rafmacalaba/datause-encoder-data huggingface dataset:
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||Str|ucture of the|East Caribbe|an General/|Property Ins|urance Ma|rket (1998)|||
|---|---|---|---|---|---|---|---|---|---|
||Antigua<br>and<br>Barbuda|Barbados|Dominica|Grenada|St. Kitts<br>and<br>Nevis|St.<br>Lucia|St.<br>Vincent<br>& the<br>Grendns|Trinidad<br>&<br>Tobago|Total|
|No. of|16|22|13|19|9|21|12|29|141|
|General<br>Insurance||||||||||
|Companies||||||||||
|% Foreign<br>Companiesa|88%|45%|95%|76%|67%|86%|85%|12%|66%|
|Property<br>Insurance<br>Premiums<br>(Gross)|$ 10.6<br>million|$ 43.0 m.|$ 5.8 m.|$8.6 m.|$6.3 m.|$11.9<br>m.|$ 9.6m.|$ 52.1 m.|$148<br>mn.|
|Gross<br>Premiums<br>% of GDP|1.8%<br>.|3.0%|2.4%|2.7%|2.4%|2.1%|3.3%|0.9%|--|

a. Of these, approximately /4 are companies from Trinidad, Barbados and Guyana.

Insurance Sector Regulatory Characteristic in the East Caribbean Markets ($US Dollars Equivalent)

||Antigua<br>and<br>Barbuda|Barbados|Dominica|Grenada|St. Kitts<br>and Nevis|St. Lucia|St. Vincent<br>and the<br>Grenadines|Trinidad<br>and<br>Tobago|
|---|---|---|---|---|---|---|---|---|
|Minimum<br>Capital<br>Requirement|$0.07mn|$1.5 mn|$0.07 mn.|$0.09<br>mn.|$0.07mn.|Local<br>$0.10m<br>Foreign<br>$0.20m|$0.07 mn.|$0.16m|
|Solvency<br>Margin<br>(Assets -<br>Liab.) above<br>premium<br>income|Min.<br>capital<br>or 10 %<br>of prem.<br>Income|$0.25mn.<br>or 25%<br>of<br>premium<br>income|Min.<br>capital or<br>10 % of<br>premium<br>income|$0.06m<br>or 10%<br>of<br>premium<br>income|Min.<br>capital or<br>10% of<br>premium<br>income|$0.07m or<br>20% of<br>premium<br>Income|Min.<br>capital. Or<br>10% of<br>premium<br>income|Min.<br>capital. or<br>20% of<br>premium<br>Income|
|Reserve<br>Requirement|na|40% of<br>annual<br>premium<br>income|30%<br>annual<br>premium<br>income|na|10% of<br>annual<br>premium|40% of<br>annual<br>premium<br>income|na|40% of<br>premium<br>income|
|Premium tax|na|5%<br>_|None<br>____|na|5%|Local: 3%<br>foreign:<br>5%|3%|6%|
|Corporate<br>tax|40%|40%|30%|30%|38%|33.3%|40%|35%|

# _Observations on East Caribbean Market Characteristics_

The proportion of residential and commercial properties in the Caribbean covered by insurance is significantly higher than in most developing countries, on account of both the susceptibility to natural disasters but also on the influence of tourism and the requisite insurance of tourist facilities. In comparison with the insurance density in the U.S. (3.3% of GDP for the property and casualty business), the average for the OECS, Barbados and Trinidad & Tobago is 2.3% of the combined countries' GDP.
**4**

shares. Due to differences in factors like the level of development, the import structures (and trade barriers) of countries in South Asia, non-OECD) East Asia, and OECD Asia are analyzed separately. Table 1 identifies the major exporters in each group while the World Bank (1992, Table Al, p. 40) provides a full listing.

Statistics on Asien countries' trade barriers were drwn from two sources. Tariff data were compiled directly from GAIT documents and then compute.ized and stored in the World Bank-UNCTAD SMART (Software for Market Analysis and Restrictions to Trarle) data base. (See UNCTAD-World Bank, 1989 for a description of the SMART system). Information on Asian countries' nontariiff barriers was drawn from UNCTAD records and also incorporated in the SMART system. Since the tariff and NTB dati were recorded at the tariff line level, available concordances were used to aggregate these data to Standard International Trade Classification (SITC) groups. This procadure allowed the UN trade data to be matched directly with Asian countries' tariff and NTB infornation.

**Im.** The Coimmodity **Structure** of Asian Trade

Three key points should be considered in an assessment of escalation issues: (1) whether Asian countries' imports reflect a major bias against semi-finished and processed products; (2) whether a similar bias is reflected in Asian commodity exports; and (3) whether Asian trade barriers escalate. If these conditions are found to exist, the subject of escalation should be addressed in Asian trade negotiations.' 'An important conceptual atl ampiical issue is how to deternine if a bias exists in Asian trade and, if it does, what its magnitude is. In this stusy, the _ mnmodity import structure of the European Community (EC) is sometimes used as a "standard' for comparison. Two po - -should be noted with regard to this choice. First, Balassa (1968), Yeats (1984) and others argue " at a signifiu.. ias against processed commodities exists in EC imports and have assessed the influence of contributing factors. Here, any Asian import bias is measured relative to that of the EC. Second, separate tests conducted by the authors show #hat the overall commodity import structure of the United States is quite similar to that of the EC although there are some differences for several individual commodities (see Box 2 for details). Thus, the conclusions of this study would not differ significantly if the United Statee rather than the EC were employed as the standard for comparison.
the effects are moving in the expected direction – we observe a decrease in the proportion of zeros in letter recognition and small increases in literacy and numeracy skills. Because we only have 21% of the study sample, we are not surprised about the lack of statistical power for the estimated treatment effect.

Table 7: Treatment Effects for Target Child on Literacy and Numeracy Test Scores: Pre-Covid _Sample 1_ March 2020

|Variables|_Literacy_<br>Prop. Zero<br>(1)|_Skills_<br>Z-score<br>(2)|_Numeracy Skills_<br>Z-score<br>(3)|
|---|---|---|---|
|Videos (T1)|-0.025|0.034|0.035|
||(0.049)|(0.109)|(0.107)|
|Observations|1,812|1,812|1,812|
|R-squared|0.265|0.369|0.320|
|Covariates|YES|YES|YES|
|Enumerator FE|YES|YES|YES|
|Baseline Mean|0.956|0.000|0.000|
|Control Mean in F-up|0.685|-0.715|0.318|

Note: Column 1 shows the results of linear probability models of the proportion of children scoring ”zero” in letter recognition. Column 2-3 show z-scores of literacy and numeracy skills, respectively.Test scores are normalized using the mean and standard deviation for the control group at baseline. All regressions have clustered standard errors at the school catchment area level. The covariates considered in all regressions are: age of child, gender of child, education of household head (baseline), availability of food (baseline) and enumerator fixed effects. *** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1 Table 8 shows that _2_ and _3_ the same conclusion as our main _Sample Sample_ provide outcome in Table 3. Overall, aspirational videos alone ( _T_ 1) did not have a significant impact on learning outcomes. It was the combination of both interventions that had a large impact on literacy and numeracy skills for the target child. In addition, we also observe that the impact on learning outcomes is slightly higher for the sub-samples a smaller proportion of school closures. This suggests potential complementarities of school activities and home learning using the mobile apps.

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|Panel A<br>Hours Idle<br>Time Chores<br>Time Social<br>Time Ration<br>(1)<br>(2)<br>(3)<br>(4)<br>(5)<br>(6)<br>(7)<br>(8)<br>Female<br>Male<br>Female<br>Male<br>Female<br>Male<br>Female<br>Male<br>Work<br>-0.092<br>0.127<br>0.135<br>-0.147<br>0.251<br>0.060<br>-0.067<br>-0.128<br>(0.161)<br>(0.112)<br>(0.281)<br>(0.203)<br>(0.195)<br>(0.186)<br>(0.162)<br>(0.100)<br>Cash<br>0.029<br>0.129<br>0.007<br>-0.306<br>-0.248<br>0.088<br>-0.011<br>-0.167<br>(0.221)<br>(0.144)<br>(0.285)<br>(0.202)<br>(0.214)<br>(0.234)<br>(0.168)<br>(0.115)<br>Test: Cash = Work<br>0.530<br>0.985<br>0.562<br>0.377<br>0.016<br>0.895<br>0.590<br>0.561<br>Shrp. q-val Cash=Work<br>0.795<br>1.000<br>0.795<br>1.000<br>0.070<br>1.000<br>0.795<br>1.000<br>Test: Male = Female<br>0.812<br>0.812<br>0.824<br>0.824<br>0.158<br>0.158<br>0.381<br>0.381<br>Shrp. q-val Male = Female<br>1.000<br>1.000<br>1.000<br>1.000<br>1.000<br>1.000<br>1.000<br>1.000<br>Observations<br>223<br>503<br>223<br>503<br>223<br>503<br>223<br>503<br>**Notes:** This table reports the treatment efect of the employment and cash arms on time use outcomes separately for male and female participants.<br>Each column shows the OLS estimates of Equation (1) including dummies for each treatment arm and controlling for the baseline measure of the<br>dependent variable (ANCOVA), camp and enumerator fxed efects, and sociodemographic controls as determined through a double-selection<br>LASSO procedure (Belloni, Chernozhukov, and Hansen, 2014), run separately by respondent gender. Each row presents the coefcients of the<br>specifed treatment relative to the control group (which received a small weekly cash payment).<br>All outcomes are measured in number of hours spent on the activity per day. ‘Idle’ is the average number of hours respondent reports being idle per<br>day. ‘Chores’ is the amount of time reported spend on household chores such as cooking or fetching water. ‘Social’ is the amount of time spend<br>socializing with others. ‘Ration’ is the amount of time spend getting household rations. ‘Market’ is the amount of time spend at the market. While<br>not an exhaustive list of all reported activities, these activities make up the bulk of the waking (daytime) hours of the average refugee respondent.<br>Efects on all activities available upon request.<br>Standard errors are clustered at the block level. The row labeled “Shrp. q-val Work” refects p-values, referred to as ‘sharpened q-values,’ adjusted<br>for multiple hypothesis testing using Anderson (2008). The adjustments are made within gender for all outcomes in this table (both Panel A and B).<br>The row labeled “Test Male = Female” refects the unadjusted p-value for the test of equality between the Male and Female work treatment efects<br>based on a pooled regression (not shown) where the treatment indicators were interacted with the gender of the respondent. The row labeled “Shrp.<br>q-val Male = Female” refects the adjusted p-value for this test of equality across all fve outcomes.|
|---|

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# **1. Introduction**

The 2007-2008 crisis is one of historical dimensions – few would dispute it as one of the broadest, deepest, and most complex crises since the Great Depression.<sup>1</sup> Its origins were in the United States (US) subprime housing finance market, which showed signs of trouble in the first half of 2007. Initially, this seemed to be a crisis of rather limited scope and many thought countries would be able to “decouple” from events in the US.<sup>2</sup> But after Lehman Brothers’ collapsed in September 2008, the crisis spread rapidly across institutions, markets, and borders. There were massive failures of financial institutions and a staggering collapse in asset values in developed and developing countries (see Figure 1). Nonetheless, the transmission of the US crisis was heterogeneous across markets around the globe with some countries showing higher comovement with the US than others (see Figure 2).<sup>3</sup> This paper empirically investigates the factors that determine stock markets’ vulnerability to the 2007-2008 crisis across 83 countries. We focus on stock markets’ reactions during the crisis because financial markets were the first to feel the effects of the crisis. Also, analyzing the behavior of stock markets is important because equity holdings have become a significant source of wealth for individuals around the world and, hence, a decline in asset values could affect consumption and other real variables. Finally, monthly stock market data over the crisis period is readily available for a significant number of countries, whereas at best annual data could be used for other variables like GDP or employment.

To study the transmission channels behind the current crisis, we explore the factors that drove the comovement between local and US stock market returns.<sup>4</sup> As argued by Claessens, Dornbusch, and Park (2001), an analysis based on correlations of stock returns sheds light on the cross-country transmission of shocks as they reflect cross-market linkages. In particular, we

> 1 The April 2009 Global Stability Report produced by the IMF estimates that write-downs in developed markets could reach US$4 trillion and those in emerging markets could amount to US$ 800 billion or 7 percent of banking assets. See http://www.imf.org/External/Pubs/FT/GFSR/2009/01/pdf/text.pdf.

> 2 The subprime market represented 15 percent of US total residential mortgages in 2006 and the latter accounted for 25 percent of US total debt (see Agarwal and Ho, 2007).

> 3 For each country, comovement with the US is measured by the coefficient of a regression of monthly local stock returns (i.e., the percentage change in the stock market index)  on US stock returns during the period July 2007 – April 2009.

> 4 See Didier, Mauro, and Schmukler (2008) for a discussion of the different channels through which crises can be transmitted.
incumbents fare better when they respond to disasters with emergency relief: disaster relief increases lead to voters’ rewards.

Bechtel and Haimuller (2010) explore the short and long-term electoral returns to disaster aid using the 2002 Elbe flooding in Germany as a natural experiment. Their findings extend previous results by Healy and Malhotra (2009) who show that voters reward incumbents for disaster relief but not for the more efficient disaster preparedness spending.

Our paper also relates to a different set of literature using quasi-experimental methods to show electoral response to government transfers. In general, these aim at providing empirical evidence in support of leading political economy theories that focus in trade-offs between consumption and political ideology. For example, Manacorda, Miguel and Vigorito (2011) estimate the causal effect of government transfers on political support for the incumbent party using data from Uruguay’s conditional cash transfer program called PANES. Arguing that PANES’ assignment near the threshold was as “good as randomly assigned”, they find that beneficiaries were between 25 and 33 percentage points more likely than non-beneficiaries to favor current government. In addition, they find that the effect of government transfers on political support is significantly larger among poorer households and among those near the center of the political spectrum as they are less attached to extreme political ideologies. In a similar study, Pop-Eleches and Pop-Eleches (2009) analyze the case of a Romanian program that awarded low income families with school age children vouchers for purchasing new personal computers and find that it had a significant impact on political attitudes and electoral behavior. In particular, voucher recipients were more likely to report vote intention in upcoming elections, and governing parties reaped most of the benefits of increased participation. They also find some evidence of vote switching from the main opposition party to the current incumbents and this effect was substantially stronger in towns where the governing parties controlled the local government.

This paper is structured as follows: Section 2 briefly describes the electoral context in Mexico and the Weather Index Insurance program. Section 3 presents the data. Section 4 discusses the statistical methodology and presents the main results. Finally, section 5 discusses the implications of the results found from the perspective of the study of political behavior and voter responsiveness to relief aid after a natural disaster.

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evaded, a greater redistributive role is found for indirect tax instruments (Boadway et al., 1994; Huang and Rios, 2016).<sup>48</sup> This discussion suggests that an extended model with direct tax instruments, even constrained, would lead to less optimal redistribution through consumption taxes. Jointly studying the optimal direct and indirect tax instruments over development would, however, require additional empirical moments, including how opportunities to evade income taxes vary along a country’s income distribution and as the country develops. Such an undertaking is beyond the scope of this paper.

# **7 Conclusion**

In this we how with household income both paper study consumption patterns vary within and across countries and derive implications for the optimal design and redistributive potential of consumption taxes. We consider two channels for redistribution: the de facto tax exemption of informal expenditure and the de jure tax exemption of necessities, in particular food. To measure informal expenditure, we harmonize expenditure surveys across 31 developing countries which contain information on the place of for each transaction. We each of to the informal or purchase assign place purchase formal sector using a robust assignment rule, and calculate the informal budget share at the household level. This enables us to characterize Curves: we Informality Engel find that informal budget shares decrease with household income in every country. This implies that the de facto exemption of the informal sector is progressive. We then extend the standard optimal commodity tax model to allow for informal consumption and calibrate it to our data to the effects of different tax on study policies inequality. Contrary to the existing consensus, we find that consumption taxes are redistributive, lowering inequality by as much as personal income taxes in developing countries. This effect is mainly driven by the existence of the informal sector. Once informal consumption is accounted for, reduced rates on necessities only have a limited impact on inequality.

Our findings have sharp implications for the use of reduced rates on necessities and on food items in particular. We find that differentiating rates across goods has limited redistributive potential once informal consumption is accounted for. In particular, these have no redistributive in some of the countries in our policies impact poorest sample. As practice shows, removing reduced rates on food and other necessities is often met with fierce resistance. An equity-improving policy would most likely have to combine

> 48For example, Huang and Rios (2016) find that incorporating income tax evasion and the existence of a consumption tax reduces the optimal income tax by 28%. Note that Boadway et al. (1994) and Huang and Rios (2016) both assume consumption taxes are perfectly enforceable. Given our findings, incorporating consumption tax evasion in their models would further reinforce the redistributive role of indirect taxes.

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percentage of the sample, however, – 28.8% - had access to safety nets, the most common being food-related safety net programs such as VGF and VGD (13.1%), followed by secondary school stipends/training programs (12.4%) while 9.2% of households had access to other cash-based safety net programs.

# Insert Table 2

The cross-sectional nature of the dataset poses some limitations for the analysis of vulnerability and risk-management approaches. For instance, the literature suggests households can adopt both _ex ante_ and _ex post_ coping strategies. One illustration is that households in a community may undertake _ex ante_ income-smoothing strategies and adopt a low-return, low-risk crop portfolio (Morduch, 1990; Dercon, 1996) or informally agree to insure each other in the event of crises (Rosenzweig, 1988; Besley, 1995). Panel data is ideal to capture both changes in any measure of vulnerability as well as the various coping strategies. The survey we use collects information mainly on _ex post_ coping strategies and, thus, we cannot account for any _ex ante_ coping mechanisms households may have adopted.

One other limitation arises from the scope of the questions asked in the survey. In particular, the questionnaire does not include any information on the impact or severity of the shock. For this reason, as we discuss below, we emphasize the capacity of households to deal at all with shocks, as reflected by their ability to find (ex-post) coping mechanisms to manage the shock.

In general, however, this survey allows us to gain new insights into, not only the shocks experienced by households in Bangladesh, but also the instruments used to cope with them. Next we discuss the key findings from the survey in this respect.

# **3. Shocks and coping mechanisms: A descriptive analysis**

In this section we present the data on the distribution of shocks in our sample. The objective is to present a description of what kinds of shocks occurred over the survey period, who was affected by them and what kind of coping mechanisms were adopted. We also analyze the extent to which the answers to these questions depend on the socio-economic status or geographic location of households.

5
**Figure 5: Decomposing Changes in Earnings by Percentile**

<!-- Start of picture text --> Overall<br>2003-1995 2012-2003 2012-1995<br>0.60<br>0.40<br>0.20<br>0.00<br>-0.20<br>-0.40<br>10 20 30 40 50 60 70 80 90 10 20 30 40 50 60 70 80 90 10 20 30 40 50 60 70 80 90<br>percentile<br>Observed difference Endowments Structure<br>Endowments<br>2003-1995 2012-2003 2012-1995<br>0.40<br>0.20<br>0.00<br>-0.20<br>10 20 30 40 50 60 70 80 90 10 20 30 40 50 60 70 80 90 10 20 30 40 50 60 70 80 90<br>percentile<br>Structure<br>2003-1995 2012-2003 2012-1995<br>0.20<br>0.00<br>-0.20<br>-0.40<br>-0.60<br>10 20 30 40 50 60 70 80 90 10 20 30 40 50 60 70 80 90 10 20 30 40 50 60 70 80 90<br>percentile<br>Total<br>2003-1995 2012-2003 2012-1995<br>0.20<br>0.00<br>-0.20<br>-0.40<br>-0.60<br>10 20 30 40 50 60 70 80 90 10 20 30 40 50 60 70 80 90 10 20 30 40 50 60 70 80 90<br>percentile<br>Education Potential Experience Demographics<br>Geography Informality Minimum Wage<br>log difference<br>log difference<br>log difference<br>log difference<br><!-- End of picture text --> Note: The graph shows the generalized kernel-weighted local polynomial Oaxaca decomposition for each centile of the earnings distribution. See notes in Table 3 for details on the individual effects included in each category.

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