, public sector workers can capture rents by exerting low effort, in its most blatant form not showing up for work. Bold et al. (2016) show that low effort, in particular in the form of absenteeism, is rife among teachers in public primary schools in Sub-Saharan Africa. Analyzing nationally representative data from 7 countries (Senegal, Tanzania, Kenya, Uganda, Togo, Mozambique and Nigeria), they find that during a random unannounced spot check, almost half the teachers (45 percent) are not in the classroom teaching. In Mozambique, almost two-thirds (61 percent) are not in the classroom teaching. As a result, students are missing out on half the scheduled teaching time each day and the authors find that this is negatively related to learning. Across five low- and middle-income countries (Bangladesh, Ecuador, Indonesia, Peru and Uganda), Chaudhury et al. (2006) find that 35 percent of health workers were absent from the facility during an unannounced visit. Bold et al. (2012) report that clinicians in Senegal spend an average of 39 minutes per day counseling patients, while in Tanzania the number is even lower at just half an hour.

5
# **Vulnerabilities of Networked Energy Infrastructure: A Primer**

**Amy Schweikert**<sup>**1,2**</sup> **, Lindsey Nield**<sup>**2**</sup> **, Erica Otto**<sup>**2**</sup> **, Magdalena Klemun**<sup>**3**</sup> **, Sanna Ojanpera**<sup>**4**</sup> **, Mark Deinert**<sup>**1,2**</sup>

1Payne Institute for Earth Resources, Golden, Colorado, USA 2The Colorado School of Mines, Golden, Colorado, USA 3 Massachusetts Institute of Technology, Cambridge, Massachusetts, USA 4 Oxford University and The Alan Turing Institute, London, UK

**JEL classification:** Q54, H54, O13

**Keywords** : Resilience; critical infrastructure; natural hazards
# Table 2. OLS Estimation Results for Private Investment Function

# Dependent variable is real private investment/real GDP

||(1)|(2)|(3)|(4)|
|---|---|---|---|---|
|Constant, C(1)|-2.406|-4.019|-2.390|-3.790|
|Real RateofInterest, C(2)|-|-0.849*<br>(-2.08)||-0.769*<br>(-1.82)|
|Flow of re4lcredi.to private<br>sector as a ratio to GDP, C(2)|0.065<br> <br>(4.02)|--|0.062"'<br>(3.80)|<br>--|
|Foreign Exchange Reserves<br>as a rati' to GDP, C(3)|0.332"'|0.353"'|<br>0.326|0.350**|
|(2 year sum, lagged 1 period)|(7.98)|(7.44)|(7.82)|(6.65)|
|Real Exchange Rate, C(4)|0.460"'|0.607"'|<br>0.452|0.557"'|
|(Trade weighted, 1985-<br>100)|(3.52)|(4.35)|(3.48)|(3.19)|
|Infrastructure<br>Public Capital|1.073**|0.516|1.102"'|<br>0.562|
|stock as a ratio to GDP, C(5)|(2.95)|(1.33)|(3.05)|(1.37)|
|Lagged GDP growth rate C(6)|--|--|0.994<br>(1.13)|1.270<br>(1.13)|
|R-BAR SQUARED|0.96|0.93|0.96|0.93|
|F-STATISTIC|103.8|69.0|84.0|50.5|
|DURBIN-WATSON|1.41|1.69|1.42|1.80|
|N|20|20|20|20|
|White Statistic|1.432|--|5.771||

Note: 1. All variables are in natural log.

2. The sample includes annual data from 1969 to 1988.

16
ASIA DEPARTMENT V

# 1. **gtimates** and Projections

|**Tow**|Pwietuio|So.<br>ratio|Psi-fed|Growth<br>**ratd,**|cm|cm|TFR|@0<br>**Mates**|@0<br> Fmotme|<br>in|q<br>£1000|
|---|---|---|---|---|---|---|---|---|---|---|---|
|19l5|169699|73.3|1965-89|1.97|28.9|9.1|3.54|58.3|61.9|71|97|
|**1990**|16726|67.6|1990-94|1.73|25.7|8.3|3.05|61.2|64.6|55|_n_|
|**1995**<br>**2000**<br>2005|20422?<br>**220429**<br>23S485|61.4<br>57.1<br>52.0|1995-99<br>2000-04<br>2005-09|1.53<br>1.32<br>1.20|22.9<br>20.1<br>18.8|7.5<br>6.9<br>6.8|2.66<br>2.32<br>2.19|63.9<br>66.3<br>67.5|67.1<br>69.5<br>70.9|43<br>34<br>30|55<br>42<br>36|
|2010|24999|48.1|2010-14|1.14|18.2|6.8|2.17|68.8|72.4|25|31|
|2015|264409|46.0|2014-19|1.05|17.3|6.8|2.14|70.1|74.0|20|25|
|2020|276863|45.9|2020-24|0.94|16.3|6.9|2.12|71.4|73.6|16|19|
|2025|292345|46.8|2025-49|0.64|14.3|7.9|2.09|74.6|79.4|8|10|
|2050|343013|57.4|2050-74|0.24|12.8|10.4|2.07|78.2|83.5|4|5|
|2075|364520|65.2|2075-99|0.12|12.3|11.0|2.06|80.1|86.1|3|4|
|2100|375781|69.5|2100-24|0.07|11.9|11.2|2.06|81.6|88.0|2|3|
|212<br>2150|382885<br>38448|72.1<br>n.4|2125-49|0.04|11.8|11.4|2.06|82.1|88.8|2|3|

# 2. **Age** structure (percent): 1990 and 2025

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# 3. Distribution of population (percent): 1990 and 2025

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study of financial inclusion a challenge. Fortunately, we are able to overcome that challenge using a new dataset on bank branch penetration at the district-level described in Allen et al. (2011), matched with household surveys of financial usage for Kenya that were conducted in 2006 and 2009. We find that Equity Bank presence is strongly associated with increases in households’ usage of bank accounts and bank credit between the two surveys. These effects are particularly strong for under privileged households—those with low income and less education, who do not own a permanent house, and those that lack any member with a salaried job.

We also find differences in branching strategies between Equity Bank and other commercial banks. While all bank types (including Equity Bank) open a greater number of branches in urban, highly populated and English speaking districts, Equity Bank was more likely to expand to underdeveloped districts than other types of banks between 2006 and 2009— less densely populated areas and areas where the dominant language was not English or Swahili. This different branching strategy is consistent with our earlier result that population density is a major obstacle for financial sector development in Africa; more importantly, our results are encouraging in the sense that private institutions can arise in such an environment to help overcome the ‘no finance, no growth trap’ in many African countries.

The rest of the paper is organized as follows. Section II describes the data, methodology for benchmarking financial development across countries, and results on examining whether the actual financial sector development in Africa is below what the fundamentals would predict using other developing countries as benchmarks. Then Section III examines whether the variables associated with banking development in other developing countries from our base models are related in a similar way to African banking development. Section IV presents results on the determinants of firm access to external finance in Africa and other developing countries. Section V presents results on financial access in Kenya and on the effect of bank branch expansion, particularly Equity Bank 7
Our model identifies a role for expected political alternation as a restraint on “corruption contracts” in countries with weak formal constraints on corruption. We measure cumulative political alternation between 1989 and year _t,_ where _t_ ∈ {1996, 1998, 2000, 2002, 2003, 2004, 2005, 2006}<sup>15</sup> and use that measure as a proxy for expectations as of time _t_ of political alternation in the future. We treat the period 1996-2006 as one “slice of time.” Our hypothesis is that the cumulative number of political alternations is positively related to governance indicators. We estimate the reduced form equation, where the subscripts _i_ and _t_ index, respectively, countries and years. Χ _it_ is a vector of covariates

> and<sup>γ</sup> _i_<sup>are  country dummies.</sup><sup>_ALTit_is cumulative political or ideological alternation</sup>

(“alternation,” for short).

The dependent variable is the KKM _rule of law_ measure.<sup>16</sup> It is defined as the extent to which agents have confidence in, and abide by, the rules. The measure focuses on the quality of contract enforcement and property rights. Among the available governance measures, we believe that this measure captures most closely the phenomena with which we are concerned—the quality of governance affected by the exercise of political power. The KKM data are composite indicators based on pre-existing sources, which include expert surveys and estimates by government, NGOs, and credit rating agencies. Since, as discussed above, the indicator is scaled so that the average and median value for the world is 0, a country’s score gives its relative position in the world. However, among those pre-existing data sources that are measured in absolute terms, there is no clear time trend between 1996 and 2006. Thus, KKM conclude that the measures for each country over time can be read as absolute changes. Relative to the world as a whole, there is a slight upward time trend in the mean rule of law

> 15 This set is all years for which KKM measures of governance were available.

> 16 Later we also use another KKM measure, control of corruption.

24
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- 27 -

The number of transfers, which can be interpreted as a proxy of the connectivity among bus routes, is particularly important for travelers within CUA where more bus routes are available. Outside of the CUA, the current suburban bus routes are lineal. The transferability does not matter too much.<sup>10</sup> Travelers within CUA are more worried about safety. Females have lower demand particularly within CUA. Suburban residents who own a car have particularly low demand for informal public buses, presumably because they can drive. On the other hand, vehicle ownership does not seem to affect the demand of city residents for public transport significantly. Vehicle ownership is minimal anyway, and people may not use their vehicles within CUA because of the current traffic congestion.

**<u>Table 4. Demand functions differentiated by distance travelled</u>**

||Pooled model:<br>|||Separat<br> <br>|e regressi<br>|on:<br>||
|---|---|---|---|---|---|---|---|
||Without OD dummies|With O|D dumm|ies<br>dCUA=|0|dCUA|=1|
||Coef. Std.Err.|Coef.|Std.Err.|<br>Coef.|Std.Err.|Coef.|Std.Err.|
|lnP*(dCUA=0)|-0.006 (0.020)|0.002|(0.020)|0.007|(0.022)|||
|lnP*(dCUA=1)|-0.055 (0.022) **|-0.054|(0.022)|**||-0.063|(0.026) **|
|Trip purpose: Work|0.326 (0.060) ***|0.267|(0.059)|***<br>0.286|(0.072)|*** 0.231|(0.106) **|
|lnTIME|-0.032 (0.010) ***|-0.034|(0.011)|***<br>-0.024|(0.015)|*<br>-0.047|(0.015) ***|
|dTRF|-0.060 (0.039)|-0.072|(0.041)|*<br>-0.013|(0.061)|<br>-0.158|(0.052) ***|
|lnTRF|0.067 (0.025) ***|0.066|(0.025)|***<br>0.032|(0.033)|<br>0.113|(0.039) ***|
|dSUBU|0.008 (0.018)|0.015|(0.021)|0.015|(0.024)|<br>-0.015|(0.039)|
|dBOTH|-0.093 (0.033) ***|-0.078|(0.035)|**<br>-0.068|(0.037)|*<br>0.028|(0.141)|
|dCUA|0.297 (0.170) *|0.374|(0.177)|**||||
|dUNSAFE|-0.037 (0.020) *|-0.037|(0.020)|*<br>-0.009|(0.031)|<br>-0.066|(0.025) ***|
|dUNCOMF|0.008 (0.018)|0.007|(0.018)|-0.052|(0.027)|*<br>0.078|(0.023) ***|
|dFEMALE|-0.030 (0.016) *|-0.028|(0.016)|*<br>-0.008|(0.022)|<br>-0.045|(0.022) **|
|dCAR|-0.071 (0.031) **|-0.064|(0.032)|**<br>-0.094|(0.046)|**<br>-0.024|(0.042)|
|dMOTO|0.013 (0.017)|0.011|(0.018)|0.014|(0.026)|<br>0.002|(0.023)|
|dBIKE|-0.033 (0.020)*|-0.031|(0.020)|-0.032|(0.027)|<br>-0.017|(0.030)|
|Obs|3,133|3,133||1,367||1,766||
|F-statistics|25.58|35.01||27.25||9.33||
|R squared|0.181|0.211||0.238||0.211||
|No. of OD dummy var.|<br>0|96||61||35||
|Ramsey RESET test|3.19**|1.81||1.56||3.19|**|

Note: Only selected coefficients are presented. See the full result in Appendix. The dependent variable is ln _Q_ . Robust standard errors are shown in parentheses. *, ** and *** indicate statistical significance at the 10, 5 and 1 percent level, respectively.

10 The average number of transfers made is the same between the two groups. It is 1.2 times for travelers within CUA and 1.3 times for those who travel from and to the outside of the CUA.
state variables, **a** := _c_ is the decision variable, and the transition laws are which can be written as **S** _t_ +1 = **G** ( **S** _t,_ **a** _t, ✏t_ ).

In ENLCEQ, we choose _T_<sup>b</sup> = 200 which has been large enough for the solutions in the periods of our interest (i.e., the first 20 periods). If we are interested in solutions at more periods, then we can also increase _T_<sup>b</sup> . In the step 2 of Algorithm 1 for solving problems at time _s_ , we let **S** _t_ +1 = **G** ( **S** _t,_ **a** _t,_ 0), that is, _At_ +1 = _G_<sup>_A_</sup> ( _At,_ 0), which in fact means that we replace _At_ by its mean conditional on the realized values of _As_ : _A⇡t,s_ , for all _t ≥ s_ , where _A_ = ( _A_ 1 _, A_ 2 _, A_ 3) and _⇡t,s_ is a column vector representing the probability distribution conditional on the realized values of _As_ . If the realized values of _As_ is _Ai_ , we have _⇡t,s_ = _P_<sup>_t−s_</sup> _⇡s,s_ , where _⇡s,s_ is a length-3 column vector with 1 at the _i_ th element and 0 everywhere else.

In the GAMS code attached in the end of the appendix, we compute 1,000 simulated paths of the first 20 periods which are the periods of interest. It took 15 minutes on a laptop, much slower than NLCEQ, as ENLCEQ solves 20,000 optimization methods while NLCEQ just needs to solve a few (e.g., 33 optimization problems to obtain a degree-10 Chebyshev polynomial of _k_ for each of three values of _A_ to approximate the optimal policy functions) for this simple illustrative case. But as discussed in Section 2, ENLCEQ can solve many problems that NLCEQ cannot or is less efficient or less accurate.

Each simulation path _i_ from ENLCEQ contains a pair of ( _kt,i_<sup>ENLCEQ</sup> _, A_<sup>ENLCEQ</sup> _t,i , c_<sup>ENLCEQ</sup> _t,i_ ) at each time _t_ , where _A_<sup>ENLCEQ</sup> _t,i_ is simulated exogenously. We also apply value function iteration to solve this simple problem and then get the optimal policy function for consumption: _C_<sup>_V F I_</sup> ( _k, A_ ), which is approximated by a degree-20 Chebyshev polynomial of _k_ on [0 _._ 5 _,_ 5] for each possible value of _A_ . We then compare _c_<sup>ENLCEQ</sup> _t,i_ and _C_<sup>_V F I_</sup> ( _kt,i_<sup>ENLCEQ</sup> _, A_<sup>ENLCEQ</sup> _t,i_ ) for all 1 _ i _ 1000 and _t _ 20. We find that the approximate _L_<sup>1</sup> relative error is 3 _._ 7 _⇥_ 10<sup>_−_3</sup> and the approximate _L_<sup>_1_</sup> relative error is 5 _._ 5 _⇥_ 10<sup>_−_3</sup> . This tells us that ENLCEQ can achieve 2 to 3 digit accuracy, which is consistent with the accuracy of NLCEQ in Cai _et al._ (2017).

29
<!-- Start of picture text --> Figure 3. Extended Debt as Share of GDP<br>Extended Debt<br>40 35.5 5<br>33.4 32.9<br>35<br>30.1<br>30 28.4 27.7 27.2 4<br>24.5<br>25 3<br>20<br>15 2<br>10<br>1<br>5<br>0 0<br>1992 2000 2004 2005 2006 2007 2008 2009<br>Debt/GDP Extend debt/GDP GFD/GDP<br>Debt/GDP, Per cent GFD/GDP, Per cent<br><!-- End of picture text --> Source: Reserve Bank of India and author‘s calculations. Latest data on guarantees are available only till 2009.

Along with the deterioration in state finances during the second phase (1998–99 to 2003– 04), was the fiscal stress experienced by the central government. The combined center-state fiscal deficit rose from 7.3 percent of GDP in 1997–98 to an average of 9.3 percent over the period. Studies indicate that although India has had primary deficits, it has avoided an explosive rise in debt, mainly because of high economic growth rates relative to the interest rate paid on government debt. Milan (2011) analyzed the decomposition of India‘s public debt trajectory using the method of debt dynamics, and concludes that the strong rate of economic growth compared to the interest rate paid on debt helped avoid an explosive debt trajectory. The situation was similar for states‘ finances, where the lower rate of interest on debt and the higher revenue buoyancy (Thirteenth FC, p. 126) from both own taxes and share in central taxes enabled improvements in the fiscal stance.

Fiscal correction set in after 2004–05, with the onset of reforms that went beyond the ―realm of fiscal space‖ (World Bank 2004, 11). These included reforms on the expenditure side to contain spending, restrict recruitment, and curb growth in administrative expenditures; and some states cut the cost of pension schemes and reduced subsidies (through power sector reforms), including closure of and privatization of selected PSUs. On the revenue side, reforms aimed to enhance revenue receipts by revising tax rates and broadening the base, while focusing on improving tax compliance. Institutional reforms reflected a paradigm shift, with the adoption of medium-term fiscal frameworks and fiscal responsibility legislation at the state level (Howes, Lahiri, and Stern 2003).

16