rafmacalaba/datause-encoder-data huggingface dataset:
5-8 in Table 8. In regression 7, the interaction Fiscal policy/GDP * Bonds/assets is negative and significant, indicating that debtholders benefit more from the PEPP if firms are more dependent on bond finance. In regressions 5 and 7-8, the interactions of Fiscal response/GDP with included financing variables are insignificant. Overall, Table 8 provides evidence of a positive effect of the interaction of monetary policy in the form of the PEPP and fiscal policies on equity and debt valuations, as prior fiscal measures are shown to enhance the positive effect of the PEPP announcement.

# **4. Conclusion**

<mark>The PEPP is a key element of the European policy response to the COVID-19 pandemic. The PEPP enables the ECB to spend an additional €</mark> 750 billion to purchase debt securities including corporate bonds. By increasing demand for corporate bonds, the ECB makes it easier for corporations to issue additional bonds, thereby improving firms’ chances of surviving the pandemic.

Using an event study methodology, this paper finds that investment-grade firms benefit especially from the PEPP as evidenced by higher share prices and lower CDS spreads, which is likely to reflect that the ECB is restricted to purchasing investment-grade debt securities.

However, non-investment grade firms benefit from the PEPP as well relative to firms without a credit rating, due to spillovers of anticipated ECB purchases to the non-investment grade bond market. Firms in euro as well as non-euro area countries were positively affected by the PEPP, providing evidence of PEPP spillovers to non-euro area countries.

<mark>While on average both shareholders and debtholders gain from the PEPP announcement, the division of the overall gains between shareholders and debtholders is found to reflect the preannouncement financial strength of the firm. In particular, the share of shareholder gains in total</mark> 27
measure. Irwin (1997) received an average of 3.4 abstract views and 0.8 downloads per month from the NBER working paper series during 2009. Then on February 16, 2010, Paul Krugman blogged about the paper on his “Conscience of a Liberal” blog, resulting in 940 abstract views and 151 downloads in February 2010.<sup>7</sup> The paper then went back to averaging 0.8 downloads a month and 5.3 abstract views a month from April 2010 through March 2011.

Similar patterns occur for other blogs. Figure 2 gives the example of Landry et al. (2006), which was averaging 14.4 abstract views and 5.2 downloads per month in the year before Freakonomics blogged about the paper, and then had 1,478 abstract views and 144 downloads in the month it was blogged about. Figure 3 shows abstract views and download statistics for Arai and Thoursie (2006), which averaged only 1.5 abstract views and 0.67 downloads per month in the year before Chris Blattman blogged about the paper, then had 57 abstract views and 11 downloads in the month the paper was blogged about.

# _2.2 Formal Estimation_

We systematically searched the blogs of the 50 most read finance and economics blogs (defined according to one list based on blogs with public traffic logs<sup>8</sup> ) for references to research papers in RePEc. In addition we also looked at six other popular blogs that were not included on this list (Aid Watch, Chris Blattman, Economix, Freakonomics, Paul Krugman, and Dani Rodrik). We included only papers where the blogger directly linked to the RePEc version of the working paper, and where the paper had been out for at least three months before being blogged about. We also exclude blogs which only linked to one or

> 7 The paper is in the NBER working paper series, which is gated (requiring payment) to readers from institutions which do not subscribe to the NBER series, limiting downloads.

> 8 <u>http://www.gongol.com/lists/bizeconsites/ [March 2011 data, accessed August 2011].</u>

7
areas have higher autonomy, for both religions, as do women who have had some level of education compared to those who have never been to school.

The results from the triple-difference specification (reported in table S.3 in the supplemental appendix) point in a similar direction. We see from the first column that women actually exposed to the reform have significantly better outcomes; the coefficients are in fact larger in magnitude compared to the difference-in-difference coefficients for all outcomes. The second column shows that the reform did not have a significant effect on the outcomes for the non-Hindus, barring the mobility variables. The third column indicates that, among the women married pre-reform, there is no significant difference between the Hindu and the non-Hindu women in the reform states.

# _Results of Placebo Tests_

Table 3 documents the results of the placebo tests. The coefficients reported are _δ_ 1 from specification (3) for the Hindu and non-Hindu samples based on different cutoffs for additional robustness—each row represents a specification with treatment group defined as women married after a certain number of years prior to the reform. For simplicity, the outcome variable I use here is an average of all the decision-making and mobility outcomes;<sup>15</sup> for comparison, the first row reports the coefficients from using this aggregated outcome variable in my initial diff-in-diff specification, which are positive and significant for Hindus and insignificant for non-Hindus. The coefficients for the Hindu women are quite close to zero and statistically insignificant, supporting the common trends assumption for difference-in-difference. The nonHindu coefficients are insignificant for most specifications but not all. This also suggests that the estimates from the diff-in-diff specification are more credible than triple difference, since the difference in trends between Hindus and non-Hindus were not always parallel across reform and non-reform states, violating the triple difference assumption. Moreover, as discussed earlier, the non-Hindu counterparts of the ‘treated’ women in the reform states, though identical to the ‘non-treated’ non-Hindu women for all the decision-making outcomes, appear to fare _worse_ in terms of the

> 15. Appendix table S.4 shows the coefficients for all outcome variables for the case where _τ_ = 10.

11
**Table 6 (Continued)**

||1|**Food C**<br>IHPS<br>2|**onsumption Score**<br>1|FIAS<br>2||
|---|---|---|---|---|---|
|**Weather Shocks†**||||||
|Weather Shock|-1.625||1.149|||
||(1.170)||(1.919)|||
|Current Shock||-1.368||1.084||
|||(1.201)||(2.004)||
|Sequential Shock||-6.616|***|-2.619||
|||(2.089)||(2.962)||
|**Aid * Shock**||||||
|Aid|-3.352|-4.327|4.645|3.649||
||(3.362)|(3.443)|(4.370)|(4.501)||
|Aid * Weather Shock|-1.861||-2.697|||
||(3.674)||(4.875)|||
|Aid * Current Shock||-3.085||-2.711||
|||(4.124)||(5.849)||
|Aid * Sequential Shock||10.583|**|5.220||
|||(4.779)||(5.537)||
|**Explicit HH Targeting Characteristics**||||||
|Dummy, Female Head|-0.972|-1.074|-1.911|-1.959||
||(1.043)|(1.046)|(1.547)|(1.550)||
|Dummy, Children<3|-0.085|-0.051|0.085|0.134||
||(.680)|(.681)|(1.020)|(1.018)||
|Dummy, All Members>59|-3.172|-3.275|*<br>-5.347|**<br>-5.745|**|
||(1.973)|(1.983)|(2.466)|(2.465)||
|Dummy, HH Member Disabled|-1.937|***<br>-1.957|***<br>-1.362|-1.347||
||(.750)|(.748)|(1.092)|(1.096)||
|**Wealth**||||||
|Wealth Index|32.252|***<br>32.356|***<br>33.435|***<br>33.571|***|
||(3.420)|(3.437)|(5.632)|(5.699)||
|# Mobile Phones|1.284|***<br>1.296|***<br>0.035|0.113||
||(.442)|(.447)|(.678)|(.693)||
|**Constant**|54.481|***<br>52.410|***<br>60.828|***<br>58.898|***|
||(10.065)|(10.094)|(15.520)|(15.444)||
|# of Obs.|4412|4412|1999|1999||
|R2 (within)|0.200|0.198|0.247|0.243||
|R2 (between)|0.578|0.578|0.470|0.469||
|R2 (overall)<br>**Notes:*****/**/* denote statistical si<br>**Weather Shock**is a dichotomous vari|0.452<br>gnificance at<br>able that is eq|0.450<br>1/5/10 percent res<br>ual to 1 if the house|0.373<br>pectively. Standard<br>hold has been expo|0.371<br>Errors in parenthes<br>sed to a weather sho|es.**†**<br>ck in|

**Notes:** ***/**/* denote statistical significance at 1/5/10 percent respectively. Standard Errors in parentheses. **† Weather Shock** is a dichotomous variable that is equal to 1 if the household has been exposed to a weather shock in the current period (irrespective of potential exposure to past weather shocks), and 0 otherwise. **Current Shock** is a dichotomous variable that is equal to 1 if the household has been exposed to a weather shock _only_ in the current period, and 0 otherwise. **Sequential Shock** is a dichotomous variable that is equal to 1 if the household has been exposed to a weather shock in consecutive periods, and 0 otherwise.

35
# **The Evolution of Agricultural Trade Flows**

## **M. Ataman Aksoy and Francis Ng**<sup>**1**</sup>

JEL classification: F01, F10, F13, Q17, Q18 Keywords: Agriculture, agricultural trade, agricultural prices, agricultural growth, net food exporters and importers

> 1 Development Research Group – Trade and International Integration at the World Bank. Corresponding Emails: ataman.aksoy@gmail and fng@worldbank.org.  The authors would like to thank Soamiely Andriamananjara for his helpful comments. The findings, interpretations, and conclusions expressed herein are those of the authors and do not necessarily reflect the views of the World Bank and its affiliated organizations.
Table 5: Fishing decision in 2007

||Owners||||Non|-owners||||
|---|---|---|---|---|---|---|---|---|---|
|VARIABLES|Fish in 07|||Fish in 07||||Report catch in|07|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|
|**AGGSHOCK**||||||||||
|Fraction of bad aid boats|-0.156|-0.504***|-0.687***|-0.796***||-1.071***|-0.830***|||
||(0.144)|(0.157)|(0.179)|(0.182)||(0.270)|(0.180)|||
|Bad0|||||0.151|||||
||||||(0.120)|||||
|Bad50|||||-0.479***|||||
||||||(0.0959)|||||
|Ln(number of good aid boats by 07)||||||||0.0631**|0.0419|
|||||||||(0.0321)|(0.0296)|
|Ln(number of bad aid boats by 07)||||||||-0.288***|-0.263***|
|||||||||(0.0641)|(0.0618)|
|D_bad house in 07||||-0.230**|-0.224**|-0.231**|-0.227**|-0.176*|-0.219**|
|||||(0.0900)|(0.0902)|(0.109)|(0.0945)|(0.0937)|(0.0992)|
|Boat owner and captain pre-tsunami|0.0413|-0.0601|0.00713|0.00233|0.00593|0.0437|-0.0191|-0.0485|0.00405|
||(0.0903)|(0.0965)|(0.105)|(0.111)|(0.111)|(0.126)|(0.115)|(0.114)|(0.122)|
|D_large boat pre-tsunami|-0.0349|0.0610|0.0366|0.0372|0.0291|-0.0241|0.0289|0.0240|-0.00797|
||(0.0763)|(0.0814)|(0.101)|(0.110)|(0.110)|(0.140)|(0.113)|(0.111)|(0.117)|
|Ln(number of NGOs in 2007)|-0.0995|-0.324***|-0.796***|-0.936***|-0.943***|-1.571***|-0.883***|-0.700***|-0.314|
||(0.0836)|(0.109)|(0.241)|(0.255)|(0.246)|(0.330)|(0.259)|(0.262)|(0.209)|
|D_debt to Toke Bangku|0.234***|0.150|0.176|0.193|0.193|0.204|0.200|0.108|0.222|
||(0.0718)|(0.151)|(0.144)|(0.152)|(0.144)|(0.207)|(0.152)|(0.161)|(0.177)|
|High education pre-tsunami|-0.162|-0.125|-0.112|-0.117|-0.0988|-0.186|-0.133|-0.134|-0.0738|
||(0.124)|(0.100)|(0.103)|(0.0999)|(0.104)|(0.159)|(0.0984)|(0.0947)|(0.101)|
|Number of surviving captains in 05||0.00563***|0.00752**|0.00994***|0.00755*|0.0109***|0.0100***|0.0118***|0.00793***|
|||(0.00157)|(0.00325)|(0.00335)|(0.00395)|(0.00408)|(0.00350)|(0.00368)|(0.00300)|
|Observations|154|148|147|147|147|115|147|147|128|
|Sample|Original||||Or|iginal||||
|Mean of dependent variable|0.740|0.453|0.449|0.449|0.449|0.574|0.463|0.463|0.445|
|Seasonality is controlled|Y|Y|Y|Y|Y|Y|Y|Y|Y|
|Regional fixed effects|Kab|Kab|Kec|Kec|Kec|Kec|Kec|Kec|Kec|

*** p<0.01, ** p<0.05, * p<0.1; Estimates in column (1)-(9) are marginal effects of probit model.  Column (6) drops unemployed households.  Column (9) drops households living in two kecamatans (Lhok Nga and Pulo Aceh).  Robust standard errors clustered at the village level are in parentheses.
Finance Commissions declined from 65 percent during 1969-74 to 58 percent of total net transfers in 1992-93 (World Bank 1995, p.44). (2) Transfers from the federal governmen **t** o state development plans. Such transfers are authorized by the Planning Commission, whose major responsibilities include formulating national five-year plan as well as annual plans. The plan transfers consist of formula-based unconditional transfers and specific purpose transfers some of which are matching grants. In 1992-93. transfers authorized by the Planning Commnission amounted to 38 percent of the total transfers (World Bank 1995, p.45). (3) Local government borrowing authorized by the central government. These are not transfers in the stnct sense.

T he following table provides a summary of the relative magnitude of the three types of transfers. Table 2.4. The Composition (%/o) of Transfers from the Center to the States, 1969-93

||Finance Commission<br>Transfers|Planning Commission<br>Transfers|Other<br>Transfers|
|---|---|---|---|
|Fourth Plan<br>(1969-74)|64.6|24.4|11.0|
|Seventh Plan<br>(1985-90)|61.0|35.1|3.1|
|1992-93|58.9|38.3|2.7|

Source: World Bank (1995), p.<sup>45</sup> .

lhe Finance Cmmiissiox, which is appointed every five years, is the agency that suggests the method for allocating the transfers based on revenue-sharing. It is not a standing body, however; it is dissolved after it has mnade the recommendation on transfer formula. Since the independence of India, there have been ten Finance Commissions, and the transfer formula suggested by the Tenth Finance Commission will cover the period 1995-2000 (Gurumurthi 1995). Currently, the pool used for transfers allocated by the Finance Commission consists of 85 percent of income tax and 45 percent of union excise duty. The tenth Finance Commission has proposed that for fiscal year 1995-96 the pool includes 47.5 percent of the reformed union excise duly (MODVAT) and 77.5 percent of income tax.

On the distribution method, all the commissions up to the Eighth Finance Commission (1984) followed what is known as the "gap-filling" approach. This consists of assessing the revenue receipts and expenditure based on the actual numbers and recommending non-plan deficit grants to fill the financing gaps arrived at on this basis. This approach has encouraged the state governments to understate the predicted growth oF their own tax revenues, to increase their commitments on non-plan expenditure, and to run deficit budgets in the expectation that their financing gaps would be filled by grants from the Finance Commission. Apart from encouraging inefficiency, this approach also resulted in relatively better off states qualfing for such grants while some poor states were not eligible (Gurumurthi 1995).

he tenth Finance Commission has suggested that the allocation adopt the following new criteria: (a) 20 percent on the basis on population; (b) 60 percent on distance of per capita income from the highest income 19
related to the absence of risk of no-confidence vote and the separation of powers under a presidential regime.<sup>32</sup> Our findings do not confirm this prediction.

There is, however, an increase, albeit sporadic, in the number of cases of significant impacts of _Pilkada_ on district spending in specific sectors during election years and in sometimes in the preelection year in the districts that had direct elections later (i.e., in 2006 and 2007). For example, expenditures in social protection increase significantly in 2005 and 2006 (between 45 and 52 percent) accompanied by large compensatory reductions in expenditures in environment and infrastructure over the same years so that total expenditures remain unaffected over the same years. After elections, the increases in social protection wither away. Similarly, expenditures on health increase significantly in the year of election and before in the set of districts that had direct elections in 2007.<sup>33</sup> In contrast to the pattern observed in the districts that had elections earlier, the increase in district spending in specific sectors during the election year is not accompanied by compensating reductions in the spending in other sectors in the 2007 election year, thus resulting in an increase in total district expenditures in that year. The increase in total expenditures, however, is not sufficiently high to be associated with a significant decline in the budget surplus (see shaded rows of Table 6).

Table 5 reports the estimates for regression equation (2) using the alternative classification of district expenditures in both levels (in log) and as a share of total expenditures. Expenditures by economic classification include total expenditures aggregated across the different sectors on: (i) capital, defined as expenditures on assets with durability of more than 12 months, (ii) goods and services, defined as expenditures on services and assets with durability of less than 12 months, (iii) personnel, which includes the salary of civil servants in each of the sectors, and (iv) “other”, which consist of expenditures (not necessarily in order of significance) on subsidies, interest payments, shared revenues with and support grants to subdistricts related primarily to increasing their fiscal capacity, unforeseeable spending related to natural disasters, and social assistance spending aiming to increase people’s welfare. The expenditures on personnel as well as those on “other” may be considered as especially amenable to “buying” political support around election time. Suharnoko et

> 32 Based on cross-country and time-series data, Persson and Tabelinni (2004a and 2004b) estimate that a constitutional reform that switched the form of government from parliamentary to presidential in a country drawn at random from their sample of 83 democracies in the 1990s would reduce the total size of government spending by about 5 percent of GDP in the long run.

> 33 This may be in part explained by increased spending on the local health insurance scheme ( _Jamkesda_ ), which left considerable discretion to local governments. _Jamkesda_ is discussed in more detail later in the paper.

19
prime leisure tourist sites such as the Western Peninsula or the islands and regions of biodiversity conservation.

Figure 6 below shows that revenue per tourist is one of the highest in Sierra Leone, and compares favorably with a high-end tourist destination of the region, the Seychelles. But that assumption will be a misleading one. First, the length of tourist stay is much longer in Sierra Leone than Seychelles, not because of its tourist attractions but merely the fact that majority of tourists who come to Sierra Leone are business travelers and that requires them to stay longer. Second, it has to be remembered that a bulk of the tourist expenditure in Sierra Leone is spent in airfare which is approximately 30 percent more expensive than neighboring countries (World Bank, 2009). This is because an English airline company Astraeus has recently combined flight operations with another English company BMI<sup>iii</sup> (owned by Lufthansa) for the London-Freetown route. The other scheduled flight from Europe, SN Brussels, is also 45% owned by Lufthansa. This has made the flight operations between Europe and Sierra Leone a virtual monopoly and this has resulted in artificial inflating of pricing. The same is true for airport-to-town transfer, which is currently done either on a helicopter, a hovercraft or a speed boat because of the absence of reliable public transport.<sup>iv</sup> And visa fees are at least 20 percent more expensive than neighboring countries. Finally, added to this is the problem that Sierra Leone lacks the capacity to develop goods and services that may contribute to a tourism value chain within the country. Most of the essential goods and services for tourism, ranging from construction materials to build lodging facilities to supply of food in restaurants are imported. Unless these issues are addressed properly, the economic gains from tourism are going to remain very low within Sierra Leone.

**FIG 6: Receipts per tourist (USD), 2007**

<!-- Start of picture text --> Seychelles<br>Sierra Leone<br>Mauritius<br>Ghana<br>Cape Verde<br>Namibia<br>Senegal<br>Mozambique<br>Gabon<br>0 200 400 600 800 1000 1200 1400 1600<br><!-- End of picture text --> <!-- Start of picture text --> Source: UNWTO stat<br><!-- End of picture text --> 14
_51_ In terms of hopes for improvement, 90 percent of the respondents hoped for improved benefits and supporting services such as health insurance and health services. In addition, 70 percent hoped for prenatal care and birth-control programs, and 50 percent would like day-care for their children. Other facilities desired are for dental and eye case services.

# **VI. Main Findings and Policy Recommendation**

The main findings of this survey can be summarized as follows. As with other studies, the main characteristics of the women workers surveyed are that they are mainly young, with 70 percent in the 15-24 age group, single and have less than three years work experience. This phenomenon does not necessarily mean that there is a high turnover of women workers. It simply reflects the reality that after marriage and having children, these women often do not go back to industrial work. Further, due to an abundant supply of labor, their positions are easily filled by others. It should be noted that the main difference between this study and others is the number of respondents (300), and that it is contemporary research which has the advantage of being undertaken after changes have occurred, in particular with regard to the minimum wage.

The main finding is that these is a mixed record with regard to the success of government stipulated regulations in terms of compliance, workers' knowledge, and effectiveness. This implies that government regulations and the government approach to policy need to be reevaluated, including consideration of workers' awareness of their rights, particularly to enable "self regulation" and thereby for companies to improve compliance to labor legislation. Clearly, in line with Agrawal (1995) the role of the labor union needs to be reviewed.

More specifically, if we go back to the five main questions raised by this study, the main findings and their policy implications are as follows.

# _VI I Minimum Wages_

The minimum wage legislation which has received a great deal of attention since 1993 appears to have a high compliance rate (80 per cent) among the respondents, especially with large companies and in two of the sectors, namely textiles and garments. As expected, a lower percentage of women workers in large scale companies (15 per cent) receive lower than the