rafmacalaba/datause-encoder-data huggingface dataset:
estimator. Second, I leverage the “Dumsor” power crisis in Ghana to estimate the effect of unreliable electricity provision on entry of foreign firms using greenfield foreign direct investment (FDI) as a proxy. Specifically, I utilize unique data on greenfield FDI projects from fDiMarkets<sup>6</sup> and the synthetic control method to estimate the effect of the crisis on FDI in the non-energy-and-construction sectors<sup>7</sup> of the Ghanaian economy.

The main of the is that exert a non-trivial finding paper electricity outages negative impact on employment. From the cross-country analysis, I find that outages reduce employment by about 13.5 percentage points (pp). The results of the country case studies in Ghana and Nigeria also show a negative effect of outages on employment that are economically and statistically significant, albeit with relatively low magnitudes: the DID estimates from Ghana suggest that the “Dumsor” power crisis increased unemployment by 4.7 pp, while the fixed effects estimates from Nigeria suggest that outages are associated with a 5.7 pp increase in unemployment. Thus, overall the estimates suggest that outages are associated with a 4.7 pp to 13.5 pp increase in unemployment in the region. Additionally, evidence from the paper suggests that the effects are largely concentrated in employment in non-agricultural sectors and skilled jobs. Employment of unskilled workers are unaffected by outages. The null effect of outages on employment of unskilled workers provides support to the identification strategy in estimating the causal impact of outages on employment as, in practice, we do not expect unskilled tasks or jobs reliant on manual labor to be affected by outages. I also find suggestive evidence that the job losses associated with unreliable electricity provision are largely concentrated in the private sector. Interestingly, employment in the public sector increases with unreliable electricity provision, albeit the level of increase is relatively lower than the job losses in the private sector, hence the overall reduction in employment.

> 6a subsidiary of the Financial Times. See https://www.fdimarkets.com/

> 7The exclusion of FDI to the energy and construction sectors is motivated by two main factors: (i) FDI into the energy sector may increase in direct response to the power crisis, thereby leading to reverse causality; (ii) FDI to the construction sector in Africa is mainly concentrated in the real estate sub-sector which is less reliant on energy. Hence, the power crisis is less likely to affect FDI to the sector.

4
|Table 4. Sri Lanka: Ba<br>(Percent of <br>|selin<br> GDP|e Sce<br>unles<br>|nario <br>s indic<br>|(Res<br>ated <br>|truct<br> other<br>|uring <br>wise)|Scena|rio)<br>||||
|---|---|---|---|---|---|---|---|---|---|---|---|
|Pred.||Med|um-term|project|ion|||Exten|edproje|tion||
|2002|-232|04|<br>2025||eT|2|0|2|<br>2||2|
|Public:debt<br>1281|1112|1085|1078|1088|1044|1015|O85|O56|O88|OF|ATS|
|Change in public debt<br>8|4170|27|OOF|||||D|O||R|
|Contribution of identified Mow<br>79||D|D||D||R||R|A||
|Primarydeficit<br>c|e|:|a||||||e|:||
|<br><br>Moninterest reverues<br>a4|09|133|149|150|151|151|181|151|161|154|151|
|Noninterestexpenditures<br>120|«118|«125|126|127|128|128|i128|128|128|128|i128|
|Automaticdeb<sup>dynamics</sup><br>49|42|99|OF|O88|OFF|OOF|OO|1|ot|oOo|oOo|
|<br>Realinteresitaie andretalive inflation<br>«=«||1||||2|30|28|28|28|27|
|eal interest rate<br>316|232|34|17|19|18|17|160|4615|«#16|«416|(18|
|Relative inflation<br>18|49|42|fF|15|168|165|14|19°|«13°0:|«O12||
|Real grewth raie<br>8|40|«18|28|<1|42|41.|40|-2O|29|«2B|2?|
|Olher identified flows<br>oo|25|12|08|O88|OO|OOF|of|OO|of|OO|oO|
|Contingent liabilities<br>oo|oof|OOF|Fo|8|OO|OF|BO|OO|OO|of|oO|
|Olher transactions<br>oo|25|12|028|O88|OO|OF|DFO|OO|OO|of|oO|
|Contribution of residual<br>59|-160|239|O04|O84|O21|28|05|D4|4|Dd|Da|
|Grossfinancingneeds<br>345<br> <br>|266<br>|179<br>|184<br>|169<br>|142<br>|191<br>|140<br>|122<br>|#125<br>|122<br>|115<br>|
|<br><br>alwhich:deblservice:<br>309|259|17|177|182|185|154|16835|145|148|148|138|
|<br><br>Localcurrency<br>215|214|145|121|149|#130|111|119|10|03|02|OB|
|Foreigncurrency<br>4|468|#42|#45|433|35|43|43|45|44|43|39|
|Memo:||||||||||||
|Real GOPgrowth (percent)<br>47|20|15|28|30|44|44|aio|o31|44|434|Bf|
|Inflation (GOP deflator; percent)<br>466|300|7|56|62|51|50|50|50|50|50|50|
|NominalGOPgrowth(percent)<br>339|220|123|83|B84|43|@&3|83|83|433|43|B83|
|<br><br>Effectiveinterestrate (percent)<br>a7|72|#72|7S|72|69|OF|8F|BF|«AF|88|469|
|Contri<br>|bution|to ch|ange in <br>|public|debt<br>|||||||
|||perce|t<br>|||||||||
|a0<br><br>|||ofGDP}<br>||a0<br>|||<br>|<br>|<br>||
|||||||||<br>|<br>|<br>||
|pet<br>_.<br><br>|||||<br>|||<br><br>|<br><br>|<br><br>||
||||||||||<br>|||
|==<br><br><br>||||||||<br><br><br>|<br> <br><br>|<br>||
|«20<br>|||||i<br>40<br>|||d<br>meee Q<br>|<br>epreciati<br>ther low<br>|on<br>s||
|<br><br> <br><br>|||||<br>||||<br><br>|||
||||||<br>|||||||
|ad<br>2013 2015 20t7 2019 2021<br>2023 202|5 2027|2029|20s1<br>2|033|<br><br>7"|<br>Cumulati|<br>;<br>ve:|s|<br>ett|||
|Stallcommentary: Public debt increaseddrastically in 202<br>reduction aver the longer berm resis on continued fiscal d-|0-22, du<br>cipline.|etoexc|hangera|le depre|ciation,|large fis|cal defici|ts,SOE|borrowin|g, and||
_Table 4-1 : Sales Equation Estimation Results, Jordan and Georgia_

|Dependent variable: sales change since COVID-19 outbreak (percent change<br>year-on-year)<br>|Jordan|Georgia|
|---|---|---|
|Temporarilyclosed due to COVID-19 (weeks of closure) (CC)<br>|-2.371***|-3.903***|
||(0.542)|(0.572)|
|Total score of infection risk(higher = more risk) (I)<br>|0.163|-0.241*|
||(0.143)|(0.144)|
|Whether supplyofgoods,materials,and inputs decreased(1=yes) (EI)<br>|-17.87*|-18.83***|
||(9.745)|(5.692)|
|Change in export share of sales(percentage difference) (E)|0.323*|n.s.|
||(0.195)||
|Supplies of goods and materials decreased (1=yes) * change in export sales<br>(EI*E)|-0.418**|n.s.|
||(0.205)||
|The firm had export sales in 2018 (1=yes) * global demand shock (yoy change of<br>US and EU imports,with imputation)<br>|-0.058|0.259***|
||(0.111)|(0.073)|
|Started or increased business activityonline(1=yes) (A)|n.s.|n.s.|
|Residuals from digital adaption equation|n.s.|n.s.|
|Observations|371|413|
|Adjusted R-squared|0.240|0.388|

Notes: ***p<0.01, ** p<0.05, *p<0.1. Robust standard errors in parentheses. In the case of Jordan, bootstrapped standard errors are reported. N.S. indicates that the variable was omitted due to the lack of statistical significance. Although not all variables found to be insignificant are included in the final specification, we include some in the table to aid the exposition.

# 4.2 Primary Drivers of Job Loss and Risk

In the final stage of model estimation, the job loss equation, we examine the relationship between labor demand and supply shocks experienced by the firm and its change in permanent workforce levels. We regress the percentage change in the firm’s permanent workforce on the percentage change in sales, , whether or not firms experienced financial difficulties, , whether they received policy support ( ), digital technology QQ adaptation ( ), the percentage of workers under permanent contracts (all on the labor demand side), the FF PP percentage of workers working remotely ( ), and workers’ infection risk (I ), weeks of closure (C ) and AA essentialness (E ).<sup>23</sup> We also included generalized residuals from earlier stage equations for Jordan, where EEIIssh CC these were significant, to account for possible simultaneity and selection bias (due to incomplete responses). EE Our controls for selection and simultaneity bias mattered only for our results in Jordan.<sup>24</sup> 23 Some regressors were ultimately excluded as they were not significant and had no effect on the other coefficient estimates.

24 In the equation to predict whether all key variables were available and estimate controls for non-response, we found that for Jordan the probability of completed responses was higher if the respondent to the COVID follow-up survey was the same person as in the 2019 WBES; for medium sized firms (but not large ones); and for managers with more experience in the sector. For Georgia, full response probabilities were not related to any of these variables. Unfortunately, we were unable to control for non-response there, because none of the Z variables were significant determinants of joint non-response.

18
# **5.3 Effects on Group Composition**

An additional challenge of the comparison across remote survey methods relates to the likelihood of altering the composition of the respondent group. That is, a potential concern is that one survey method at midline has a differentiated impact on future attrition than the other. This is important because there are two competing ways through which a survey method can modify an observational outcome of interest. For instance, in a context of low response rate, a change in the composition of the group of people who complete the survey can confound the impact of a program. Similarly, different survey methods can alter the likelihood of reporting a particular response, which can be especially salient in surveys about sensitive issues.<sup>36</sup> In our context, this matters because researchers want to know not only the extent to which a particular method affects the size of the group that completes the survey, but also whether it alters the group composition in ways that correlate with the outcome of interest. Of course, our research design does not allow us to causally disentangle both possibilities, but we address this issue by exploiting the experiment within which this survey is embedded. Specifically, we do so by comparing the effect of the survey method on the level of stress reported by respondents.

Table 5 measures the effect of the phone-based survey on self-reported stress level and perceptions of the effectiveness of physical punishment in two different time periods. In columns (1) and (2), we show the impact of the phone-based survey on reporting a high level of stress in the midline survey, while columns (5) and (6) report similar estimates at the endline survey. Similarly, for perceptions of the effectiveness of physical punishment, coefficients for the midline survey are in columns (5) and (6), and for the endline survey, in columns (7) and (8). Given the high level of non-response in both remote survey methods, we would like to know whether the coefficients in columns (1) and (2) capture either a change in willingness to

> 36We examine this issue in Table 5. Columns (1)–(4) show estimates of differences in the willingness to report sensitive information by type of survey mode during our remote survey experiment (midline survey). Using our measures of stress and perceptions of the effectiveness of physical punishment, we find that participants who completed the survey over the phone are less likely to report higher levels of stress, relative to those assigned to the WhatsApp survey mode. At the same time, we do not observe significant differences when comparing the outcome binary variable _Believe that physical punishment is effective_ . At least in the case of stress, these findings show a large difference among those who completed the survey by phone.

19
|(9)<br>PCA<br>index<br>cols. 1-8|-0.682|(0.423)|0.13<br>2724|les.||
|---|---|---|---|---|---|
|(8)<br>coping<br>strategy<br>index|1.283|(1.748)|0.09<br>2783|h percenti||
|(7)<br>food<br>insecurity<br>score|0.319|(0.228)|0.07<br>2781|0th and 90t||
|(6)<br>any day<br>w/reduced<br>meals|0.110|(0.093)|0.07<br>2789|orized at the 1||
|(5)<br># food<br>groups<br>consumed|-0.299|(0.302)|0.17<br>2805|01<br>ies are Wins||
|(4)<br>per adult<br>equivalent<br>calories|-82.769*|(50.278)|0.07<br>2820|WP.<br>_∗∗∗p <_0_._0<br>. Total calor||
|(3)<br>food<br>cons.<br>score|-5.580*|(3.089)|0.12<br>2805|d with P<br>_∗p <_0_._05_,_<br>, 7, and 8|fects.|
|)||||e<br> <br> 6|e|
|(2)<br>ln (p.c.<br>food cons.<br>last week|-0.171|(0.122)|0.16<br>2820|n instrument<br> _∗p <_0_._10_, ∗_<br> of columns|erview fxed|
|(1)<br>ln (p.c.<br>food exp.)<br>last week|-0.124|(0.225)|0.15<br>2776|participatio<br>the EA level.<br>food security|d week-of-int|
|ble:|in MASAF|||HH MASAF <br>clustered at <br>dicate worse|e district an|
|varia|pated|days|ns|ates. <br>rrors <br>es in|nclud|
|ndent|artici|t 30|ared<br>vatio|estim<br>ard e<br>r valu|ates i|
|Depe|HH p|in pas|R-squ<br>Obser|2SLS <br>Stand<br>Highe|Estim|

42
uncertainty. This aspect is crucial to understanding the political economy of reforms and why most governments in the region have put subsidy reforms at the top of their agenda in recent years.

Figure 2: Commodities and Oil Prices, January 1992–May 2016 <!-- Start of picture text --> 300 160<br>140<br>250<br>120<br>200<br>100<br>150 80<br>60<br>100<br>40<br>50<br>20<br>0 0<br>Commodity Food and Beverage Price Index ‐ Monthly Price (left scale)<br>Commodity Fuel (energy) Index ‐ Monthly Price (left scale)<br>Crude Oil (petroleum); Dated Brent ‐ Monthly Price (right scale)<br>Jan‐1992 Jan‐1993 Jan‐1994 Jan‐1995 Jan‐1996 Jan‐1997 Jan‐1998 Jan‐1999 Jan‐2000 Jan‐2001 Jan‐2002 Jan‐2003 Jan‐2004 Jan‐2005 Jan‐2006 Jan‐2007 Jan‐2008 Jan‐2009 Jan‐2010 Jan‐2011 Jan‐2012 Jan‐2013 Jan‐2014 Jan‐2015 Jan‐2016<br><!-- End of picture text --> _Sources:_ Elaborated from http://www.indexmundi.com/ and World Bank Commodities Prices database.

The next issue to consider is the regulated prices that MENA countries were confronted with when they were forced to introduce reforms. Table 2 shows the average regional prices for four energy products—gasoline, diesel, kerosene, and LPG—comparing the main regions of the world. It is striking how the MENA region distinguishes itself from the others by having, by far, the lowest average regional prices. For example, the price of gasoline, on average, was US$ 0.67, about half the price of other regions. The price of LPG was US$ 0.4, which is less than a third of the average price in South Asia and about a third of the price in East Asia and the Pacific. Similarly, for diesel and kerosene, the prices in the MENA region were a fraction of the prices in other regions. Hence, the worldwide push for subsidy reforms that took place in virtually all regions of the world since 2010–11 was particularly acute in the MENA region when growth took a negative turn due to political instability. These two factors together with the economic 9
|both in GATT/WTO|1984-99|Indicator for both countries in trade pair members in<br>GATT/WTO|Rose (2004)|
|---|---|---|---|
|only one in GATT/WTO|1984-99|Indicator for only one country in trade pair a member in<br>GATT/WTO|Rose (2004)|
|generalized system of<br>preferences (GSP)|1984-99|Indicator for one country extending GSP privileges to<br>the other|Rose (2004)|
|regional trade agreement|1984-99|Indicator for both countries in trade pair members in a<br>regional trade agreement|Rose (2004)|
|log of product of telephone lines<br>per 1,000 people|1984-99|log of product of telephone lines per 1,000 people in<br>trade pair|WDI, The World Bank|
|log of product of road length per<br>1,000 people|1984-99|log of product of road length per 1,000 people in trade<br>pair|WDI, The World Bank|
|average weighted tariff|1988-99|Average of bilateral weighted tariff for trade pair. When<br>only one value exists, that is the one that is taken|TRAINS, The World Bank|
|sum of absolute latitude|-|Sum of absolute latitudes of countries in trade pair|CIA Factbook|
|ethnic fractionalization|-|Sum of probabilities of two people randomly meeting<br>someone not from their own ethnic group|Alesina et al. (2003)|

Sources in detail: Alesina, Alberto, Arnaud Devleeschauwer, William Easterly, Sergio Kurlat and Romain Waicziarg (2003), "Fractionalization, _Journal of Economic Growth_ 8 Djankov, Simeon, Rafael La Porta, Florencio Lopez-de-Silanes and Andrei Shleifer (2003), "Courts", _The Quarterly Journal of Economics_ , May Rose, Andrew (2004), "Do we really know that the WTO increases trade?", _American Economic Review_ 94(1) CIA Factbook,http://www.cia.gov/cia/publications/Factbook/ ICRG, http://www.icrgonline.com/ The Heritage Foundation, http://www.heritage.org/ 35
**Figure 5: Location of commercial maize farms in 2001/4 with smallholder kebeles and their yields in 0-25, 25-50, 50-100, and 100-150 km distance bands**

<!-- Start of picture text --> Legend<br>Kebele boundary 2014<br>Yield (q/ha) by quintile<br>3 - 15<br>16 - 21<br>22 - 28<br>29 - 36<br>37 - 78<br>Region boundary<br>Buffer around Maize farm (14)<br>Distance (km)<br>25<br>50<br>100<br>150<br><!-- End of picture text --> 30
Using the methods to be described in Section 5 we have also checked to see whether the period under analysis was marked by differences in the implementation of other development projects in the project and non-project communes. For a long list of potential interventions for which information is available in our data, we find no evidence that project or non-project communes were treated differently.<sup>9</sup> Based on the findings, we are confident in attributing any differences in outcome changes over time to rural road rehabilitation and construction.

# **_3.2 The SIRRV data_**

Collected specifically for evaluating the impacts of the rural roads rehabilitated under RTPI, the "Survey of Impacts of Rural Roads in Vietnam" (SIRRV) consists of a panel of 200 communes and 3000 households. The survey design implicitly takes the commune as the project’s zone of influence. This is justified by the project objectives ─ namely to link commune centers (where key social, economic and administrative facilities are located) with road and market networks ─ and because the commune is an enumeration level at which data is commonly collected in Vietnam.

The baseline was collected pre-project starting in June 1997, while subsequent rounds followed in the summers of 1999, 2001 and 2003, tracing the implementation process and schedules of prior rounds. The analysis for this paper makes use primarily of the first and last rounds, though we will also test impact dynamics using the 2001 round.

Project (“treatment”) and non-project (“comparison”) communes, and households within them, were surveyed in six of the 18 provinces participating in the project ─ Lao

> 9  We looked at education, health care, family planning, child nutrition, reforestation, opening up new land, anti-opium, job creation and TV and radios distribution programs, as well as various schemes that are part of the Hunger Elimination and Poverty Reduction Program, including credit loans, school fee and health care exemptions, free land allocation and new infrastructure.

13
Contracts vary significantly in the case of force majeure. In the Kosovo airport PPP, the contract asks the parties to agree on the terms of termination. The contracts of road PPPs in Argentina mandate that the government compensate the private party for the whole accumulated investment amount without penalties.

In India (not one of our sample countries), PPP contracts include a compensation scheme that aligns with the PPI database’s conceptualization of PPP financing. Road concession agreements, for example, express compensation as a percentage of debt and equity financing of the project, depending on the source of termination of the contract. In the case of default by the project company, the concession agreements do not foresee any compensation on equity; in the case of default by the public authority, the contract entitles the private sponsor to 150 percent of its equity, which can be seen as an approximation of the return on equity invested, as stipulated in the contracts mentioned before. In the case of a force majeure event indirectly caused by a political event, the private sponsor of an Indian road PPP is entitled to 110 percent of the equity it invested in the project.

When a project is terminated, it is assumed that the government loses all its equity. As a result, equation (4) can be rewritten as follows: E � D D + P + P E �, (5) � ii ii ii ii ii EEiiDDEE where<sup>�</sup> is the predicted probability of early termination of project = PPE �DDeeDDtt EELLEE DDPPiicc E PPiittEE iiP ttee E PPiittEE , EELLEE is the debt of project , D D is the proportion of debt lost because of early termination, P is ii ii PPE ii DDeeDDtt the government equity in project , P is the private equity in project , and ii ii EELLEE DDPPiicc E PPiittEE E is the proportion of private equity lost because of early termination. ii ii iiP ttee E PPiittEE ii EEiiDDEE EELLEE Three scenarios are considered: • Low: D D = 0.793; E = 0 • Medium: D D = 1; E EEiiDDEE = 1 EELLEE EELLEE • High: D D = 1; E EEiiDDEE= 1.5 . EELLEE EELLEE In the low scenario, the proportion of debt covered by the government because of early EEiiDDEE EELLEE EELLEE In the low scenario, the proportion of debt covered by the government because of early termination is assumed to be the recovery rate estimated by Moody’s Investor Service (2019). The government does not cover the loss of private equity, losing only its own equity in the PPP. In the medium scenario, the proportion of debt and private equity covered by the government because of early termination are assumed to be 1, so that the government guarantees the total financing of the project but does not compensate for the forgone return. This outcome is equivalent to compensating the project company for the accumulated investment amount in the PPI database’s financing framework. In the high scenario, on top of the debt, the government 23
20

|T<br>SELECTED INCIDENCE STUD|ABLE1<br>IES FOR DEVELO<br>Allbl|PED COUNT|iWES<br>bli||
|---|---|---|---|---|
|Studv, Country, Year of Data|ocaes<br>(Net of Transfer)|Transfers<br>|Puc<br>Goods<br>|Total|
|Reynolds and Smolensky, United States, 1950|- (4)|+(W)|J+|+|
|Gillespie, United States, 1960|||||
|Scenario A|o(*|+(+)|(+)|oW(+|
|Scenario B|o()|+(+)|(o)|-|
|Scenario M|o(+)|+()|.|-|
|Reynolds and Smolensky, United States, 1961|-|+(4.)|Jl+)|.|
|Henriot and Miller, United States, 1968|||||
|Scenario C|-(+)|+ (+)|od(+)|o(+)|
|Scenario A|-<br>(+)|+(+)|-e|-|
|Scenario M|- (+)|+(+)|.||
|Musgrave, Case, and Leonard, United States, 1968|||||
|Scenario POP|+ then- (+)|+ (+)|-(||
|Scenario INC|+then-<br>(+)|+ (+)|-o(o)|-(+)|
|Scenario CAP|+then<br>(-)|+(+)|b(t|.<br>-(|
|O'Higgios and Ruggles, United Kingdom, 1981|||||
|Scenario I|+ theno(+)|(i)|A(+)|o(+)|
|Scenario 2|+ theno (+)|(i)|-<br>(o)|-<br>(+)|
|Scenario 3|+then o(+)|(iL|h(+)|,W|
|Leu. Frey. and B<br>_hM Swierdalnd 1980|NA(<br>)|L<br>_<br>-;|J (4.)|NA (|
|O!dake, West Germany, 1963-78|- (0)|NA(+)|0<br>()|-<br>(+)|
|a"+"- pro-poor, "of- neutral, and"-<br>pro-rich<br>distibution of expediues across income deciles. T<br>expenditure as a percentage of income in each decile<br>including more extensive notes on the various metho<br>bTypicailyincludes defense, intemational securit<br>Distributed one-balf by households and one-half<br>dDistributedby households.<br>'Distributed by household income.<br>*aital's<br>(1975) calculations based on Aaron and<br>public or general.)|. The reported progs<br>he dgns in parthes<br>. For a more detaile<br>dologies, see Selden a<br>y, public health, and o<br>by household incom<br>McGuire (1970) (tre|Sivity reuts<br>are progess<br>d presentation o<br>nd Wasyleak<br>ther general ex<br>e.<br>ats aU non-tran|am basd o<br>ivity result<br>f these resu<br>o (1990).<br>penditures<br>sfer expendi|n the<br>s based on<br>lts,<br>.<br>tures as|
|<br>SDisti.buted by individuals.<br>'Distibuted by capital income.<br>Transfes induded with allocable expenditumes<br>in<br>JDistributed one-half by population and one-half b|first colm<br>y income.||||
|SOURCES:<br>Reynolds and Smolensky, 1977, pp.<br>174; Heriot and Miller, 1972, pp. 4<br>294, and 300; O'Higgins and Ruggl<br>p. 353; Griske, 1985, pp. 245-248.|112-114, 124-129; G<br>6, 49; Musgrave, Cas<br>es, 1981a, pp. 307-3|illespie, 1965,<br>e, and Leonar<br>08; Leu, Frey, a|pp. 138, 13<br>d, 1974. pp.<br>nd Buhma|9, and<br>285, 292,<br>nn. 1985,|