rafmacalaba/datause-encoder-data huggingface dataset:
potential of untapped hydropower resource that offers a great opportunity for clean and green power production.

<!-- Start of picture text --> 14,000 12.0% 350, 000 50%<br>Bangladesh India<br>12,000 10.0% 300,000<br>40%<br>10,000 250, 000<br>8.0%<br>30%<br>8,000 200, 000<br>Hydro (MW) 6.0% Hydro (MW)<br>6,000 Total (MW) 150,000 Total (MW) 20%<br>Hydro Share (%) 4.0%<br>4,000 100, 000 Hydro share (%)<br>10%<br>2,000 2.0% 50,000<br>0 0.0% 0 0%<br>1,000 10 0% 30000 80.0%<br>Pakistan<br>Nepal 70.0%<br>800 Hydro (MW) 80 % 25000<br>Total (MW) 60.0%<br>600 Hydro Share (%) 60% 2000 0 50.0%<br>1500 0 Hydro (MW) 40.0%<br>400 40 % Total (MW) 30.0%<br>1000 0<br>Hydro Share (%) 20.0%<br>200 20 %<br>500 0<br>10.0%<br>0 0% 0 0.0%<br>4,500 120%<br>Not e: Bhutan is excluded as most of its capacities are for<br>4,000 ex p ort to India; Data are not available for Afghanistan.<br>Sri Lanka 10 0%<br>3,500<br>3,000 Hydro (MW) 80 % So De u velopment Board online database; India: CEA (2012) and rce: Bangladesh: IAEA (2013) and Bangladesh Power<br>2,500 Total (MW) 60 % Mi nistry of Power Website:<br>2,000 htt p s://powermin.nic.in/en/content/power‐sector‐glance‐all‐<br>1,500 Hydro Share (%) 40 % ind ia; Nepal: Kafle (no date) and NEA Annual reports various<br>iss ues; Pakistan: Khan and Ashraf (2015) and WAPDA various<br>1,000 20 % re p orts; Sri Lanka: Cylon Electricity Board Statistical Reports,<br>500 var ious issues.<br>0 0%<br>1980 1990 2001 2008 2012 2017 1961 1969 1980 1990 1997 2007 2012 2018<br>1970 1980 1990 2002 2007 2012 2015 2017 1960 1970 1980 1990 2000 2010 2017<br>1970 1980 1990 2000 2010 2017<br><!-- End of picture text -->

**Figure 5: Historical growth of hydropower vis a vis total electricity generation capacity**

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We assembled a new database with information on budget allocations for every social protection program implemented in response to the pandemic in 155 countries. The database derives the universe of social protection programs from the Global Database on Social Protection Responses to COVID-19 (Gentilini et al. 2022), with a cut-off date for policies announced before June 1, 2021. Using these data, we estimate the relative contributions of income protection and job protection policies to the overall social protection response.

Our empirical model estimates the relationship between four economic outcomes (GDP growth, unemployment, rates of labor inactivity, and poverty) and variables representing expenditures on the two types of social protection policies and country-level controls. Our analysis shows that, in the short run, higher expenditure on job protection is associated with a more robust economic recovery, increased employment, and decreased inactivity and poverty rates; expansion of income protection measures appears to have no significant economic effect. We conduct a series of robustness checks with empirical specifications that use alternative definitions of our dependent variables. These estimations qualitatively confirm our main results on the greater effectiveness of job protection policies in promoting economic recovery during the second half of 2020 and the first half of 2021.

We then take our analysis further to account for the effects of pre-pandemic characteristics of social protection systems. We find that social protection response had a significant economic impact in countries with weaker pre-pandemic social insurance systems. In countries with broader social insurance coverage, income and job protection programs appear to have a limited impact on post-pandemic recovery. We speculate that some programs implemented during the pandemic - particularly those protecting jobs - may have been redundant in these countries as automatic stabilizers were already in place.

This paper contributes to the emerging literature on the effectiveness of various types of social protection programs in facilitating post-pandemic recovery. To our knowledge, it offers one of the first empirical cross-country analyses of the short-term impact of job protection and income protection programs on economic growth, unemployment, labor inactivity, and poverty. The findings of this paper could be used to design more effective policies to address the main challenges in the post-pandemic world and find the right balance between job preservation and 4
percent) report zero bribe payments, we check our main results using as dependent variable a dummy equal to 1 if the firm reports positive bribe payments and 0 otherwise ( _Incidence of Overall Corruption_ ).

# 2.3.2 _Petty corruption_

The ES also contains information on instances of corruption that firms experience in soliciting the following: obtaining electricity connection, obtaining water connection, obtaining construction permit, obtaining import license, obtaining operating license, and inspections or meetings with tax officials. Based on this information, the ES compiles two separate measures of petty corruption that we use as dependent variables. The first measure is the incidence of petty corruption defined as a dummy variable equal to 1 if a firm experienced a bribe payment or request in one or more of the six transactions listed above and 0 otherwise ( _Petty Corruption Incidence_ ). The second measure is the depth of petty corruption defined as the percentage of the six transactions for which the firm experienced a bribe payment or request for one ( _Petty Corruption Depth_ ).<sup>3</sup> In the baseline sample, the mean value of the incidence of petty corruption equals 0.17 and the standard deviation equals 0.38. The corresponding figures for the depth of petty corruption equal 13.47 and 31.70 percent.

# 2.3.3 _Correlation between overall and petty corruption_

The overall corruption measure described above aims to capture all types of corruption that the private firms experience in running their business. Thus, it is ideally suited for our purpose since

> 3 The two measures of petty corruption are available for only those firms that solicited the public services listed above or were inspected by tax officials. For the bribery depth measure and following the ES methodology, a refusal to answer a question on whether bribes were requested or expected is considered as an affirmative answer.

9
As a final recommendation, the BCRP should try to address the problem of large bidask spreads. Further research should investigate whether interventions affect the bid-ask spread, and even whether the volatility of the exchange rate affects such spread. This is very important for participants in foreign trade and investment who are involved in foreign exchange transactions in general.

# **References**

Armas, A. (2005). “Forex interventions in Peru: 2004 – 2004.” In _Foreign exchange market intervention in emerging markets: motives, techniques and implications_ , BIS Papers #24.

Armas, A. and F. Grippa (2005). “ Targeting inflation in a dollarized economy: the Peruvian experience”. Unpublished manuscript, Banco Central de Reserva del Peru.

Bank for International Settlements (2005). “Foreign exchange market intervention in emerging markets: motives, techniques and implications.” BIS Papers #24.

Heckman, J. (1978). “Dummy endogeneous variables in a simultaneous equation system. Econometrica 46, 931 – 959.

Humala A. and G. Rodriguez (2009). “Foreign Exchange intervention and exchange rate volatility in Peru.” Working Paper Series # 2009-008, Banco Central de Reserva del Peru.

Johnson N.L. and S. Kotz (1972). “Distribution of statistics: continuous multivariate distributions.” Wiley, New York.

Lee, L. (1978). “Unionism and wage rates: a simultaneous equation model with qualitative and limited dependent variables.” International Economic Review 19, 415 – 433.

Morris, S. and H.S. Shin, 2006b. Global games: Theory and Applications” in <u>Advances in Economics and Econometrics, Ed. M. Dewatripont, L.P. Hansen, and S.J. Turnovsky,</u> Cambridge University Press.

Mundaca, G. and B. Øksendal (1998). “Optimal stochastic intervention control with application to the exchange rate.” Journal of Mathematical Economics 29, 225 – 243.

Mundaca, G. (2001). “Central bank interventions and exchange rate band regimes.” Journal of International Money and Finance 20, 677 – 700.

31
coverage on consumer prices for any of the commodities.<sup>26</sup> These findings are in line with our earlier findings that gross margins did not fall, even for cowpeas. They are also consistent with our model, which predicts a reduction in the spatial variance of prices but not necessarily an effect on average prices across surplus or deficit areas.<sup>27</sup> Using the intra-annual coefficient of producer price variation as the dependent variable, we find that mobile phone coverage reduces the average intra-annual coefficient of variation by 6 percent for cowpea, with a statistically significant effect at the 1 percent level (column 4). Given that the pretreatment intra-annual coefficient of variation of cowpea price is 26 percent, this represents a significant reduction in intra-annual cowpea price risk. We find negative but not statistically significant effects for millet (column 8) and sorghum (not shown). These findings are also in line with the model predictions in that better spatial arbitrage reduces week-to-week price variation, but only when this role cannot be assumed by inter-temporal arbitrage.

A limitation of the market-level data is that mobile phone coverage at the village level was still relatively limited by 2008. Thus, we might be underpowered to estimate effects on producer prices levels. To gain more insight into this issue, we use the farm household survey data collected by one of the authors in 2009, by which time mobile phone coverage had begun to reach more rural areas. Across the 100 villages in our sample, all had mobile phone coverage by the end of 2009, and 30 percent of the farmers in these villages owned mobile phones. Although there are obvious limitations to using this crosssectional survey, the data complement the market-level data.

Our dependent variable of interest is the log of producer prices received for a variety of commodities, namely millet, sorghum, cowpea and peanut. The estimating equation is (18) , where _Y_ j is the log price of commodity _i_ received by farmer _j_ , _mobileown_ is a binary variable for whether the household owns a mobile phone, _Xj_ is a vector of farmer-specific covariates to control for factors that potentially affect mobile phone ownership and prices received (such as land ownership, asset ownership, 20
broad definition of the industry, then<sup>their decision, like that of the ITC</sup> minority, would have been based on the aggregate<sup>data listed in the bottom of</sup> the table. The first three indicators trended<sup>upwards for the industry as a</sup> whole, and while the fourth trended downwards,<sup>members of the ITC minority</sup> argued that this was due to the inclusion<sup>of increased expenditures for</sup> capital and research and development<sup>within the financial data. Thus, the use</sup> of a broad industry definition would<sup>have led the ITC to conclude that the</sup> industry was healthy and not injured by<sup>unfair imports.</sup> Have these affirmative decisions translated<sup>into actual protection for</sup> U.S. growers? No. The protective effect<sup>of the mass filings has been modest,</sup> despite the imposition of dumping and<sup>countervailing</sup> duties. Consider the case of Colombia which accounts for<sup>nearly all of the imports affected by CVD</sup> and AD rulings. In the CVD case,<sup>the DOC ruled that a 1.09 percent ad valorem</sup> duty be applied to imports of miniature<sup>carnations from Colombia. (Miniature</sup> carnations had been excluded from the<sup>earlier CVD case filed against roses and</sup> other cut flowers.) However, like all<sup>previous CVD cases, Colombian growers</sup> quickly surrendered any benefits and<sup>the U.S. government did not apply</sup> countervailing duties. In the AD cases, Colombian<sup>imports of carnations and</sup> chrysanthemums were assessed dumping duties of 3.53<sup>percent ad valorem. These</sup> duties, while troublesome, are not<sup>onerous. They also do not support U.S.</sup> growers' claims that Colombia's market<sup>dominance is based on unfair trade,</sup> particularly since the DOC's methodology biases dumping margin<sup>calculations</sup> upwards.

# B. No winners

No one appears to have been the winner in these<sup>trade actions. Although</sup> U.S. growers used the unfair trade laws<sup>until they obtained affirmative injury</sup> decisions, the actual protection they received<sup>was modest and certainly much</sup> less than they expected. Similarly, even though<sup>the trade sanctions imposed</sup> on Colombians were modest, the litigation and<sup>other unmeasurable costs have</sup> been significant.

21
|4|Construction of buildings|29234|2528|8.0|3.9|
|---|---|---|---|---|---|
|5|Manufacture of textiles|4806|2188|31.3|3.3|
|6|Human health activities|2083|1183|36.2|1.8|
|7|Manufacture of food products|3925|1061|21.3|1.6|
|8|Food and beverage service activities|5175|741|12.5|1.1|
|9|Financial service activities, except insurance,<br>pension|1886|352|15.7|0.5|
|10|Wholesale trade, except of motor vehicles and<br>motorcycles|4416|231|5.0|0.4|
|11|Accommodation|512|194|27.5|0.3|
|12|Fishing and aquaculture|1189|158|11.7|0.2|
|13|Land transport and transport via pipelines|15307|114|0.7|0.2|
|14|Insurance, reinsurance and pension funding|721|112|13.4|0.2|
|15|Manufacture of basic pharmaceutical products|617|76|11.0|0.1|
|16|Telecommunications|853|71|7.7|0.1|
|17|Manufacture of beverages|313|45|12.6|0.1|
|18|Employment activities|61|23|27.4|0.0|
|19|Manufacture of furniture|2076|16|0.8|0.0|
|20|Water collection, treatment and supply|255|3|1.2|0.0|
||Total employees (in thousand)|233022|65645|22.0|100|

Source: ILOSTAT database, International Labor Organization Even the latest National Sample Survey (NSS) report on employment and unemployment in India (2013)<sup>14</sup> highlights that about 75 percent of female workers were engaged in the agricultural sector while only 44 percent of the male workers were engaged in the sector during this period. Similarly, 26 percent and 30 percent male workers were engaged in secondary and tertiary sectors and the corresponding proportions for female workers were 17 percent and 8 percent, respectively. Moreover, the industrial distribution of the workers in the urban areas was distinctly different from that of rural areas. In urban areas, the share of the tertiary sector was dominant followed by that of secondary sector while agricultural sector engaged only a small proportion of total workers for both male and females. In rural areas, nearly 59 percent of the male workers and 75 percent of the female workers were engaged in the agricultural sector while the share of urban workforce in agricultural sector was nearly 6 percent for male workers and 11 percent for female workers. In urban areas, about 59 percent of male workers and 55 percent of the female workers were engaged in the tertiary sector.

> 14 Key Indicators of Employment and Unemployment in India, 2011-12, NSS 68th Round, Ministry of Statistics and Programme Implementation, Government of India, June 2013.

15
Table B.3b: Changes in extreme poverty for the countries using <u>phone surveys</u>

|Ct|2019|2020 (co|unterfactual)|2020 (|COVID-19)|Net COVID-19|
|---|---|---|---|---|---|---|
|ounry|Rate,%|Rate,%|Change, pp|Rate,%|Change, pp|impact,pp|
|Burkina Faso|30.00|28.90|-1.10|31.50|1.50|2.60|
|Bulgaria|0.90|0.80|-0.10|1.00|0.10|0.20|
|Central African Rep.|67.70|66.90|-0.80|67.80|0.10|0.90|
|Congo, Dem. Rep.|62.30|62.10|-0.20|62.60|0.30|0.50|
|Gabon|2.40|2.40|0.00|2.70|0.30|0.30|
|Ghana|22.20|21.20|-1.00|22.00|-0.20|0.80|
|Guinea|13.40|12.50|-0.90|14.20|0.80|1.70|
|Gambia, The|10.30|9.80|-0.50|11.00|0.70|1.20|
|Guatemala|6.90|6.80|-0.10|7.40|0.50|0.60|
|Honduras|12.70|12.00|-0.70|15.60|2.90|3.60|
|Croatia|0.20|0.20|0.00|0.40|0.20|0.20|
|Kazakhstan|0.00|0.00|0.00|0.00|0.00|0.00|
|Kenya|25.20|24.00|-1.20|25.90|0.70|1.90|
|Lao PDR|6.60|5.70|-0.90|6.90|0.30|1.20|
|Mali|14.40|13.70|-0.70|17.20|2.80|3.50|
|Myanmar|1.10|1.00|-0.10|1.10|0.00|0.10|
|Mongolia|0.40|0.30|-0.10|0.70|0.30|0.40|
|Mozambique|63.20|63.00|-0.20|64.40|1.20|1.40|
|Mauritius|0.00|0.00|0.00|0.20|0.20|0.20|
|Malawi|69.10|68.50|-0.60|69.10|0.00|0.60|
|Niger|50.10|48.80|-1.30|49.60|-0.50|0.80|
|Nigeria|30.90|31.00|0.10|32.30|1.40|1.30|
|Philippines|4.10|3.50|-0.60|9.70|5.60|6.20|
|Poland|0.00|0.00|0.00|0.00|0.00|0.00|
|West Bank and Gaza|0.50|0.50|0.00|1.00|0.50|0.50|
|Romania|2.10|2.00|-0.10|2.30|0.20|0.30|
|Sudan|21.20|22.70|1.50|24.00|2.80|1.30|
|Senegal|9.00|7.90|-1.10|8.20|-0.80|0.30|
|Solomon Islands|24.80|24.80|0.00|27.10|2.30|2.30|
|El Salvador|1.40|1.30|-0.10|1.80|0.40|0.50|
|South Sudan|71.90|70.50|-1.40|72.30|0.40|1.80|
|Tajikistan|3.50|3.20|-0.30|3.60|0.10|0.40|
|<br>Tunisia|0.00|0.00|0.00|0.20|0.20|0.20|
|Uganda|40.50|39.30|-1.20|40.40|-0.10|1.10|
|<br>Uzbekistan|30.10|28.00|-2.10|30.20|0.10|2.20|
|Zambia|61.10|61.20|0.10|61.70|0.60|0.50|
|Zimbabwe|39.70|39.40|-0.30|42.50|2.80|3.10|

_Note_ : This table reports the poverty rates (%) for each country using the high frequency phone surveys. For 2020, poverty rates and the percentage points change in poverty from 2019 is reported for both the observed distribution (with COVID) and the counterfactual distribution (no COVID). The last column captures the percentage points difference between the 2020 distribution with COVID-19 and counterfactual distribution. pp - percentage points.

49
# 2.3 Regulatory and Institutional Features

Regulatory and institutional features of the labor<sup>marketinMoroccothat</sup> are important for understanding wage and employment formation<sup>includethe</sup> minimum wage, hiring and firing regulations, the matching<sup>processbetween</sup> supply and demand for labor, the structure of nonwage labor<sup>costs,andthe</sup> role of trade unions.

## **2.3.1 Minimum Wage Regulation**

Morocco adopted as early as 1936 a minimum wage legislation<sup>thatsets dif-</sup> ferent wages floors for urban and rural labor markets,<sup>andfordifferent age</sup> categories. Minimum wages are revised according to a<sup>formalprice indexa-</sup> tion mechanism, namely as soon as the consumer price<sup>indexincreasesby5</sup> percent. In practice, however, revisions are irregular and rather<sup>independent</sup> of the indexation rule. In fact, they are usually the<sup>outcomeofpolitical</sup> and discretionary decisions, following overwhelming<sup>pressurefromthe trade</sup> unions. The few available studies reveal that the urban minimum<sup>wagereg-</sup> ulation (which concerns mostly unskilled workers) is pretty<sup>wellenforcedin</sup> the private formal sector, through an active role of the administration<sup>and</sup> the trade unions. According to Benhayoun et al. (2001),<sup>only13percentof</sup> the wage earners in the private formal sector are unfairly<sup>paida salary lower</sup> than the minimum wage.

During the period 1970-2000, the nominal urban minimum<sup>wagehasin-</sup> creased by an average of 6 percent a year (see Panels 1<sup>and4,Figure2).</sup> In real terms, the urban minimum wage fluctuates, though<sup>slightly,dueto</sup> the ad hoc nature of the revision process. Over the<sup>period1970-2000,the</sup> real urban minimum wage rose on average by around<sup>1.1percenteveryyear.</sup> Over the 1990s only, it has increased by around 1.3<sup>percent,aratefaster</sup> than the rate of growth of labor productivity in the industrial<sup>formalprivate</sup> sector. These increases adversely affected the demand<sup>forunskilledlabor,</sup> and contributed to a rise in Morocco's unit labor<sup>costs,therebyaffecting</sup> Morocco's external competitiveness.

The urban minimum wage represents 50 percent<sup>of theaveragewagein</sup> the formal private sector and 178 percent of GDP per capita<sup>in 2000-a fairly</sup> high level in comparison to other developing countries.<sup>Benhayounandalii</sup> (2001) have found that minimum wage legislation influences,<sup>thoughweakly,</sup> the overall wage structure in the private formal sector.<sup>Theirstudyalso</sup> 12
Clearly, when biodiesel is less expensive than diesel, fuel blenders have an incentive to blend as much biodiesel as possible.<sup>23</sup> The U.S. Environmental Protection Agency (2007) recommends that biodiesel blends containing more than 20 percent of biodiesel should be evaluated on a case-by-case basis because of possible damage to the vehicle’s engine. Therefore, we set the share of biodiesel in the fuel blend to 20 percent in this scenario (column 8 in Table 2). The consumer fuel price decreases by 3 cents relative to the no-policy baseline because of two effects: ( _i_ ) biodiesel is less expensive than diesel and ( _ii_ ) blenders do not charge fuel consumers the compensatory payment as it was the case in the previous scenarios. The reduction in the fuel price has considerable and positive effects on fuel consumers whose welfare increases by $19.5 million. Diesel tax revenues decrease by $32.3 million, but this fiscal loss is more than offset by the revenues from taxing biodiesel, $36.5 million. Overall, therefore, the social welfare increases by $23.7 million. The foreign exchange balance worsens, however, relative to all previous scenarios because of a substantial increase in imports of soybean oil.

The scenario above is likely to underestimate the positive welfare effects and overestimate the negative foreign exchange balance, however. With increasing efficiency of biodiesel production in Zambia in the long run, especially if the industry is able to keep biodiesel production costs below the diesel price, one could expect more land to be converted into soybeans production which would result in more soybean oil and soybean meal produced, both of which have positive effects on the trade balance. Moreover, the welfare of soybean producers would increase as well.

_Comparison of the Welfare Efficiency of Alternative Biofuel Policies in Zambia_ While the blend mandate is currently the most debated biodiesel policy in Zambia, it is of interest to also analyze other policy options that could be used to produce the same amount of biodiesel. Thus, in this section we compare the market effects and efficiency, as measured by the societal welfare, of three alternative biofuel policies: a blend mandate, a biodiesel production subsidy, and a tax exemption,<sup>24</sup> for the same quantity of biodiesel production. The exogenous

> 23 Our sensitivity analysis shows that if the biodiesel production cost in Zambia is below $0.84/liter (for the current diesel price and fuel tax), the blending of biodiesel will increase the total social welfare relative to the no-biodiesel baseline. Moreover, for a biodiesel production cost below $0.815/liter, fuel blenders will blend the maximum share of biodiesel that is technologically allowed.

> 24 A soybean production subsidy is not considered because it cannot directly affect biodiesel production (especially when a compensatory payment is required to induce biodiesel production).

17
**<u>Table 4: Plot Level Characteristics by Treatment Category</u>**

||**No certificate**<br>**2004-2007**|**With certificate**<br>**2004-2007**|
|---|---|---|
|Plot size in hectares|0.31|0.34|
|Number of years possessed|20.84|21.44|
|Good soil quality|0.38|0.35|
|Medium soil quality|0.37|0.43|
|Flat land|0.57|0.72|
|Gently sloped land|0.34|0.23|
|Irrigated land|0.04|0.04|
|Number of observations(plots)|4699|3972|

**Source:** Own computation from AAU/Gothenburg/WB Survey

21