rafmacalaba/datause-encoder-data huggingface dataset:
- Private-sector representatives from the following organizations: the Chamber of Industry, Costa Rica Chamber of Agriculture and Agribusiness, and the Federation of Livestock Chambers.

From its beginning, then, the CRGT had a participatory structure that was both inter-organizational and inter-disciplinary, attempting to connect the most important actors in water resource matters. The structure was nevertheless imperfect, and some of its deficiencies are discussed later.

With this original composition, the stakeholders sought official recognition of the CRGT by the national government. Rather than proceed through the more timeconsuming and politically difficult process of obtaining recognition through a law passed by the Costa Rican legislature, the stakeholders and the office of the Minister of the Environment agreed to use the swifter action of a ministerial decree recognizing the commission. Although this choice expedited official recognition of the commission, it later proved somewhat regrettable, since ministerial decrees are more easily altered or disregarded by subsequent administrations than are national laws.

The CRGT was officially recognized on April 29, 1993 by Executive Decree No. 22156-MIRENEM (MIRINEM being the previous acronym for what is now MINAE, the Ministry of Environment and Energy). Although the decree recognized the CRGT’s legitimate existence as a collaborative body for information development and sharing, communication, and planning, it did not transfer to the CRGT any formal responsibility or capacity for undertaking water management projects.

In December 1993, a follow-up decree, Decree No. 22712-MIRENEM, was published adding representatives from the Municipal Institute for Promotion and Consultancy (IFAM) and the Foundation for Urban Development (FUDEU) as permanent members of the CRGT. With these additions, the commission had 19 members, of which five were nongovernmental organizations and six were municipalities (Figure 2).

|**COM**<br>Figure 2:|**POSITION OF THE**|**TARCOLES RIVER BA**<br>TARCOLES RIVER<br>BASIN COMMISSION|**SIN COMMISSION (CR**|**GT)**|
|---|---|---|---|---|
|GOVERNMENTAL<br>INSTITUTIONS|AUTONOMOUS<br>INSTITUTIONS|PRIVATE<br>COMPANIES|NON-GOVERNMENTAL<br>ORGANIZATIONS|LOCAL<br>GOVERNMENT|
|_S Bll_<br>MINAE<br>MIDEPLAN<br>MIN. SALUD|_2003_<br>ICAA<br>ICE/CNFL<br>UCR<br>IFAM|INDUSTRY<br>CATTLE RANCHERS<br>FARMERS|FUDEU<br>FECON<br>CEDARENA|ALAJUELA/OROTINA<br>HEREDIA/SAN RAFAEL<br>SAN JOSE<br>CURRIDABAT|

_Source: Ballestero (2003)_ During 1993 and 1994, the CRGT conducted an awareness-raising campaign about river basin management. There were motivational activities in the municipalities and with 15
This is underscored by the high immunization rates in the high INPRES districts (83%) as well as the relatively small gap between high and low INPRES districts.

We also examine children’s dietary diversity. 95% of children born to mothers from high INPRES districts meet the minimum requirements of dietary diversity. Consequently, it is not surprising that we do not detect effects of the school construction on children’s diet. Similarly, there are no differences in terms of maximum dietary diversity.

Finally, we examine effects on investments in children’s education. Mothers who were exposed to larger doses of the program are 8 p.p. more likely to know the location of the closest pre-school. This effect size translates to a 12% increase in knowledge of preschools among high INPRES districts. Likewise, the program led to a 5 p.p increase in the children ever-attending a pre-school. Given that only 28% of mothers in the high INPRES districts report that their child ever-attended preschool, the point estimate is a substantial increase of 20%. Lastly, the program improved the next generation’s early education investments by inducing longer months of enrollment in preschool by 31%.

# **5 Conclusion**

The evidence presented in this paper should reaffirm to policy makers the critical role of ensuring access to education for all. The evidence shows that there are lasting benefits - not just to the individuals who acquire greater schooling themselves but to a wide array of developmental markers for their children. We find significant and meaningful evidence of intent-to-treat effects not only in terms of long-run health status (extreme stunting and to a lesser extent extreme wasting) but also in terms of cognitive outcomes (language and mathematics scores in early grades of primary school) as well as in terms of children’s social behavior (measured using both the SDQ and the EDI).

We find evidence to support the fact that the effects of maternal education operate through a multitude of related and self-reinforcing pathways. Treated mothers are likely to belong to better resourced households. Importantly, treated mothers engage in better parent-child interactions 24
**Table 2. Other data used for the LCOE calculation**

|Data source|Capacity<br>factor (%)|Economic<br>life<br>(year)|Fixed O&M<br>costs (%) *|Variable<br>O&M costs<br>($/MWh)|Fuel price<br>($/GJ)|Heat rate<br>(GJ/MWh)|
|---|---|---|---|---|---|---|
|PV(Utility)|25%|20|1.1%||n.u.|n.u.|
|CSP|32%-68%|25|1.8%||n.u.|n.u.|
|Wind Onshore|35.0%|20|2.6%||n.u.|n.u.|
|Wind Offshore|45.0%|20|2.3%||n.u.|n.u.|
|Gas CC|85.0%|25|1.7%|3.25|7.5|5.9-7.3|
|Gas GT|85.0%|20|1.9%|5.62|7.5|8.0-10. 5|
|Geothermal|85.0%|25|4.0%|1.16|n.u.|n.u.|
|Hydro|55.0%|40|1.6%|1.39|n.u.|n.u.|
|Coal|85.0%|40|1.5%|5.42|3.1|9.0-12.7|
|Nuclear|90.0%|45|1.9%|4.60|0.8|11.0|
|Biomass|75.0%|25|3.6%|5.50|0.9|11.6-14.2|

n.u. -- not used; * Expressed as a percentage of overnight construction costs.

Notes: the natural gas price is the average European price for 2019; coal price is the average Australian coal price in 2019; the discount rate is 6%.

Sources: IRENA (2018), Lazard (2019), IEA (2015) and EIA (2020) for capacity factors and economic life and fixed O&M costs; World Bank (2020) for fuel prices

# **3. Results and Discussion**

The maximum, minimum, and median values of the LCOEs are presented in Figure 1. As illustrated by the figure, if we compare the minimum values of LCOEs across the technologies, all renewable energy technologies, excluding CSP and offshore wind, have lower LCOEs than the fossil-fuel technologies. If the electricity generation technologies are compared in terms of maximum values of their LCOEs, fossil fuel-based technologies are cheaper as compared to renewable energy technologies.

Median values of LCOEs could provide a better ground for a comparison of these technologies. In terms of median values of LCOEs, hydro, solar PV, onshore wind, and geothermal are cheaper as compared to the remaining technologies. Hydro is the cheapest, followed by solar PV, onshore wind, and geothermal. Offshore wind and CSP are the most expensive.

12
(2013)). There may, however, be some overlap of FOMC announcements with the CPI data release, as the latter is announced on Wednesdays or Fridays.

Indeed, in our sample, while the payroll data releases do not coincide with FOMC meetings, there is some overlap between CPI data releases and FOMC announcements--there are 7 occasions when CPI data releases and FOMC announcements are on the same day. We include dummies for CPI data releases in our benchmark regressions in Equation 1. The results, not reported here for brevity, indicate that the spillover coefficients of unexpected FOMC announcements on emerging economies are not sensitive to CPI data releases.

We also ask, as an extension, whether financial markets in emerging economies react to the employment and CPI data releases, and specifically to the surprises inherent in them. For this we estimate regressions similar to those in Equation 1, but instead of focusing on days around FOMC meetings we now focus on days around the payroll or CPI data releases. Our exact specification is given in Equation 6, where, as before, ��,� is the exchange rate, equity price, or bond yield in country i on day t; ���� ������� is the difference between the actual payroll data or CPI inflation and their consensus forecasts (in thousands for payroll surprises, and in percentage points for CPI surprises); t is the day of the respective data release; �� indicates country-fixed effects, included to account for country-specific, time invariant elements; and ���,� is the stochastic disturbance term capturing the effect of all other information and developments happening during period t and t+k. And, as before, we regress changes in emerging economies exchange rate, equity prices and bond yields between day t+k and t-1, where k ranges from one to three days, as per Equation 6.

Results are in Table 9A for payroll surprises and in Table 9B for surprises on inflation data. Results indicate that positive payroll surprises, i.e. when actual payroll figures exceed the consensus forecast, do not have a significant impact on exchange rates, have a small positive effect on equity prices, and a definite positive effect on bond yields in emerging economies. Positive CPI surprises, i.e. when actual inflation data exceeds the consensus forecast, on the other hand imply that emerging economies’ exchange rate appreciates, equity prices increase, but there is no definite impact on bond yields. These results indicate a less uniform impact of payroll and CPI data releases on emerging economies, compared to that of FOMC announcements; and the direction of impact seems consistent with the positive data releases indicating robust economic activity in the US, rather than being a harbinger of tighter monetary policy per se.

23
<!-- Start of picture text --> $0000 2019 Containership Newbuild Price/TEU<br>><br>#<br>5ao2 20000<br>Q<br>2<br>ag<br>FS"5® 10000<br>Zz<br>0<br>i 8 A<br>2g 63 63 og og<br>- 6 e ¢€ g 8 g 8<br>>ze*’® 5a- 38®8> 8¢° ry2e (o}888 in22 a38° zBa3@ >pS in6©e5 Oo5aE idg@2s in-E8> 6>ae<br>&8N g&$5 ira§35B @i$8a co2£g$8 2#gs8 &g§eS FES#5 FEB55 &®&a8o Sgs8=: Ss8S8¢ bedF=8 cy8B¢s 8gS&s<br>se8s=> §8©°& ~ % “ zg8gs8 Py88§8 * 8Ss= &os= F 8S2 8aa<br>Sa oO oO<br><!-- End of picture text --> <!-- Start of picture text --> 8e+05 2019 Dry Bulk Newbuild Prices (DWT/USD)<br>a<br>no<br>2 6e+05<br>E<br>a<br>® 4e+05<br>8<br>a<br>z<br>5@Fa 2e+05<br>za<br>Oe+00<br>5<br>i=<br>xe © e 2%<br>28 og 5 Paé *E xg &3 a3 §5<br>2 2 2 5 5 5 a = é<br>5s =5S Ees= é£ EB&= Ebé= fa)Si= 65a 5ax<br>ax8oO ax$2 ax8= 5xio aa83 Fea®2 x&%i bex8é Sao3<br>“ ° © * = = ®g<br>3a<br>6<br><!-- End of picture text --> <!-- Start of picture text --> Fuel Economies in Containerships<br>600<br>><br>oO<br>2<br>oOi<j<br>S<br>= 400<br>fe)<br>ire<br>aod<br>&<br>2<br>S 200<br>2<br>a<br>0<br>° 3 3= 3= g& gN<br>TEU<br>Vessel Construction Period KE 1960-2000 | 2000-2010 A 2010-2020<br><!-- End of picture text -->
- 66 -

**Figure 40. Unobserved district-specific inefficiency in contract awarding**

<!-- Start of picture text --> District specific fixed<br>effects on inefficiency<br>in contracting a contract<br>-0.72 - -0.55<br>-0.56 - -0.34<br>-0.35 - 0.15<br>0.16 - 0.48<br>0.49 - 1.00<br><!-- End of picture text --> <!-- Start of picture text --> .<br><!-- End of picture text -->

|**Table 18. Average time requir**|**ed to award**|**a road c**|**ontract after t**|**he bid ope**|**ning (days)**|
|---|---|---|---|---|---|
||Obs|Mean|Std. Dev.|Min|Max|
|World Bank project|81|99.9|<br>32.9|27|190|
|Governmentproject|73|24.6|<br>24.1|1|171|

# **VI. CONCLUSION**

Transport infrastructure is important for economic growth. In particular in rural areas, the lack of access to reliable transport infrastructure remains a significant constraint for people’s daily life and local businesses. In Nepal, about 20 percent of rural residents have to spend more than 3 hours to go to the nearest marketplace or agriculture center. Over the world, about 900 million rural dwellers are estimated to have no access to all-weather road within two kilometers―typically equivalent to a walk of 20-25 minutes.

Public procurement is an important instrument to use resources wisely and efficiently. How to design an efficient procurement system is still a challenge in the infrastructure sector, because infrastructure projects are often technically complex, highly customized and politically sensitive. Therefore, the markets tend to be vulnerable to collusive bidding and corruption. The bid evaluation process tend to take long time. Contracts tend to be far from complete; many infrastructure projects undergo massive cost overruns and project delays. In addition, road assets will deteriorate quickly if not well maintained.
<!-- Start of picture text --> Election Turnout Share of Votes for Left<br>Year Year<br>Share of Votes for Center Share of Votes for Right<br>Year Year<br>Electoral competition among candidates<br>Year<br>2000 2003 2007 2011 2015 2019 2000 2003 2007 2011 2015 2019<br>2000 2003 2007 2011 2015 2019 2000 2003 2007 2011 2015 2019<br>2000 2003 2007 2011 2015 2019<br>.08 .08<br>.06 .06<br>.04 .04<br>.02 .02<br> (95% CI) 0 (95% CI) 0<br>β β<br>-.02 -.02<br>-.04 -.04<br>-.06 -.06<br>.08<br>.06<br>.08 .04<br>.06 .02<br>.04 0<br> (95% CI) .02 (95% CI) -.02<br>β 0 β -.04<br>-.02<br>-.06<br>-.04<br>-.06<br>.08<br>.06<br>.04<br>.02<br> (95% CI) 0<br>β<br>-.02<br>-.04<br>-.06<br><!-- End of picture text --> Figure D.3. Parallel Trends for the Common Support – Mayoral Elections Discrete Treatment Variable _Notes:_ The figure illustrates the results of an event study estimation for the municipalities in the common support of the predicted pscores. We interacted the discrete variable PEP, which takes the value of one in the municipalities where there are PEP holders, with mayoral electoral years excluding the 2015 election, the last one before 2018, when the PEP program was implemented. The estimates include municipality, electoral year, and department electoral-year fixed effects, and they control for the interaction between year dummies and a set of predetermined municipal characteristics, measured before the beginning of our period and listed in Table C.2. Standard errors were clustered at the municipality level and bars represent 71
Table 4 reports the summary of transition intensities through the workforce pooling the entire 1983 to 2002 sample. These average results show that duration in unemployment is very short (a bit more than one month) while inactivity (OLF) is close to ten months with the probability of acceding to a job correspondingly higher in the former. Relative durations of employment types are similar to those found elsewhere for Mexico and Argentina: informal salaried workers show the lowest duration (2 months), informal self employed the next longest (4 months) and formal salaried the longest at roughly 10 months.<sup>12</sup> Raw durations (Figure 4) show substantial secular decline of roughly 35% in the formal sector across the period and complementary movements in Informal Salaried and unemployment from the late 1980s. Both may be consistent with the shifting of longer tenure workers from the formal to the informal salaried sector and greater difficulty in leaving unemployment.

To study cyclical movements, we first time-aggregate the underlying monthly data to quarterly averages. We then follow Shimer (2005a) and remove the trends of the quarterly averages of each variable using a Hodrick Prescott Filter. Finally, we smooth the results by computing moving averages of the filtered series with a centered window of three quarters. The middle periods of these rolling windows are depicted in Figure 4 along with filtered GDP. As is the case in Mexico and the US, countercyclical movements are observed in the durations of the three employment sectors. However, in the early 1990s, the pattern becomes muddier. The very high correlations between detrended durations of formal and informal salaried employment (0.83) suggests that the factors determining turnover (i.e: macroeconomic conditions dictating quitting or firing) affect formal and informal jobs in a similar fashion. The non-employment sectors also reveal the patterns now standard in the mainstream literature and in Mexico: duration of unemployment moves countercyclically reflecting the ease of finding jobs during upturns, while duration in OLF is procyclical, likely reflecting voluntary inactivity.<sup>13</sup>

> 12 See Bosch and Maloney (2005)

> 13 Our findings are consistent with those of Flinn and Heckman (1983) for the US that, in Brazil as well, OLF and unemployment are distinct labor market states.

11
the entrepreneur, like her social networks that contribute to have higher profits and are captured in _ψi_ . To overcome this endogeneity, we use a two-stage least square (2SLS) estimation in which the first-stage equation is: _opportunityi_ = _α_ + _βGDPgrowth_ _~~t~~_ 0 _s_ + Γ<sup>_T_</sup> _Xi_ + _εi_ (2) where _GDPgrowth_ _~~t~~_ 0 _s_ , the instrument, is the GDP growth observed in state _s_ where the entrepreneur _i_ lives, in the year _t_ 0 she decides to open her business. Given that our survey measures profits and management performance in 2014, our assumption is that state GDP growth at the time when the business was set up is exogenous to profits (and management performance) various years later and only influences them through the choice of starting a business out of necessity or opportunity.<sup>7</sup> When we use a 2SLS specification we include years of opening fixed effects in both stages, in this way, we compare firms that opened in the same year and have similar characteristics.

Table 3 presents the estimation results. Panel A and B show the effect of the _opportunity_ dummy on weekly profits and the CBP score, respectively. Panel C, shows the first stage of the 2SLS presented in column 2. Column 1 shows that when estimating equation (1) by OLS, the effect of _opportunity_ on weekly profits is positive and significant at 1 percent in Panel A, and so is the effect of this same variable on the CBP score in Panel B. In column 2, controlling for the potential endogeneity of this variable yields a much larger positive coefficient for opportunity for both profits and the CBP score, significant at 10 percent for both. The estimate in Panel A implies that women who open their business out of opportunity in year _t_ 0, induced by higher economic growth in their state in that same year, have 2.6 times higher weekly profits than necessity ones; and that part of this effect can be explained by a 2 times better CBP score, i.e. by better management practices.

The partial F statistic shown in panel C has a value less than 10, but larger than 5, which means that the maximum IV bias we are tolerating is less than 20 percent (Staiger and Stock, 1997). The fact that the 2SLS estimates in column 2 are larger than the OLS ones in both panel A and B, might indicate that _opportunity_ is measured with error. An alternative explanation is that our instrument is identifying the effect of _opportunity_ for a specific subset of “compliers”, in our

> 7Poschke (2013) presents a model in which in each period, individuals decide whether to work or to run a firm. Selection of agents into entrepreneurship comes from the heterogeneity of the relevant outside option and the heterogeneity in firm’s performance.

8
11 treat sectoral productivity growth rates, gA, as exogenous and invariant between simulations (although it would be easy to link productivity growth with say public and/or private investment if sufficient evidence were available at the aggregate level).

The government collects taxes, and disburses subsidies, on commodities. The government also employs government workers, paying these workers an exogenous wage. Changes in public sector employment and changes in public sector wages are part of aggregate demand management common to stabilization programs. The government also has exogenously determined investment and current expenditures. Both components of government expenditures are also part of expenditure reductions in the counterfactuals. Finally, the government deficit is financed by a mix of foreign borrowing (eAB*w), borrowing from the private sector (ABh), and borrowing from the Central Bank (Aib) (see table 1). The government's budget constraint is given by: (2.4) G7Pc +Iij, + i-Pc - (AB + AB* (Net indirect tax + Import tariff) - i_ (Bb + Bh) - (i1 (eB,) In (2.4), the first three terms on the LHS are the three components of government expenditures described above (a bar over a varible, or a product of variables, indicates that the variable, or product of variables, is exogenous,; the term in parenthesis on the LHS includes the three sources of financing of the fiscal deficit. The first two terms on the RBS are revenues from tax collection, and the last two terms are the payments on
**Table 2a: The impacts of COVID-19 confirmed cases and deaths on demand for losing services**

||||Hotels a<br>|nd restaurant<br>|||||R<br>|etail<br>|||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
||hotel|hotel|hotel|restaurant|restaurant|restaurant|mall|mall|mall|shopping|shopping|shopping|
|First case|-0.23|||-0.32|||0.03|||-0.03|||
||(0.19)|||(0.20)|||(0.06)|||(0.06)|||
|First 10 cases||-0.49<sup>**</sup>|||-0.58<sup>**</sup>|||-0.13<sup>***</sup>|||-0.23<sup>***</sup>||
|||(0.24)|||(0.23)|||(0.04)|||(0.06)||
|First 100 cases|||-0.64<sup>***</sup>|||-0.66<sup>***</sup>|||-0.23<sup>***</sup>|||-0.40<sup>***</sup>|
||||(0.21)|||(0.21)|||(0.06)|||(0.06)|
|R-squared|0.88|0.89|0.91|0.89|0.91|0.91|0.90|0.90|0.90|0.83|0.84|0.85|
|Observations|8,208|8,208|8,208|8,112|8,112|8,112|8,160|8,160|8,160|8,160|8,160|8,160|
||||Tra|nsport|||||To|urism|||
||flight|flight|flight|airport|airport|airport|tourist|tourist|tourist|museum|museum|museum|
|First case|0.03|||-0.03|||-0.06|||-0.11|||
||(0.06)|||(0.06)|||(0.06)|||(0.10)|||
|First 10 cases||-0.13<sup>***</sup>|||-0.23<sup>***</sup>|||-0.17<sup>***</sup>|||-0.23<sup>***</sup>||
|||(0.04)|||(0.06)|||(0.05)|||(0.09)||
|First 100 cases|||-0.23<sup>***</sup>|||-0.40<sup>***</sup>|||-0.19<sup>***</sup>|||-0.29<sup>***</sup>|
||||(0.06)|||(0.06)|||(0.05)|||(0.05)|
|R-squared|0.90|0.90|0.90|0.83|0.84|0.85|0.84|0.84|0.84|0.89|0.89|0.89|
|Observations|8,160|8,160|8,160|8,160|8,160|8,160|7,344|7,344|7,344|7,584|7,584|7,584|

**Table 2b: The impacts of COVID-19 confirmed cases and deaths on demand for wining services**

||||<br>|ICT<br>||||Delivery<br>||
|---|---|---|---|---|---|---|---|---|---|
||Zoom|Zoom|Zoom|Skype|Skype|Skype|delivery|delivery|delivery|
|First case|0.25|||0.21|||-0.13<sup>***</sup>|||
||(0.22)|||(0.13)|||(0.04)|||
|First 10 cases||0.57<sup>**</sup>|||0.38<sup>***</sup>|||-0.06||
|||(0.24)|||(0.13)|||(0.06)||
|First 100 cases|||0.61<sup>***</sup>|||0.44<sup>***</sup>|||-0.01|
||||(0.22)|||(0.06)|||(0.10)|
|R-squared|0.86|0.87|0.87|0.74|0.75|0.75|0.88|0.88|0.88|
|Observations|8,208|8,208|8,208|8,208|8,208|8,208|7,872|7,872|7,872|

Note: The data used covers January-April of 2018-2020. For every country where google trends data is available, the search index reflects searches in English and the most dominant local language. Each regression includes weekly dummies to capture aggregate temporal trends. Standard errors, clustered at country level, are given in parentheses.<sup>*</sup> _p_ < 0.10,<sup>**</sup> _p_ < 0.05,<sup>***</sup> _p_ < 0.01.

21
# Spatial and Sectoral Heterogeneity of Occupational Choice in Cameroon<sup>*</sup>

Théophile Bougna<sup>†</sup> Pierre Nguimkeu<sup>‡</sup> _JEL Classification:_ O12, O17, C51, C52, C54, R23, R11.

> *We are grateful to Emma W. Hobson and two anonymous referees for very helpful comments and suggestions. Financial support from the Economic Performance of African Cities (nlta) is gratefully acknowledged. The content is solely the responsibility of the authors.

> †Development Research Group (decrg), The World Bank, 1818 H Street, NW Washington, DC 20433, USA; Email: tbougna@worldbank.org

> ‡Department of Economics, Andrew Young School of Policy Studies, Georgia State University,14 Mariette Street NW, Atlanta, GA 30303, USA; Email: nnguimkeu@gsu.edu.