rafmacalaba/datause-encoder-data huggingface dataset:
maintain price stability, cereal prices in the longer term will depend upon supply and demand, and hence climate, in the rest of the world as well as in India as a whole.

Results from two separate multi-sector computable general equilibrium models of the world economy, GTAP (Hertel, 2010) and ENVISAGE (van der Mensbrugghe, 2009), suggest that cereal prices will rise by around 10 percent by 2040 due to climate change. We should emphasize, however, that such forecasting is far from an exact science; indeed, since no standard errors are attached, the degree of prediction uncertainty cannot even be quantified. For GTAP, in particular, we are averaging price changes across a number of scenarios, including one in which the prices of all major cereals actually fall. Suffice it to say that our point estimate of a 10 percent increase is contained within a wide confidence interval.

# **5 Distributional Implications of Climate Change**

## **5.1 Climate change scenario for India**

We use temperature predictions from HadCM3-A1F1 (high emissions path), a commonly used climate model developed by the Hadley Centre for Climate Prediction and Research as part of the Third Assessment Report of the United Nation’s Intergovernmental Panel on Climate Change (IPPC). Our measure of warming is based on the mean monthly change in surface temperature between the baseline period 1961-1990 and the near future 2010-2039. Most other widely used climate scenarios predict very similar temperature increases in the short-run and only begin diverging significantly toward the end of the century.

Figure 5 displays the geographic distribution of projected annual temperature changes, as well as those for each agricultural season, kharif and rabi, separately. Northern India will be hardest hit by warming in the coming three decades, particularly during kharif, whereas much of the south and east will be spared the worst of the temperature increase in the principal growing season.

## **5.2 Putting the pieces together**

With predicted temperature changes and estimated price responses in hand, it remains only to calculate the weights and the other components of the formulae of section 2 to implement the distributional analysis. This is easier said than done, however, in that households in the NSS61 expenditure survey, for whom we have consumption data, are not the same as those in the 61st round employment-unemployment survey, nor are they the same as those in the 59th round farm household survey. We must, therefore, impute several key parameters based on observable characteristics common across surveys.

18
constructed using information from the Oxford Government Policy Response Tracker (see tables A.2 and A.3 for details).

The first group of dummy variables captures the variability in lockdown policies, defined as those policies which restrict work and business activities and internal mobility. As described in the background section, most countries moved from a full lockdown to a partial lockdown and then later lifted lockdown measures, with almost all work activities resuming regular schedules and free internal mobility. Some countries however, moved swiftly from a full lockdown to removing all restrictions at once. Therefore, we classify each country on a given day to be in one of the following four categories: full lockdown (which is the reference category), partial lockdown implemented after a full lockdown (captured by the dummy variable _FPc,t_ ), no restrictions in place following a partial lockdown ( _PNc,t_ ), and no restriction in place following a full lockdown ( _FNc,t_ ). Countries then progress from the reference category to either no restrictions at all or from the reference category to a partial lockdown and then later to full reopening with no restrictions in place. Importantly, we distinguish between a situation when no restrictions are in place after a partial lockdown which represents a more gradual reopening and a situation where restrictions are completely lifted directly after a full lockdown.

The second group of dummy variables focuses on school reopening policies. All countries in our sample, except Sweden, started their reopening process with schools closed (the reference category). Countries then either partially reopened schools, allowing in-person classes for some grades or groups of students (captured by the dummy variable _SPc,t_ ) or moved to almost normal, pre-COVID-19 mode of school operation ( _SNc,t_ ).

Lastly, the third group of dummy variables focuses on restrictions on public events. Countries started the reopening process with all public events canceled (reference category) and then either removed some restrictions, allowing certain public events to resume (a situation captured by dummy variable _PPc,t_ ), or removed most of the restrictions, with cancelations being exceptional ( _PNc,t_ ).

Using these notations, equation (2) specifies the model of the effect of the sequencing of reopening on economic outcomes.

12
in fact prompted trade to decrease by US$24 billion, if we allow AD to a¤ect more than the existing level of pre-AD trade (see (4)), or by US$3 billion if we assume the maximum e¤ect of AD and other tari¤ increase cannot exceed the existing trade in 2008 (see (5)). Likewise, for the EU, incorporating AD duties causes its OTRI_BE to increase by 0.1 percentage points. As a result, imports of the EU drop by US$2 billion. This exercise shows that while anti-dumping may not increase the overall level of protection by much, it is in fact the main instrument being used by the US and EU during the crisis period. Another heavy user of AD is India. Without AD duties, OTRI_M of India decreases by 0.2 percentage points from 2008 to 2009.<sup>8</sup> Once AD duties are included, the change becomes positive 0.1 percentage points, indicating that AD have made the overall level of trade restrictiveness of India worse. The net trade e¤ect of the changes in tari¤ and AD duties for India is about US$306 million.<sup>9</sup> Nevertheless, such duties hardly explain the huge collapse in trade, which further suggests that this global collapse in trade is probably not because countries are becoming more protectionist, but instead relates to factors such as demand shocks.

Figure 5 compares AD to traditional tari¤ policy. The vertical axis is the change in AD duties during 2008-2009 and the horizontal axis is the change in the OTRI due to both tari¤s and AD. The 45 degree line is also depicted in the ...gure. For the US, the change in the OTRI is entirely driven by AD duties changes, which position the US on the 45 degree line. In the case of other

> 8 For Chile, India and Japan we use OTRI_M instead of OTRI_BE to calculate OTRI_AD, since 2009 bilateral tari¤ schedules are not available.

> 9 Our estimated changes in trade in Table 3 are not directly compatible to Bown (2009b). For example, for the worse case scenarios, Bown’s estimates of the AD impact in the US, EU and India are US$7 billion, US$8 billion and US$4 billion, respectively. The di¤erences can be attributed to the following. First, our estimates are based on tari¤ line (HS 8 digit) data, rather than HS 6 digit data. In other words, within an HS 6 digit category, only those HS 8 digit goods that are a¤ected by AD are included in the calculation, while Bown’s estimates use HS 6 digit trade ‡ows. Second, we use 2008 trade value in our calculation while Bown’s estimates based on 2007 trade value. Third, our AD coverage is from June 2008 to September 2009, while Bown’s estimates are from the ...rst quarter of 2008 to the ...rst quarter of 2009. Forth, we take into account the bilateral import demand elasticities in the calculation of trade impact due to AD. Finally, we include tari¤s and AD in our calculation of trade changes, while Bown’s estimates only focus on AD. For the EU and India, the negative impacts on trade ‡ows due to AD are partially o¤set by their overall tari¤ reduction during the two year period.

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13 the just described annual rate. The annual rate for 1980 was<sup>assumed to be</sup> equal to the 1981 rate (data on interest paid by the Bank<sup>of Mexico to banks</sup> in 1980 was not available).

The term other government revenues (OR) was obtained as followss

## (9) OR = EcBal + Ipu + G + iB + iDpun - T

where: EcBal is the fiscal balance Ipu in public investment G is current government expenditure (FA source) iB is nominal interest on domestic debt iDpun is nominal intorest on external public<sup>debt</sup> T is tax revenue

# 3. Saving Rates in Mexico. 1980-90

In subsection 3.1 we compute the items in identity (2)<sup>and assess saving</sup> performance over the 1980-90 period. A comparison<sup>between our saving measure</sup> and the conventional measure is made in subsection<sup>3.2. Inspection of saving</sup> measures reveals a high degree of complementarity between<sup>public and private</sup> saving throughout the decade in both the proposed and<sup>cenventional measures.</sup> However, it becomes difficult to interpret this complementarity<sup>without a</sup> closer look at the disaggregated items in both private<sup>and public savings.</sup> Thus, we analyze the behavior of private saving<sup>components: private</sup> consumption, in subsection 3.2, and disposable income, in subsection<sup>3.3.</sup>

# 3.1 Savina Performance in 1980-1990.

Figures 1 and 2 show our measures of private saving as<sup>a percentage of</sup> GDP in 1980 pesos and in current pesos respectively. Some important differences between the figures in current and real pesos<sup>arise after 1985;</sup> these differences stei from changes in relative prices<sup>brought about by trade</sup> liberalization. In the subsequent discussion we focus<sup>on the saving measures</sup> in constant pesos (Figure 1P.

The large flow of foreign finance that Mexico received in the<sup>early</sup> 1980s, which mainly financed public sector oil investments, came to an abrupt end in 1982 (Figure 3). As the debt crisis unfolded<sup>foreign saving contracted</sup> sharply prompting an equally drastic increase in<sup>public saving (Figure 1).</sup> While higher public saving stemmed primarily from<sup>an increase in revenue,</sup> higher public tariffs until the mid 1980s and higher<sup>tax revenue towards the</sup> end of the decade (respectively OR and T in Figure 4), it<sup>also relied heavily</sup> on inflation-driven wealth transfers (Figure 5). All this,<sup>of course,</sup> affected adversely private saving. Lower investment<sup>and the partial recovery</sup> of domestic saving came along with a sizable accumulation<sup>of foreign assets by</sup> the private sector. These assets eventually played<sup>an important role in the</sup> private sector income structure (Figure 6).
to verify that the inclusion of DPOs in the sample of projects is not leading to spurious correlations. The regression results presented are robust to both these checks.

**Table 7 further examines the contribution of variables that control for TTL fixed effects for explaining the variation in project outcome ratings.** As a benchmark, column (1) demonstrates that 8% of the variation in project outcomes can be explained by using a set of country indicator controls. This means that country-specific factors or fixed effects, such as quality of institutions, economic growth trends, size of WB country office, only explains 8% of the variation in project outcomes. Including the measure of TTL quality defined above as the propensity of TTLs to implement satisfactory projects only leads to an additional 1% increase in the coefficient of determination (see column (2)). Introducing a full set of TTL indicators that control for TTL-specific factors or fixed effects explains about 46% of the variation in project outcomes (see column (3)) and this is true for projects in both FCS countries (see column (4)) and non-FCS countries (see column (5)). However, these regressions are close to saturation with many regressors compared to the number of data points, so it is hardly surprising that they achieve high statistical fit. Columns (6) and (7) estimate the correlation of TTL quality with project outcome ratings in FCS and non-FCS countries while controlling for country fixed effects. This shows that TTL quality remains positively correlated with project success, even when country environment is taken into account, although the correlation is still weaker in FCS countries.

As discussed earlier, it is important to distinguish between causation and correlation in interpreting these results and recognize that underlying project characteristics, such as the ambitiousness or inherent complexity of the project, may drive the correlations that are observed. For example, for ambitious projects it may be more difficult to obtain satisfactory outcome ratings and this may lead to more preparation and supervision time and finances. In an attempt to control for such project fixed effects a set of panel regressions are estimated in tables 10 to 12. For each project two data points are collected as a measure of satisfactory progress or outcome. In the first half of a project, satisfactory progress is defined as the absence of any problem project flags, unsatisfactory implementation or development objective rating reports<sup>7</sup> , and potential problem flags. In the second half of a project, the IEG outcome rating is used to evaluate whether the project reached a satisfactory outcome. The fixed effects regressions can be interpreted as estimating the correlation between changes to project inputs, such as supervision costs or the TTL leading the project, with changes to project outcomes, while controlling for time-constant project characteristics.

Tables 8 and 9 show summary statistics for projects that obtain unsatisfactory and satisfactory outcomes, re-

> 7For example that would be detected through the implementation status reports (ISRs).

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> - 24 -

# Table 8: 0O-ITEGRATIOD TESTS: REAL CONIODITY PRICE INDICES AID KACRO-EONWONIC VARIABLES /a

||CDRW|DF|ADF|
|---|---|---|---|
|LnICP33|1.360|-4.421|-5.049|
|LnICPAG|1.240|-4.245|-4.328|
|W.nCPMN|1.215|-4.073|-3.018|
|LnICPACF|1.187|-4.029|-4.123|
|LnICPAGNF|1.675|-6.528|-3.306|

# Note: See Table 1 for variable definitions.

- /a The co-integrated macro-economic variables included on the right-hand side of the above tests are: three-month US treasury bill as proxy for the real interest rate, the real price of oil, the real exchange rate and real CDP of the C-7 countries.
 or is eligible for tt←tt<sup>∗</sup> FFFor example, for UNPS, the households eligible for tracking are those in which at least is the household subpopulation that remains fixed from time tt<sup>∗</sup> tracking from time tt<sup>∗</sup> to time tt according to the tracking rules adopted for the survey. does not include the households of all immigrants, which contain only new people one individual was already present at time tt, and the households consisting only of people who<sup>∗</sup> and lives in the original parish until time tt. tt←tt<sup>∗</sup> FF out of their parish. entering the country after time tt<sup>∗</sup>  E comprises the households whose members consist only of immigrants or movers moved after time tt<sup>∗</sup> ]. tt<sup>∗∗</sup> ←tt<sup>∗</sup>  E includes the households whose members consist only of immigrants or movers in in the time interval [tt<sup>∗</sup> , tt<sup>∗∗</sup> tt←tt<sup>∗∗</sup> Partition 3.1 and Figure 3.1. define the representativeness of the two longitudinal samples the time interval [tt<sup>∗∗</sup> , tt]. . Herein, we refer to representativeness as the possibility of computing direct unbiased estimates of a given population from the sample tt←tt<sup>∗∗</sup> tt←tt<sup>∗</sup> tt (SSdata. and SS ) regarding the sub-populations of HH For the Sample only to the sub-population tt←tt<sup>∗</sup> tt←tt<sup>∗</sup> SS , the tracking rules limit the representativeness of sample SS to time t. tt←tt<sup>∗</sup> FF For Sample , the tracking rules restrain the representativeness of the sample data only to of households of time tt who have been eligible for tracking from time tt<sup>∗</sup> the sub-population tt←tt<sup>∗∗</sup> SS tt←tt<sup>∗∗</sup> FF and E , being of households of time tt who have been eligible for tracking from time tt<sup>∗∗</sup> to time tt. ∪E . tt←tt<sup>∗∗</sup> tt←tt<sup>∗</sup> tt<sup>∗∗</sup> ←tt<sup>∗</sup> FF can be partitioned into the two subpopulations FF , we have no people belonging to the subpopulation Ett←tt<sup>∗∗</sup> tt←tt<sup>∗</sup> tt<sup>∗∗</sup> ←tt<sup>∗</sup> ,, FF = FF who are the people entered in the population after the last panel refresh. As we have no sample tt←tt<sup>∗∗</sup> tt<sup>∗∗</sup> ←tt<sup>∗</sup> Finally, we see that in sample SSdata on this subpopulation, we cannot compute direct estimates.

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Table 5: Total Factor Productivity and Banking Sector Performance: Access to credit

|TFP estimation procedure:|I<br>OLS|II<br>Olley/Pakes|III<br>OLS|IV<br>Olley/Pakes|
|---|---|---|---|---|
|Perceived difficulty of access to<br>finance|-0.220<sup>***</sup><br>[0.068]|-0.133<sup>*</sup><br>[0.076[|||
|Perceived difficulty of cost of<br>finance|||-0.171<sup>**</sup><br>[0.071]|-0.067<br>[0.078]|
|Regional % of firms with a loan|||0.739<sup>***</sup><br>[0.245]|0.769<sup>**</sup><br>[0.304]|
|Exporter|-0.018|0.059|-0.01|0.068|
||[0.044]|[0.052]|[0.044]|[0.053]|
|Age of the firm|0.007|0.019|0.006|0.015|
||[0.019]|[0.024]|[0.019]|[0.024]|
|Size of the firm|0.018|0.004|0.014|-0.006|
||[0.049]|[0.059]|[0.05]|[0.06]|
|Foreign owned|0.167<sup>***</sup>|0.146<sup>**</sup>|0.166<sup>***</sup>|0.154<sup>**</sup>|
||[0.051]|[0.059]|[0.051]|[0.060]|
|Location in Capital Region|0.067|0.027|0.023|-0.024|
||[0.044]|[0.052]|[0.047]|[0.056]|
|Country dummies|Yes|Yes|Yes|Yes|
|Industry dummies|Yes|Yes|Yes|Yes|
|Observations|1186|1077|1186|1077|
|Adjusted R-squared|0.24|0.40|0.24|0.40|

Robust standard errors in parentheses. *, **, *** indicate statistical significance at the 10%, 5% and 1% levels, respectively. The services related variables are used in regional averages.

The two aspects of financial sector performance, although in principle not necessarily related, turn out to be highly correlated with each other in the data. Table A1 in the appendix shows the positive and statistically significant relationship between the number of days required to clear a foreign wire and the perceived cost of financing. In other words, in those regions where the financial sector seems to perform better, it does so both with respect to facilitating payment transactions and extending credit.

# **Entering all three services sectors into the regressions**

In addition to entering performance measures for each service sector separately, it is also interesting to see how far our results carry through when we simultaneously enter measures of all three services sectors into the estimation of equation (2). The question here is whether we can detect a significant association between firm productivity and the performance of a given services sector, conditional on the performance of other services. Table 6 presents these results. The models in table 6 differ in the performance measures chosen for the electricity sector, for which we have two measures in table 3.

Our results for the telecommunications and banking sectors carry through in all specifications of table 6. We find a significant positive correlation between the performance measures for these sectors and manufacturing productivity in all cases. The results for the electricity sector, 11
# **How Much Has Nepal Lost in the Last Decade Due to Load Shedding? An Economic Assessment Using a CGE Model**

## **1. Introduction**

Nepal, a small country on the lap of the Himalayas, is one of the poorest countries in the world in terms of infrastructure and economic development. In 2013, per capita electricity consumption in Nepal, for example, was 427 times lower than that in Iceland, the country with the highest per capita electricity consumption in the world; 182 times lower than that in Norway, a country with hydropower as the primary source for electricity supply, like Nepal; 101 times lower than that in the United States; 81 times lower than that in the Republic of Korea; 30 times lower than that in China; and 6 times lower than that in neighboring India.<sup>3</sup> As of January 31, 2018, Nepal has about 1,000 MW of power generation capacity for its almost 30 million population. Of this total installed capacity, which is mostly run-of-river type, 70% is not available during the dry season (March-April). The lack of electricity generation capacity and further unavailability of the existing capacity due to very low river discharge in the dry season, the country has suffered from huge load shedding over the decade starting in 2008. In the dry season in 2015 and 2016, the country faced up to 14 hours of load shedding in a typical day.<sup>4</sup> More recently, starting from 2017, the situation has improved and regular electricity supply to households in major urban areas, including the capital city, Kathmandu, has been resumed. Several factors, including an innovative load management system adopted by the state electricity utility- Nepal Electricity Authority, increased domestic supply due to commissioning of a few new power plants and increased imports of electricity from India facilitated by the completion of a 400 KV, 1,000 MW cross-border transmission line. Yet, many parts of the country still face load shedding and demand for industrial customers is yet unmet. In a personal communication, representatives from the Federation of Nepalese Chambers of Commerce and Industry (FNCCI) of Nepal claim that the current industrial sector load alone exceeds 3,000 MW, whereas the country

> 3 Calculated based on data from the World Bank’s online database available at <u>http://databank.worldbank.org/data/home.aspx.</u>

> 4 The Himalayan Times, January 03, 2016. https://thehimalayantimes.com/kathmandu/.

2
Table 1: Gender Employment and Wage Ratios

||Employm|ent Ratio|Wage|Ratio|
|---|---|---|---|---|
||1970-75|2010-18|1970-75|2010-18|
||(1)|(2)|(3)|(4)|
|**_Panel A. Sectors_**|||||
|Home|8.07|3.63|||
|Agriculture|0.27|0.27|0.56|0.73|
|Manufacturing|0.28|0.28|0.50|0.69|
|Services|0.65|0.93|0.54|0.71|
|**_Panel B. Occupations_**|||||
|Professional|0.33|0.82|0.58|0.70|
|Clerks|1.08|1.41|0.80|0.86|
|Craft, Trade, Service|0.39|0.57|0.46|0.67|
|Agricultural|0.25|0.24|0.56|0.72|
|Machine Op., Elementary|0.39|0.50|0.51|0.73|
|**_Panel C. Countries_**|||||
|India|||0.56|0.60|
|Indonesia|||0.54|0.73|
|Brazil|||0.57|0.72|
|Mexico|||0.65|0.75|
|Canada|||0.44|0.71|
|United States|||0.47|0.70|

_<u>Notes:</u>_ This table reports female-to-male employment ratios (Columns 1-2) and wage ratios (Columns 3-4) in the 1970s and 2010s. The ratios divide employment shares (average wages) of women in each occupation sector by those of men. Panels A-C report average ratios for sectors, occupations, and countries. Averages of employment (wage) ratios are weighted by the employment (income) of each occupation-sector.

countries, which decreased to 3.6 in the 2010s (Table 1, Panel A). Women remain most underrepresented in the agriculture and manufacturing sectors, which employ only 2.7 women for every 10 men, with little change over time. In contrast, women’s employment expanded rapidly in the service sector, increasing from 6.5 to 9.3 women for every 10 men over the past decades. Turning to occupations, Panel B of Table 1 shows that women were underrepresented in all occupations except clerical jobs in the 1970s. The largest catch-up occurred in professional and managerial jobs, where women’s employment increased from 3.3 to 8.2 for every 10 men. Women’s overrepresentation in clerical jobs grew further over time, rising from 11 to 14 women for every 10 men. Employment gaps in other 14