rafmacalaba/datause-encoder-data huggingface dataset:
||∆Electrici<br>(1)|ty consumpti<br>(2)|on in April 2020<br>(3)|
|---|---|---|---|
|∆light intensity, April 2020|0.579**<br>(0.230)|0.892***<br>(0.287)|1.251***<br>(0.519)|
|∆light intensity squared, April 2020||-2.789*<br>(1.607)||
|Constant|-0.188***|-0.138***|-0.135***|
||(0.0277)|(0.0393)|(0.04)|
|_N_|30|30|26|
|_light intensity_|all|all|<0.1|
|_R2_|0.184|0.266|0.195|

**Note:** Standard errors are in paranthesis. Arunachal Pradesh, Dadra & Nagar Haveli, and Daman and Diu are dropped from the analysis. * p <0.1, ** p <0.05, *** p <0.01

Table 4: Relationship between changes in electricity consumption and changes in nighttime light intensity at the state level related at the state level. To do so, we aggregate electricity consumption to monthly data and re-estimate the electricity consumption model presented above with monthly frequency. We can then compare the deviations of actual electricity consumption from the model prediction in April to the change in nighttime light intensity in April (compared to the year before).<sup>24</sup> For the whole sample, the linear coefficient is statistically significant at the 5 percent level (Table 4, column 1). However, as visible in Figure 5, since large increases in nighttime light intensity in some states have not been accompanied by increases in electricity consumption, a quadratic model provides a better fit (Table 4, column 2). This suggests that the relationship between changes in electricity consumption and nighttime light intensity is stronger if both are declining, or if nighttime light intensity is at least not increasing strongly. In fact, abstracting from those states in which nighttime light intensity increased more than 10 percent results in a strong linear relationship that is significant at the one percent level. For a one percent larger decline in nighttime light intensity, electricity consumption declined by 1.4 percent (Table 4, column 3). Note that this monthly cross-sectional relationship of the two proxies is very similar to the

> 24For this analysis we drop 3 outliers. For Arunachal Pradesh, we have issues with cleaning the night light data, so that year-on-year quarterly growth rates as well as DDH and DD for which declines in electricity have been very large.

17
Our scoring methodology was based on Dehejia and Wahba (2002) and consists of five steps that the following pages explain in detail. First, we selected background variables to use in comparing students. Second, we used a probability function to estimate a p-score for each school, and assigned each student the p-score of his/her school. Third, we sorted students by their schools’ p-score. Fourth, we separated students into groups so no significant difference existed between the mean p-score of the CONAFE and non-CONAFE students in a group. Finally, we tested separately for each background variable whether values of the variable were balanced between the CONAFE and non-CONAFE students in a group. We used this algorithm for primary and secondary schools separately.

Following this methodology, we first used three criteria to select background variables that would identify similar CONAFE and non-CONAFE schools in those databases. First, we excluded endogenous variables. For example, data on student failure rates would help identify similar CONAFE and non-CONAFE schools. But a goal of CONAFE is to lower failure rates. Since CONAFE likely affects the failure rates of its students, data on failure is an inappropriate control for isolating the effect of CONAFE. If all background data from years before when CONAFE began, then endogenous variables would effectively select CONAFE and nonCONAFE schools. Since we do not, however, have data on most CONAFE schools before CONAFE began supporting those schools, we exclude such variables from background selection. Second, we required the availability of data on a variable for at least two different years and grades. Since a school’s propensity score was constant for all five years, we wanted to avoid the potential bias of selecting an outlier data point from one grade-year.

The third criterion considered specific requirements of the propensity score match. To ensure balance between the CONAFE and non-CONAFE group, we had to exclude some 12
# **III. Main results**

Table 2 includes the basic results from the analysis. For men and women separately, I analyze four dependent variables: labor force participation, employment, employment conditional on being in the labor force and total personal income. I do not focus mainly on wages, but also on the extensive margin of employment (Chandra 2000; Heckman, Lyons and Todd 2000; Johnson, Kitamura and Neal 2000; Neal 2004). For labor force participation and employment, the probit regressions (marginal effects shown) apply to individuals 25 to 65 and controls for five year age group dummies, region dummies (as defined in the IPUMS<sup>1</sup> ), education (high school drop-outs omitted, high school graduates, some college and, college and above)<sup>2</sup> , indicators for being married, disabled and in school, the number of children under age five in the household and the logarithm of total personal income earned by the other household members. The regressions and other estimations use the sampling weights provided with the IPUMS data.

I focus on the interaction terms between race and immigration and in particular on the comparison within each racial group between immigrant and native born, but I observe that the other controls yield expected coefficients<sup>3</sup> : labor force participation, employment and personal income increase with education. Currently attending school and being affected by a disability both have a negative impact on labor market outcomes<sup>4</sup> . Being married has a positive effect on labor market outcomes for men, but negative for women, except that women who have chosen to be in the labor force are more likely to be employed if married. The number of children under 5 in the household is negatively

> 1 The nine census regions are New England, Middle Atlantic, East North Central, West North Central, South Atlantic, East South Central, West South Central, Mountains and Pacific.

> 2 The education distribution by race and immigration status is reported for each gender in the panel A of table 1.

> 3 Table A1 in the appendix shows the same results without controlling for income earned by other family members.

> 4 Being an inmate is also controlled for in the personal income regression, which is a linear regression. When entered in the labor force and employment regressions, which are probit regressions, the inmate dummy drops out as inmates are not in the labor force. This explains the slightly smaller sample sizes for those regressions, which, in effect, excludes inmates from the sample.

5
<!-- Start of picture text --> Private Consumption Com ponents<br>(as % of Private Consumptton)<br>0.<br>0.5<br>1910 1OUt ol~ 1082 1983 1984 1985 1986 1sa7 19 Wi198 1990<br>"Ws<br>l I ~Dur"f -Nu%-Ow" I<br><!-- End of picture text --> Figure 11

# 3.4 Dissaocreaation of Dis_osable income

As discussed above most of the variability of private saving rates is accounted for by changes in disposable income. Figure 4 shows all non-GDP items of disposable income as a proportion of GDP. The real interest on domestic debt (rB), other government revenue (OR), net income on foreign assets (iF-iDpr) and the inflation tax (inf*BM) all exhibit important changes during the 1980-90 period. A closer inspection at the behavior of these items is thus required to evaluate the factors underlying private saving patterns.

The large drop in disposable income, and associated decline in private saving, in the early 1980's steamed principally from the sharp increase in the inflation tan and the sharp drop in the domestic public interest service in 1982, and the more than doubling of other government revenues (OR) in 1983 (Figure 4). The impressive OR record was mainly the result of large<sup>increases</sup> in public utility prices as part of the structural adjustment process in Mexico. Disposable income recovered partially towards the mid-1980s. By and large this was due to a partial reversal of the above described changes: a temporary reduction of OR in 1985 and 1986, a substantial reduction in the inflation tax and a larger domestic public interest service. Net real income from private fore<sup>4</sup> gn assets also increased favored by a real peso depreciation and the growing size of the stock of foreign assets.

The steep decline in disposable income in 1987 is accounted for by three factors: a reduction in the real domestic debt service, a reduction in net income from foreign assets, and a now increase in other government revenue (OR). The drop in the real domestic interest service, equivalent to about 4 percentage points of GDP in 1987, was spurred by surprise inflation which hit
8, almost all employment in our firms is formal and eligible for social security and health benefits, and the mean (median) monthly wages of firms in our sample of $492 ($331) in 2018 are well above the minimum monthly salary of $248 and median monthly wage of $283.<sup>14</sup> Second, from a measurement perspective, (paid formal) employment is the best measure we have of growth in firm size. This is both a result of data coverage (formal employment data are available for more firms and over a longer time period than any of the other outcomes we consider), and of the inherent volatility in firm sales (Lewis and Rao, 2015) and potential problems of firms strategically misreporting sales because of taxation concerns (Carillo et al, 2017). For these reasons, employment is also the main measure of firm growth that Bruhn et al. (2018) highlight in their individualized consulting experiment. Finally, from a theory perspective, employment growth is a key marker of firm size and productivity as firms age (e.g. Hsieh and Klenow, 2014).

Given the heterogeneity amongst firms in initial employment size, and the differences in coverage of the different data sources, we use firm fixed effects in estimating the treatment impacts. We estimate the following equation for firm _i_ at time _t:_ Where the α� are firm fixed effects, _During_ and _Post_ indicate the periods during the individual or group interventions, and after these interventions respectively, 1(s=t) are time fixed effects, _Individual_ and _Group_ denote assignment to the individual and group treatment status respectively, and the standard errors ε�,� are clustered at the firm level. The randomization triplets are subsumed by the firm fixed effects here. We consider both levels and the inverse hyperbolic sine of employment as outcomes.

Table 5 presents the treatment impacts on employment. The first two columns use the employment data obtained from firms. While these data are available for some 145 firms for some months, only 108 of the firms have data for much of 2017. The group treatment results in a statistically significant increase in employment of 6 workers post-treatment, or 12 percent. In contrast, the

> 14 2018 numbers use an exchange rate of 3,155 COP to 1 USD. The minimum monthly salary in Colombia for 2018 was 781,242 COP, and median monthly wage was 882,500.

20
**Figure 11: Maize production Annual value of own-production (left panel), and Annual expenditure share of own-production**

_Source: KIHBS (2005/06), author calculations._

# **3.3 Welfare impact of high maize prices: Effects on household income and poverty incidence**

**In this section we estimate the welfare impact that could be achieved through a 20 percent average decline in maize prices.** The most recent reliable estimates of NCPB purchases show that these activities inflated maize prices by 20 percent on average over a tenyear period<sup>88</sup> .  Using the analysis of household demand and supply of maize, we now estimate the impact of a 20 percent decrease in prices on welfare in Kenya – i.e. what could feasibly be achieved by eliminating price increases due to recent NCPB actions.  Similar analysis can easily be applied to other changes in maize prices, whether relating to the short-term management of crisis, or to longer-term structural policy.  We know that trade policy (tariffs) and the policy towards GMO maize have also affected prices, although at this stage we lack credible estimates as to the size of those effects beyond their complementary role along with the NCPB as mentioned above. The results in this section underline the importance of maize prices and the fact that lower prices will yield significant benefits to Kenyan consumers, and most particularly the poor.  Given that we have used only the estimate of the impact of NCPB, we can consider this a lower bound of what is achievable through adjusting current policies.

**We will apply the hypothetical average 20 percent price decrease to all kinds of maize (green, dry grain, loose flour and sifted flour) across all parts of Kenya.** World Bank (2009) showed that maize prices across Kenya are fairly well integrated, suggesting that it is reasonable to assume constant geographical effects on average.  In addition, price history suggests that the margins between different steps in the value chain are fairly constant over time.  Prices for green maize were not available, but these are the smallest segment, and there is no reason to believe that prices for green maize would not move in similar patterns to those of dry grain.  In the next section we show that while margins between sifted flour, loose flour and dry grain do fluctuate over time, in the longer term they are generally very stable. The only point at which they have deviated substantially from this stability was during the aftermath of the of the 2009 price spike, when sifted flour prices fell more slowly than grain prices.

> 88 See background section.

25
surrounded by (empowered) sisters in treatment communities might raise their competitiveness in response to increased sibling rivalry over parental resources.<sup>4</sup> Second, the program might also affect the influence that sisters have on their brothers’ competitiveness through its social empowerment impacts. By challenging prevailing gender norms, the program might have threatened boys’ sense of traditional male identity [Akerlof and Kranton 2000]. This is particularly true for boys with relatively more sisters, as they are more likely to be indirectly exposed to the program. This gender identity threat can trigger an opposing response aimed at restoring the damaged self‐image [Bénabou and Tirole 2011]. If being competitive is part of the male identity under traditional gender norms, boys surrounded by sisters in treatment communities can subsequently become more competitive as a form of cognitive dissonance reduction.

# **3. Sampling and Lab Experimental Design**

## **3.1. Sampling**

The participants in our experiment were randomly drawn from the sampling frame of households surveyed by Bandiera _et al._ [2015], stratified to include both control and treatment communities for the randomized control trial impact evaluation of the ELA program. A total of 700 adolescents participated in the experiment, 40 percent of whom came from control communities and 60 percent from treatment communities. About 55 percent of the participants were girls, both in treatment and control groups.

At the end of each experimental session, participants completed a short exit survey asking basic background socio‐demographic related questions such as their age, education, marital status, and whether they have any children. The survey also contains sibling sex composition information on participants, namely the number of brothers and sisters they have. Table 1 presents summary statistics on these characteristics for the full sample of participants and separately for girls and boys in control and treatment communities.

Four points are of note. First, participants in our experiment are in their late adolescence and early adulthood: the average participant is 17 years old, and just under 80 percent are aged 14‐21 years (not shown). The bulk of the participants are thus past the critical puberty age around which gender differences in competitiveness have been documented to arise elsewhere [Andersen _et al._ 2014]. Second, the average participant has 2.6 sisters and 2.7 brothers. This large family size is in line with the fact that Uganda has one of the highest fertility rates in the world, and it provides us with enough

> 4 The notion that sibling rivalry can help shape personality traits has been extensively hypothesized in the psychology literature [Sulloway 1966]. While our lab experiment takes place only two‐years post‐intervention, the psychology literature also documents that new behaviors in one setting quickly shape preferences over behaviors in other settings [Breer and Locke 1965].

5
SUDAN’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE

**Figure 18a. Electricity is the largest obstacle to doing Figure 18b. High reliance on generators for power business in Sudan**

<!-- Start of picture text --> 70 120<br>60 100<br>50 80<br>40 60<br>30 40<br>20 20<br>10 0<br>0<br>North river Nile Khartoum Gezira Nyala Red Sea<br>Kordofan<br>% of firms that own generator % of power from generator<br>Gazira Khartoum River Nile North Kordofan Red Sea Nyala<br>Perception that electricity is the biggest onstable to doing business (% of firms)<br><!-- End of picture text --> _Source:_ Derived from World Bank (2009b).

**Figure 19. A small share of Sudan’s power consumption is for industrial activities**

<!-- Start of picture text --> 6000<br>5000<br>4000<br>3000<br>2000<br>1000<br>0<br>2004 2005 2006 2007 2008<br>Agriculture (pumps) consumption Industrial consumption<br>Government consumption Commerical consumption<br>Domestic consumption Electricity Generated<br>GWh<br><!-- End of picture text --> _Source:_ Based on statistical handbooks produced by the Central Bureau of Statistics and on data reported by the NEC, which are assumed to reflect the situation in Sudan.

Power prices are toward the lower-middle end of the range observed in Sub-Saharan Africa. At $0.09 per kilowatt-hour (kWh), the price is close to the average of countries predominantly reliant on hydropower. Nevertheless, given that thermal power has been dominant until quite recently and continues to play a significant role, tariffs look low relative to historic costs of around $0.19/kWh (figure 20).

The costs of producing power are marginally higher than the average cost of power production in Africa. The average power production cost in Sudan, at $0.19/kWh, is slightly more than that of SubSaharan Africa’s, which is around $0.18/kWh (figure 21). The costs are partially offset by generous government subsidies to the power utility. In 2003 and 2004 the government of Sudan provided subsidies valued at around $60 million. While there are no data available for later years, it is believed that the power utility continues to receive these subsides from the government. The end user ultimately pays less than half of what it costs to produce the power.

> <sup>30</sup>
# **2. Enrolment**

# _General Trends_

Vietnam made impressive progress in expanding enrolment in basic education in the 1990s. Figure 1 shows total student enrolment at the primary and secondary levels for every year from 1987 to 2000, based on official government education statistics. Vietnam has achieved high rates of literacy and school enrolment despite its low per capita income. Net enrolment in primary education was 91.4 % in 1999. Increases in enrolment rates were even higher for lower and upper secondary schools over the five years between 1993 and 1998.

Figure 1: Trends in school enrolment, **1987-98** (in millions) <!-- Start of picture text --> ,12<br>,10<br>8 +-s Prirmry<br>6 6 _ ~~~~~~~~Low er secondary<br>4~~~~~~~~ Upper secondary_<br>2 -<br>.0<br>1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000<br><!-- End of picture text --> _Source:_ Ministry of Education and Training The enrolment statistics shown in Figure '1 were collected by the Ministry of Education and Training (MOET) through its statistical reporting system. Absolute numbers of students attending primary school have increased dramatically between early 1990s to 1998, rising by 7 percent from 9.7 million in 1993 to 10.4 million in 1998. From 1998, a downward trend was observed due to demographic change. The downward trend in enrolments in the years before 1990 was reversed by the mid -1990s at secondary levels. At the lower secondary level, enrolment increased even faster by almost 43 percent, from 3 million to 5.3 million in 1998. Upper secondary school enrolment has risen even more dramatically as compared to 1993 - from 0.6 million to 1.4 million, and has far surpassed the peak in 1987.

# _Trends by Expenditure Group_

Education progress for different expenditure groups can be assessed by looking at the number of children actually in school and whether children in school are at the appropriate age'. Over the 5 year period 1993-98, net enrolments increased significantly at all levels, especially at higher general education levels. Net enrolments rates for primary schools increased from 87 1 The gross enrolment rate is defined as the ratio of the number of children currently in school at a given education level to the total number of children in the right age for that level; For example, the right age for primary school is 6-10 years. The net enrolment rate is defined as the ratio of the number of children currently in school at right age for a given education level to the total number of children in the right age for that level.

2
where P�,� is the population in city _j_ at time _t_ , τ��,� is the travel time between locations _i_ and _j_ at time _t_ , and θ is a trade elasticity parameter.<sup>17</sup> The travel time varies over the years based on geo‐referenced information on road categories, surface type and condition, information derived from the Uganda National Roads Authority (UNRA) (see the Appendix for a more detailed description). A higher value of the index indicates that households have better access to markets (where both agricultural inputs and outputs are sold) in terms of proximity to larger markets and/or shorter travel time to a given market. The variation arises both from the changes in the quantity and quality of the road network available to the household, and the demographic growth of the different markets close by.

Input use tends to increase with enhanced market access in Uganda, particularly in the Central and Western regions. Figure 4.1 examines how input use varies with access to markets. Given that market access and crop types vary across regions we present this for the four regions separately. With the exception of some outliers, farmers in villages with better access to markets have higher use rates of fertilizer and to a lesser extent, pesticide and hybrid seeds. This seems to be particularly true in the more economically integrated regions: the Central and Western regions.

**Figure 4.1: Access to markets index and input use (at the village level), by region**

<!-- Start of picture text -->
1. Use of inorganic fertilizer   2. Use of pesticide<br>Lowess smoother Lowess smoother<br>Central Eastern Central Eastern<br>Northern Western Northern Western<br>0 2 4 6 0 2 4 6<br>0 2 4 6 0 2 4 6<br>bandwidth = .8 Distance to markets index bandwidth = .8 Distance to markets index<br>3. Use of hybrid seeds<br>.8 1<br>.6<br>.4 .5<br>.2<br>0 0<br>.8 1<br>.6<br>.4 .5<br>.2<br>0 Use of pesticides (1=yes) during the year 0<br>Use of inorganic/chemical fertilizer (1=yes) during the year<br><!-- End of picture text -->

> 17 Following Donaldson (forthcoming), the elasticity of trade, θ, is equal to 3.8. Different values of the trade elasticity parameter provide alternative results for robustness checks (e.g. see Jedwab and Storeygard 2015; and Blankespoor et al. 2016).

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