rafmacalaba/datause-encoder-data huggingface dataset:
Annex 7. Results from an unweighted model A. The impact of FAIS on mean income, income distribution, and inequality

||_Mean i_|_ncome_|_Gin_|_i_|
|---|---|---|---|---|
||_Unweighted_|_Weighted_|_Unweighted_|_Weighted_|
|FAIS ∞|0.0254**|0.0230*|0.889***|−0.603**|
||(0.0110)|(0.0125)|(0.213)|(0.267)|
|Own revenues ∞|−0.00198|0.0157*|−0.199*|−0.161|
||(0.00463)|(0.00882)|(0.107)|(0.200)|
|Unconditional revenue ∞|0.0262*|−0.00833|0.286|1.107***|
||(0.0136)|(0.0194)|(0.262)|(0.398)|
|Conditional revenue ∞|−0.0324***|−0.0320***|−0.688***|0.0996|
||(0.00741)|(0.0110)|(0.201)|(0.248)|
|Direct workers ‡|0.265|−0.0629|9.087***|−2.996|
||(0.180)|(0.178)|(2.899)|(4.268)|
|Indirect workers ‡|−0.237|−0.963**|3.583|4.470|
||(0.272)|(0.430)|(4.748)|(7.566)|
|Productivity per occupied ‡‡|−0.00719|−0.00628|0.387**|0.701***|
||(0.00704)|(0.0108)|(0.166)|(0.243)|
|Private business assets ‡‡|0.00872|0.00580|−0.117|−0.169|
||(0.00569)|(0.00879)|(0.130)|(0.180)|
|Semiurban|0.0968**|0.0912**|1.870**|2.219**|
||(0.0452)|(0.0457)|(0.951)|(1.071)|
|City|0.114**|0.133***|0.440|0.889|
||(0.0471)|(0.0496)|(1.025)|(1.157)|
|Year 2010|−0.0279***|−0.0641***|−3.894***|−4.360***|
||(0.00760)|(0.00992)|(0.136)|(0.232)|
|Year 2014|0.152***|0.117***|0.452**|0.174|
||(0.00882)|(0.0108)|(0.178)|(0.236)|
|Constant|7.084***|7.713***|34.40***|34.25***|
||(0.0915)|(0.131)|(1.793)|(2.987)|
|N|6,143|6,143|6,143|6,143|
|R-2|0.250|0.360|0.300|0.496|
|Municipalities|2,154|2,154|2,154|2,154|

_Note:_ Semiurban refers to municipalities with a population between 2,500 and 15,000. City refers to municipalities with more than 15,000 inhabitants. The average income corresponds to the average per capita household income in each municipality in logarithms. The deciles of income correspond to the cut-off points of dividing in each municipality the households into ten equal groups and is expressed in logarithms. All monetary values were in real terms in August 2014. Fixed effects model by year and municipality for the years 2005, 2010, and 2014. Weighted results show the same model adding importance by size of municipal population.

∞ Fiscal revenue items in the logarithm of the average monthly per capita value three previous years. ‡ Occupied per inhabitant, employees of the economic units or indirect workers, reported in the economic census of the circa year. ‡ ‡ Productivity by the total occupied and active economic units in each municipality. Standard error in parentheses. *** p < .01 ** p < .05 * p < .1.

39
Accident costs are extrapolated from US studies (accounting for differences in accident rates). They are taken to be the same on a per vehicle mile basis for autos, microbuses, and public buses for reasons noted in Appendix B. On a per passenger mile basis accident costs are small (7 percent) relative to marginal congestion costs, reflecting the severe congestion in Mexico City.

# **3.3. Behavioral Responses**

There is a substantial body of empirical work on the responsiveness of gasoline consumption and auto mileage to fuel prices for the United States and certain other industrialized countries (for surveys see e.g., Goodwin et al. 2004, Glaister and Graham 2002). Based on this evidence, and making some allowance for the greater responsiveness of auto mileage, given the wide array of alternative mass transit options in Mexico City, we assume a gasoline price elasticity of –0.6, and auto and microbus passenger mileage/fuel price elasticities of –0.3. These assumptions are broadly in line with the one study we aware of that estimates behavioral responses with Mexican data (Eskeland and Feyzioglu 1994). (As already noted, and discussed further below, applying economy-wide evidence on fuel price elasticities to fuel tax increases that are specific only to Mexico City is problematic, as this does not capture incentives for legally, or illegally, obtaining low-tax fuel from neighboring regions of the country).

Own-fare elasticities for passenger mileage by public bus and rail are taken to be –0.5 and –0.3, again based on a body of empirical literature, largely for the United Sates (see Appendix B). To obtain the relative price coefficients when a toll is imposed on auto travel, we assume that 80 percent of the diverted passenger mileage away from auto will go onto other modes (according to their share in non-auto passenger travel) and 20 percent will reflect reduced overall travel demand.<sup>12</sup> When the price of transit increases however, many travelers are limited in their ability to substitute into auto, given that only a minority of the population owns automobiles (IAPT, 2007). For this case we assume that 30 percent of diverted travel from the transit mode whose price is increased reflects reduced overall travel demand, and of the remaining 70 percent half goes to auto and half to other transit modes (again in proportion to

> 12 For example, for reduction of 10.0 auto passenger miles in response to higher auto tolls, 4.5 passenger miles are diverted onto microbus, 1.6 to public bus, 1.9 to rail, and 2.0 reflects reduced overall travel demand.

23
# **2.2 The Program**

In the late 1990s, Colombia was hit by its worst economic downturn in 60 years: GDP shrank by 4.5 percent in 1999 alone, and the national poverty rate increased by 7.2 percentage points, largely erasing the socio-economic gains made during the early 1990s (World Bank, 2005 and 2008). A team was put together to address the social dimensions of the crisis from the government of Colombia, the World Bank, and the Inter-American Development Bank. One of the safety net programs was _Familias en Acción_ (FA), designed as an instrument to help mitigate the effects of the economic crisis on the wellbeing of poor households and protect and promote human capital formation. FA was inspired by the CCT _Oportunidades_ in Mexico, and consists of subsidies to education, nutrition, and health subsidies conditional on specific behaviors associated with school participation and attendance to health checks. The program was piloted in a few municipalities in 2001, but was scaled up in the second half of 2002 in the context of great, but still insufficient, progress in improving educational coverage and attainment.<sup>7</sup> The largest component of the program is educational conditional cash transfers. Households with children aged 7-18 receive a monthly grant per child, conditional on the child attending to at least 80 percent of school lessons. When the program started, the grant was $12,000 pesos (approximately $7) for each child attending primary school (grades 1-5) and $24,000 pesos ($14) for those in secondary school (grades 6-11). The level of benefits was set to compensate for the direct cost borne by low-income families to send their children to school.<sup>8</sup> In some urban areas, the subsidies were increased to accommodate the higher opportunity cost of secondary education in the cities, and the subsidies for primary school students were replaced by nutritional subsidies for children

> 7 Net enrollment rates for children aged 7-11 (primary school) increased from 77 to 93 percent from 1992 to 2002, and from 40 to 57 percent for those in the 12-17 age group (secondary school) (UNESCO, Institute for Statistics). The average educational attainment of people 15 years and older increased from 6.4 years in 1992 to 7.6 years in 2003. This is still low, however, when compared with other countries in the region such as Argentina (8.8 years) and Panama (8.6 years) (World Bank, 2005 & 2008). In terms of school completion, the numbers had not changed significantly since the beginning of the 1990s. At the pre-program time, still only 60 percent of children who started primary school finished fifth grade, while 57 percent of those who began secondary school finished ninth grade and only 35 percent completed eleventh grade, that is, high school (García and Hill, 2009; World Bank, 2008).

> 8 After a first expansion in 2005, the grant was increased to $14,000 pesos and $28,000 pesos, respectively. The latest change in 2007 brought the grant to corresponding $15,000 pesos and $30,000 pesos, merely keeping up with inflation.

9
TANESCO has been consistently loss-making in recent years but ran an operating profit in FY2013/14 and 2014/15 because of favorable hydrological conditions which reduced the cost of sales substantially and reduced the need for power purchases from third parties. Fuel prices have been a major factor in cost variations seen in the Philippines, which benefitted from declining fuel prices since 2013. Senegal was also able to achieve greater levels of cost recovery despite tariffs being frozen at 2009 levels, as fuel purchase costs declined 44 percent between 2012 and 2016. Since most materials and equipment are imported, Tajikistan’s Barki Tojik is highly susceptible to the devaluation of its local currency.

**Table 3: Volatility in Full-Cost Recovery during 2010-2017**

|**Country/State**|**Average**|**Min**|**Max**|**Coeff.**<br>**of Var**<sup>**b**</sup>|**Main sources of Volatility**|
|---|---|---|---|---|---|
|Colombia|104%|94%|117%|7.0%|Fuel mix (hydro availability), debt service costs, FEX<br>rate.|
|Dominican Republic|66%|62%|73%|7.0%|Fuel prices.|
|Egypt, Arab Rep.|55%|49%|68%|**14.3%**|Fuel mix (gas availability), FEX rate, fuel prices.|
|India - Andhra Pradesh|38%|36%|44%|8.9%|Debt service costs.|
|India – Odisha|91%|90%|94%|1.7%|n.a.|
|India – Rajasthan|45%|38%|57%|**16.0%**|Debt service costs.|
|Kenya|90%|80%|101%|9.0%|Fuel mix (hydro availability), capital costs.|
|Morocco<sup>a</sup>|84%|n.a.|n.a.|n.a.|Fuel prices.|
|Pakistan|80%|66%|97%|**17.0%**|Fuel prices (major factor), tariff increases (minor).|
|Peru|98%|93%|102%|3.6%|n.a.|
|Philippines|98%|95%|100%|1.7%|Fuel prices.|
|Senegal|70%|55%|87%|**16.0%**|Fuel prices.|
|Tajikistan|67%|52%|83%|**18.7%**|FEX rate.|
|Tanzania|68%|56%|90%|**22.6%**|Fuel mix (hydro availability), FEX rate, fuel prices.|
|Uganda|92%|81%|110%|**12.2%**|Capital costs, fuel prices, FEX rate.|
|Ukraine|106%|93%|115%|7.1%|FEX rate, fuel prices.|
|Vietnam|89%|86%|91%|2.1%|n.a.|

_Notes:_<sup>a</sup> Data for Morocco is only available for one year (2013).<sup>b</sup> Coefficient of variation (standard deviation divided by the geometric mean). _Source:_ World Bank staff.

# **#3: Losses due to the under-recovery of costs are absorbed by the sector in different ways, and some countries manage to maintain utilities’ financial viability better than others**

29. The case studies illustrate that there are different mechanisms to absorb the under-recovery of costs, with different implications for the utilities’ ability to adequately serve their customers. Table 4 summarizes indicators of how losses are absorbed. Notably, government support falls short of restoring utilities’ financial viability in almost all cases.

15
22 For health effects, the diesel emissions, being so fine, penetrate most deeply into the lung and, therefore, are presumably the most dangerous fraction of total particulates (TSP), Yet, the recent mortality studies cited above use TSP not fine particulates as their measure of pollution. In fact, health professionals are not at all in agreement on the particulate agents that are most dangerous to health (indeed, effects of particulates have not even been differentiated from those of S02).

Unlike ozone, changes in particulat **e** missions at one location (or transport corridor) will generally have greater affects on the surrounding area and little, if any, effects on other areas. For<sup>policies that have</sup> differential effects across space, this distinction could be important if exposed populations also differ significantly across space. EPA has programs to model corridor effects of TSP but the standard point source, Gaussian plume models would not apply to mobile sources.

S02, as a gas, will be less spatially differentiated than particulates but owing to its direct emissions from diesel sources, more spatially differentiated than ozone, which is a product of chemical transformation. In any event, as an S02 effect has not been clearly differentiated from a particulat **e** ffect, it may be acceptable to ignore S02 emissions (unless a policy of reducing sulfur in fuels in being contemplated) on the grounds of otherwise double-counting health effects, focusing on the particulate effects as capturing all of the particulate-S02 diesel effects.

Lead, as a part'-ulate, will have spatially differentiated effects if traffic patterns are altered along a specific transportation corridor. But otherwise, one can assume that mobile source lead emissions are ubiquitous. 3 On the basis of health effects, lead is by far the greatest 13. Evidence of this ubiquitousness is that very tight statistical relationships have been discovered between monthly gasoline sales in a city and blood lead levels in children.
1.6 results 37

R-squared is much higher in the ad hoc regression, the results show that the coefficients vary and inferences would be very different depending on whether the model ignores the zeros (as in column 1), corrects for them (as in columns 2-4), or makes an atheoretical ad hoc adjustment (column 5).

Finally, Silva & Tenreyro (2006) point out that the presence of heteroskedasticity in trade data actually requires the use of _non_ log-linearized models. Their method allows the inclusion of zero flows in the sample by estimating the gravity model with original (non-log) FDI as the dependent variable. The results from their Poisson Pseudo Maximum Likelihood (PPML) method in column 6 do not show coefficients on the FDI-related policy variables to be statistically significant, but some variables have surprising coefficients. The elasticity of distance, for example, drops by about two-third as in the main bias-corrected model. An issue with the method proposed by Silva & Tenreyro (2006) in the context of FDI flows is that it does not seek to explain zeros as anything special. In my model, zeros are intimately linked to heterogeneous productivity of firms. A zero bilateral flow indicates that firms are not finding it profitable to invest abroad because, for a given level of foreign demand, either the firm productivity is low, or the fixed and variable costs induced by distance and policy are prohibitive.

Addressing the zero observation problem (regardless of whether the selection bias is serious or not) still does not give us consistent estimates when firms are heterogeneous. In earlier trade models that follow Krugman (1979), firms are symmetric and all firms export. Only their volume is constrained by trade costs, not the decision of firms of whether to export. When trade barriers are infinite, foreign varieties are still consumed, but zero quantity of each. The effect of trade costs is only on the intensive margin of trade. Helpman et al. (2008) show that the correction of selection bias is inadequate when the assumption of symmetric firms is rejected and firms are not affected by FDI barriers in an _identical_ manner.
assumes that violence has no impact on the relatively less victimized. If everyone who lives through a period of conflict — regardless of their victim status — is affected, estimates of the marginal impact of greater conflict exposure may present a biased assessment of the overall social cost of violence.

In this paper, we estimate the impact of a specific episode of civil conflict, Kenya’s postelection crisis, on the risk preferences of a broad sample of young adults who lived through it. The post-election crisis was a months-long period of protests, rioting, and ethnic violence that began immediately after a disputed presidential election. The election, in which Raila Odinga challenged incumbent Mwai Kibaki, took place on December 27, 2007. Amidst allegations of electoral fraud by observers, and after three days of uncertainty following the national polls, the incumbent president was both declared the winner and sworn into office on December 30, 2007. Ethnic tensions rose, and rioting ensued. The following two months of civil conflict left more than a thousand people dead and hundreds of thousands more internally displaced. The crisis largely ended when, on February 28, 2008, the two candidates signed a power-sharing agreement.<sup>1</sup> We estimate the impact of Kenya’s post-election violence on individual risk preferences, which we measure using lottery choice questions embedded in a longitudinal survey. The Kenyan Life Panel Survey (hereafter KLPS2) is a survey of more than 5,000 young adults who were enrolled in rural primary schools in 1998. The second round of the survey was administered between August of 2007 and December of 2009. 1,180 respondents (23.3 percent) were interviewed prior to the crisis, while the remainder were surveyed after experiencing the period of civil conflict. Thus, Kenya’s post-election violence interacted with the timing of the survey to create a natural experiment in exposure to conflict.

We employ two complementary identification strategies to estimate the impact of the crisis on risk aversion. First, we estimate the impact of the crisis in straightforward linear and nonlinear frameworks, using several strategies to control for any time trends or seasonal shocks. Second, we exploit the fact that survey respondents were randomly assigned to one of two waves of interviews; Kenya’s election crisis interrupted the first wave of surveys, allowing us to instrument for conflict

> 1 This description of Kenya’s 2007-2008 post-election crisis draws upon several sources: World Bank (2006), World Bank (2008), World Bank (2009), World Bank (2010a), World Bank (2010b), World Bank (2011), World Bank (2012), Van Praag (2010), Waki (2008), Hjort (forthcoming), McCrummen (2007), BBC (2008a), and BBC (2008b).

3
accounting year-end.

As an extension, rather than a binary treatment variable, we also calculate a continuous measure reflecting the number of pounds the firm is below the AIA ceiling. The intuition being that firms with more unspent allowance, have a greater incentive to increase their investment. We use this continuous measure of the AIA allowance available to firms as a robustness test below. That the magnitude of the response to the AIA policy is likely to increase with the gap between a firms historical capital investments and the AIA threshold also helps to provide an argument for why the DID approach adopted in the paper is preferable to, for example, a regression discontinuity (RD) design. An RD design would measure the effects of the AIA for firms around the threshold, thereby ignoring those for which the change in the AIA threshold is most significant.<sup>27</sup> We note some differences in the specification when using the capital investment or worker data and that on cloud, big data and AI. When studying the effects of AIA on capital investment or demand for data workers this data is available on an annual basis and therefore the treatment status of the firm is determined by these annual changes in the marginal incentive to invest by year. The presence of firm/worker and year fixed effects in Equations 1 and 2 along with the changes to the AIA policy across years means the regression belongs to the two-way fixed effects models with staggered treatment design that has received much recent criticism about ‘forbidden comparisons’, i.e. comparing treated firms to previously treated firms as well as those not treated (Goodman-Bacon, 2021; Borusyak et al., 2021; Callaway and Sant’Anna, 2021). As our preferred method, we use the approach of Callaway and Sant’Anna (2021), which accounts for staggered treatment designs and heterogeneous treatment effects under semi-balanced panel data. The approach presents event study estimates of investment of treated firms (for each cohort of AIA changes), by comparing against those firms that are never treated and not yet treated as a control group.<sup>28</sup> We also examine

> 27We are not able to estimate a regression kink design due to relatively small samples for our technology regressions.

> 28The results are similar if we only use never treated firms as controls.

17
The rate of non-compliance that resulted was 12%. In those cases, the household received zero CFLs.<sup>33</sup> All study households were visited again in the spring of 2014, one year following the intervention, for a follow-up survey. Of the original 1,000 study households, 101 addresses were identified as having new tenants in the year since the intervention. We interviewed all households currently living at the original addresses, as it was difficult to know exactly when residents moved or if the CFLs had moved with them.<sup>34</sup> After completion of the follow-up survey, survey respondents were again offered 150 KGS to compensate for their time.

# **4 Study sample**

## **4.1 Data**

We use several datasets, including electricity utility data, baseline and follow-up household survey data, and GIS spatial data, which are matched at the household-level. All of these data are then matched to heating degree day data from a nearby weather station. A description is provided in the Appendix.

**Electricity utility data:** We utilize electricity utility records identifying both transformers and households served by them, as well as the monthly household electricity bills. These data start in October 2010 and continue through September 2014. This provides observations 30 months prior to and 18 months following the intervention, held in March 2013. One important feature of this time period is that electricity prices remained constant at 0.02 USD per kWh.<sup>35</sup> The utility, however, was not collecting transformer-specific outage data at the time of the intervention.<sup>36</sup>

**Household survey data:** As indicated in the previous section, we conducted two rounds of household surveys, one immediately prior to the intervention (baseline) and one a year

> 33For intent-to-treat estimates, these households are considered treated. Compliance is discussed below.

> 34Survey enumerators made at least four attempts to survey the household. If enumerators were informed that the previous respondents had moved, then the new residents were surveyed. A total of 835 original respondents were re-interviewed for the follow-up survey.

> 35A tariff reform was introduced in late 2014 (see McRae and Meeks (2016) for details of reform) and therefore we end our analysis in September to avoid conflating the CFL intervention with the tariff change.

> 36Because electricity consumption data are monthly, we cannot use them to calculate timing of outages.

13