rafmacalaba/datause-encoder-data huggingface dataset:
6 opportunity to shift away from populist policies and improve political incentives for broad public goods.

# **Budget composition and household demands**

Table 1 shows the summary statistics of the share of capital and recurrent social spending in the budgets of 14 major states over the period 1980-2005. On average, states allocate 13 percent of their total spending, excluding interest payments and debt servicing, to capital works, compared to 39 percent on recurrent spending on social programs including education health and welfare. The single largest category of spending is education, accounting for 21 percent of total spending. As a proportion of developmental spending on public services alone, excluding other kinds of administrative spending, capital spending accounts for 16 percent, recurrent spending on social programs for 49 percent, and within it, education accounts for 27 percent. These averages mask variation across states and over time, which this paper will explore in the next section.

This distance between the share of capital and social spending in state budgets is at odds with household survey evidence on reported citizen prioritization of village problems. In a large survey of over 5000 rural households in the southern Indian states of Andhra Pradesh, Karnataka, and Tamil Nadu undertaken by Besley et al (2004) between September-November 2002, the data show that 89 percent of respondents list water or infrastructure as among their top 3 priorities, while less than 6 percent list education.<sup>5</sup> These three states are at the sample average for the shares of spending devoted to capital versus social programs and education. Ban and Rao (2009) report that for a sub-sample of these households, for which they gathered transcripts of village assembly meetings with public officials, 38 percent rank water as their top priority, another 38 percent rank roads, 7 percent each rank electricity and housing, 5 percent rank health, 2 percent

> 5 I am grateful to Biju Rao for providing access to this data.
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**Figure 3. Shares of Guaranteed Savings and Shared Savings Contracts**

<!-- Start of picture text --> 100%<br>90% Shared savings<br>Guaranteed savings<br>80%<br>70% 0.55 0.60<br>0.69 0.67<br>60% 0.75<br>0.91 0.88<br>50% 1.00<br>40%<br>30%<br>20% 0.45 0.40<br>0.31 0.33<br>10% 0.25<br>0.09 0.13<br>0% 0.00<br>2004 2005 2006 2007 2008 2009 2010 2011<br>Source : Author’s survey data.<br><!-- End of picture text -->

# **V. Main estimation results**

The 3SLS estimation is performed; the results are shown in Table 3. The results indicate that, by-and-large, multidimensional auctions are functioning well in the ESCO market. The competition effect is found to be significant to increase energy savings. The elasticity is estimated at 0.569. At the same time, however, intense competition may increase payment for the contractor as well. The elasticity is 0.376. Therefore, given a particular increase in competition, expected savings are likely to be larger than expected payment increases.<sup>21</sup> This is consistent with the existing auction literature (Kessel, 1971; Gupta, 2002). Market competition is still an important driver of procurement efficiency even in the multidimensional context.

The weight variables have largely consistent coefficients with prior expectation. The weights are interdependent on each other, but the own score impacts may be of particular interest. The coefficient of _wPAY_ is found to be significantly negative at -0.24. This means that if more weight is put on the low payment criterion, the proposed service fees would decline, i.e., be more competitive. Similarly, the coefficient of _wDUR_ is significantly negative in the duration equation.

> 21 Recall that energy savings and annual payment are almost the same in many ESCO projects, this means that intensified competition can bring net benefits to asset owners.
**6)** . From 1999 to 2004 poverty in Moldova declined by 31 percentage points, from 78 to 47 percent, using the World Bank Europe and Central Asia regional poverty line of $2.5/day. Although the rest of the note is based on regional thresholds for poverty and the middle class (further described below), it is key to note that the strong declining poverty trend is also observed in the national poverty numbers, which point to a reduction from 68 percent in 2000 to 27 percent in 2004.

**Progress in poverty reduction continued in the second half of the 2000s, but at a slower pace** . From 2006 to 2011 poverty (based on the regional $2.5/day poverty line) declined from 23 percent to 18 percent. The sharpest decline occurred from 2006 to 2007; between 2007 and 2009, which covers the crisis years, poverty rates changed by half a percentage point only, actually increasing slightly in 2008.

# **Figure 6. Poverty declined significantly in Moldova, particularly in the early 2000s**

Regional extreme poverty line ($2.5/day) and national general poverty line, 2000-2004 and 2006-2011 <!-- Start of picture text --> 90.0<br>80.0<br>70.0<br>60.0<br>50.0<br>40.0<br>30.0<br>20.0<br>10.0<br>0.0<br>2000 2001 2002 2003 2004 2006 2007 2008 2009 2010 2011<br>$2.5/day National general poverty line<br><!-- End of picture text --> Source: Calculations based on HBS data.

Notes: The poverty series breaks between 2004 and 2006 given methodological changes to the survey, which included changes in the sampling design of the Moldova HBS and a more comprehensive consumption diary. The national poverty line was also adjusted to reflect these changes, in contrast to the regional constant $2.5/day line. Although the two periods are, again, not comparable, these changes could explain the reversed gap in the poverty rates captured by the national and regional headcount ratios.

**Poverty is mostly a rural phenomenon in Moldova (Figure 7)** . Urban poverty rates - at the extreme poverty line for ECA of $2.5/day - were 3.5 percent in 2011, compared to 14.1 percent for rural areas. The gap between urban and rural poverty widened in the late 2000s, particularly in 2008 and 2009 at 16.2 percentage points, as urban poverty continued its decline and rural poverty increased or remained stagnant. The gap started to narrow in 2010 and 2011 as the economic recovery process took place.

7
0 _._ 5<sup>_◦_</sup> _×_ 0 _._ 5<sup>_◦_</sup> . The lack of temporal variation due to the cross-sectional nature of the dataset coupled with relative low spatial resolution (0 _._ 5<sup>_◦_</sup> _×_ 0 _._ 5<sup>_◦_</sup> ) poses a challenge to the use of this dataset as an instrument to causally estimate the effects of outages.

Recent scientific literature however shows that observed lightning intensity is proportional to the product of convective available potential energy<sup>19</sup> (CAPE) and precipitation rate (i.e., the amount of precipitation that would cover a given area ( _m_<sup>2</sup> ) per second) (Romps et al., 2014; Dewan et al., 2018). In an attempt to find granular data on lightning intensity and aid future projections of lightning intensity associated with climate change, Romps et al. (2014) proposed a proxy for measuring lightning intensity: where _F_ is the lightning flash rate per area ( _m_<sup>_−_2</sup> _s_<sup>_−_1</sup> ), while CAPE (J kg<sup>_−_1</sup> ) and _P_ (kg _m_<sup>_−_2</sup> _s_<sup>_−_1</sup> ) represent the convective available potential energy and precipitation rate respectively. _η/E_ , a constant of proportionality, is the ratio of the conversion efficiency factor ( _η_ ) and the energy discharge (in joules) per flash ( _E_ ).<sup>20</sup> Romps et al. (2014) validate this methodology by showing that _CAPE × P_ explains about 77% of the variance in actual lightning flash rate in continental United States. Dewan et al. (2018) also using data from Bangladesh show a significant correlation between actual lightning strikes and _CAPE ×P_ with the latter explaining about 89% of the variance in the former on a monthly time scale.

Therefore, following Romps et al. (2014) and Dewan et al. (2018), I use _CAPE × P_ as a proxy for lightning intensity. Specifically, using time series data on CAPE and precipitation rate from the ERA5 Global Reanalysis Database by the Copernicus Climate Change Service<sup>21</sup> , I compute _CAPE × P_ at a 0 _._ 25<sup>_◦_</sup> _×_ 0 _._ 25<sup>_◦_</sup> grid-cell level and use it as an instrument

> 19This measures the amount of energy a parcel of air would gain if raised to a specific height in the atmosphere. See: https://study.com/academy/lesson/ convective-available-potential-energy-cape-definition-use-in-forecasting.html 20See Romps et al. (2014) for details.

> 21https://cds.climate.copernicus.eu/cdsapp#!/home

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consider the higher estimate, the country will have large room, about 11,000 MW hydropower resources, to export.

# **3.2 Need for Hydropower for System Balance**

Since electricity is a non-storable commodity and its demand varies across hours, days and months, required installed capacity should be higher than the maximum demand or peak load plus reserve margins. Due to these hourly changing load patterns, the capability of quickly increasing or decreasing (or ramping up or down) of power generation along with the load is an essential characteristic of a power supply system. However, many technologies (e.g., steam turbine technologies), cannot be switched off and on so frequently for technical reasons. Hydropower, particularly, storage type, is very useful in such a situation. Therefore, power systems with predominantly steam turbine technologies, such the Indian power system, need enough storage hydropower capacity for load balancing as well as meeting peak load. In India, with 60% of the total installed capacity based on coal, a higher share of hydropower is needed to provide flexibility to the system.<sup>7</sup> However, at present, hydropower supplies only 13% of the total capacity installed. In the 1960s, half of the total electricity in India was supplied through hydropower. Considering the important role of hydropower to provide more flexibility to the power system, the Government of India (GOI) periodically introduces promotional policies for hydropower. These policies include differential pricing, introduced in the 2006 National Tariff Policy for peak and off-peak power, which provides extra incentives to storage hydro that is mainly used for meeting peak load, and the 2008 mega project policy that provides additional incentives, such as a 10-year tax holiday,

> 7 Government of India, Ministry of Power Website. https://powermin.nic.in/en/content/power-sector-glance-all-india

18
14 training (15 percent received secondary vocational education followed by institutional training; and 18 percent received secondary vocational education followed by formal on-thejob training). In principle, workers could have attended all three types of training program, though this possibility is not illustrated in the chart.

# Occupational Training Maps: Some Empirical Results:

In Charts 2 and 3, we present occupational training maps for women bookkeepers and male fitters, respectively.9 Each map contains three sections, relating to (i) non SENA-trained workers; (ii) short SENA-trained workers; and (iii) long SENAtrained workers. Given the sampling procedure adopted for the sample of non-SENA trained workers (see Section E), we may not compare the three major training alternatives in terms of absolute numbers. Rather, we consider workers in each of the three training categories separately, each in terms of the percentage distribution of workers across the various identified training paths.

In each map, the shaded section on the left relating to non-SENA training depicts three major types of training program: (a) occupationally-related vocational training; (b) institutional training; and (c) formal on-the-job training). This non-SENA section parallels the illustrative map in Chart 1. The middle section (shaded) and rightmost section (unshaded), representing short and long SENA-trained categories of workers, respectively, report on five types of training: (a) occupationally-related vocational education; (b) institutional training that preceded the SENA course, (c) the SENA training itself, and (d) institutional training acquired after the SENA program; and (e) formal on-the-job training.

Female Bookkeepers. Chart 2 summarizes the training histories of the 752 women bookkeepers for which sufficiently detailed information was available. We will first 9 Because of limitations of space, we present only two maps in the body of the paper. The other six occupational training maps appear, without discussion, in the Annex.
interest in this study.<sup>16</sup> These are our key independent variable, programmatic parties, logged GDP per capita and land area, as well as other institutional factors (competitiveness of legislative and executive elections, ACLP democracy coding, and whether the system is presidential or parliamentary). Scores for various governance indicators from _International Country Risk Guide_ (bureaucratic quality, ethnic tension, corruption, and rule of law, all measured on the ICRG six-point scale) are also identical between the samples. In all cases, the means are within the overlapping areas of the respective confidence intervals, indicating that they are statistically indistinguishable between the two sets of countries.

A further concern is that it is not party organization that drives the results we observe, but the different political processes that programmatic and non-programmatic parties may pursue prior to asking for World Bank loans. For example, it could be the case that countries that have programmatic parties agree to World Bank loans only after they have obtained broad support for them; those without such parties agree to the loans after a few top officials assent to them. Since the former process is more likely to yield successful loans, such a difference between programmatic and non-programmatic countries would yield a bias in favor of finding that programmatic parties lead to public sector reform success.

In fact, though, all the countries in our sample share a similar legislative starting point. Legal authority to sign loan agreements with the World Bank rests with the executive branch and, within the executive, with the responsible sectoral minister and the finance minister. In the case of a health loan, for example, the minister of health and the minister of

> 16 For comparability, the subsample means are calculated for the range of country-years starting with the first year in which a country in the region engaged in reforms.  Results are robust to using other ways of determining the time frame. For example, results are identical for using the first year that any country engaged in public sector reforms or for using the exact start date of reforms for the countries engaging in public sector reform.

41
and compensation for work accidents or occupational diseases. However, in many countries and also in Bhutan the enforcement of labor laws is relatively lax and some employers are able to avoid compliance (cf. Fandl, Kugler, Vodopivec and Weber, 2011, for details on compliance with Bhutan’s Labor and Employment Act). Against this backdrop, the BLFS 2014 collects data for regular paid employees, casual paid employees and contract / piece paid workers in state-owned or private enterprises, and data on their _de facto_ coverage by fringe benefits.

As monetary values of fringe benefits are hard to quantify, they will not be considered in the subsequent analysis of the public/private wage differential. However, Table 2 still lists the prevalence of these benefits separately for public and private sector workers. The table shows that coverage is incomplete both in the public and the private sector. At the same time, public sector workers are more likely to be eligible for each of the eight types of benefits and all differentials are both statistically significant and economically meaningful. In fact, for each of the eight benefits, coverage among public sector workers easily exceeds 90 percent. The benefit with the lowest prevalence among public sector workers is paid annual leave. 94.3 of public sector workers are covered by this benefit. On the other end of the spectrum, 96.5 percent of public sector workers receive compensation for work accidents or occupational diseases. In contrast, only between around 25 percent and 65 percent of private sector workers are covered by each particular benefit. Only 27.0 percent of private sector employees are paid for overtime. Paid sick leave is the most common benefit in the private sector. 64.5 percent of private sector workers receive this benefit. It seems safe to assume that differences between public and private sector would be even more pronounced if data on benefit coverage were also collected for workers other than regular paid employees, casual paid employees and contract / piece paid workers in stateowned or private enterprises.

# **4.2. Selection Equation, Wage Regressions and Baseline Decomposition**

Turning to the initial step of the three-step procedure outlined above, Table 3 reports outputs from an empirical estimation of the sector selection equation (2). These outputs provide insights into the determinants of self-selection into labor force participation and into public and private sector. Not directly reported in Table 3 but available upon request are the coefficients of the multinomial logit model used to estimate equation (2). What is displayed in Table 3 are marginal effects on the likelihood of self-selection into the public sector, the private sector or inactivity. This is because marginal effects

- 17 -
The combination of the importance of foreign ownership and the positive effect of inward FDI on productivity growth suggests a prominent role for FDI in the emerging European economies. Indeed, Eastern Europe has received large volumes of FDI since the 2004 EU expansion. Theory and experience indicate that openness to foreign investment helped these economies generate employment, upgrade technology, and improve managerial knowledge to accelerate productivity growth. In this regard, business regulations play an important role in attracting FDI, even after controlling for market size and factor endowments (Wagle, 2010; Demekas et al., 2007).

# **5.2 EU15 Results**

Among the more developed nations of the EU15, firm-level characteristics predominate over country-level variables. The exclusion of country dummies from the regression on manufacturing firms reduces the explanatory power of the model by 19 percent; for the service firms, the model loses 11 percent. Running the regression without firm characteristics reduces its explanatory power by 25 percent in both manufacturing and service (see Appendix VII).

Ownership, size, and age are important correlates of productivity growth in the EU15 region.<sup>40</sup> Global headquarters firms grow more quickly than purely domestic firms: 2.3 percentage points more quickly in manufacturing industries and 2.9 percentage points in service industries. Foreign-owned firms also perform better than their purely domestic counterparts: 1.8 percent better in manufacturing and 2.4 percent in services. Unlike in the EU12, size does matter in the EU15: larger firms realize greater productivity growth. Firms that have between 50 and 500 employees grow more than firms with 10 to 49 employees: 1.5 percent more in manufacturing, and 1.2 percent in services. Older firms in service sectors grow more quickly than the younger firms; in manufacturing, age is not statistically significant.

Country-level variables remain a factor: locating in one country or another can net productivity gains of up to 7 percent for manufacturing firms and 5 percent for services firms. However, country performances differ widely across sectors: Norway realized the greatest productivity growth in services but also the least growth in manufacturing.<sup>41</sup> Table 3 presents the EU15 estimation results for Eq. (1). In manufacturing, productivity gains are correlated with increases in workforce education and stock of outward FDI; in services, these are not significant. Improving business regulations—especially trade, tax, and labor regulations— produces gains in labor productivity growth, both in manufacturing and in services.

> 40 For a second method of evaluating the relative impact of firm-level variables, see Appendix XIII.

> 41 For a discussion of results from Sample 2, see Appendix VIII.2.

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<!-- Start of picture text --> 300 rt<br>I|<br>||<br>||<br>||<br>||<br>I|<br>I |<br>200 I |<br>>||<br>&1 |<br>vo | |<br>—||<br>D<br>LL Median: 0.90|1 || Mean: 1.16<br>||<br>100 I!<br>bit<br>| |<br>0<br>0 1 2 3 4 5<br>PME<br><!-- End of picture text -->