rafmacalaba/datause-encoder-data huggingface dataset:
of power supply in terms of shortages or unreliability. World Bank [2004] shows that for the average business in India, power outages occur almost every other day. In contrast, outages occur once every two weeks in China and once a week in Brazil. There is also significant variation in this indicator within India itself. On average, cities such as Surat, Ahmedabad, Nagpur, Nashik , Mumbai and Calcutta /Howrah experience less than 5 power outages each month. On the other end, cities in the North such as Jalandhar, Ludhiana (Punjab), Gurgaon and Faridabad (Haryana) and Ghaziabad and Noida (U.P) experience more than 20 outages a month.

As a result of the high frequency of power outages, the average manufacturer in India loses 8.4 percent a year in sales compared with less than 2 percent for the average manufacturer in China or Brazil. Outages can lead to loss of sales by forcing downtime (or idle capacity) on managers. They can also cause waste of materials. This happens when power disruptions cause damage to materials in process—materials that cannot be used when power and thus production resumes. Power disruptions also damage equipment, adding maintenance and repair costs that are directly attributable to the outages.

_Transport_ : In addition to examining bottlenecks in power supply, we calculate travel times from each city to the closest port to examine if proximity to transhipment hubs and output markets influence the location decisions of manufacturing firms. In addition to the direct effects on facilitating access to markets (both national and international), transshipment nodes (such as ports) have historically been important in the evolution of urban centers. In fact, through path dependency such urban centers continue to be prosperous (and efficient) even after the initial advantage of the hub access becomes irrelevant (Fujita and Mori, 1996).

10
23 outlays that takes place as a result of the price rise from P0 to P1 (A), plus the loss in consumer surplus as a result of the price increase and corresponding demand response in consumption (B). All losses in consumer surplus in this paper are calculated as a percentage of total expenditures (Areas (A + B)/Expenditures)).

_Calculating welfare losses._ We estimated the welfare loss associated with various different price changes (Table 8). For illustrative purposes, the effects on different income groups of an 80% increase in energy prices is outlined -- an increase not deemed unrealistic in terms of what is needed for energy prices, overall, to reach "efficient" levels, as outlined in section II (Table 2).

Additional simulations are presented in Appendix tables 3 - 7 which show the welfare loss effects of price increases ranging from 20% to 120%. As the additional simulations show, the welfare loss corresponding to a _zero elasticity is linear with respect to the price change,_ so the welfare loss associated with a 80% price increase would be twice that associated with a 40% price rise. For other elasticities (less than zero) the welfare loss will increase less than proportionately in prices because of the effect of the second term in the equation which is squared in the price change. That is, when demand is elastic, a price increase of 80% will be associated with _less than twice_ the welfare loss of a 40 % price rise.

# _Who hurts most?_

As would be expected from looking at the share of energy consumption by various income groups, _the welfare loss of higher energy prices is greater for the non-poor than for the poor._<sup>_17_</sup> Assuming a zero elasticity of demand, the poor's welfare declines by 5.9%, while that of the richest quintile declines by 8.2% (Table 8). For all consumers taken together, the welfare loss associated with an 80% increase in prices is between 4.6% and 7.6 % of their total budget, depending on the price elasticity of demand that is assumed --the more elastic is demand, the less the welfare loss.

> 17/ We define "poor", as the lowest quintile and non-poor as all those above that level. Preliminary results from the _poverty assessment_ for Hungary indicate that the poverty headcount for Hungary is less than 20%.
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35
rises above the floor. The final benefit level of a household is given by the maximum benefit level minus 30% of household “net countable income,” which is the household’s gross income minus expenses considered indispensable. Examples of indispensable expenses include the standard utility allowance (SUA) determined by each state to cover utility expenses, rental costs, and a homeless deduction for living expenses conditional on being homeless (e.g., transportation). Some components of the formula, such as the federal maximum benefit level and the state-determined SUA, are determined by government policy, whereas others, such as household gross income and rental costs, are determined by household characteristics.

We first introduce the instrument using a few examples before laying out the details. To construct the instrument, we use the SNAP benefits formula to simulate what a fixed sample of participants would have received throughout the sample period, by allowing only policy parameters to vary over time while holding household economic variables such as household gross income and rental costs fixed, using a sample of about 50,000 SNAP participating households surveyed in the pre-period of 2006 from the SNAP QC data. We then sum over these simulated benefits for each recipient to construct our IV: simulated (used interchangeably with “synthetic”) benefits per population at the state-level.

Variation in the instrument is almost entirely driven by two policy changes. First, federal maximum benefits were raised and state-specific adjustments to the SUA were made during the Farm Bill. We illustrate these changes in Figure 5 by again focusing on the comparison between New Hampshire and Wyoming, two states that had the largest difference in percentage changes of the instrument during the Farm Bill. In Figure 5a, we show that New Hampshire raised the heating and cooling SUA (HCSUA) by about 29% in October 2008 while Wyoming lowered it by about 23%. These changes led to a much larger percentage increase in synthetic benefits per recipient for New Hampshire as shown in Figure 5b. The two states had almost identical synthetic benefits per recipient until they diverged during the Farm Bill.

Second, the ARRA also raised federal maximum benefits, as illustrated in Figure 6 which juxtaposes Missouri and New York, two states that had the largest difference in percentage changes of the instrument during the ARRA. In Figure 6a, we show the two states had only small changes in the HCSUA during the Farm Bill and the ARRA, with New York raising the HCSUA in February 2009 instead. However, the ARRA led to a 20.26% increase in the instrument for Missouri but only a 15.31% increase for New York. The reason is that Missouri has a lower synthetic benefits per recipient, because it has continually had a lower HCSUA relative to New York. Because the ARRA raised federal maximum benefits by the same absolute magnitude for all states, Missouri had a higher percentage change in the instrument as it started from a lower base level, determined largely by the lower SUA.

8
16 these measures to estimate the elasticity of value-added exports to changes in value-added REER and compare these estimates with our estimates of the standard REER to gross total exports. Specifically, the first row in Table 4 presents the same results as in Table 1 but for a shorter time series and less countries due to data availability. In the second line of Table 4, we regress value-added exports on the standard REER. In the last two rows, instead of using the standard REER and gross exports, we regress value-added exports on two measures of the REER corrected for GVC participation taken from Bayoumi _et al._ (2013): the REER in Tasks Value Added (a measure that is close to the value-added REER by Bems and Johnson, 2012) and the REER in Goods Value Added.<sup>15</sup> We establish two main results that corroborate the importance of GVC integration as a determinant of the decline in the responsiveness of exports to real exchange rate changes. First, the value-added REER elasticity does not present the same decreasing pattern as the standard REER elasticity does. This result is independent of the specific measure of the value-added REER. Importantly, this finding suggests that the decrease in the REER elasticity is, at least in part, an optical illusion that results from the fact that REER and exports are measured in gross rather than value-added terms. Second, we can see that the elasticity in each of the two regressions in value-added terms is lower than the corresponding ones in the first row. This seems to indicate that the impact of a currency depreciation on exports is substantially less when measured in value-added terms.<sup>16</sup> There is one caveat to this second result that we need to bear in mind. The REER adjusted for value-added terms use GDP

> 15 In our regressions, we use the measures developed by Bayoumi _et al_ . (2013) which are publicly available. See IMF (2013) for a discussion and comparison of the different approaches used in the literature to obtain REER in value-added terms. Patel _et al._ (2014) develop a theoretical framework to compute REERs at the country-sector level. Assessing the impact on (value-added and gross) exports of these new REER measures at the country and sector levels would be a further refinement to the present analysis.

16 It could be argued that the value-added component of exports should be more responsive to REER changes but it may also be possible that complementarities between foreign and domestic value added explain the slightly lower responsiveness of domestic value-added exports to the REER. Similarly to the exercise in this section, the independent work in IMF (2015) investigates the impact of value-added REER on value-added exports differentiating between sectors more integrated in GVCs, defined as GVC sectors, and sectors where GVCs are less relevant. Consistently with our results, they find that the competitive impact of a depreciation is stronger for the latter as they rely less on imported inputs.
disposal versus how much money they have released for new projects). To discuss the findings from these reports, the regional water department hosts meetings with DWEs from each of the districts within the region. During these meetings, DWEs present the opportunities and challenges they face in their districts, while the regional engineer discusses issues that affect the region as a whole. PO-RALG then compiles these reports and submits the data contained in them to the MoWI. These data include the number of water points, COWSOs, and ongoing projects that exist across the region.

To varying degrees, COWSOs, village and ward governments, the DED, departments for finance and planning, and community development, NGOs, MoWI and PO-RALG are the main institutions that district water and sanitation departments work with. However, there are a number of other institutions to which employees of a district and water sanitation department report to through ongoing meetings and formal reporting processes. These reporting processes include submitting written reports. The DWE reports to the committee for security, which a district commissioner chairs and which coordinates activities with regional security committees. She or he also reports to a Local Authorities Accounting Committee, as well as a number of standing committees within the district council (1.finance, administration and planning, 2. education, health and water, 3. economic affairs, work and environment, 4. HIV/AIDS). These committees within local government require the DWEs to provide frequent updates on the status of ongoing projects, their department’s expenditures, and the contracts they have with the private sector. DWEs also meet with the Central Management Team (CMT) on a weekly basis. The CMT is composed of department heads in the district.

District water and sanitation departments are accountable to other departments and committees in the district and to higher levels of government. This is the result of a number of 35
<!-- Start of picture text --> 1992 1994 1996 1998 2000 2002<br>Year<br>Max Min Average<br>80<br>60<br>40<br>20<br>Mexican NAFTA ad valorem tariffs<br>0<br><!-- End of picture text --> Figure 2: Import Tariffs Drop under NAFTA (Source: Secretaria de Economia, Mexico) we plot present the distribution of the gap between each individual firm-level productivity and the “productive frontier”, in years 1993 (dashed line), 1998 (dotted line) and 2002 (continuous line). The gap is defined as the log of the ratio between the average productivity of the top five firms in the sector and the individual firm labour productivity.<sup>11</sup> A larger ratio identifies firms more distant from the productive frontier. In figure 3 we observe the distribution moving rightward, indicating that the gap between the average, or the median, Mexican firm and the “productive frontier” is expanding. At the same time, we see that the right tail of the distribution is also getting fatter implying an increase in the density of firms with larger gaps.

This expansion in productivity inequality is even more interesting if we consider that during this period we observe the exit of a significant number of less productive firms. In fact, fig. 4 depicts the number of number of exiting plants in Panel (a), and shows in Panel (b) that the average productivity of exiting plants (dashed line) is always substantially smaller than the average productivity of surviving ones (continuous line). In a nutshell, after NAFTA was implemented besides a substantial exit of less productive plants, which should compress the productivity distribution by trimming its left tail, we observe an increase in productivity inequality among surviving Mexican firms.

productivity among firms even within narrowly defined sectors got larger during the period under analysis.

11To avoid the risk of having an excessively narrow definition of sector we define the “productive frontier” at 4 digits.

7
36 Neuhauser, D. 1972. "The Hospital as a Matrix Organization." Hospital Administration 17:8-25.

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# **Table 2.0: Test Scores and Governance Indicators**

||W|ithout CountryFixe|d Effects||With Fix|ed Effects||
|---|---|---|---|---|---|---|---|
||Average|Average Primary|Average|Average|Average|Average|Average|
|Dependent Variable|Score|Score|Secondary Score|Score|Score|Score|Score|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|
|**Civil Rights**|-0.012|0.23|-0.183|**0.698****|-0.022|**-0.508***||
||(0.26)|(0.29)|(0.3)|(0.32)|(0.34)|(0.29)||
|Globalization|<br>0.155***|<br>-0.026|<br>0.105|<br>0.149*|<br>0|<br>0.002||
||(0.05)|(0.06)|(0.06)|(0.08)|(0.07)|(0.07)||
|Democracy|0.478|0.201|0.760**|-0.192|-0.039|0.524||
||(0.31)|(0.36)|(0.38)|(0.78)|(0.68)|(1.07)||
|**Economic Freedom**|<br>0.053|<br>0.196**|<br>0.025|<br>**0.177***|<br>**0.168***|<br>0.028||
||(0.07)|(0.08)|(0.08)|(0.1)|(0.1)|(0.12)||
|Openness (in||||||||
|<br>percent)|0.020*|0.013|0.031**|0.060**|0.038|-0.003|-0.001|
||(0.01)|(0.01)|(0.01)|(0.03)|(0.03)|(0.03)|(0.03)|
|Log Population|1.207***|0.701|0.507|4.798|-<br>13.259*|57.878**|43.967**|
||(0.37)|(0.46)|(0.38)|(6.82)|(7.44)|(24.64)|(20.38)|
|Log GDP per Capita|<br>3.114***|<br>2.623***|<br>3.728***|<br>-3.146|<br>-4.375|<br>-6.801**|<br>-6.908**|
||(0.64)|(0.72)|(0.78)|(3.05)|(3.93)|(2.62)|(2.71)|
|**Lag of Civil Rights**||||||<br>**0.437***|<br>**0.604****|
|||||||(0.22)|(0.23)|
|Lag of Globalization||||||<br>-0.130*|<br>-0.106*|
|||||||(0.08)|(0.06)|
|Lag of Democracy||||||-0.056|-0.014|
|||||||(0.35)|(0.32)|
|Lag of Economic||||||||
|<br>Freedom||||||0.065|0.083|
|||||||(0.1)|(0.11)|
|Lag of Openness (in<br>percent)||||||0.002|0.002|
|||||||(0.03)|(0.03)|
|Lag of Log||||||-|-|
|Population||||||65.913***|50.916***|
|||||||(22.26)|(19.21)|
|Lag of Log GDP per<br>Capita||||||<br>-1.936|<br>-1.17|
|||||||<br>(4.41)|<br>(4.19)|
|R-Squared|0.632|0.497|0.587|0.171|0.936|0.652|0.622|
|Observations|186|122|120|186|186|138|138|
|Number of||||||||
|Countries|95|84|72|95|95|91|91|

**Notes:** Dependent Variable: Score averaged over all test score domains. Columns (1), (2) and (3) report OLS estimations. Columns (4), (5), (6) and (7) report Fixed Effects Estimations. Column (4) includes country fixed effects, Column (5) includes both country and time fixed effects, and Columns (6) and (7) include country fixed effects as well as lagged variables. All regressions are estimated with robust standard errors, clustered on the country level.

30"
especially the cash-flow rights relative to control rights variable, the results can be interpreted as evidence that the small shareholder expropriation occurs to a large extent by families.

We next explain the existence of a valuation discount for the other types of control ownership. Tables 6 and 7 investigate the effects on valuation of firms for cash-flow and control ownership for those corporations where respectively widely-held financial institutions, the state and widely-held corporations are the largest controlling block-holders. Since country-by-country regressions indicate some important differences for financial institutions in the case of Japan, we also include in the regression a separate dummy for ownership by widely-held financial institutions in Japan versus other countries.

We find that cash-flow ownership by financial institutions is positively associated with corporate valuation and that control rights are negative associated with valuations (Table 6, columns 1 and 2). The magnitude of the coefficient on the control rights is somewhat less than that for family control, but higher than control for all classes of ownership. We find further evidence of expropriation as the coefficient on CASH/CONTROL is positive and significant, and as the coefficient for the interactive variable for high control stakes is also positive and significant. The magnitude of the coefficient on the CASH/CONTROL variable is less than for family control as well as for control for all classes of ownership, suggesting expropriation by financial institutions is less than that by families. It appears that the negative effect of control by financial institutions arises from the role of financial institutions in Japan as the interactive dummy with CASH/CONTROL is positive and significant for Japan, but is not significant for the other countries (column 5). This finding supports the results in Kang and Stulz (1998), which show that Japanese firms whose debt had a high fraction of bank loans in 1989 performed worse from 1990 to 1993, possibly as Japanese financial institutions extract a rent from their 20