rafmacalaba/datause-encoder-data huggingface dataset:
**Table 1 Dependent and Explanatory Variables Influencing Private Investment in Renewable-based Energy**

|**Vribl**|**Dfinitin**<br>**E Sin **|
|---|---|
|**aaes**|**eo**<br>**xp. g **|
|**Itdti f fdi**|**SECTORALGOVERNANCE VARIABLES**|
|**nroucon o ee-n**<br>**tariffs**<br>_FIT_|= 1 since the year of establishment of a feed-in tariff system<br>= 0 otherwise<br>**+**|
||**ECONOMYWIDEGOVERNANCE VARIABLES**|
|**Control of Corruption**|= measures the perceptions of the extent to which public power is<br>exercised for private gain, including both petty and grand forms of<br>corruption, as well as "capture" of the state by elites and private<br>interests.(Kaufmann and KrayWorld Governance Indicator)<br>**+**|
|**Degree of democracy**<br>**_Polity2_**|= Revised Combined Polity Score (Polity 2): This variable is a modified<br>version of the POLITY variable to facilitate the use of the POLITY<br>regime measure in time-series analyses. It ranges from +10 (strongly<br>democratic) to -10 (strongly autocratic) based on 6 indicators<br>**+**|
||, .<br>**SHORT RUN FINANCIALCRISES**|
|**East Asian and Russian**<br>**Crisis**<br>_d_FC1_|= 1 for the 3 years after 1997<br>= 0 otherwise<br>-|
|**Latin American Crisis**<br>_dFC2_|= 1 for the 3 years after 2001<br>= 0 otherwise<br>-|
|___<br>**LO**|**NG RUN ENVIRONMENTALSUSTAINABILITY FACTORS**<br>|
|**Price of oil**|= value of the internationalprice of oil<br>**+**|
|**Kyoto Protocol**|= 1 for the years after 2005<br>= 0 otherwise<br>**+**|
|**PPP Transmission**<br>**i**|= investment in transmission committed by the private sector in (US$)<br>**+**|
|**nvestment**<br>**GDP**<br>_ln_GDP_|**LONG RUN ECONOMIC FUNDAMENTALS**<br>= GDP, PPP terms (constant 2005 international $)<br>**+**|
|**Population**<br>_ln_Pop_|= total population<br>**+**|

# **_Sectoral and Economy-Wide Governance_**

**Hypothesis 1A** The likelihood of attracting private investors to provide electricity from renewables is higher in countries where support mechanisms, including feed-in tariff, have been put in place.

**Hypothesis 1B** The level of investment in renewable-based energy is expected to be higher in countries using such incentives.

Private investors are more likely to choose to invest in renewable energy if the country offers sufficient incentives from the regulatory side. This is because most renewable sources of energy produce electricity at a higher cost than electricity from conventional sources, and they also typically entail higher upfront capital costs. After recouping the capital cost, private investors can benefit from the lower operational costs of generation from renewables. This is because, for instance, producers of electricity from wind and solar do not have to manage fuel cost or fuel price volatility.

14
More recent rounds of the DHS also include questions on decision making and attitude toward domestic violence. The decision making questions include whether or not women are involved in decisions on their own health care, purchases, and visits to family. The decision making index is the sum of all the decision making items in which the woman is involved individually or jointly. A higher index suggests higher decision making power for women. The questions on attitude toward domestic violence include whether it would be justified for a husband to beat his wife if she goes out without telling her husband, neglects the children, argues with her husband, refuses sex, and burns the food. The index for the attitude toward domestic violence is the sum of all the items with which the woman agrees. A higher index suggests a higher acceptance of domestic violence.

# **2.2 Empirical strategy**

## **2.2.1 Estimation**

We use an event study approach based on sharp changes in women’s labor market outcomes observed around the time of marriage, _t_ = 0, for individuals at age _a_ observed in calendar year _y_ . Ideally, the estimation uses individual-level panel data, in which case relative time indicators for marriage and childbirth can be included simultaneously, along with individual and year fixed effects, in a single event-study regression where labor market participation is the outcome Kleven et al. (2023).

However, in the absence of such data, following Kleven (2022), cross sectional data can be used to create pseudo-panel data. For married individuals, we observe the age of marriage, and therefore their location the post-marriage event space, _t ≥_ 0. For unmarried individuals, we cannot observe their timing of marriage, and therefore only observe that they are in the pre-marriage event space, _t <_ 0, but not their precise location in this space. The pseudopanel approach matches married women to unmarried women with the same demographic characteristics to form a pre-marriage counterfactual estimate of the outcome, transforming the cross sectional data into a pseudo-panel across the pre and post-marriage event spaces.

Specifically, woman _i_ is observed in the year of her marriage _t_ = 0 in calendar year _y_ at age _a_ , with demographic characteristics _Xi_ . She is matched to a surrogate observation in the 7
have tended to find the strongest gains to alleviating malnutrition in reduced levels of stunting (e.g., Fernald, Gertler, and Neufeld (2009); Rivera, Sotres-Alvarez, Habicht, Shamah, and Villalpando (2004); Maluccio and Flores (2005)).

Tables 8 and 9 show the same outcomes as tables 6 and 7, but only include new children born to women in households where the woman was interviewed at baseline. These are presumably the children most likely to be influenced by the changes in behavior shown in table 5, which only focused on the mothers with new children. There are no indications of any improvements in cognitive or anthropometric measures for these youngest children.

If complying with suggested best practices, such as exclusively breastfeeding a child for six months, one particular outcome that should improve among the youngest children is a reduction in rates of diarrhea. This would occur both because of increased nutrients from drinking breastmilk and from reduced likelihood of contamination from dirty water. For new children born since the baseline in both of the treatment groups, there is a substantial, albeit insiginficant reduction, in the likelihood of having diarrhea, as shown in column 8 of table 9. Though the reduction is only between 1 and 2 percentage points, this is a 15-25% lowering of the likelihood of having diarrhea over the 6.9 percent of children in the control group who had diarrhea in the past thirty days. This suggests that the treatments may have improved child outcomes, and perhaps some of the broader insignificant results are due to the short time window in which we observe the children. Since the intervention only took place over nine months, it may not have been enough time to sufficiently capture the gains, but by following up in the future we may be able to more accurately measure improvements in child outcomes if they have indeed improved.

This analysis has focused only on participants in the endline survey. The midline survey results are left to an appendix because of issues with attrition discussed in section 3. However, we follow the procedure in Lee (2009) to estimate upper and lower bounds to correct for the sample selection issues stemming from attrition. Table A3 estimates the upper and lower bounds on the treatment effects for the indices reported in the main tables. Estimates are reported separately for the information only versus control group and the information plus cash versus control group. The bounds for the most part cannot rule out the findings from the endline, which implies that the two survey waves can be consistent with each other.<sup>14</sup>

> 14Specific tables that mimic the exact main results for the midline survey waves are available on request, but are left out to save space.

14
significant impacts are seen for the vocational training treatment, which lowers the intention to migrate by 5 to 7 percentage points, and of considering the backway by 9 percentage points.

Columns 7 and 8 show that these youth have lower intentions to migrate to Senegal in the next five years, with only 12 percent of the control group saying they “surely” will, and 22 percent of the control group sure or likely to migrate. The Senegal treatment increases these intentions by 3 to 7.5 percentage points. This impact on surely or likely migrating also holds after multiple testing corrections.

Overall, these results show our interventions had lasting impacts on migration knowledge and intentions, with the change in intentions stronger for the policy interventions providing alternatives to irregular migration.

# **5.3 Treatment impacts on backway, Senegal, and internal migration**

Table 4 provides the estimated treatment effects on our different primary migration outcomes. Columns 1 to 3 examine impacts of the different treatments on migration to Senegal. We see that 11 percent of the control group has gone to work in Senegal at some point in the 18-month followup period. However, most of this migration is short-term and shorter distance, with only 1.7 percent having gone to Dakar, and only 1.6 percent residing in Senegal at the time of the follow-up survey. None of our treatments had sizeable or significant impacts on migration to Dakar, but the Senegal and vocational training interventions increased the likelihood of residing somewhere in Senegal at the time of the follow-up survey by, respectively, 2.2 and 2.6 percentage points, which more than doubles the rate in the control group. These effects continue to hold after adjusting for multiple hypothesis testing.

Our survey data provides descriptive information on this form of migration. The migrants are about evenly split between urban and rural areas in Senegal. Very few are involved in agricultural activities, unlike in their rural areas of origin, with the main occupations being working in construction and self-employment activities such as small-scale trading. We do not have income data for many of these migrants, but for those that we do, the average reported income is 6,333 Gambian dalasi (GMB) per month. This is in line with the wages of 75,000 to 100,000 CFA (6,500 to 8,700 GMB) reported by the Gambian migrants residing in Senegal who were interviewed for our informational video, and is more than 80 percent higher than the average earnings of 3,452 29
Table 9: Climate Change Impacts on Probability of Irrigation Choice by AEZs (%)

||Africa<br>A|EZ1|AEZ2|AEZ3|AEZ4|AEZ5|
|---|---|---|---|---|---|---|
|Baseline|0.501|0.564|0.488|0.488|0.477|0.510|
|2020|||||||
|CCC|0.038|0.010|0.036|0.026|0.030|0.029|
|PCM|-0.025|0.099|-0.055|-0.186|-0.090|-0.076|
|2100|||||||
|CCC|0.155|0.036|0.144|0.091|0.116|0.120|
|PCM|-0.019|0.107|-0.023|-0.168|-0.064|-0.046|
||AEZ6<br>A|EZ7|AEZ8|AEZ9|AEZ10|AEZ11|
|Baseline|0.480|0.479|0.517|0.515|0.513|0.559|
|2020|||||||
|CCC|0.026|0.056|0.026|0.050|0.061|0.033|
|PCM|-0.147|-0.012|-0.076|-0.027|-0.020|-0.072|
|2100|||||||
|CCC|0.107|0.207|0.150|0.193|0.208|0.153|
|PCM|-0.123|-0.025|-0.071|-0.008|-0.059|-0.058|
||AEZ12|AEZ13|AEZ14|<br>AEZ|15<br>A|EZ16|
|Baseline|0.432|0|.470|0.421|0.498|0.492|
|2020|||||||
|CCC|0.030|0|.026|0.027|0.028|0.030|
|PCM|-0.028|-0|.160<br>|-0.039|-0.051|-0.167|
|2100|||||||
|CCC|0.132|0|.119|0.123|0.114|0.126|
|PCM|-0.008|-0|.147<br>|-0.019|-0.025|-0.142|

30
<!-- Start of picture text --> 3<br>2.9<br>2.8<br>Average<br>SWB<br>(1-4)<br>2.7<br>2.6<br>2.5<br>2010 2011 2013 2015 2016 2017 2018<br>Year<br><!-- End of picture text --> <!-- Start of picture text --> 0.60<br>0.55<br>0.50<br>0.45<br>Net<br>affect<br>0.40<br>0.35<br>0.30<br>0.25<br>2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020<br><!-- End of picture text -->
- 85 -

# Table 28: EARNINGS FUNCTIONS, BY LOCATION

||Abi|djan|Other|Cities|Rural A|reas|
|---|---|---|---|---|---|---|
|Intercept|10.17|(72.2)|9.99|(28.1)|9.45|(22.0)|
|Years of Primary Schooling|.027|(0.9)|<br>.005|<br>(0.1)|<br>.069|<br>(0.7)|
|Years of Secondary Schooling|.124|(4.0)|<br>.149|<br>(4.0)<br>|<br>.156<br>1|<br>(0.9)|
|Years of Higher Schooling|.129|(2.3)|<br>.120|<br>(1.7)|||
|Years of Current Job Experience<br>|.099|(6.1)|<br>.069|<br>(3.2)|<br>.153|(1.8)|
|(Years of Current Job Experience)<br>2|-.0019|<br>(3.5)|-.0008|<br>(1.1)|-.0054|<br>(1.5)|
|Years of Previous Job Experience<br>|.082|(2.2)|<br>.078|<br>(2.1)|<br>.037|<br>(0.4)|
|(Years of Previous Job Experience)<br>2|-.0054|<br>(1.8)|-.0043|<br>(1.7)|<br>.0016|(0.3)|
|Years of Apprenticeship|.014|(0.6)|-.001|(0.0)|<br>.169|<br>(1.3)|
|Years of Secondary VTE|.073|(2.1)|<br>.037|<br>(0.5)|<br>.025|<br>(0.2)|
|Years of Other VTE|.052|(0.4)|<br>.037|<br>(0.4)|<br>.463|<br>(0.8)|
|CEPE|-.098|(0.5)|<br>.573|<br>(2.0)|<br>.254|<br>(0.4)|
|BEPC|-.324|(1.0)|<br>.483|<br>(1.3)|<br>.038|<br>(0.0)|
|Secondary VTE Diploma|.023|(0.1)|<br>.736|<br>(2.1)|-. 199|(0.2)|
|Other VTE Diploma|.650|(1.6)|<br>.711|<br>(1.7)|||
|Other Diploma|.344|(0.9)|<br>.666|<br>(1.7)|<br>-|_|
|Sex (1-female)|-.064|(0.6)|-.201|(1.1)|<br>.346|<br>(0.6)|
|Non-lvorian|-.074|(0.6)|-.206|(1.2)|-.546|(1.3)|
|Seasonal Job|-|_|.064|<br>(0.2)|-.923|(2.4)|
|Lambda|-.275|(2.3)|-.120|(0.6)|<br>-|_|
|R<sup>2</sup>|.5|8|.|63|.|59|
|F|19.|13|14.|12|3.|66|

# Note: Absolute t - values in parentheses.
**Table 45. Importance of IPA services: Pre-investment assistance (e.g. site visits, briefings, meetings with stakeholders)**

|VARIABLES|(1)<br>OLS|(2)<br>OLS|(3)<br>OLS|(4)<br>OLS|(5)<br>Ologit|(6)<br>Ologit|(7)<br>Ologit|(8)<br>Ologit|
|---|---|---|---|---|---|---|---|---|
|Services|0.00137|0.00312|||-0.0216|-0.00589|||
||(0.0549)|(0.0534)|||(0.109)|(0.112)|||
|Export % of sales|0.000642|0.000140|0.000466|-3.41e-05|0.000709|0.000108|0.000299|-0.000344|
||(0.000729)|(0.000730)|(0.000733)|(0.000735)|(0.00143)|(0.00151)|(0.00150)|(0.00158)|
|Share of sourcing via imports|-5.21e-05|-0.000165|-4.67e-05|-6.85e-05|0.000214|-0.000372|0.000307|-5.45e-05|
||(0.000799)|(0.000831)|(0.000793)|(0.000820)|(0.00160)|(0.00174)|(0.00165)|(0.00177)|
|High-income source country|-0.207***|-0.142*|-0.212***|-0.156*|-0.445***|-0.318*|-0.480***|-0.372**|
||(0.0789)|(0.0793)|(0.0805)|(0.0803)|(0.163)|(0.174)|(0.173)|(0.182)|
|>250 employees|0.000996|0.0234|-0.0298|-0.00899|-0.0513|0.0141|-0.118|-0.0587|
||(0.0650)|(0.0668)|(0.0647)|(0.0662)|(0.128)|(0.138)|(0.132)|(0.142)|
|Cumulative inv. >US$10M|-0.0181|0.0485|-0.0151|0.0506|-0.0123|0.0962|0.00690|0.114|
||(0.0638)|(0.0626)|(0.0631)|(0.0620)|(0.127)|(0.129)|(0.129)|(0.131)|
|Years in country|-0.00267|-0.00400*|-0.00237|-0.00380*|-0.00493|-0.00762*|-0.00435|-0.00726*|
||(0.00218)|(0.00206)|(0.00220)|(0.00204)|(0.00402)|(0.00393)|(0.00414)|(0.00399)|
|Level of foreign ownership (%)|-0.00118|9.35e-05|-0.00112|-4.14e-05|-0.00186|0.000882|-0.00190|0.000398|
||(0.00106)|(0.00109)|(0.00105)|(0.00108)|(0.00213)|(0.00223)|(0.00216)|(0.00226)|
|Country fixed effects||X||X||X||X|
|Subsector fixed effects|||X|X|||X|X|
|Observations|2,424|2,424|2,424|2,424|2,424|2,424|2,424|2,424|
|R-squared|0.011|0.069|0.043|0.099|||||

Standard errors in parentheses

*** p<0.01, ** p<0.05, * p<0.1

The dependent variable in all models is an ordered categorical variable corresponding to responses to the following question: "How important are the following services offered by Investment Promotion Agencies to your company in this country? Please rate each on a scale from 1 to 4 where 1 is not at all important and 4 is critically important. [Pre-investment assistance (e.g. site visits, briefings, meetings with stakeholders)]". Higher numbers denote more greater importance. Subsector fixed effects are at the ISIC A38 level.

Coefficients for constants (for OLS), cut points (for ordered logit), and fixed effects not shown in table.

62
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structure.” Green (1997) on the other hand, while recognizing the initial problems of price caps in the UK has a much more positive view about its usefulness as regulatory mechanism: “The high profits of the early 1990s were due largely to unanticipated, onetime productivity gains following privatization, which are unlikely to be repeated. The established method for resetting price controls makes further “mistakes” unlikely. In the future, price controls can be expected to give companies an incentive for efficiency without allowing them to earn excessive profits”.

# **3.3 Regulation of Service Quality**

In addition to setting tariff levels, another critical (but often neglected) area of economic regulation is quality of service. The implementation of a sound regulatory framework for quality of service is essential in any regulatory regime, but becomes particularly critical when a price cap regime is in place.

Incentive regulation mechanisms that provide incentives only for cost reduction, simultaneously create an incentive to reduce service quality, since cost and quality are closely related. It is therefore essential to complement price regulation with performance standards and associated penalties and rewards for the regulated firm that falls above or below these performance norms. These mechanisms are used by several U.S. states and in other countries that have liberalized their electricity sectors, such as New Zealand and the Netherlands (Joskow, Incentive Regulation and Its Application to Electricity Networks 2008), (Joskow 2014).<sup>13</sup> The UK regulator (Ofgem) has developed several incentive mechanisms targeted at various dimensions of distribution network service quality. Ofgem uses statistical and engineering benchmarking studies and forecasts of planned maintenance outages to develop targets for the number of customer outages and the average number of minutes per outage for each distribution company. These _Quality of Service Guaranteed Standards_ must be met by each distribution company and have been set to guarantee a level of service that is reasonable to expect companies to deliver in all cases. If a distribution company fails to meet the level of service required, it must make a payment to the customer to compensate for the inconvenience caused by loss of supply (subject to certain exemptions). (Ofgem n.d.) Similarly, the Australian Energy Regulator established the Service Target Performance Incentive Scheme (STPIS) to incentivize businesses to maintain or improve the quality of their services. The STPIS counterbalances incentive-based regulation and the expenditure incentive schemes in place, which encourage businesses to become more efficient by spending less. The goal is to avoid cost reductions coming at the expense of service quality. Different schemes exist for distribution and transmission networks. Penalties and rewards under the distribution STPIS are calibrated to how willing consumers are to pay

> 13 <u>http://www.nber.org/chapters/c12566.pdf</u>

7