rafmacalaba/datause-encoder-data huggingface dataset:
CAPE VERDE’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE Despite high levels of safety, TACV suffers from operational management problems that tend to undermine the reliability of scheduling and baggage-handling services. The entry of a new operator competing with TACV on West African routes would serve to create pressure for service improvements.

**Table 4. Benchmarking air transport indicators for Cape Verde and selected West African countries**

|**Country**|**Cape**<br>**Verde**|Ghana|Côte<br>d’Ivoire|Senegal|Kenya|Tanzania|
|---|---|---|---|---|---|---|
|**Traffic (2007)**|||||||
|Domestic seats (millions per year)|**0.58**|0.14|0|0.13|2.09|1.87|
|Seats for international travel within Africa (millions per year)|**0.54**|0.91|0.85|1.26|3.14|1.27|
|Seats for intercontinental travel (millions per year)|**0.06**|0.83|0.30|1.23|2.76|0.59|
|Seats available per capita|**2.40**|0.08|0.06|0.23|0.28|0.12|
|Herfindahl index—domestic market (%)|**100.0**|100.0|—|100.0|60.5|31.0|
|Herfindahl index—international market (%)|**42.0**|6.4|9.8|10.3|34.1|13.0|
|**Quality**|||||||
|Percent of seat km in medium or smaller aircraft|**93.6**|15.7|52.3|39.3|23.3|48.6|
|Percent of seat km in newer aircraft|**99.1**|96.8|90.8|98.3|80.2|79.3|
|Registered carriers on EU blacklist|**0**|0|0|0|0|0|
|FAA/IASA audit status|**Pass**|Fail|Fail|No audit|No audit|No audit|
|Percent of carriers passing IATA/IOSA audit|**50.0**|0|0|50.0|11.1|33.3|

_Source:_ Bofinger 2009. Derived from the AICD national database (http://www.infrastructureafrica.org/aicd/tools/data).

_Note:_ EU = European Union; FAA = Federal Aviation Administration; IASA = International Aviation Safety Association; IATA = International Air Transport Association; IOSA = IATA Operational Safety Audit.

# Power

## **Achievements**

Cape Verde has achieved a relatively high rate of electrification that—at 66 percent of the population—compares favorably with other middle-income countries in Sub-Saharan Africa (table 5). This outcome reflects a strong political commitment to provide basic services to the entire population.

The national power and water utility has a minority private sector stake. Power is supplied by the multi-utility ELECTRA, which is also responsible for water supply on at least some of the islands. In 1999 ELECTRA became a public-private partnership through the sale of 51 percent of the share value of the production assets of electricity and water. The government retained 34 percent of the share value, and the municipalities retained 15 percent. As a result of a series of failed negotiations between the private stakeholders and the government, the state regained the position of major shareholder (51 percent) in May 2008. Private stakeholders retained 34 percent, and the municipalities retained 15 percent.

10
# Water Catchment IPF Matrix

Figure 9 shows the results of plotting projects (by their SEI and FEI scores as x and y coordinates) onto a water catchment prioritization matrix, region by region. Yellow project points denote projects that were actually approved for funding. Interestingly, these results suggest different patterns with respect to funding preferences. In Maule and Valparaiso, for example, approved projects are those that had the highest FEIs, whereas project selection in O’Higgins and Biobio does not directly reflect high FEI or SEI.

Figure 9. Water Catchment IPF Matrices, by Region <!-- Start of picture text --> Biobio Maule<br>100 P14 100 P18<br>90 90<br>80 80 P19<br>P25 P27<br>70 70<br>P23<br>60 P6 60 P16 P26<br>50 50<br>P17<br>40 P1 40 P24 P21P15<br>P8<br>30 P12 30<br>P13<br>20 P7 P4 P11P10P2 P3 20 P20 P22<br>10 10<br>P5 P9<br>0 0<br>FEI FEI<br>O'Higgins Valparaiso<br>P49<br>100 P44 P45 100 P59<br>90 P38 90<br>80 P40 P39 P48 80<br>P47<br>70 P32 P42 P30 70<br>P33 P41 P28<br>60 60 P53<br>P34 P37<br>50 P43 P29 P31 50 P52<br>40 40 P61 P60<br>30 P35 30 P50 P55 P54<br>P51<br>20 20 P57<br>P56 P58<br>10 10<br>P36 P46<br>0 0<br>FEI FEI<br>SEI SEI<br>0 10 20 30 40 50 60 70 80 90 100 0 10 20 30 40 50 60 70 80 90 100<br>SEI SEI<br>0 10 20 30 40 50 60 70 80 90 100 0 10 20 30 40 50 60 70 80 90 100<br><!-- End of picture text --> Source: Authors’ calculations 33
# **Table 15a.  Impact of 20 Percent CO2 Reduction through Market Based Cap and Trade on Output by Sector and Regional Market. (Results are Percentage Change from Base Year)**

|Good|**`Central`**|**`Siberia`**|**`South`**|**`North`**|**`Urals`**|**`Far East`**|**`Moscow `**|**`St. Pete.`**|**`Tumen`**|**`Northwest`**|
|---|---|---|---|---|---|---|---|---|---|---|
|**`NFM`**|18.3|8.1|15.2|9.2|19.3|6.9|26.4|25.6||202.6|
|**`MAR`**|4.5|2.4|3.0|2.1|3.2|3.5|4.3|2.8|-2.7|3.7|
|**`OTH`**|5.0|3.0|1.9|1.1|3.0|1.4|0.7|0.0|-4.4|6.4|
|**`TPP`**|0.4|1.6|-1.6|-0.7|-0.4|14.3|-1.7|0.3|-4.9|10.6|
|**`FOO`**|3.3|1.0|2.1|0.3|1.5|1.2|2.8|1.1|-3.3|6.0|
|**`OIL`**|5.3|0.5|1.2|0.9|2.6|0.3|1.2|0.3|-4.9||
|**PST**|1.0|0.6|0.7|0.6|0.9|0.4|0.6|0.4|0.2|1.6|
|**`TMS`**|0.6|0.4|0.3|0.2|0.5|0.0|0.2|0.0|0.1|1.0|
|**CLI**|1.9|0.1|0.0|-0.4|1.1|-0.9|0.3|0.0|-3.6|3.2|
|**`AGR`**|3.0|-0.7|0.2|-1.5|1.0|-1.4|0.5|-0.3|-6.3|4.6|
|**HEA**|0.0|-0.1|-0.4|-0.2|-0.1|-0.4|-0.6|-0.3|-0.1|0.1|
|**`FIN`**|-0.3|-0.2|-0.3|-0.2|-0.3|-0.3|-0.3|-0.3|-0.6|-0.5|
|**`CON`**|0.9|-0.3|-0.6|-1.0|-0.6|-1.1|-0.2|-0.6|-4.1|2.6|
|**`CRU`**||-0.6|-0.5|-0.6|-0.5|-0.7|||-1.3|-0.1|
|**`SCI`**|-1.2|-1.0|-1.1|-0.9|-1.5|-1.7|-0.3|-0.8|-2.3|-0.5|
|**TRD**|-2.9|-1.4|-0.8|-0.7|-1.2|-0.7|-1.5|-1.6|0.9|-3.1|
|**`TRO`**|-1.5|-2.3|-1.6|-1.7|-1.7|-1.6|-1.7|-1.8|-1.9|-1.7|
|**`OTI`**|0.3|-2.7|-1.1|-2.9|-0.7|-2.9|-0.2|-1.3|-7.3|0.3|
|**`MWO`**|-0.7|-5.7|-1.9|-3.6|-2.8|-5.4|2.8|-1.2|-7.1|5.7|
|**`TRK`**|-3.3|-2.8|-2.5|-2.3|-2.8|-2.0|-3.1|-2.9|-0.9|-3.0|
|**HOU**|-2.1|-2.9|-3.1|-3.1|-2.6|-3.5|-3.4|-2.9|-2.7|-1.7|
|**`RLW`**|-3.9|-4.8|-2.3|-3.1|-3.9|-2.1|-1.3|-2.1|-2.1|-5.4|
|**`AIR`**|-1.2|-2.6|-3.4|-4.7|-1.7|-5.4|-3.2|-5.0|-6.8|-0.4|
|**ELE**|-4.0|-3.8|-4.1|-3.8|-4.1|-3.8|-3.8|-3.4|-5.7|-4.5|
|**`CNM`**|-2.3|-6.3|-3.5|-5.4|-3.7|-6.0|-5.9|-5.8|-7.5|-1.7|
|**`PIP`**|-9.3|-8.7|-6.8|-7.5|-6.0|-7.0|-17.9|-7.9|-3.9|-23.8|
|**`CHM`**|-6.7|-18.1|-10.1|-10.5|-11.0|-11.3|-9.8|-12.6|-23.0|-0.6|
|**`GAS`**||-13.1|-11.7|-13.0|-10.5|-13.7|||-20.7|-5.7|
|**`COL`**|-17.7|-19.3|-18.4|-18.8|-19.5|-19.1|||||
|**`FME`**|-21.1|-22.5|-26.2|-22.7|-21.6|-21.7|-28.4|-54.2||-18.0|
|`See tabl`|`e 1 for s`|`ector cod`|`es.`||||||||
|**`Source:`**|**`Authors`**|**`' estima`**|**`tes.`**||||||||

54
To generate a measure of consumption, which is not directly surveyed by the SECC, we predict consumption in a district-level survey (IHDS-II, 2011-12) that contains the same asset, income and land data as the SECC. We then impute consumption for each individual in the sample following the small area estimation methodology of Elbers et al. (2003), allowing us to test not only for impacts of roads on mean consumption but also for distributional effects.<sup>7</sup> Appendix A contains additional details of this process.

Firm data comes from the Sixth Economic Census (2013). This covers every non-crop producing economic establishment in India, including public and informal establishments. It contains detailed information on location (which we match to the 2011 Population Census), employment, industry and a handful of other firm characteristics, but includes no variables on wages, inputs or outputs. We trim outliers to eliminate villages where the number of workers in village nonfarm firms is greater than the total number of workers resident in the village. Results are not substantively changed by this restriction.

Remote sensing data is used to measure outcomes otherwise unavailable at the village level. Nights lights provide a proxy for total village output. As no village-level agricultural output data exists in India, we use a satellite-based vegetative index (NDVI) for the primary (kharif) growing season (late May - October) to proxy for village-level agricultural production. To control for differences in non-crop vegetation, we measure the maximum growing season vegetation minus early cropping season vegetation.<sup>8</sup> We use village boundary polygons purchased from ML Infomap to map gridded remote sensing data to villages and to determine treatment spillover catchment areas.

The 2001 and 2011 Population Censuses provide village infrastructure, demographics, transportation services and the running variable for the regression discontinuity design (pop-

> 7 Standard errors for all imputed consumption and poverty regressions are produced using the bootstrapping procedure outlined in Elbers et al. (2003).

> 8Table A1 shows that this measure is highly correlated with two other proxies for agricultural productivity and per capita consumption at the village level, as well as annual agricultural output at the district level. See Appendix A for additional details.

10
and the students at risk for dropping out in a particular grade are those who stayed in school from the previous grade.

In this study, we analyze nationwide data on standardized tests in math and Spanish combined with administrative data on beneficiary receipt, school characteristics and on student and parent characteristics to study program effects on 1) enrollment, 2) type of school attended and 3) academic achievement. We construct a panel of students in sixth grade enrolled in primary schools in high and very high poverty areas in 2008, whom we observe in 2009, 2011 and 2014. Our estimates derive both from fixed-effects regression models and propensity score matching models. We study whether impacts are heterogeneous by gender and by indigenous status. We include a rich set of control variables at the individual, household, school and locality level. We study impacts of PROSPERA in medium, poor and very poor areas, as defined by margination indexes developed by Conapo (National Mexican Population Council “Consejo Nacional de Poblacion”), where a majority of beneficiaries reside.

We begin by describing the program and our research hypotheses, then turn to previous literature, a description of our data and methodology, followed by results and conclusions.

# **II. Background and Hypotheses**

PROSPERA began operating in small rural communities in 1997, following a macroeconomic crisis in Mexico in 1995, and was part of a transition towards implementing targeted anti-poverty programs and eliminating general food subsidies. It quickly grew over time and currently covers six million families, or about one-quarter of all families in Mexico. Although the program has expanded into urban areas, it remains largely rural, with about two-thirds of its household beneficiaries deriving from communities with fewer than 2,500 inhabitants.

The program conditions cash payments to families on children regularly attending schools and on family members visiting health clinics for checkups. Program take-up was exceedingly high when the program began, with 97 percent of families who were offered the program participating (Skoufias and Parker, 2001). Program rules allow students to fail each grade once, but if a student repeats a grade twice, the schooling benefits are discontinued permanently. The program also provides some additional subsidies for school supplies and a transfer linked to regular visits to health clinics. Children and youth age 21 and younger are eligible to receive the school subsidies.

Table 1 shows the monthly grant levels for children between the third grade and the twelfth grade in the second semesters of 1997 and 2003 (when the exchange rate was about 8 and 11 pesos per U.S. dollar, respectively). Originally, the program provided grants only for children between the third and ninth grades, but in 2001, the grants were extended to grades 10-12. At grades seven and above, the grants are slightly higher (by about 13 percent) for girls than boys, a ~~response to historically lower enrollmen~~ t rates among girls than among boys after primary school.

4
4 Table la compares summary statistics from the Chilean manufacturing censuses of 1967 and 1979. The figures indicate that during tile restrictive quota-ridden (QR) trade regime of 1967, price-cost margins were large compared with the liberalized trade regime of 1979. The increase in concentration (and decline in the number of firms not shown here) between the two census years is dramatic given that the manufacturing sector was of roughly the same size in 1967 and in 1979. Full adjustment to the new trade regime was not complete, however, since the uniform tariff structure of 102 with no QRs had just been achievad in June 1979 when census data were gathered. It is noteworthy that increased concentration was accompanied by lower price cost margins (P1Ms), which is consistent with the removal of protection forcing more competitive pricing because firms face a more elastic demLnd.

In Korea, the legacy of Korea's development strategy between 1973 and 1979 focussing on heavy and chemical industries has been an extremely concentrated industrial structure by international standards. For example, in 1982, the top 50 Korean firms accounted for 37 percent of total sales while in Japan the top 100 rirms accounted for 27 percent of total sales. The figures in table lb show higher PCMs in the more concentrated sectors. Furthermore, mean PCMs are higher in the highly protected sectors. Also PCMs are lower for sectors with higher export shares. Both results suggest that sectors that compete in international markets price more competitively.

Further evidence taking into account factors other than protection (e.g. differences in capital/labor intensity across sectors) support the conclusions drawn from table 1. For Chile, structural change tests by de Helo and Urata, (1986) based on a cross-sectoral simultaneous equations model of structure and performance applied to the two census years for 41 industrial sectors confirmed
# Table 6: **Output and Investment in Star Firms**

This table reports estimates from the following panel regression model: The dependent variable is Sales/Invested Capital or CAPEX/Invested Capital or XRD/Invested Capital. _ROIC_<sup>_T OT_</sup> Star is a dummy variable that takes the value 1 if the firm _i_ s _ROIC_<sup>_T OT_</sup> is above the 90th percentile of _ROIC_<sup>_T OT_</sup> across all firms in a particular year _t_ and 0 otherwise. Log(Assets) is the logarithm of total assets. _Markups_<sup>_T OT_</sup> are based on the estimation in De Loecker and Eeckhout (2017) using operating expenses as a variable input of production and include correction for intangible capital. _Q_<sup>_T OT_</sup> is Tobins Q corrected for intangible capital. All regressions include industry and year fixed effects and are estimated using ordinary least squares with standard errors clustered at the firm level. Detailed variable definitions are in the Appendix. (<sup>_∗∗∗_</sup> ) _,_ (<sup>_∗∗_</sup> ) _,_ (<sup>_∗_</sup> ) denote statistical significance at 1%, 5%, and 10% levels respectively.

||(1)|(2)|(3)|(4)|(5)|(6)|
|---|---|---|---|---|---|---|
||Sales/Invested|Sales/Invested|Capex/Invested|Capex/Invest|ed<br>XRD/Invested|XRD/Invested|
||Capital|Capital|Capital|Capital|Capital|Capital|
|L. ROICTOT Stars|0.804***|1.378***|0.014***|0.038***|-0.008***|-0.028***|
||(0.028)|(0.093)|(0.002)|(0.006)|(0.002)|(0.006)|
|L. MarkupsTOT|-1.052***|-0.992***|-0.001|0.001|0.048***|0.046***|
||(0.039)|(0.039)|(0.003)|(0.003)|(0.003)|(0.003)|
|L. ROICTOT Stars x L.MarkupsTOT||-0.452***||-0.019***||0.016***|
|||(0.062)||(0.004)||(0.005)|
|L.Log(Assets)|0.058***|0.057***|0.004***|0.004***|-0.001***|-0.001***|
||(0.005)|(0.005)|(0.000)|(0.000)|(0.000)|(0.000)|
|L.Log(Age)|-0.014|-0.013|-0.008***|-0.008***|-0.012***|-0.012***|
||(0.013)|(0.013)|(0.001)|(0.001)|(0.001)|(0.001)|
|L.QTOT|-0.021***|-0.019***|0.008***|0.008***|0.006***|0.006***|
||(0.005)|(0.005)|(0.000)|(0.000)|(0.000)|(0.000)|
|N|70094|70094|69582|69582|70667|70667|
|Adj. R-sq|0.355|0.357|0.359|0.360|0.465|0.466|
|Fixed Efects|— —|———————|———- Industry|, Year ———|———————|—- —|
household’s gender and traces the effects of labor market imperfections and credit market constraints on productivity. The model’s inclusion of “gender-specific effective off-farm work time” (GSEOW) borrows from studies by López (1984, 1986), and is defined as the total time required to perform off-farm work which includes not only the actual working time but also commuting time faced by men and women. López (1984, 1986) examines how time allocation between on-farm and off-farm work has different connotations on utility as a consequence of commuting time required by off-farm work. He shows how the consideration of commuting time leads to a model in which the household acts as having different preferences between on-farm and off-farm work, even if preferences are defined purely in terms of leisure. In this setting the optimization problem of the household becomes non-separable and thus the production decisions would be directly linked to the household’s characteristics and consumption decisions. Our model predicts that GSEOW leads to labor productivity differences between men and women, with women as heads of household devoting a higher proportion of their time to agricultural activities.

Predictions derived from the model are empirically illustrated using nationally representative data from Malawi. We explore gender differences in productivity at the plot level. We specifically compare plots managed by female heads of household with plots managed by male heads of household. Our examination of gender differences mostly pertains to female-headed households in which a male spouse is absent.

The econometric approach adapts a decomposition method from labour economics, most notably in the analyses of the gender wage gap, union wage gap, and growing wage inequality (Oaxaca, 1973 and Blinder, 1973). Our study specifically decomposes the average differences in agricultural labour productivity between plots managed by male-headed households and those 5
through an experiment and measure the effects on spending using the administrative data.

The remainder of the paper is structured as follows. Section 2 provides a theoretical framework to motivate our experimental design and develop our hypotheses. In Section 3, we summarize the setting, research design, and implementation of the experiment. In Section 5, we describe additional data sources and provide descriptive statistics. Section 6 reports the results, and Section 7 concludes.

# **2 Theoretical Framework and Hypotheses**

We use a standard model of intertemporal consumer choice to motivate our experimental design. Letting subscript _t_ denote the time period, we assume that the consumer faces an exogenous stream of nominal income _Yt_ and can have positive or negative holdings of an asset _At_ that pays an exogenous nominal interest rate _Rt_ . There are four types of consumption goods, which we can classify according to their durability and tradability: durable tradables (denoted _Xt_<sup>_T_),durable</sup> nontradables ( _Xt_<sup>_N_), nondurable tradables (</sup><sup>_C_</sup> _t_<sup>_T_), and nondurable nontradables (</sup><sup>_C_</sup> _t_<sup>_N_).We assume</sup> that durable goods depreciate at a rate of _δ_ , tradable goods (both durable and nondurable) have an exogenous price _Pt_<sup>_T_,andnontradablegoods(bothdurableandnondurable)haveprice</sup><sup>_P N_</sup> _t_<sup>.</sup> The consumer gets utility _U_ ( _Ct_<sup>_N, X_</sup> _t_<sup>_N, C_</sup> _t_<sup>_T, X_</sup> _t_<sup>_T_)fromagivencombinationofgoods,whichis</sup> concave in each of its arguments and has a discount factor _β_ .

The consumer’s optimization problem can thus be summarized as follows: subject to We denote the exogenously given rate of inflation from period _t_ to _t_ + 1 as _πt_ +1, which is as follows:

- _Z_  ̄ is the average value of variable _Z_ in the economy.

We make the following simplifying assumptions. First, an increase in inflation cannot be 6
small changes at lower levels of food insecurity due to the small base. Next, the three economic growth indicators show similar variation around the mean, but the relatively rapid rate of food inflation over this period means that the GDP growth deflated by the food CPI has a mean of only 0.4, whereas deflating by the GDP or CPI deflators results in means of 2.7 percent and 2.9 percent, respectively. Thus, food inflation typically exceeded nonfood inflation. Turning to table 5, it is noteworthy that the correlations among different price indices are quite large, as high as 0.82 in the case of the relationship between food inflation and total inflation. Table 5 also presents some bivariate evidence that changes in food security are significantly related to both economic growth and overall inflation but not to our estimates of relative food inflation.

**Table 4. Descriptive Statistics for Dependent and Independent Variables**

||**Count**|**Mean**|**Std. De.**|**Min.**|**Max.**|
|---|---|---|---|---|---|
|Change in food insecurity|296|0.2|7.2|−31.0|24.0|
|Percent change in food insecurity|290|4.9|31.6|−83.0|200.0|
|Economic growth (GDP deflator)|291|2.9|5.7|−17.6|32.1|
|Economic growth (CPI deflator)|276|2.7|8.4|−27.1|41.1|
|Economic growth (food CPI deflator)|271|0.4|8.4|−34.2|34.0|
|Total CPI inflation|276|8.6|7.5|−8.9|51.8|
|Food CPI inflation|276|10.9|9.6|−11.5|67.6|
|Nonfood CPI inflation|276|6.4|6.2|−9.5|34.4|
|Relative food inflation|276|4.4|7.7|−20.8|41.8|

_Source_ : Food insecurity is from the GWP (Gallup 2011 **)** . Economic growth data are from the IMF (2010), and all inflation data are from the ILO (ILO 2011).

_Note_ : All data are in percent or percentage points. Economic growth is reported with three different means of deflation: the GDP deflator, the CPI deflator, and the food CPI deflator. Relative food inflation is the change in the ratio of the food CPI to the nonfood CPI.

20