rafmacalaba/datause-encoder-data huggingface dataset:
<u>.</u> 2016. "Micro, Macro, and Strategic Forces in International Trade Invoicing: Synthesis and Novel Patterns." _Journal of International Economics_ 102: 173-187.

Greenstone M., and A. Looney. 2012. "What Immigration Means For U.S. Employment and Wages." Brookings Institution, May 4. Washington, DC.

Helbling, T., R. Huidrom, M. A. Kose, and C. Otrok. 2011. "Do Credit Shocks Matter? A Global Perspective." European Economic Review 55 (3): 340-353.

Hirata, H., M. A. Kose, and C. Otrok. 2013. "Regionalization vs. Globalization." _Global Interdependence, Decoupling and Recoupling_ in Frank Westermann and Yin-Wong Cheung eds., MIT Press.

Huidrom, R., M. A. Kose, and F. Ohnsorge. 2016. "Challenges of Fiscal Policy in Emerging and Developing Economies." CEPR Discussion Paper 11347. Centre for Economic Policy and Research, London.

Hunt, J., and M. Gauthier-Loiselle. 2010. "How Much Does Immigration Boost Innovation?" _American Economic Journal: Macroeconomics_ 2 (2): 31–56.

IMF (International Monetary Fund). 2013. "2013 Spillover Report." International Monetary Fund, Washington, DC.

<u>.</u> 2014. "2014 Spillover Report." International Monetary Fund, Washington, DC.

<u>.</u> 2015a. "2015 Spillover Report." International Monetary Fund, Washington, DC. <u>.</u> 2015b. "Spillovers from Dollar Appreciation." International Monetary Fund, Washington, DC.

<u>.</u> 2016. World Economic Outlook: Subdued Demand: Symptoms and Remedies. Washington, DC: International Monetary Fund.

<u>.</u> 2007. World Economic Outlook: Spillovers and Cycles in the World Economy. Washington, DC: International Monetary Fund.

Jansen, W. J., and A. C. Stokman. 2004. "Foreign Direct Investment and International Business Cycle Comovement." ECB Working Paper 401, European Central Bank, Frankfurt. Kose, M. A. 2003. "Globalization and Business Cycles in the United States," United States - Selected Issues, IMF Country Report, No. 03/245, Chapter VIII, 74-80. Washington, DC: International Monetary Fund.

Kose, M. A., Prasad, E. S. and M. E. Terrones. 2004. "Volatility and Comovement in Globalized World Economy: An Exploration," in Macroeconomic Policies in the World Economy, edited by H. Siebert, 89-122. Berlin-Heidelberg: Springer-Verlag.

30
# **Table A16: First stage: Log Refugees at 100km instrumented by distance-based IV with decay function represented (i.e. IV generated using 1)mean border distance,** **<u>2)square of mean border distance and 3) square root of mean border distance) including Kampala district</u>**

|||||||Log of Refug|ees (100km)||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|(10)|(11)|(12)|
|Log of Distance IV (mean border distance)|5.45962***|5.46204***|5.44187***|5.41763***|||||||||
||(1.29524)|(1.29575)|(1.47595)|(1.51948)|||||||||
|Log of Distance IV (square of mean border<br>distance)|||||3.48017***|3.48165***|3.46261***|3.45146***|||||
||||||(0.75025)|(0.75062)|(0.87676)|(0.90701)|||||
|Log of Distance IV (square-root of mean border<br>distance)|||||||||9.58775***|9.59222***|9.73207***|9.71222***|
||||||||||(2.52359)|(2.52434)|(2.84412)|(2.92283)|
|HH head age||0.00127|0.00112|0.01260||0.00148|0.00152|0.01407||0.00111|0.00082|0.01162|
|||(0.00174)|(0.00196)|(0.01342)||(0.00173)|(0.00199)|(0.01357)||(0.00175)|(0.00194)|(0.01342)|
|Age squared||-0.00000|-0.00001|-0.00008||-0.00001|-0.00001|-0.00010||-0.00000|-0.00000|-0.00006|
|||(0.00002)|(0.00002)|(0.00012)||(0.00002)|(0.00002)|(0.00012)||(0.00002)|(0.00002)|(0.00012)|
|HH head sex (male)||0.00762|0.01463|0.05132||0.00785|0.01407|0.06046||0.00748|0.01528|0.04529|
|||(0.00601)|(0.01202)|(0.06551)||(0.00588)|(0.01151)|(0.06405)||(0.00628)|(0.01293)|(0.06955)|
|Household size|||0.00040||||0.00032||||0.00049||
||||(0.00101)||||(0.00099)||||(0.00104)||
|Married monogamously|||-0.03815**||||-0.03710*||||-0.03966**||
||||(0.01921)||||(0.02113)||||(0.01786)||
|Married polygamously|||-0.03523*||||-0.03471||||-0.03589*||
||||(0.02024)||||(0.02191)||||(0.01907)||
|Separated (divorced/widowed)|||-0.01405||||-0.01311||||-0.01454||
||||(0.01824)||||(0.02019)||||(0.01686)||
|Completed primary sch|||-0.00036||||-0.00195||||0.00059||
||||(0.01019)||||(0.01016)||||(0.01025)||
|Commercial farming|||0.01338||||0.01075||||0.01545||
||||(0.01538)||||(0.01504)||||(0.01585)||
|Wage employment|||0.00293||||0.00264||||0.00238||
||||(0.00953)||||(0.00939)||||(0.00985)||
|Non-agricultural self-employment|||-0.01368**||||-0.01308**||||-0.01434**||

23
# Table IV.7

# Electronic Parts Sample: Usefulness of Collective Marketing Support

||**Early**<br>**Expor**|<br>**ters**<br>|**Late**<br>Expor<br>|ters|**Larg**<br>**Expo**|**e**<br>**rters**|
|---|---|---|---|---|---|---|
|1Numberof Finns|6||19||8||
|2. Average Relative Importance of Support by Public<br>or Non Profit Agency for your Firm to Penetrate<br>Export Markets.<br>________<br>a. At First Export|3.3||2.6||3.0||
|b. In 1991|1.7||1.3||1.6||
|3. Proportion of Firms Receiving Service Provided by<br>such an Agency and iLs Relative Usefulness.|Ever<br>Used'|Usfl|Ever<br>Used|Usfl<br>|Ever<br>**Used**|**Usfl**|
|Information on Export Opportunities|.50|3.7|:11|5.0|.38|3.7|
|Introduction of Buyers|.50|2.0|.11|3.0|.38|2.0|
|Participation in Dosnestic Trade Fair|_.50_|3.3|.33|3.0|.50|3.2|
|Participation in Overseas Trade Fair|.33|3.0|III|3.0|.25|3.0|
|Support of Exploratory Ma&leting Trips|.17|2.0|.1 i|2.0|.25|2.0|
|Export - Import Agency|O||-<br>O||O||
|Obtaining Foreign Standards|O||O||O__||
|OverseasDistribution of Promotional Material|0|-|0||0||
|Joint Venture Technology Ties with Foreign Firn|0|-|0||0||

2. Average Relative Importance of Support by Public or Non Profit Agency for your Firm to Penetrate Export Markets. ________

- a. At First Export

- b. In 1991

- Ever used = Average probability of ever having used. Usfl = Average usefulnes **s** core on a scalz of 1 (low) to 5 (high).

Note: Early expoiters began exporting before 1980. Late exporters began 1980.

64
Indirect taxes on the purchase of (non-real-estate) goods and services also contribute approximately twofifths of all tax revenues. In FY15, there was a general sales tax system that applied to goods and services alike as well as a set of customs duties applying to certain imported goods. Public revenues from nontax sources—for example, property and asset income or income from sales of goods and services or grants— represents just under 30 percent of all revenue in FY15. Property income, which includes rents generated by public control of economic enterprises like the Suez Canal and petroleum royalties (among others), accounts for the largest share (61 percent) of all nontax revenues.

At 13 percent in FY16,<sup>17</sup> Egypt’s tax revenues as a percent of GDP (or tax ratio) could be higher. Compared to other lower-middle income economies, Egypt fairs better than India and Indonesia (Figure 5), but lags behind Morocco (21.4 percent) and Jordan (15.4 percent)—countries with lower GDP per capita in 2016.<sup>18</sup> Egypt suffers from a large informal economy that shrinks the tax base and makes tax collection efforts difficult. Tax implementation and collection processes are also not effectively enforced by the tax authority, making reaching the taxable base difficult.

To address some of these issues, Egypt moved from the GST to a VAT law in September 2016, and increased its rate from 13 to 14 percent in July 2017 compared to a GST that was 10 percent. This new law, like its predecessor, has many exemptions, including many food products such as dairy products, breads, meats, and fish. Health and education, insurance services, broadcasting services, some transportation services, butane and natural gas, and banking transactions are also exempt, causing an erosion of the tax base. In FY13 the effective tax rate on the GST was 7.1 percent.<sup>19</sup>

**Figure 5. Tax Revenues as a percent of GDP** , **Egypt and Comparison Countries**

<!-- Start of picture text --> 30%<br>25%<br>20%<br>15%<br>10%<br>5%<br>0%<br>India Indonesia Egypt Malaysia Jordan Turkey Morocco South Africa<br>Source: IMF International Financial Statistics Database (http://data.imf.org/?sk=4C514D48-B6BA-49ED-8AB9-<br>52B0C1A0179B&sId=1390030341854 )<br><!-- End of picture text --> The income tax suffers from problems of its own. It is also affected by the informal sector and low tax collection. In 2015, the government implemented some reforms to the income tax, increasing the

> 17 And 12.5 percent in FY15.

> 18 Measured in PPP (constant 2011 international $). Source: WDI, accessed August 9, 2018.

> 19 Due to limited data, this is the most recent estimate.

15
14 Table 1: Coefficients of Variation for Crude Oil and Products (9M)

||_________<br>_<br>1970-1990|1980-1990|
|---|---|---|
|Crude Oil|59.1|31.1|
|Gasoline|55.7|27.0|
|Diesel|55.4|28.0|

# Source: International Trade Division, The World Bank.

We also examined the volatility of refined products and crude oil using weekly data for the period 1988 to 1990. The volatility was calculated for the whole period and for a sub-period which excluded the 1990-91 Gulf War. This was done because a number of analysts have claimed that the volatility of products, and in particular jet fuel, increased significantly more during the Gulf War than it did for crude oil. Table 2 shows that for both the total period and for the subperiod, crude oil volatility was above product volatility but not significantly (except for gasoline). This result holds whether we consider Gulf Coast/N.Y. or MARAVEN's prices for the refined products (MARAVEN is one of the three principal operating subsidiaries of PDVSA).

Table 2: Volatility of Crude Oil and Products Prices: Weekly Data (%)

||1988-90|1988-90 (excluding the Gulf War)|
|---|---|---|
|Gasoline<br>|14.64|10.79|
|Diesel<br>|17.28<br>|12.09|
|Diesel (MARAVEN)<br>|16.43|13.03|
|Jet fuel<br>|19.96|12.93|
|Jet fuel (MARAVEN)<br>|19.39|14.02|
|Fuel oil<br>|18.08|15.25|
|Fuel oil (MARAVEN)<br>|17.50|13.84|
|Crude (Tia Juana)<br>|20.81|15.06|
|Crude (Bonny Light)|20.11|13.69|

Source: International Trade Division, The World Bank.

Notes: Gasoline, regular unleaded gasoline (87), Gulf Coast; Diesel, Gulf Coast and MARAVEN; Jet Fuel, Gulf Coast and MARAVEN; Fuel Oil, 2.2% sulfur, New York and 2.8% sulfur, MARAVEN. Source of raw price data: Platt's.
<!-- Start of picture text --> o°-<br>Electricity Access<br>1.00<br>10°s-4 0.75<br>o 0.50<br>ae]<br>P=5oO 0.25<br>i<br>0.00<br>20°S -<br>= ___ coastalRadial andhighways<br>30°S -<br>70°w 60°w 50°w 40°w<br>Longitude<br><!-- End of picture text --> <!-- Start of picture text --> o°-<br>Electricity Access<br>1.00<br>10°s-4 0.75<br>o 0.50<br>ae]<br>P=5oO 0.25<br>i<br>0.00<br>20°S -<br>= ___ coastalRadial andhighways<br>30°S -<br>70°w 60°w 50°w 40°w<br>Longitude<br><!-- End of picture text -->
occurred—collapsing the data by municipality and year. By our definition, a new school is introduced in a municipality if a new unique school identification number appears in the school panel.<sup>12</sup> Information on municipality codes allows us to locate every school in Brazil to the corresponding municipality. The school census provides information on primary school enrolment, the availability of nursery classrooms, and the number of pre-school classrooms, which we use as control.

_Childbearing data_ Data on birth outcomes come from the microdata of Brazilian vital statistics, which cover approximately 45 million births occurring between 1997 and 2009. Vital statistics data are based on birth certificates issued by health institutions or midwifes attending homebirths and are collected through the states’ health secretariats. The vital statistics microdata are publicly available through the System of Information on Life Births (SINASC) of the Brazilian public health system (DATASUS). These data provide information on the age and municipality of residence of the mother, as well as gestational length of the pregnancies, and the mother-reported race of the child.

For each year, we collapse these data to create a summary measure of births by municipality and mother’s age at conception.<sup>13</sup> We calculate age at date of conception using information on gestational length recorded in the birth certificates to provide a municipal panel of births by mother’s age at conception.<sup>14</sup> Brazilian vital statistics data show close to universal coverage of all occurring births; information from the 2010 population census shows that more than 99% of all births occurring between 2000 and 2010 were registered and entered into the vital statistics data

> 12 We restrict the school count to schools that are active in a given year and report a positive number of enrolled students to limit measurement error in the school panel.

> 13 This works in the Brazilian context, as the school year coincides with the calendar year.

> 14 Using date of conception rather than date of birth of the child, allows us to capture the time when fertility decisions are taken and relate this to the timing of the introduction of schools.

10
# **Table S29. Prevalence of engagement in educational activity by consumption quintile**

||Education Activity<br>|
|---|---|
|Ethiopia|0.011|
||(0.024)|
|Nigeria|0.437***<br>(0.028)|
|Uganda|0.439***<br>(0.025)|
|Quintile 2|0.012<br>(0.042)|
|Quintile 3|0.051<br>(0.039)|
|Quintile 4|0.108***<br>(0.038)|
|Quintile 5|0.179***<br>(0.037)|
|Ethiopia-Nigeria|0.000|
|Ethiopia-Uganda|0.000|
|Nigeria-Uganda|0.931|
|Observations|6,316|
|R-squared|0.185|

_Note_ : The table reports the result from a single regression of a binary variable equal to 1 if the household has a school-aged child that has been engaged in some educational activity since schools closed and 0 otherwise. The question is asked conditional on the household containing a school-aged child. The binary variable is regressed on indicators for each country, with Malawi as the base case, and each pre-COVID-19 household annual per capita consumption quintile, with the lowest consumption quintile as the base case. The second panel reports p-values for Wald tests of the simple linear hypothesis that the coefficient on one country indicator equals the coefficient on an indicator for a different country. Data are only from the first phone survey round in each country. Robust standard errors are reported in parentheses (*** p<0.01, ** p<0.05, * p<0.10).

56
# **Table A9: The effects of temperature on poverty and inequality – Heterogeneity analysis**

|Poverty $1.90|Gini index|
|---|---|
|(1)|(2)|
|_Panel A: Regime type (Reference group: Democracy)_||
|Temperature*Hybrid regime<br>0.735*|-0.004|
|(0.444)|(0.003)|
|Temperature*Authoritarian regime<br>1.395***<br>(0.431)|0.008**<br>(0.003)|
|_Panel B: Location (Reference group: Countries near equator)_||
|Temperature* Countries near equator<br>0.943***<br>(0.293)|0.011***<br>(0.004)|
|_Panel C: Share of agriculture in GDP (Reference group: Low s_|_hare)_|
|Temperature*High agriculture share<br>0.155***<br>(0.051)|0.001***<br>(0.000)|
|_Panel D: Share of manufacturing in GDP (Reference group: Lo_|_w share)_|
|Temperature*High manufacturing share<br>-0.076**<br>(0.039)|-0.001***<br>(0.000)|
|_Panel E: Share of trade in GDP (Reference group: Low share)_||
|Temperature*High trade share<br>-0.005<br>(0.003)|0.000<br>(0.000)|
|Controlling for rainfall<br>Yes|Yes|
|Subnational FE<br>Yes|Yes|

_Notes:_ Results of panel fixed effects model. Robust standard errors in parentheses. Standard errors are clustered at the subnational level. Regressions are weighted by region population. *** p<0.01, ** p<0.05, * p<0.1 57
training from CMC. Among the poor and non-poor households, no training has been received by 98% and 100% respectively. Most of the BPL families (94%) are further unaware of a nearby shelter they can shift to when flood waters start rising. However, in case of poor and non-poor households, more than 80% of families are aware of nearby shelters. Thus, the poorest families do not know where to shift when flooding occurs thereby increasing their vulnerability further.

91% of BPL households also do not get any early warning and those who get some warning do so through the community alarm. However, in the case of poor families, 25% get some type of early warning and this percentage is higher at 37% for non-poor families. The most important source of warning for the poor and non-poor families is radio or television followed by community alarm as seen in Figure 10. Police, ward office, cell phones and local cable network are other sources through which warning is issued to the people.

_Figure 20: Sources of flood warning_ <!-- Start of picture text --> Local ward<br>office<br>6% Police<br>Community<br>10% alarm<br>25%<br>Cell<br>Radio/TV phone<br>50% 7%<br>Local cable<br>network<br>2%<br><!-- End of picture text --> For families who receive some warning, the average time for warning is about an hour. 32% have stated that they receive warning about floods an hour in advance, 20% receive it barely 15 minutes in advance and another 23% have claimed receiving warning 12 hours in advance. About 17% of families have also stated that they get the warning 24 hours in advance. These percentages are only among families who receive some form of warning.

Respondents have also been asked to name the agency they contact when flood waters start rising. 65.6% have responded that they do not contact anyone. 20% contact the Municipal Council, 10% contact the police station and only 4% contact the DS office.

27
share of subjects indicating the highest tax morale by more than 1.7 percentage points. Thus, a higher degree of perceived corruption crowds out tax morale. If taxpayers notice that many public officials are corrupt and many other taxpayers evade taxes, they might get the feeling that their intrinsic motivation is not recognized or honored. Thus, taxpayers get the feeling that they too can be opportunistic. The moral costs to evade taxes decrease.

Interestingly, Torgler (2003) also observe that the self-employed have a lower tax morale than full-time employees. This result is not surprising, especially in transition countries where self-employed individuals are confronted with and restricted by high transaction costs imposed by unduly inefficient government activities<sup>13</sup> . States‘ position and its reputation was affected due to numerous mistakes in the tax practices since the beginning of the transformation process: ―The state‘s attitude in many instances can be described as blatantly irresponsible. This usually took the form of continuously modified tax policy concepts (partially implemented). When observing the politicians‘ behavior in the area of taxes, one could get the impression that they juggled with tax instruments without fully realizing that these ―toys‖ could go off one day. This attitude is typical of almost all governments of post-communist countries where the applied fiscal tools fail to ensure financial stability but their reform is opposed by the society‖ (Owsiak, 2007, p. 202). This induced a higher level of tax awareness, reputation costs, and a higher sensibility towards unfair treatments: ―All attempts to improve the tax system on the part of the state are treated with suspicion and highly criticized by the media‖ (p. 194).

# **VIII. CONCLUSIONS**

It seems to be important to consider the moral dimension of complying with societies‘ rules, underlying legal structure and countries‘ security of property rights. A failure of a country‘s legal system undermines tax morale and tax compliance. Also, regulatory restraints and bureaucratic procedures limit the operation of markets, enhancing the

> 13 Djankov et al. (2002) show with data from 75 countries that in general heavier regulation of entry goes in line with a higher corruption and a higher shadow economy. On the other hand, countries with more democratic and limited governments have less entry regulations.

53
# Figure 5 Latin America The Movement of MCY over time

<!-- Start of picture text --> 0.7<br>0.6<br>0.5<br>o.4I<br>0.3<br>0.<br>0.1C<br>'<br>----------- ~ --~ - - --- --- -~ ---<br>W w ~~~- 5* ''''''''<br>_1 ,-y >e,_ ---.__-'<br>O-|| _|I{tI I I Il I I I<br>1977 1978 1979 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991<br>year<br>Argentina - Brazil - Chile<br>------- Colombia ------- Mexico Venezuela<br><!-- End of picture text -->
# **1. Introduction**

The increasingly prominent threats of climate change have inspired a significant body of economic research on a variety of outcomes, such as agriculture (Deschênes and Greenstone, 2007; Schlenker and Roberts, 2009), labor productivity (Somanathan _et al._ , 2021), human health (Deschênes and Greenstone, 2011), and crime and conflict (Burke _et al._ , 2015a; Heilmann _et al._ , 2021). In particular, since climate change could reduce economic growth (Dell _et al._ , 2012; Newell _et al._ , 2021), this might, in turn, translate into slower progress with poverty reduction. Furthermore, climate change may unevenly affect different countries and population groups with the most harmful consequences of income decline being borne by less affluent groups; these would likely result in increasing inequality both across and within countries (Diffenbaugh and Burke, 2019; Hsiang _et al._ , 2019).

A possible explanation for the lack of empirical evidence on the impacts of global warming is the challenge of obtaining appropriate measures of poverty and inequality. While household surveys—the main source of official poverty statistics—have become increasingly available, these surveys are still unavailable or infrequently collected in many countries, particularly in poorer regions.<sup>1</sup> Another explanation is that poverty and inequality can widely vary within (and across) countries. Consequently, ignoring subnational variations could easily mask the dynamic relationship of these outcomes with climatic conditions, which have long been known to be location specific. Indeed, recent studies suggest that analysis using spatial aggregation of data at the country level may not reveal the true effects of climate change on economic growth, which can be improved with analysis using more disaggregated data at the subnational level (Damania _et al._ , 2020; Kalkuhl and Wenz, 2020).

> 1 A recent survey by Beegle _et al._ (2016) indicates that just slightly more than half (i.e., 27) of the 48 countries in Sub-Saharan Africa had two or more comparable household surveys for the period between 1990 and 2012. Dang _et al._ (2019) find that a 10-percent increase in a country’s household consumption level is associated with almost one-third (i.e., 0.3) more surveys.

2