rafmacalaba/datause-encoder-data huggingface dataset:
is where probabilistic catastrophe risk models can add value (see introduction and Appendix). Measures of meteorological drought therefore serve our objectives. Since we are using crop yield loss indices based on rainfall variability and tables of stationary data on crop and soil characteristics<sup>12</sup> , our regression analysis described in Section 3 asks the question:

- “What is the relationship between our selected poverty indicator (reduction in household consumption) and crop yield losses arising from rainfall variability?”

The household survey data forming the basis of the regression methodology contains self‐reported crop damage arising from sources other than rainfall variability. This field is controlled for in the regression methodology in order to extract as direct as possible a relationship between household consumption and meteorological drought.

Using a rainfall‐based index also confers the advantage of objectivity in the measure, removing challenges such as reporting bias that are present when working with reported crop yield statistics (Verma et al, 1988, Fermont and Benson, 2011). It is also more plausibly exogenous than a self‐ reported measure. However, we acknowledge the limitations of working with a modelled estimate rather than a direct measure of yield impact.

The relative importance of the Belg and Meher seasons for each Woreda was accounted for by taking a weighted‐index of the two seasonal values for crop yield shortfall. The regional summary is shown in table 2.1 below:

**Table 2.1: Weighting of cropping seasons, by region**

||Cropping<br>|Season<br>|
|---|---|---|
||Meher|Belg|
||Prod. Share|Prod. Share|
|Tigray|0.99|0.01|
|Afar|0.85|0.15|
|Amhara|0.99|0.01|
|Oromia|0.95|0.05|
|Somali|0.89|0.11|
|B.G.|0.99|0.01|
|S.N.N.P.R|0.81|0.19|
|Gambella|0.75|0.25|
|Harari|0.97|0.03|
|Addis Ababa|n/a|n/a|
|Dire Dawa|0.98|0.02|
|**Total**|**0.95**|**0.05**|

Source: Central Statistics Agency documentation, see annex for details.

The key questions to be answered in terms of the household regression model have been agreed as:

- To what extent can impacts statistically associated with a drought hazard measure be causally attributed to the drought (internal validity/robustness)?

> 12 See Box 1 for data inputs, which include additional non‐stationary, but low‐variability climate data

10
Social distance is proxied by the proportion of individuals in the district who share the same language, religion, and ethnic group. This is implemented as follows. From the census we have information on ethnic, religious, and language diversity in all districts of the country. From these we construct an index of similarity between individual h and the population of each district. Let m denote a speci fi c trait — e.g., ethnicity, religion or language — and let p<sup>m</sup> s<sup>bethe</sup> proportion of the population of district s that has trait m. Consider the trait mh of individual h. We expect h’s chances of fi nding a job, etc, to increase in the proportion of individuals in the district of destination who share the same trait. We therefore construct, for each destination and each migrant, a variable p<sup>m</sup> s<sup>h</sup> equal to the proportion of members of h’s with trait mh. For this migrant, the social distance between two locations i and s is p<sup>m</sup> s<sup>h</sup> − p<sup>m</sup> i<sup>h</sup> . The idea behind this measure is that individual h ‘ fi ts’ better in district s if the proportion of like individuals is higher than in his district of origin. We construct similar indices for language and religion. Note the similarity between p<sup>m</sup> s<sup>h</sup> and the commonly used index of ethno-linguistic fractionalization (ELF). The ELF index measures the probability that two individuals taken at random belong to the same ethnic or linguistic group. Variable p<sup>m</sup> s<sup>h</sup> measures the probability that an individual taken at random belongs to the same ethnic or linguistic group as the migrant and is thus the individual-equivalent of the ELF index for groups.

We seek to control for price di ff erences across locations. This is di ffi cult because we do not have detailed price data. We are mostly concerned about housing costs and prices of common household goods.

We use the price of rice as a proxy for the price of common household goods. This is not entirely satisfactory but in the absence of a district-level consumer price index this is the best we can do. Given the mountainous nature of the country, rice cannot be grown in many parts of the country. The price of rice thus tends to rise with altitude and geographical isolation, as we 18
**Table 4: Bivariate probit model of the use of destructive fishing gear**

||_Use Destr_<br>_Fishing G_|_uctive_<br>_ear (I)_|_Fish R_<br>_Species_|_eef_<br> _(II)_|_Use Dest_<br>_Fishing G_|_ructive_<br>_ear(III)_|_Fish Pe_<br>_Species _|_lagic_<br> _(IV)_|
|---|---|---|---|---|---|---|---|---|
|_Independent_<br>_Variables_|_Coefficients_|_Standard_<br>_Errors_|_Coefficients_|_Standard_<br>_Errors_|_Coefficients_|_Standard_<br>_Errors_|_Coefficients_|_Standard_<br>_Errors_|
|Own destructive fishing gear (IV|||||||||
|<br>estimate)|4.484***|0.535|1.842***|0.389|4.590***|0.579|0.294|0.341|
|<br>Rent fishing gear|1.727***|0.232|0.287|0.330|1.741***|0.236|0.534**|0.249|
|Large boat/dhow|0.515|0.320|0.817***|0.222|0.458|0.321|1.139***|0.305|
|Outrigger or dugout canoe|0.567***|0.192|1.184***|0.209|0.523***|0.199|1.077***|0.250|
|<br>Fishing primary activity|0.174|0.244|0.281|0.185|0.150|0.250|0.474**|0.222|
|<br>Food secure|-0.404**|0.211|-0.114|0.256|-0.398*|0.245|-0.259*|0.155|
|Household size|0.074**|0.028|0.006|0.033|0.068**|0.025|-0.020|0.034|
|Female head of household|0.883***|0.228|-0.357|0.393|0.900***|0.245|-0.176|0.397|
|Mean male education|-0.073**|0.037|0.020|0.023|-0.077**|0.037|-0.006|0.029|
|Mean female education|0.042|0.029|-0.017|0.026|0.046|0.028|-0.024|0.025|
|Own land|-0.231|0.276|-0.111|0.201|-0.249|0.277|-0.099|0.123|
|Land area cultivated|-0.002|0.026|0.008|0.018|-0.011|0.025|0.029*|0.017|
|Access to credit|0.465**|0.229|0.250*|0.141|0.439**|0.218|-0.064|0.179|
|Value of physical assets|-3.04e<sup>-07**</sup>|1.77e<sup>-07</sup>|-5.05e<sup>-07***</sup>|1.75e<sup>-07</sup>|-3.26e<sup>-07</sup>*|2.02e<sup>-07</sup>|-1.17e<sup>-07</sup>**|8.42e<sup>-07</sup>|
|Participates in village meetings|0.098|0.064|-0.004|0.043|0.101|0.066|-0.166***|0.058|
|<br>Participates in AIGA|-0.993***|0.330|0.203|0.193|-0.978***|0.318|0.248|0.263|
|<br>Aware of MPA affairs|-0.054|0.213|0.145|0.213|-0.055|0.191|-0.091|0.193|
|MPA village|0.189|0.201|-0.399***|0.163|0.201|0.201|-0.181|0.131|
|Observations|655||655||655||655||
|Log likelihood|-5337||||-5156||||
|Wald test of rho=0:|chi<sup>2</sup>(1)=5.08|;Prob>chi<sup>2</sup>|=0.02||chi<sup>2</sup>(1)= 1.15|;Prob>chi<sup>2</sup>|=0.28||

Notes: *** , **, * indicate significance at the 99, 95, and 90 percent confidence interval, respectively Robust standard errors reported, adjusted for clustering effect <u>Coefficient for dummy variables for each of the MPAs and constant not reported</u> 31
65

## _Energy Policy and Prices_

Among the measures in the goverunent policy package set in motion in A982 were depreciation of the Zimbabwean Dollar by 25 percent between December 1982 and January 1983 and a major increase in taxes and utility tariffs, including electricity. Given that nearly 38 percent of domestic electricity consumption was imported from Zambia at the time, increases in domestic utility prices were a important measure of the effect of devaluation in consumer prices. A general increase in low domestic electricity prices was also undertaken to mobilize revenues from the mosty state-owned electricity sector. Consequently, a sizable increase in domestic electricity prices was implemented between 1983-84. when prices were increased by nearly 95 percent Figure S.1 shows the changes in domestic energy prices in Zimbabwe during 1982-84.

_Figure 5.1: Energy Price Changes in Zimbabwe percentagep.a)_ <!-- Start of picture text --> 50%<br>40%<br>30%<br>20%-<br>D 10% -<br>0%<br>-10%<br>-20%<br>1982 1983 1984<br>U Diesel a Electricity O Gasoline U Kerosene<br><!-- End of picture text -->

# **Effeet of Enery Price Changes**

_Flectricity Price Increases 1983-84: The Industrial Sector_ Given fte donunance of electricity consumption by the industrial sector it is reasonable to expect an increase in electricity prices to have had significant consequences for ihe industrial sector. What was the impact of 1983-84 electricity price increase for tis sector? To assess the impacts it is important to examine
rated firms for the other pollutants as well, but without the striking differential for poorly-rated firms. The same general pattern holds in Table 8, with generally-declining effluent concentrations across all rated firms and the largest impacts among poorly-rated firms. Although some concentration results are highly significant, the overall significance level is somewhat lower than for pollution intensities. Across both tables, red-rated firms exhibit stronger responses than black-rated firms.

# **6.  Summary and conclusions**

This study has employed a new panel data set to test the impact of environmental performance rating and disclosure (PRD) on polluting firms in China. The data include ex ante and ex post pollution measures for both rated and unrated firms, enabling us to control for confounding factors such as time-variant technology improvement and selection bias. Our results strongly suggest that Green Watch has significantly reduced pollution from rated firms, with particularly strong impacts on firms with poor ratings. Among poorly-rated red and black firms, the impact is generally greater on red-rated firms that are closer to compliance with regulations. The reasons for these responses can be that the incentive for improvement that the Green Watch generates is stronger for firms with poor ratings than those with good ratings, and that the abatement costs for the red-rated firms to achieve compliance are lower than those black rated firms, even though the pressure for improvement can be stronger with the black-rated firms than the red-rated firms.

This research also adds some insights to the growing comparative literature on PRD’s. After studying PRD experiences in Indonesia (PROPER) and the Philippines (EcoWatch), Dasgupta et al. [6] argue that PRD programs are most effective in moving moderately non-compliant firms
# **2. Setting the context: The Philippine economy and the crisis**<sup>**1**</sup>

The Philippines enjoyed high growth throughout the 1990s and into the early 2000s. These years were also witness to a decline in poverty, as the proportion of Filipinos leaving below $1.25/day fell from 34.9 to 22.0 between 1985 and 2003. This is equivalent to a 35.2% reduction in poverty during the period. Despite this general trend, however, estimates using the $1.25 benchmark show that poverty increased in recent years, consistent with official estimates. Between 2003 and 2006, the Philippines’ $1.25/day poverty increased from 22.0 to 22.6 percent (or 1.7 million people). Meanwhile official estimates show an even higher increase in poverty, from 30.0 to 32.9 percent.

As in many developing countries, there is wide variation in Philippines in the incidence of poverty across regions and between rural and urban areas. Rural poverty rate was 46% in 2006, compared to an urban poverty rate of around 20%. Regional variation in poverty rate was as significant – ranging from 10% in NCR to 47% in Mindanao. In addition, poverty rates were around 20% or below for Central Luzon and Calabarzon, and 35% or higher in Other Luzon, Visayas and Mindanao.

In addition to income poverty, there are myriad development challenges facing the country. The country has a high Gini coefficient (an index of income inequality) as compared to its Asian neighbors, and many key MDG targets, especially those related to universal primary education and certain health indicators, are unlikely to be achieved by 2015.

The financial crisis, coupled with the food and fuel crisis of 2008, is likely to have a significant impact on poverty in the Philippines, which has been suffering from lagging labor market outcomes since the start of the decade. Real wages have been declining since 2001 (especially since the food price shocks of 2007‐08), and unemployment was persistently high, averaging 7.4% in 2008. 50% of the unemployed were in the 15‐24 age group and there is a high incidence of unemployment even among the college‐ educated.

An interesting feature of the Filipino economy is that there has historically been a low (and often slightly negative) output elasticity of employment, which translates to low responsiveness of employment rate to economic growth. Although the exact reasons for this are unknown, it is hypothesized that this is due to either an increase in returning Overseas Filipino Workers (OFWs), increased labor market participation from spouses and children, or increased employment in informal sectors, accompanied by a decline in the wage and salaried employment.

## **_Macroeconomic projections of crisis impact_**

The nature of the Filipino economy, as well as real‐time evidence that has emerged during the crisis, suggest that the impact of the crisis in Philippines would be transmitted primarily through the following channels.

> 1 All figures cited in this section, as well as the ensuing discussion, are based on findings from World Bank (2009a) and World Bank (2009b).

2
- 10

-

Table 2: DOMESTIC INTEREST RATES, 1981-88 (annual averages; % p.a.)

||1980|1981|1983|1984|1985|1986|1987|
|---|---|---|---|---|---|---|---|
|Deposit rates /_|6.0|<br>6.0|<br>14.6|<br>18.0|14.5|<br>14.6|<br>17.0|
|Real deposit rates /b|- 11.0|-10.7|<br>2.8|<br>7.5|<br>9.8|8.8|<br>7.5|
|Average lending rates<br>(all banks) /c|15.0|15.0|<br>18.7|23.0|<br>20.5|<br>20.2|<br>21.2|
|Real lending rates /d|- 10.0|<br>4.8|<br>4.4|<br>11.0|13.9|<br>13.1|<br>11.4|

/a 6 month deposit rate of state banks /b Using CPI-17 cities as deflator /c Weighted average interest rates for non-priority credits /d Using non-oil GDP deflator Source: Bank Indonesia and World Bank staff estimates.

regarding the appropriate role of monetary policy in determining domestic interest rates. A second issue is the persistent downward trend of the nominal exchange rate and its impact on inflation. Finally, also at issue is the role of monetary policy in controlling inflation. On the one hand, inflation is considered to be primarily determined by structural forces (rice price behavior, exchange rate changes, international inflation). On the other hand, it has been argued that inflation is purely a monetary phenomenon.
to the school/community level improved grade progression (and reduced repetitions) but the impact on students’ learning outcomes, measured by test scores, was insignificant.

Pradhan et al. (2010) assessed randomized interventions to strengthen school autonomy in Indonesia. They found that, nearly two years after implementation, measures to reinforce existing school committee structures, the grant and training interventions, demonstrate limited or no effects, but measures that foster outside ties between the school committee and other parties, linkage and election, lead to greater engagement by education stakeholders and to an improvement in learning outcomes. The findings suggest that the way to combine different elements of school autonomy together with appropriate trainings is important to improving learning outcomes.

The second group of SBM impact evaluation studies combines panel data and matching methods to control endogenous program placements and potentially differentiated trends between treatment and comparison schools.<sup>6</sup> Skoufias and Shapiro (2006) evaluated the impact of increased school resources and decentralized management decisions combined in Mexico. They find that the reform decreased drop-out, failure and repetition rates, but the magnitude of the impacts is generally small. Related to the current study, Khattr, Ling and Jha (2012) recently used school-level test score data in the Philippines to assess the impact of SBM on learning outcomes. They showed that the introduction of SBM had a significant impact on students’ test scores but its magnitude is quite small, that is, an increase by about 1.5 points in percent score over two years.

# **3. Background**

> 6 King and Ozler (1998) and King, Ozler and Rawlings (1999) assessed the SBM impacts in Nicaragua by distinguishing between _de facto_ and _de jure_ autonomy. They found that the _de facto_ devolution of decision making to the school/community level matters but the _de jure_ devolution does not. Similar to many other studies, they only found significant impacts on progression/repetitions, not on learning outcomes. Parker (2005) basically confirmed the same conclusion in Nicaraguan schools – the SBM impact on learning outcome is ambiguous.

5
to its marginal return on investment.

Note that the bailout hinges on the assumption that the advanced country’s government has a better enforcement mechanism to ensure repayment than the private sector. This allows the advanced country’s government to make loans to the developing country, when the private sector is no longer willing to lend even at a higher interest rate.

# **6 Conclusion**

This paper presents a simple model to examine a transmission mechanism in which a productivity shock originating from an advanced economy can have potentially large and persistent impacts on a developing country’s investment and output. The transmission works through the global integration of asset markets and the credit constraint of the developing country. The impact can be particularly devastating if the developing country is highly leveraged. The results find some empirical support with the 2008-2009 crisis data.

To the extent that relaxing credit constraints in bad times will help developing countries raise capital and boost investment significantly, some amount of assistance, in the form of grants or long term debt, from advanced economies or international financial institutions can help developing countries recover from a recession caused by a negative external shock.

# **References**

- Bernanke, B. S., M. Gertler, and S. Gilchrist (1999, April). The financial accelerator in a quantitative business cycle framework. In J. B. Taylor and M. Woodford (Eds.), _Handbook of Macroeconomics_ , Volume 1 of _Handbook of Macroeconomics_ , Chapter 21, pp. 1341–1393. Elsevier.

- Blanchard, O., H. Faruqee, and M. Das (2010). The impact effect of the crisis on emerging market countries. _Mimeo_ .

- Caballero, R. J. and A. Krishnamurthy (2001). International and domestic collateral constraints in a model of emerging market crises. _Journal of Monetary Economics 48_ (3), 513–548.

- Calvo, G. A. (1998). Capital flows and capital-market crises: The simple economics of sudden stops. _Journal of Applied Economics I_ , 35–54.

19
# **2. Data and Methodology**

# _2.1 Data_

The paper uses specialized multi-national cross-sectional data from the first round of the 2020 COVID-19 Disparities Survey to identify the multidimensional impacts of the COVID-19 pandemic on cismen, ciswomen, transgender, and non-binary people. The first round of the 2020 COVID-19 Disparities Survey was collected between April–May 2020 using the social networking app Hornet. Hornet is a queer social networking app with over 30 million users worldwide, including in South Asia, East Asia and Pacific, Europe and Central Asia, Latin America and the Caribbean, Middle East and North Africa, North America, and Sub-Saharan Africa (Santos et al. 2020). Hornet users aged 18 years and older were invited to participate in the survey and 7,427 responses were obtained. While the survey may not be representative of the LGBTI population, nonetheless, it collects valuable information from Hornet users on the impact of COVID-19 on their economic condition, mental distress, and HIV prevention, testing, treatment, and care.

For this study, we categorize gender identity based on the survey question asking respondents to define their identity. We group respondents as cismen, ciswomen, transgender, and non-binary people based on their responses.<sup>2</sup> Note that the gender identity response categories of cismen, ciswomen, transgender, and non-binary people capture respondents of varying sexual orientations such as heterosexual, gay, lesbian, bisexual, and others.<sup>3</sup> The sample in this study consists of the cismen, ciswomen, transgender, and non-binary people for whom all the information on the selected income, consumption, mental distress indicators, and individual, household, and regional characteristics are available. Table 1 summarizes the sample distribution of these individuals by their gender identity. It is important to note that the sample

> 2 As respondents were allowed multiple responses to identify their gender identity, we consider individuals with more than one response non-binary.

> 3 In the survey, respondents were also asked regarding their sexual orientations. Due to data limitations in each category of sexual orientations (heterosexual, gay, bisexual, and others) of respondents, this paper only focuses on analyzing indicators based on respondents’ gender identity.

3
owned by the state and is prohibited to be sold.<sup>14</sup> The scope of rights included in the questionnaire was influenced by Schlager and Ostrom’s (1992) theoretical framework which focuses, in the context of natural resources, on issues related to access, withdrawal, management, exclusion and alienation while defining a bundle of rights.

**Table 1: LSMS+ Survey Questions on Rights and Decision-Making**

|**Rights over land parcels:**|
|---|
|1) **Sell:**Are you among the individuals who have the right to sell the land, even if you need permission or<br>consent from someone else?|
|2) **Bequeath:**Are you among the individuals who have the right to bequeath the land, even if you need<br>permission or consent from someone else?|
|3) **Collateral:**Are you among the individuals who have the right to use land as collateral, even if you need<br>permission or consent from someone else?|
|4) **Rent:**Are you among the individuals who have the right to rent out the land, even if you need permission<br>or consent from someone else?|
|5) **Improvement**: Are you among the individuals who have the right to make improvements/invest in the<br>land, even if you need permission or consent from someone else?|
|**Additional ownership and decision-making variables:**<br>1)**Documented ownership**: Is your name among the names listed as owners on the document for this land?<br>2)**Economic ownership**: If the parcel were to be sold today, would you be among the individuals that<br>would decide how the money would be used**?**|
|3)**Parcel decision-making:**Are you among the decision-makers about the parcel, regarding the timing of<br>crop activities, crop choice, and input use?|

Notes: In Ethiopia, the question on selling right was not asked. Questions were asked to individuals who report themselves as the owner or have use rights. In Malawi, questions were asked only to owners.

Additionally, the LSMS+ modules further asked landowners to identify whether ownership is joint or exclusive, and separately whether permission is needed to exercise rights. Up to three household members and two non-household members can be listed who share ownership/give permission. The design and implementation of these modules therefore acknowledges that there are varying

> 14 Ethiopia’s constitution established that the right to ownership of land belongs “exclusively to the State and the peoples of Ethiopia.” Individuals have the right to use land allocated to them; however, they do not have the right to sell the land. Additional background information on land tenure in Ethiopia can be found in Crewett et al. (2008a), Crewett et al. (2008b), and Dokken (2015).

7