rafmacalaba/data-use-mentions-extended huggingface dataset:
# **5 Theoretical model**

In light of the existing evidence for the US, our finding that an expansion in the supply of municipal preschool does not crowd-out local private enrollment in Brazil might be somewhat surprising. In this section we present a simple theoretical framework in order to rationalize these mixed empirical results.

Consider a market for institutional preschool education with potentially two providers: one private and one public. The private provider offers preschool services at quality _q_ 1 and price _p_ . For simplicity, we take a short-term perspective by assuming that the quality level is fixed, making price the only choice variable of the private provider. The public provider offers preschool education at quality _q_ 0 for free (zero price).<sup>14</sup> We assume that the public provider has lower quality than the private provider: _q_ 1 _> q_ 0. This assumption is fully consistent with the descriptive statistics on the Brazilian child care market presented in Section 3, which show that the quality indicators are systematically higher for private providers.

We assume that consumers have unit demand, where each consumer demands either one unit of child care from the most preferred provider, or zero units if that is the utilitymaximizing choice. The net utility of buying one unit of preschool education is given by where _v_ + _θqi_ is the willingness to pay for one unit of child care of quality _qi_ , with _θ_ being the marginal willingness to pay for quality. A key assumption of our model is that consumers are heterogeneous with respect to the parameters _v_ and _θ_ . More specifically, we assume that demand for preschool services comes from two different demand segments, henceforth referred to as Segment _H_ and Segment _L_ , where each consumer in Segment _H_ is characterized by _v_ = _vH_ and _θ_ = 1, whereas each consumer in Segment _L_ is characterized

> 14The assumption of fixed quality is consistent with the evidence presented in Tables 8 and 9 in Section 4, which shows that an expansion of public supply of preschool has no systematic effect on
-|||

_Note:_ Unit of observation is one rider in column (1) and one platform survey respondent in columns (2) and (3). Sampling weights are applied to platform survey observations. Standard errors in parentheses, clustered at rider level. For risk of harassment questions, women were asked to imagine a rider similar to themselves who takes the same commuting route, while men were asked to imagine an average woman who takes the same commuting route. Crime data from Instituto de Seguranca Publica do Rio de Janeiro is reported as number of crimes per 100k residents in the area.
but has fallen some due to steps taken in the power sector reform. As the data in annex 8 indicate, Liberia and Malawi may also fall into this group, but they are not shown in the figure because data are available for only one year and there are issues in both countries that raise questions about data accuracy.

The second group are those countries with historically medium levels of QFD (25 to 100 percent of revenues). In this group, the QFD has remained relatively stable. The third group are those countries for which the QFD has increased since the AICD period. Kenya and Botswana are notable examples. Both had close to zero QFD by the end of the AICD study period, and but medium to large QFDs in this study. For Botswana, the main driver has been the commissioning of the Morupule plant, as mentioned in section 7.1. Ethiopia may also fall into this group, where there has been major capital expansion (with significant direct support from the state, as discussed in section 5.1).

These results provide a more detailed analysis of the lack of profitability found in Dobozi (2016), which looked at a sub-sample of 16 utilities in SSA from 2000 to 2013. The paper suggests limited improvement over the 13 years covered: 13 percent of utilities reported a net profit in 2000, increasing to 30 percent in 2010 and falling to 20 percent in 2013. The results presented here are broadly consistent with this conclusion, but the data reveal positive trends in a number of countries, as well as worrying digressions in others.

56
on the sample, with 1 assigned to the smallest value and ties are assigned the same value, so that the sum of the ranks is preserved. For communities the ranking is based on the sample within a zone.

number of communities within a zone is ten, while for five zones there is only one community in the survey.

Although it would be advantageous to have more information at the community level, the set of possible variables is limited by the lack of information available at that level from published census reports.

# **3.3 Individual Data**

As discussed earlier, we surmise that the effect of family planning on fertility is highly dependent on a woman’s schooling. The lower a woman’s education, the more likely she is to benefit from access to family 10
|22|
|Sickness<br>or maternity|6|12|18|19|
|Work InJury|13|21|23|23|
|Unevployment|0|4|5|8|
|Family<br>Allowance|0|6|14|14|

Industrial market economies (19 out of 19 reported some program as of 1985)

|Type of benefits|1925|1945|1965|1985|
|---|---|---|---|---|
|Old age, disability,<br>or death|13|17|19|19|
|Sickness<br>or maternity|12|16|17|18|
|Work Injury|18|19|19|19|
|Vnemploymcnt|12|18|19|19|
|Family<br>Allowance|0|11|17|19|

Note: Gabon has been added to the upper middle-income countries. See note I to Table 1.1.

Sources: Program Information: U.S. Social Security Administration (1986), Social Security Programs Throughout the World 1985, Washington, D.C. Incom categorles: World Development Report 1987, World Development Indicators, Table 1, World Sank, Washington, D.C.-
versus congestion before and after the curfew for each hour of the day).

There are increases in speed both during the daytime hours and during the pre-curfew hours, though the increase is twice as large during pre-curfew hours (Table 4).<sup>7</sup> This demonstrates that the combined effect of changes in density and behavior is an increase in the speed that individuals drive. Additionally, given that we see no effect on crashes during the daytime hours (if anything the coefficient is negative, indicating a decrease in crashes), it seems that the increase in speed from the drop in congestion does not lead to increases in crashes. This is in line with the findings from Green et al. (2016).

When we control for vehicle density using the congestion delay data from Waze, “relative” speed decreases during the daytime hours the COVID-19 policies are implemented. During the pre-curfew hours there is still a significant increase in “relative” speed. This demonstrates that beyond the increase in speed that can occur when the number of vehicles decreases, individuals are driving at even higher speeds during the pre-curfew hours. We also look at this in a triple-difference framework, comparing the increases in speed between the pre-curfew hours and the daytime hours, before and after the curfew period in 2019 and in 2020, controlling for the level of congestion. We see a large positive increase in speed during the pre-curfew period as compared to the other daytime hours. This increase in speed that goes beyond the mechanical change in speed that occurs when congestion decreases could help to explain the increase in the probability of a crash during the pre-curfew time interval.

> 7Note that during the curfew hours, there seems to be a decrease in speed as measured by Google Maps (Figure 1b). The number of vehicles on the road at this time drops to close to 0; therefore, the measured decrease in speed may be a function of how Google calculates driving time in the absence of vehicles. To confirm the data,
(2.281)|
|**Daughter Dummy**|**-74.1*****|**-74.1*****|**-65.2*****|**-58.5*****|
||(8.694)|(8.694)|(9.700)|(9.729)|
|Intercept|426.9***|426.9***|461.0***|507.7***|
||(5.420)|(5.420)|(5.882)|(9.728)|
|No. of Children||||-21.6***<br>(3.749)|
|No. Observations|37144|37144|32499|32499|

Notes: (1) The data used are IHDS 2005 and NSS 1995 respectively. (2) Robust standard errors in parentheses; *** p<0.01, ** p<0.05, * p<0.1.
of wages. However, the impact of wages on land allocated to forests is indeterminate. Thus, the discussion here serves to highlight the fact that there are various channels through which migration and remittances may affect forests but some of their individual effects as well as their joint impact on forests are not clear theoretically.

# **4 Data**

We combine satellite data for land use and land cover change together with two rounds of nationally representative household surveys to create a unique village-level panel dataset spanning 1995-2004. In this section we describe these data in detail.

## **4.1 Satellite Data**

We use satellite imagery from the Multispectral Scanner (MSS), the Thematic Mapper (TM), and the Enhanced Thematic Mapper (ETM+) for various years to derive a measure of land use and land cover change. Ideally, we would have liked the land use data to coincide perfectly with our base years of interest, particularly 1995 and 2003. However, this was not possible due to unavailability of the satellite data for particular years and due to excessive cloud cover. Additionally, given the particular vegetation phonology in Nepal, most of the vegetation contains a minimum amount of leaf moisture during the months of October and February rendering scenes captured during these months best suited for measurement and analysis of land cover. This put a further restriction on the images we could use for our analysis. In the end, we were left with MSS images for 1975 and 1976, TM for 1991, 1992, and 1996 and ETM+ for 2000, and 2001. In this paper, we obtain land use data for the years 1975, 1995 and 2003 using TM and ETM+ imageries respectively.

Shadows in the mountainous areas especially above 2,400m elevation, and mostly those with aspects facing east posed a significant challenge. We tried correcting for these shadows using ATCOR 3 in Erdas Imagine 9.3, but did not have much success. Additionally, there were cloud covers in MSS (1975-1976) images and
Our high-frequency surveys did not ask the amount spent on transportation and exit fees per visit to Corabastos, but we did ask these in the six-month and 12-month follow-ups. The mean control group user averages 29,659 COP per visit, and the ITT pooled treatment effect over these two rounds is 3,556 COP per visit (s.e. 1297, p=0.008), reflecting fewer heavy goods to transport. Combining this reduction in the cost per visit with the reduction in the number of days per week going to Corabastos of 0.17 in the short-run gives an estimated short-run cost savings of 20,556 COP per week (US$6.66), which is equivalent to approximately 0.5% of baseline weekly sales, or 4.9% of baseline weekly profits. Using our statistically insignificant pooled effect over the year of a 0.09 reduction in travel days would reduce this to 18,054 COP (US$5.85) per week.

Finally, panel D of Table 3 examines whether this reduction in travel time and travel costs is accompanied by a change in work-life stress. We asked a set of eight questions about general life and work satisfaction, whether they find work stressful, as well as about whether the owner feels tired, lacks time, and frequently has no time for family. These variables are coded as binary variables in the same direction so that one indicates more stress, and zero less. At baseline, 90 percent of owners say they are satisfied with their life and their work, but one-quarter report feeling tired most days per week, and 40 percent say they frequently feel work forces them to spend less time with their family. We see that the offer (and use) of Agruppa results in lower work-life stress, which is significant at the six-month horizon but not at the 12-month. Table A5 shows the impacts on each contributing item to this index, with the largest changes seen in questions about whether they lack time to do all
*|0.122|
|Lower Income<br>|dropped|dropped|6.090**|57.336***|
|Lower income * Nonrenewable resource<br>Dependence|0.078|-0.362***|0.129**|-0.756***|
|High-Low education ratio|-0.418***|-|-0.159|-|
|High-Low education ratio square|0.015***|-|0.008*|-|
|Consumer Price Index (2005)|-0.010*|-|-0.003|-|
|Services value added share|0.130***|-|0.117**|-|
|Manufacturing value added share|-0.006|-|0.064|-|
|Agriculture value added share|-0.175***|-|-0.154**|-|
|Log Labour Force|-|-8.651***|-|-26.131***|
|Log Gross fixed capital formation|-|0.975|-|5.280***|
|Log Exports of goods and services|-|2.663**|-|6.042***|
|Log REER|-|5.057***|-|3.325|
|World Oil price index|-|0.081***|-|0.091***|
|WorldMetalpriceindex|-|0.018|-|-0.001|
|Observations|667|707|580|580|
|R2|0.096|0.311|0.911|0.864|

Note: * ** and *** denote significance at the 10% 5% and 1% levels,
standard errors from linear models of the primary outcomes on intervention group, fixed effects for randomization strata, fixed effects for province, and intervention-by-province interaction terms. Standard errors are clustered by cluster (group of villages). There were 121 clusters in total. The WASH institutions index was calculated by rescaling each variable in the index (e.g., presence of WASH committee) so that higher values imply better outcomes, then standardizing relative to the control group, following Kling _et al_ . Effects are in standard deviation units. The Wald F is a test statistic for the null hypothesis that all of the interaction term coefficients are zero. * p<.1, ** p<.05, *** p<.01"
number of continuous days after suffering an illness in day 1.<sup>7</sup> Conditional on missing a day of work due to unexpected illness, 28 percent of men and 44 percent of women are still not working 7 days later. A nontrivial fraction of the shocks continue even longer, especially for women: 31 percent of women are still not working 21 days after an unexpected illness, compared to 10 percent of men.

# **2.2 Summary Statistics**

Table 1 presents summary statistics of the working-age adults in the high frequency sample.<sup>8</sup> The sample is relatively young (an average age of 34 for men and 36 for women) and well educated by developing country standards: the average male has 11.2 years of education and the average female has 9.4 years of education. The typical household extends beyond a nuclear family; the average male is in a household with 5.6 total working-age adults and the average female is in a household with 6.1 working age adults. In Heath et al (2019) we show that the sample closely matches the characteristics of adults in urban Ghana surveyed in the Ghana Living Standards Survey conducted in 2012 and 2013.

Both male and female labor supply is high in this context: 72 percent of men and 66 percent of women report being employed at baseline (having steady work that they were doing regularly), and 65 percent of men and 56 percent of women worked in a given week over the

> 7 While these reports are suggestive of the typical length of an illness, these daily data are less suitable for precisely isolating illness shocks, given that we can identify illness only in days in which the individual was planning to work but did not.  That is, a series of several consecutive days off would only appear if each day after the original illness shock, the individual was planning to go back to work but still could not.  As a result, the rate at
industry, and region—collected data for Egypt in 2004, 2007, 2008, 2013, 2016, and 2020. Each survey year is representative of the business environment that year. Of these, this analysis will focus on a panel of 198 firms in 2013, 2016, and 2020. See Table 2 for mean values of our main variables.<sup>9</sup> We supplement the ES data with information on the universe of firms captured by the 2017 Census of Establishments in Egypt and with data from the United Nations International Trade Statistics Database (UN COMTRADE), specifically information on world imports from Egypt over the period of study.

# **_4.2 Stylized Facts of Egypt_**

# **_a. Exporter Characteristics_**

We begin the data analysis at the aggregate level, by comparing total Egyptian exports from the ES with world imports from Egypt from UN COMTRADE. As Figure 5 shows, the ES data represent a reasonable proxy for Egypt’s exports to the world. In 2020, wood manufacturing (ISIC=20) and vehicle manufacturing (ISIC = 34) stand out as the only two sectors overrepresented in the ES relative to the UN COMTRADE data.

Figure 5 aggregates exports from all ES firms. Looking at the data more closely, we see that few Egyptian firms export, either directly or indirectly (Table 3).<sup>10</sup> In 2020, only 27 of the 198 firms in the panel exported.

# **_b. Cross Section_**

As Figure 6 shows, exporters tend to be significantly larger than non-exporters in every year of the survey. In 2020, the average employment size for exporters is 363, statistically different

> 9 Appendix A reports descriptive statistics for firms that survived between 2016 and 2020 with those that did not. Overall, the firms in both groups are broadly similar except in terms of size. Larger firms are more likely to survive. 10 Firms exporting directly are those that sell to other countries
.

14 The peak period of government assisted transmigration was in the Third Five Year Plan period (1979-84) when it is estimated that slightly over 350,000 households or around 1.5 million individuals were resettled outside Java. In this brief five year period more households were shifted outside Java than in any other period in Indonesia's history. The share of transmigrants going to all provinces of Kalimantan, Central and Southeast Sulawesi and Irian Jaya also rose steeply at this time.

15 Of all provinces, East Kalimantan recorded the most rapid increase in net-migration relative to the size of its population in 1971, mainly related to employment opportunities created by resource booms (first timber and later oil and gas), and supported by movement of households through the transmigration program.

16 Urban unemployment rates remained high throughout this period, although they did not increase significantly more rapidly than in other provinces. Data for 1976-77 and 1991/92 indicate that unemployment rates were highest in resource rich provinces such as East Kalimantan, Riau and Maluku in both periods.
The regions and samples of proposed projects considered via MCE and IPF are summarized in Table 3.

Table 3. Water Catchment Project Sample

|Region|Number of Projects in|
|---|---|
||IPF Sample|
|Biobio|14|
|Maule|13|
|O’Higgins|22|
|Valparaiso|12|

To compare IPF and MCE prioritization outcomes, projects were first assigned scores and ranked in regional groups, as in practice. They are also presented in ranked order as a pooled (all regions combined) group in the IPF analysis.

# Water Catchment Project Indicators

<mark>The selection of indicators used to construct the SEI reflects some of the key policy goals of the government with respect to developing water catchments. As discussed in Chapter 2, many of the intended benefits associated with improving water resources are strategic, environmental, and social – and these are often difficult to quantify and monetize. Therefore, economic analyses often result in calculated internal rates of return (IRRs) below the 6% threshold required for favorable recommendation. These low IRRs are likely due to undervaluation of some long-term benefits. Recognizing the importance of water resource development, however, the government</mark> implemented the Multi-Criteria Evaluation approach to assess these projects.

<mark>The IPF draws from the MCE’s components to select the sets of input indicators for the SEI and FEI. Data previously gathered for the MCE were simply re-organized to fit the format of the IPF approach, with three important changes. First, the selection of input variables required paring down the extended list of MCE indicators. The resulting IPF indicator set excluded some MCE variables either because (a) they brought redundant information to the analysis or (b) exhibited no variation across projects. Second, some indicators used in IPF drew directly on project data in natural units rather than the ordinal score (e.g., 0, 5, 10) assigned to projects in the MCE approach. Third, some
**Table 3: Ratio of high to low educated workers by sector, 1990-2002**<sup>_ab_</sup>

|Sector/Period|1990-1993|1995-1998|1999-2002|Change<sup>_c_</sup>|
|---|---|---|---|---|
|Sugar growing|0.01|0.03|0.06|500%|
|Sugar processing|0.07|0.12|0.56|700%|
|Agriculture other than sugar|0.04|0.05|0.08|100%|
|Industry other than sugar|0.45|0.56|0.85|89%|
|- Food Industry|0.89|0.97|1.2|35%|
|– Beverages|1.21|1.72|2.55|111%|
|Services|1.01|1.36|1.94|92%|

> _a_ Source: PNAD surveys, 1990-2002. See data appendix for more details.

> _b_ The ratio is taken as the average number of workers in the sector with 9 or more years of education divided by the number of workers in the sector with less than 5 years of education. Similar patterns are found when using the number of workers below and above the average education level in the survey which is around 7 years.

> _c_ The change is measured as the percentage change in the rati of high to low educated workers between the 1999-2002 period and the 1990-1993 period.

33
more likely to respond to or complete the diary. Overall figures suggest that, out of a total of 5,506 households who received the diary, 1,055 never made any entry, leading to a nonresponse rate of slightly below 20%. For those who started, however, attrition was relatively low, i.e. more than 90% persevered in filling the diary until it was collected during the second visit of the survey.<sup>6</sup> Across regions, completion rates are lower in urban areas and highest in the West but lowest in the Center. Given the very limited training effort and the modest cost of the exercise, results are encouraging.

To provide insights regarding potential biases, Table 2 compares descriptive statistics between agricultural households who did or did not start filling the diary (col. 2 and 3) and, conditional on starting to fill, those who completed and those who dropped out (col. 4 and 5). Three conclusions stand out. First, those with high opportunity costs of time were less likely to either start or complete the diary. Those with higher levels of education and a cement floor (which is less widespread and more suitable as an indication of wealth than iron roofs) are significantly less likely to respond or complete. Second, a response is more likely from those who are full time in agriculture, cultivate larger areas, and have higher yields (not output). Finally, being a member of local committees or having been affected by more widespread

> 5 A reasonably complete crop calendar is available at <u>http://www.fao.org/agriculture/seed/cropcalendar/searchbycountry.do.</u> 6 Unfortunately, no record was kept of who made a particular entry or which of the household‟s plots the harvest came from. Given the interesting gender dynamics, exploring the potential for adding such information would be high on the agenda for potential future experiments of this sort.

7