rafmacalaba/datause-encoder-data huggingface dataset:
Two additional variables are introduced in all cases: road length and fraction of the total investment financed by debt. The debt variable only applies to projects that secured financing.

The larger the tendered _premium payments_ to the government, the higher the probability that national highway PPPs were later canceled. Similarly, the smaller the tendered payment from the government to the sponsor as capital grant or annuity payment, the higher the probability that national highway PPPs were later canceled. _Premium-payment_ contracts are more likely to be canceled than _annuity-based_ contracts when no other project characteristics are included (column 1). However, the relationship is not statistically significant when controlling for the length of the road (column 2). When using the net present value of the _premium payments_ to government, either by itself or as a share of investment in the project, the coefficient turns again statistically significant—even when controlling for road length and fraction of the total investment financed by debt (columns 4–9). The findings indicate that as the sponsor and the project company’s financial burden increases, the likelihood of cancellation of the project increases. Alternatively, the premium-based contracts may create an unsound incentive structure that encourages overoptimistic bids from the private sponsor to win the PPP contract in order to access funds which can be channeled to other connected construction companies.

**Table 4 Logit regression estimates of likelihood of cancellation of India national highway public-private partnerships**

||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|
|---|---|---|---|---|---|---|---|---|---|
|Base case: Annuity||||||||||
|Capital grant|0.68|0.58|1.16|||||||
||(0.82)|(0.83)|(1.37)|||||||
|Premium|1.40<sup>*</sup><br>(0.812)|1.18<br>(0.84)|1.95<sup>+</sup><br>(1.20)|||||||
|NPV of payments to||||0.003<sup>**</sup>|0.002<sup>*</sup>|0.002<sup>*</sup>||||
|government||||(0.001)|(0.001)|(0.001)||||
|NPV of payments to|||||||0.74<sup>**</sup>|0.54<sup>+</sup>|0.92<sup>*</sup>|
|government as share<br>of project investment|||||||(0.36)|(0.37)|(0.50)|
|Road length (log)||1.20<sup>**</sup>|2.219<sup>***</sup>||0.978<sup>*</sup>|1.93<sup>**</sup>||1.14<sup>**</sup>|2.10<sup>**</sup>|
|||(0.49)|(0.823)||(0.53)|(0.90)||(0.50)|(0.83)|
|Debt financing share|||9.302<sup>*</sup>|||8.98<sup>*</sup>|||8.923<sup>*</sup>|
|of project investment|||(5.271)|||(4.62)|||(4.73)|
|Constant|-2.20<sup>***</sup>|-7.68<sup>***</sup>|-20.54<sup>***</sup>|-1.42<sup>***</sup>|-5.93<sup>**</sup>|-17.62<sup>***</sup>|-1.31<sup>***</sup>|-6.65<sup>***</sup>|-18.39<sup>***</sup>|
||(0.75)|(2.43)|(6.06)|(0.24)|(2.48)|(5.66)|(0.23)|(2.37)|(5.48)|
|Observations|123|123|109|123|123|109|123|123|109|

_Source:_ Authors’ calculations. _Note:_ Standard errors in parentheses.<sup>+</sup> _p_ < 0.15,<sup>*</sup> _p_ < 0.1,<sup>**</sup> _p_ < 0.05,<sup>***</sup> _p_ < 0.01.

The likelihood of cancellation increases with the share of the investment financed through debt. This finding is important from a fiscal risk perspective as roughly 80 percent of the debt to finance 29
Table 2. Determinants of civil war risk: 1946-2003 Natural resource endowments _aggravating_ the problem of weak states

|Context variable|Natural resources<br>Context variable<br>Interaction|Fearon and Laitin<br>(2003) civil war|Prio \ Uppsala civil war|
|---|---|---|---|
|Ethnic<br>fractionalization|Quantitative measures of resources endowments|||
||Fearon and Laitin oil export dummy (observations 6912)|0.465(0.512)|1.261***(0.337)|
|Model 16|Ethnic fractionalization|0.306(0.387)|1.196***(0.292)|
||**Interaction**|**0.411(0.810)**|**-1.145**(0.567)**|
||Humphreys oilproduction(observations 5139**) **|-4.668**(2.324)|1.175(1.509)|
|Model 17|Ethnic fractionalization|0.353(0.403)|0.886***(0.303)|
||**Interaction**|**21.041***(6.677)**|**2.333(6.874)**|
||Humphreys oil reserves(observations 5171)|-1.024(0.691)|-0.081(0.174)|
|Model 18|Ethnic fractionalization|0.374(0.395)|0.869***(0.310)|
||**Interaction**|**2.698**(1.344)**|**0.741(0.943)**|
||Humphreys diamondproduction(observations 5139**) **|-2.518(3.372)|-2.576(3.580)|
|Model 19|Ethnic fractionalization|0.488(0.417)|0.923***(0.297)|
||**Interaction**|**3.448(4.688)**|**2.962(4.648)**|
||Ross oil rentsper capita(observations 5634)|-0.00154(0.00173)|0.00015*(0.00008)|
|Model 20|Ethnic fractionalization|0.480(0.395)|1.095***(0.299)|
||**Interaction**|**0.00305 (0.00266)**|**-0.00016 (0.00041)**|
|Excludedpopulation|Quantitative measures of resources endowments|||
||Fearon and Laitin oil export dummy (observations 6589)|0.767**(0.352)|0.738***(0.272)|
|Model 21|Excluded Population|0.913**(0.423)|0.863***(0.300)|
||**Interaction**|**-0.322(0.993)**|**-0.439(0.772)**|
||Humphreys oilproduction(observations 4872**) **|0.731(0.684)|0.996(0.646)|
|Model 22|Excluded Population|0.722(0.448)|0.659**(0.317)|
||**Interaction**|**11.114*(6.232)**|**7.338(4.696)**|
||Humphreys oil reserves(observations 4897)|0.014(0.073)|-0.056(0.042)|
|Model 23|Excluded Population|0.842**(0.426)|0.612*(0.316)|
||**Interaction**|**-0.005(0.345)**|**0.721***(0.262)**|
||Humphreys diamondproduction(observations 4872**) **|-0.451(0.476)|-0.258(0.279)|
|Model 24|Excluded Population|0.797*(0.461)|0.822**(0.329)|
||**Interaction**|**0.701(1.608)**|**-0.858(1.514)**|
||Ross oil rentsper capita(observations 5322)|0.00013**(0.00005)|0.00007(0.00005)|
|Model 25|Excluded Population|1.060**(0.418)|0.862***(0.306)|
||**Interaction **|**-0.00027(0.00042)**|**0.00040* (0.00023)**|

Note: Results are from logit models where the dependent variable is 1 for years with war onset and zero otherwise. Table shows coefficients and standard errors in parentheses. Stars reflect levels of statistical significance: *** for 1%; ** for 5% and * for 10%.

32
some value of _Gt_ that corresponds to heavy spending, when the scope for using cheap interventions has been exhausted. The basis of the estimation and the steps involved are set out in detail in Bell, Devarajan and Gersbach (2006). The said four conditions yield the values of the parameters _at_ , _bt_ , _ct_ and _dt_ for the years 2000, 2010 and 2020 in Table 8. The associated functions _qt_ ( _Gt_ ; _D_ = 1) are plotted in Figure 1.

Table 8: The <u>parameter</u> values of the functions _<u>qt</u>_ <u>(</u> _Gt_ <u>;</u> _D_ = 1)

||2000|2010|2020|
|---|---|---|---|
|_at_|0.9073|0.9863|1.3852|
|_bt_|0.0169|0.0187|0.0266|
|_ct_|0.3124|0.3395|0.4768|
|_dt_|1.2152|1.1128|0.8066|

Source: Bell et al. (2004) It should be remarked that the approach employed in deriving these results rests on a rather sharp distinction between preventive measures and treatment. Some would argue, however, that such a distinction is not always easy to draw, especially in highprevalence environments. Granted as much, the general import for our approach is clear: the absolute slopes of the functions in Figure 1 at _Gt_ = 0 are too large; for in these settings, there are many infected individuals other than prostitutes and truckdrivers, and the cost-efficient bundle of interventions at low levels of spending will also involve HAART, at least in some measure.

# **6.2 School-attendance subsidies**

The two reference cases involve the following levels of the direct costs of education: _σt_<sup>1= 185and</sup><sup>_σ_</sup> _t_<sup>2= 800</sup><sup>_∀t ≥_90.Theformerprovestobenodeterrenttofullprimary</sup> education for all from 1990 onwards, even in the face of the epidemic. The size of the latter, though clearly smaller than the opportunity costs of time spent in secondary 38
# **Table A4: List of selected accidents in power plants**

Accidents with more than 5 fatalities were reported; also presented are accidents with large media coverage, such as Fukushima.

|**Date & Accident**|**Description**|**Impacts**|
|---|---|---|
|1963:<br>Vajont Dam, Italy<sup>a</sup>|On October 9, 1963, 260 million cubic metres of rock<br>broke off from the top of Monte Toc and fell into the<br>reservoir of the Vajont Dam on the border between<br>Veneto and Friuli Venezia Giulia. It caused overflow of<br>watercausingfloodinginthe downstreamvalley|Destroyed several villages in the valley and killed almost<br>2,000 people|
|1975:<br>Shimantan/Banqiao<br>Dam Failure,<br>Henan Province,<br>China<sup>b</sup>|On August 8, 1975, the Shimantan and Banqiao dams<br>built on the Ru River in Central China collapsed due to<br>heavy rainstorm inundating thousands of square<br>kilometers|It caused 26,000 deaths directly and another 145,000 deaths<br>through subsequent epidemics and famine.|
|1979: Machchu-2<br>dam, Gujrat, India<sup>c</sup>|On August 11, 1979, unprecedented flooding caused by<br>torrential rain triggered the collapse of the Machchu-2<br>dam on the Machhu river and inundated Morvi town of<br>Rajkot district ofGujrat|1,500 deaths|
|1986: Chernobyl<br>Nuclear Power<br>Plant, Ukraine<sup>d</sup>|On 26 April 1986, Chernobyl 4 nuclear reactor exploded<br>dissipating the large amounts of radioactive materials|Of 600 workers present on the site during the accident, 134<br>received high doses and suffered from radiation sickness. Of<br>these, 28 died in the first three months and another 19 died<br>in 1987-2004 of various causes. 530,000 people who were<br>involved in recovery operations between 1986 and 1990<br>have got minor radiations and bear a potential risk of late<br>consequences suchas cancerand otherdiseases.|
|2009:<br>Sayano–<br>Shushenskaya<br>Dam,Russia<sup>e</sup>|Water flooded a turbine room at the Sayano–<br>Shushenskaya Dam located more than 3,000 km east of<br>Moscow|72 fatalities|
|2011: Fukushima<br>Daiichi nuclear<br>disaster, Japan<sup>f</sup>|On 11 March 2011, a tsunami triggered by massive<br>earthquake disabled the cooling systems of Fukushima<br>Daiichi nuclear reactors Unit 1, 2, 3 and 4 thereby<br>resulting in meltdowns and explosions and releasing<br>radioactivematerialsforthree days|No immediate death linked to radiation due to the accident,<br>but some studies (Normile, 2012; Hoeve and Jacobson,<br>2012) estimate, based on the linear no-threshold theory of<br>radiation safety, 130–640 deaths in the long-run due to the<br>radiation|

a Expecting Disaster: The 1963 Landslide of the Vajont Dam. Arcadia, 2011, No. 8. http://www.environmentandsociety.org/arcadia/expecting-disaster-1963landslide-vajont-dam 39
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would no longer be used to influence the pattern of consumption. Thus, after a suitable rearrangement, the trade tariff system would be more or less focused on a single objective -- that of providing protection as deemed necessary by trade and industrial policy.

23. Similarly, the surtax could be made the primary revenue raiser within the system of indirect taxes by removing the protective features that were built into it. Thus, even though in general the interaction of instruments is crucial in evaluating the effects of the tax system on objectives, in the case of Malawi, the number of instruments in use were few and their effects were potentially separable. Hence, existing instruments could be redesigned to address specific objectives such as generating revenue, providing equity and providing protection. Whatever other effects they might have as by-products would be minor compared to their principal roles.

24. It is interesting to see how these reform principles were applied in the framing of specific proposals to improve the performance of the major tax instruments in Malawi. At no point in the exercise did the tax study team take the position that there was only one correct answer to a problem. This made it possible to explore ideas that initially were not considered feasible. It is important to emphasize that the focus was on the reform of the existing tax system rather than on designing a new one from scratch. In other words, every attempt was to be made to build on existing instruments and to take advantage of the familiarity that tax administrators already had with them. The instruments that were retained in the reformed system were those with many, but not all, of the desirable characteristics of the best tax for a given objective, provided there was evidence that each of these instruments had generated an increasing amount
- 6 J.C. Duque, N. Lozano-Gracia, J.E. Patino, P. Restrepo, W. Velazquez / LCR Flagship Report on Cities and Productivity

There are two different nighttime light products from DMSP-OLS that can be used to delineate urban areas: the stable or ordinary product (NTL), and the radiance-calibrated (RC) product. For this work we use the latter, given that such data include a correction for the saturation issue likely to be an important issue for most large cities in the region. Further, previous work has suggested that the RC information provides a better proxy for socioeconomic variables than the stable products (Hsu et al., 2015; Ma et al., 2014). Radiance-calibrated annual composites for 1996, 2000 and 2010 obtained from NOAA National Centers for Environmental Information<sup>1</sup> were used to delineate urban extents. These composites have a spatial resolution of 30 arc-seconds (about 1 km at the Equator).

Another known issue of the DMSP-OLS products is the “overglow” effect: dim lighting detected from light in surrounding areas of cities because of the scattering of lights in the atmosphere (Wu et al., 2014). A novel deblurring process was applied to address the issue of over glow in the radiancecalibrated products. This process involves the use of two sequential filters, a standard deconvolution and the frequency of illumination maxima, to withdraw the light from the surroundings back and restacking it vertically on its source pixels at city centers (Abrahams et al., 2016).

# _3.2.1 Calibration and interannual correction of deblurred RC data_

Deblurred DSMP-OLS RC annual composites for the years 1996, 2000 and 2010 were previously inter-calibrated and corrected for a multi-temporal analysis of urban form and city productivity in Latin America (Duque et al., 2017a). In that work, the three nighttime images were used to delineate urban extents in each year for most of the Latin American and Caribbean cities that had more than 50,000 people in 2010 using a threshold approach. Figure 1 shows the workflow of urban extent delineation in Duque et al. (2017a). Figure 2 shows three examples of the obtained urban extents from the 2010 NTL image composite. Figure 3 shows the location of the obtained urban extents in Latin America and the Caribbean region.

> 1 Image and Data processing by NOAA's National Geophysical Data Center. DMSP data collected by the US Air Force Weather Agency. https://ngdc.noaa.gov/eog/dmsp/download V4composites.html.
significant. Thus we cannot reject the null that the extra casual work of women came from unpaid domestic work or leisure.

In summary, we find that household electrification increased both male and female labor supply. For men, regular wage work increased, much of it coming from casual wage work, and some from other activities, including leisure. For women, there was an increase in casual work.

Using the wage rates reported in the village-survey we can also test whether there is any sign that village electrification affected wage rates. We used the data on harvest wage rates to compare the changes in mean wage rates for those villages that became electrified between the two survey rounds with those for the villages that already had electricity in 1982. The DD estimate gave a small gain of Rp. 2.43 per day (in 1999 prices) for women and a small loss for men of Rp -1.39; however, neither is significantly different from zero (standard errors of Rp.4.27 and 4.11). We also repeated this analysis using the number of years the village was electrified as the treatment effect; again, there was no significant impact on wages. We find no evidence in these data that village electrification increased real wage rates. This does not, of course, rule out impacts on the demand for labor, given that we also find positive effects on labor supply.

When the estimated impacts on days of labor supply are valued at the sample mean wage rates the implied impact on total labor earnings in 1999 is Rp 2277 per household.<sup>32</sup> Recall that the implied mean impact on household consumption is Rp 1450. This gap has two possible explanations. The first is the existence of foregone income from market labor supply due to displaced activities within the household. In terms of the model in Section 3, this would entail that the extra market labor supply comes from domestic work in producing marketable commodities. The second explanation is not something we modeled in Section 3, namely savings; by this interpretation, about one third of the income gain was saved or invested directly.

We cannot say which of these explanations is the right one. However, some support for the first explanation can be found in past estimates of the household income foregone in taking up new employment opportunities provided by public works projects in India. Datt and Ravallion (1994) estimate a foregone income of about one quarter of the gross wage rate in Maharashtra. Dutta et al (2012) estimate a mean forgone income of about one third of the gross wage rate in

> 32 The Addendum provides details on the wage rates used for imputation. A potentially contentious assumption we make is in using the casual wage rate for agricultural work in valuing the income effect of the impacts on days of self-employment in agriculture. As a sensitivity test we also tried using 50% of the casual wage rates; the impact of total earnings rose to Rp 2363.

28
distribution, such as the seemingly strong assumption of welfare equality within the household (Haddad and Kanbur, 1990). This data constraint points to the desirability of supplementary indicators of individual attainments. Data on child health and mortality has understandably been given high priority in looking for such indicators.

So why aggregate in the form of a MIP? Alkire and Santos (2010b, p.7) argue that their index “... goes beyond previous international measures of poverty to identify the poorest people and aspects in which they are deprived. Such information is vital to allocate resources where they are likely to be most effective.” But is it the MIP or its components that matter for this purpose? Following Alkire and Foster (2007), the Alkire-Santos MIP has a neat decomposability; we can reverse the aggregation. This is useful, for only then will we have any idea how to go about addressing the poverty problem in that specific setting. But the question remains: why do we need the aggregation in the first place?

Consider the following stylized example of a policy problem. Suppose that there are two dimensions of welfare, “income” and “access to services.” Assume that an “income-poor” but “services-rich” household attaches a high value to extra income but a low value to extra services, while the opposite holds for an “income-rich” but “services-poor” household.<sup>8</sup> There are two policy instruments, a transfer payment and service provision. The economy is divided into geographic areas (which could be countries) and a given area gets either the service or the transfer. We then calculate a composite index like the Alkire-Santos MIP based on survey data on incomes and access to services. There is bound to be a positive correlation between average income and service provision, but (nonetheless) some places have high income poverty but adequate services, while others have low income poverty but poor services. The policy maker then decides whether each area gets the transfer or the service. Plainly, the policy maker should not be using the aggregate MIP for this purpose, for then some income-poor but service-rich households will get even better services, while some income-rich but service poor households will get the transfer. The total impact on (multidimensional) poverty would be lower if one based the allocation on the MIP rather than the separate poverty measures—one for incomes and one

> 8 Sufficient conditions are that there is declining marginal utility to both income and services and that the marginal utility of income (services) is non-decreasing in services (income).

7
# **_4.3 Descriptive Statistics_**

For our analysis, we pool together all individuals sampled in all the 2016–2020 Sakernas rounds. Since the first COVID-19 case in Indonesia was recorded in early March 2020, we consider the 2016 February until the 2020 February Sakernas rounds to be the pre-pandemic baseline period, while the August 2020 round is taken as the pandemic (treatment) period. We limit our data to individuals aged 15 to 64 years old to represent the main working population. This gives us a total of 3,254,366 observations for the analysis.

We examine the impact of the COVID-19 pandemic on several key labor market outcomes, including: (i) employment status; (ii) unemployment; (iii) working hours; and (iv) wages. Consistent with Statistics Indonesia’s definition, employment is defined as either working for at least one hour in the past week, or usually working but have not been working in the past week (seasonal workers, on paid leave, etc.). Sakernas collects information on individuals’ working hours and wages at both their main occupation and their side occupation. Statistics Indonesia defines the main occupation as the job/occupation that takes up a higher proportion of the respondent’s time.<sup>17</sup> Throughout our analysis, we only report the impact of the pandemic on working hours and wages derived from respondents’ main occupations.

Table 1 presents the overall, as well as sex-disaggregated, unweighted demographic characteristics and employment outcomes of respondents in our sample. The sample contains a relatively equal gender proportion (roughly a 50:50 split). Overall, Table 1 suggests that the male and female respondents in the sample have a similar distribution in terms of age, urban/rural proportion, household size, education level, and the number of children below the age of five. On

> 17 If the amount of time being used is equal across jobs, then the main job is defined as the job that gives the highest contribution to the respondent’s total income.

15
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- Garicano, L. and E. Rossi-Hansberg. 2006. “Organization and inequality in a knowledge economy”. _Quarterly Journal of Economics_ , 121, 1383-1435.

- Garmaise, M. and T. Moskowitz, 2004, “Confronting Information Asymmetries: Evidence from Real Estate Markets,” _Review of Financial Studies_ , 16(4), 1007-1040.

- Giroud, X. 2013, “Proximity and investment: evidence from plant-level data”. _Quarterly Journal of Economics_ , 128(2), 861-915.

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- Li, Lixing and Guangrong Ma 2015. “Government Size and Tax Evasion: Evidence from China.” _Pacific Economic Review_ , 20(2), 346-364.

- Li, Yunsen and Cheryl Long 2013. “Historical Events and Regional Development: Evidence from China’s Third Front Construction.” Manuscript, Xiamen University.

- Markevich, A. and E. Zhuravskaya, 2011, “M-form hierarchy with poorlydiversified divisions: A case of Khrushchev’s reform in Soviet Russia”, _Journal of Public Economics_ 95, 1550-1560.

26