rafmacalaba/datause-encoder-data huggingface dataset:
# C O N T E N T S

|Table o|f Conte|nts|ii|
|---|---|---|---|
|List of|Figures||iv|
|List of|Tables||iv|
|Acrony|ms||v|
|1<br>coo|rdin|ating tax reforms: can lost tariffs be recouped?|1|
|1.1|Introd|uction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .|1|
|1.2|Cross|-Country Evidence on Revenue Recovery . . . . . . . . . . . . .|5|
||1.2.1|Econometric Model<br>. . . . . . . . . . . . . . . . . . . . . . . .|5|
||1.2.2|Data<br>. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .|7|
||1.2.3|Estimation Method . . . . . . . . . . . . . . . . . . . . . . . . .|8|
||1.2.4|Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .|9|
|1.3|Joint|Trade-Fiscal Reform: A Case Study<br>. . . . . . . . . . . . . . . .|13|
||1.3.1|Theoretical Motivation . . . . . . . . . . . . . . . . . . . . . . .|14|
||1.3.2|Simulation Model . . . . . . . . . . . . . . . . . . . . . . . . . .|19|
||1.3.3|Data<br>. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .|21|
||1.3.4|Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .|25|
||1.3.5|Robustness . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .|34|
|1.4|Relate|d Issues in Tariff Reform . . . . . . . . . . . . . . . . . . . . . .|36|
||1.4.1|Change in Domestic Prices and Production . . . . . . . . . . .|36|
||1.4.2|Collected and Statutory Rates . . . . . . . . . . . . . . . . . . .|37|
|1.5|Concl|usion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .|39|
|App|endix|. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .|47|
|1.A|How|the Model in TRIST Works<br>. . . . . . . . . . . . . . . . . . . . .|47|
|1.B|Addit|ional Tables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .|51|

iii
30 one to the sample maximum of seven reduces the fiscal costs of bank crisis as a fraction of GDP by approximately 31 percentage points when M,/GDP is at its lowest value (0.17), but increases the fiscal costs of bank crisis by 57 percentage points at the highest (1.87).2?

<!-- Start of picture text --> Table 1: Checks and balances and the magnitude of banking crises<br>hite-adjusted standard errors in parentheses<br>Dependent (1) (2) Q)<br>variable: Fiscal Base Controlling for Controlling for<br>costs of crises specification income/capita terms of trade<br>constant 29.3 29.1 18.3<br>checks 7.6 -8.0 4.6<br>(3.3) (3.8) (3.0)<br>checks * M,/GDP | 10.7 11.1 7.0<br>(4.3) (5.0) 3.4<br>M,/GDP 28.3 24.6 -16.2<br>(12.8) (14.2) (11.7)<br>No. of years from | 1.6 15 27<br>First year ofcrisis to | (1.5) (14 (1.2)<br>year of next<br>election<br>GDP/capita -0.12<br>(.28)<br>Terms of trade -18.7<br>shock 13.2<br>R 0.21 0.22 0,34<br>N 40 39 31<br><!-- End of picture text -->

Note: Regression 2 excludes Taiwan because income per capita not available for Taiwan from International Financial Statistics, IMF. Observations are “clustered”, such that independence is assumed across countries but not between observations from the same country (Argentina, Indonesia, Malaysia, Turkey, and Thailand). The standard error of checks and M,/GDP is evaluated assuming the interaction term is zero.

29 The coefficients on the linear and non-linear terms actually suggest that for sufficiently high costs of favors to voters (4,/GDP), an increase in the number of veto players has a positive effect on the size of fiscal transfers. This is not predicted by the theory, but is easily explained. It is plausible that the profits earned by special interests from financial sector favors, and the size of the financial sector, are both related to the number of veto players. For example, expropriation of financial assets is more difficult when the number of veto player is larger. Given this, it is easy to show that the net effect of an increase in checks and balances can be to increase the magnitude of banking crises.
# **6.1 Transit Is Extremely Vulnerable to Rent-Seeking Activities**

# _The Link between Transit and Overheads_<sup>_38_</sup>

Several overheads are associated with transit processes such as bonds. However, other overheads are not transit overheads since they apply to both transit and domestic trade such as port charges. Finally, some costs do exist in both cases, but are substantially higher in the case of transit trade and hence contribute to overheads such as agents’ fees.

The main categories of logistics overheads related to transit operations are the following: _Corruption and “Facilitation” Payments en Route or at Origin and Destination_ A well-known and documented<sup>39</sup> phenomenon is the multiplication of facilitation payments at scheduled/unscheduled roadblocks. This is serious on some corridors (for instance, roadblock in West Africa add 10% to overheads and these roadblocks may occur every 30 kilometers, or even shorter distances). These are usually small and predictable payments made to local police, military, or customs agents. Transit initiation, or border crossing, carry the potential of much bigger payments between transit operators, customs and/or transport parastatals staff.

Corruption may be severe at border crossings. In Central Asia, Megoran _et al._ (2005) show that above the official mandatory costs for a Kyrgyz truck transiting through Uzbekistani territory, around 450 USD<sup>40</sup> , there are the unofficial costs to be paid for speeding up the process. These can range between 150-200 USD for a single truck. In total, a Kyrgyz truck entering Uzbekistan has to pay approximately 700 USD to cross the border, a quarter of which are unofficial costs. Weighbridges may also be a source of

> 38 Since transit overheads are the most important in terms of delays and unpredictability, we focus on transit-related overheads in this section. However, terminal and handling charges, excluding port charges, may be considered as overheads and similarly compulsory change of vehicles (for example from India to Bangladesh (Petrapole/Benapole)) or compulsory warehousing is common in Africa and in Chamber of Commerce owned facilities.

> 39 Including in Western Africa, where with World Bank assistance ECOWAS created an observatory of those practices.

> 40 The breakdown in 2004 was the following: USD 300 for a transit charge, USD 75 for insurance, USD 60 for sanitary control, USD 10 for a visa, road user and escort fee charges, and an environmental tax (Data provided by the Osh Chamber of Commerce).

34
(average temperature and precipitation) and income, as well as between climate variables and life expectancy. While it is always dangerous to make inferences about changes in time from cross-section estimates, these relationships can at least be used to gauge the likely direction and magnitude of effects of climate change in Chile.

Two different types of climate change will be assessed. First, the documented recent climate change in each of the 333 municipalities, as estimated from average monthly temperature series from 1948 to 2008 for all the Chilean meteorological stations that have contributed systematically to the Monthly Climatic Data for the World (MCDW) publication of the US National Climatic Data Center. Second, we will use the predictions of the Fourth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC4) climate models to simulate the likely effects of projected future climate change in Chile.

The rest of the paper is organized as follows. Section 2 describes the data sources and provides descriptions of the key variables. Section 3 estimates the cross-municipality relationships between climate and human development, controlling for other key variables that also affect development. Section 4 analyzes past climate change for 7 meteorological stations across Chile, and estimates average trends for each municipality. Section 5 uses the findings from sections 3 and 4 to simulate the effects of past climate change on incomes in each of the 333 municipalities in Chile. Section 6 summarizes the climate changes that are expected for Chile during the next 50 years, and section 7 simulates the likely effects of these changes on incomes. Section 8 concludes.

# **2. The data**

The data used for this paper consist of both cross-section data and time series data. The municipal level cross-section data-base, which is used to estimate the relationship between climate and development in Chile, is constructed using data from different sources. Table 1 lists the variables, their definitions, and the sources of the information.

3
67 4,365,900 in September 1985, net immigration of 200,000 does not seem justifiable for the 1980-1985 period. Although these stock figures are not complete and they do not include illegal migration, it is unlikely that net migration could have been so large during that period. I would recommend net immigration of only 50,000 for 1980-1985 and 1985-1990, 40,000 for 1990-1995, and 30,000 for 1995-2000. Moreover, I would give a slight edge to male immigrants between 1980 and 1990 (perhaps 55 percent of the total) since male immigrants have predominated so far during the 1980s.

# France

France is another country with a considerable difference between the World Bank and United Nations figures. For 1985-2000, the United Nations assumes that there will be no net international migration, whereas the World Bank assumes net immigration of 155,000, 100,000 and 50,000 for the three five-year periods. Information on immigration to and emigration from France is quite scanty, which might account for the differences between the two sodrces of estimates. The stock of foreign population in France increased by 237,000 between the two censuses in 1975 and 1982 (SOPEMI, 1988). By far the ;_argest number of immigrants came from Algeria and Portugal. The annual household survey of employment, conducted by INSEE, found that the stock of foreign labor in France increased by 200,000 between 1980 and 1984. There is some information on the inflow of foreign population through 1984, but no information on emigration. According to SOPEMI (1988), between 1979 and 1984 the annual inflow of foreign workers (subject to the control of the National Immigration Office) who obtained a permanent work permit plus family members of already established workers varied between 45,800 and 57,500 in most years. The only exceptions were 1981 and 1982 which witnessed the regularization of large numbers of illegal workers.

Given the sparse data available for France, it is difficult to reach a conclusion for the period 1985-2000. Legislation promulgated in 1985, 1986, and 1987 to stem clandestine work appears to be having some effect already, which suggests a possible decrease in illegal immigration. In addition, immigration from France to the United States and Canada currently-exceeds 4,000 persons per year. I would recommend a lower figure (105,000) than the World Bank previously proposed for net immigration for 1985-1990 and a decline to 80,000 in 1990-1995 and to 70,000 in 1995-2000.

# United Kingdom

International migration data are also deficient in the case of the United Kingdom. There is no emigration control from the United Kingdom and there is no information on flows in either direction between the United Kingdom and Ireland. Illegal immigration is a relatively minor problem and the immigration authorities are very efficient at rooting out overstayers. In all, only about 1,000 illegal immigrants are detected by immigration officials each year (Coleman, 1987) and many of these cases are simple technical violations of the law. Immigration to the United Kingdom for settlement purposes has continued the decline that started a decade ago. The number of
# **Online Appendix**

## **A. Data: Summary Statistics, Sample Quality, and Variable**

Table A1: Additional Descriptive Statistics

||Mean|Std. Dev.<br>N|
|---|---|---|
|||**Panel A: Worker panel**|
|**_Controls (pre-global fnancial crisis)_**|||
|Male|75.2%|43.2%|
|Age|32.98|9.62|
|Higher education or more|16.1%|36.8%|
|Previous formal sector attachment|67.3%|32.8%|
|**_Other_**|||
|Young (16-49)|95.0%|37.62%|
|Old (50-65)|5.0%|37.62%|
|Low informality rate|57.8%|49.4%|
|High informality rate|42.2%|49.4%|
|Low sector concentration|76.5%|42.4%|
|High sector concentration|23.5%|42.4%|
|Not-State owned|98.5%|12.2%|
|State-owned|1.5%|12.2%|
|||**Panel B: Firm panel**|
|**_Controls (pre-global fnancial crisis)_**|||
|Importer dummy|59.6%|49.1%|
|Firm avg. growth in total employment|10.7%|30.8%|
|Firm avg. growth in total wages|13.1%|32.3%|
||**Panel **|**C: Worker panel - full RAIS**|
|Male|72%|45%<br>240 669 880|
|Age|33.9|10.4<br>241 057 542|
|Higher education|10%|30%<br>241 057 542|
|Previous formal sector attachment|37%|37%<br>241 057 542|
|Average months worked per year|6.9|3.8<br>177 895 587|
|Average monthly real wages|2.3|2.1<br>98 146 366|

_Note:_ The worker-level statistics in panel A are based on the sample of workers in firms with a non-missing GFC firm shock measure in 2008 used in the main regressions. The firm-level statistics in panel B are based on all firms with non-missing GFC shock measure in 2008 used in the main regressions. The worker-level statistics in panel C are based on all workers in the RAIS database.

40
# **1. Introduction**

In line with the recent intense migration flows from emerging and developing to advanced countries, the transfers of funds by migrants back to their countries of origin have become increasingly significant. According to IMF (2005), these transfers of funds have grown five-fold between 1980 and 2003 reaching $91 billion, that is 1.6 percent of these countries’ total GDP<sup>1</sup> . Interestingly, after inward foreign direct investment they are now the second largest source of capital inflows to emerging markets and developing countries.

Economists and policymakers’ increasing attention to migrants’ transfers of funds is not only due to their size, but also to their “good” features. In particular, as again pointed out by IMF (2005), they are much more stable and less cyclical than other capital flows. The rationale for this would be that migrants’ transfers of funds are mostly driven by altruistic reasons, i.e. migrants’ desire to enhance the welfare of relatives still living in the country of origin, rather than by investment-like motives.

These “good” properties of migrants’ transfers of funds, along with their increasing size, make them a good candidate to reduce macroeconomic instability in the receiving country. In particular, migrants’ transfers of funds, being inflows of foreign currencies that can be used to repay foreign debt, often denominated in foreign currency, without adding to the stock of external debt and generating any debt-servicing cost, could contribute to reduce the probability of financial crises.

This paper focuses on the empirical relationship between workers’ remittances, by far the main component of migrants’ transfers of funds, and the occurrence of financial crises. As far as we know, this potential effect of workers’ remittances has not been explored yet. So far the empirical literature on migrants’ transfers has mainly focused on their potential influence on growth, poverty and output volatility. IMF (2005) documents that they may actually contribute to reduce poverty and volatility of output, consumption and investment in the receiving

> 1 Admittedly, these figures are underestimated due to transfers of funds not channeled through the banking system and other official intermediaries which are often the only source of official statistical data.
Table A5: Predicting RDT Positivity With Observables

|X|(1)<br>RDT Positive|
|---|---|
|Fever|0.373<sup>_∗∗_</sup><br>|
||(0.168)|
|Chills or Excessive Sweating|0.189<sup>_∗_</sup><br>(0.108)|
|Nausea, Vomiting, or Diarrhea|0.381<sup>_∗∗∗_</sup><br>(0.0960)|
|Reduced Appetite|0.00691<br>(0.102)|
|Headache|0.240<sup>_∗_</sup>|
||(0.126)|
|Cough|-0.159<sup>_∗∗_</sup>|
||(0.0783)|
|Weakness, Fatigue, or Reduced Consciousness|0.139<br>(0.0976)|
|Duration of Illness in Days|-0.0182<sup>_∗∗_</sup>|
||(0.00908)|
|Age Patient|-0.00339<br>(0.00562)|
|Patient Under 5 Years Old|-1.378<sup>_∗∗∗_</sup><br>(0.216)|
|Under 5 _×_ Age|0.226<sup>_∗∗_</sup>|
||(0.0969)|
|Patient is Male|1.051<sup>_∗∗_</sup><br>(0.414)|
|Patient is Pregnant|-0.348<sup>_∗_</sup>|
||(0.205)|
|Ethnic group: Bambara|0.124|
||(0.0854)|
|Respondent Speaks French|-0.223<br>(0.138)|
|Respondent is Literate in French|-0.510<sup>_∗∗∗_</sup><br>(0.144)|
|Respondent Has Primary Education or Less|-0.140<br>(0.120)|
|Patient Answered Clinic Survey|-0.367<sup>_∗∗_</sup><br>(0.174)|
|Pseudo R-Squared|0.141|
|N|1093|

_Notes_ : Standard errors clustered at the clinic level in parentheses. Respondent refers to individual who answered clinic survey. ***, **, and * indicate significance at the 1, 5, and 10 percent significance levels respectively.

A3
## **Table** 12

# COWARZSG4 OF RELATIVE BENES AND RELATIVE COSTS OF YTE _AWND_ GENERL EDUCATION

<!-- Start of picture text --> Secondary education Post-secondary education<br>Ratio of Expenditure Ratio of Expenditure<br>Return to year of marginal returns ratio Return to year of: mrginal returns ratio<br>By group Average all Avrage Non- By group Average all Avera g<br>Technicail General (a)/(b) four groups 1971-77 university University (g)/(h) four groups 1970-82<br>(a) (b) (c) (d) (1) (g) (h) (1) U) (k)<br>Wage am I oyeee<br>Lim. 4.43 5.9% .07 84% 90.3x 9.9<br>OUAs 7.1X 7.9% 0.9 4.2% 4.9 9.9<br>1.1 1.1c 0.9 0.6<br>Self-euiloyed<br>Line 10.2% 2.9% 3.6 9.2% 6.53 1.1<br>OUAs -3.2a 4.6x -0.7 7.5X 8.7% 0e9<br><!-- End of picture text -->

**a,b** From table 10. c Twice **as** high in 1978 **and** 1979 (the It two years for which data **available).**
30

- Granger, **Colin** W. J. and Paul Newbold (1986): Forecasting Economic Time Series. Second Edition. San Diego: Academic Press.

- Herring, Ronald J. and Rex M. Edwards (1983): "Guaranteeing Employment to the Rural Poor: Social Functions and Class Interests in Employment Guarantee Scheme in Western India". World Development 11: 575-592.

- Lieberman, S. S. (1985): "Field-Level Perspectives on Maharashtra's Employment Guarantee Scheme". Public Administration and Development 5:109-127.

- Narayana, N.S.S., K.S. Parikh, and T.N. Srinivasan (1988): "Rural Works Programs in India: Costs and Benefits". Jourral of DeveloDment Economics 29:131-156.

- Pesaran, M. Hashem, and Bahram Pesaran (1987): Microfit. An Interactive Econometric Software Package. Oxford: Oxford University Press.

- Programme Evaluation Organization (PEO) (1980): Joint Evaluation Report on EmRloyment Guarantee Scheme of Maharashtra. Planning Commission, Government of India, New Delhi.

- Ravallion, Martin (1990a): "On the Coverage of Public Employment Schemes for Poverty Alleviation". Journal of Development Economics, forthcoming.

- (1990b): "Reaching the Poor through Rural Public Employment:

- A Survey of Theory and Evidence". Discussion Paper 94, World Bank, Washington DC. (Shortened version forthcoming in World Bank Research Observer.)

- (1990c): "Market Responses to Anti-Hunger Policies: Wages,

- Prices, and Employment". Forthcoming in Jean Dreze and Amartya Sen (eds) The Political Economy of Hunger. Oxford: Oxford University Press.

- Subbarao, K. (1989): "Interventions to Combat Household-Level Food Insecurity: A Review of India's Experience", mimeo, World Bank.

- World Bank (1989): India: Poverty. Employment and Social Services. A World Bank Country Study, Washington DC: World Bank.

- _(1990): World Development Report 1990. Poverty. Washington DC:

- World Bank.
32 cut education expenditures, but the effects were alleviated by rising school enrollments. And Morocco maintained its food subsidies.

Ecuador and Indonesia. Ecuador and Indonesia are more a study in contrasts than in similarities since they have little in common in their adjustment experience except the need to adjust to a fall in oil revenues. Indonesia carried out its adjustment program without asking for support from international institutions, suggesting strong internal political support for adjustment.Y Like Malaysia, Indonesia also benefitted from a relatively light debt burden when the crisis occurred (see Table 2), due largely to the constitutional requirement for a balanced budget. Its continued access to foreign financing helped cushion the adjustment. Investment in Indonesia was al; o considerably stronger than the average for fuel exporters. By contrast, Ecuador had recourse to support from the intemational organizations and in a broad sense did not "own" its adjustment program, and so had difficulty getting the support needed to carry out its structural reforms. Nor did Ecuadofr have access to external financing when it was most needed. These problems notwithstanding, both Ecuador and Indonesia did much better than other fuel exporters (Table 2), resuming growth in the last period and cutting back less on investment expenditures.

Indonesia not only achieved stabilization but also carried out structural reforms that helped to increase credibility of its adjustment program. In particular, financial sector reforms leading to convertibility of the rupiah inspired confidence in the expenditure-switching and-reducing measures that were being implemented concurrently. Much like Malaysia, Indonesia began its adjus,ment from a position of high growth and high investment. What is remarkable is that while Indonesia w;3 undergoing structural adjustments that reduced its dependence on exports of nonrenewable resources for foreign exchange earnings and government revenues and improved the efficiency of resource allocation and investments (see the article by Thorbecke in this issue), the country was also reducing poverty and undernutrition. Data from household surveys suggest a much lower incidence of poverty
**Stylized Fact 6.** _Between 1969 and 2009, the fuel consumption per container was roughly the same for all ship sizes. The fuel consumption economies of scale became more visible only starting in 2010. The overall fuel efficiency for all ship sizes has also visibly improved starting in 2010._

An increase in fuel efficiency in the 2010s has coincided with a rapid increase in the number and capacity share of the eco-ships with electronic engines. As a result, their capacity share in the total liner fleet has increased from 0% in 2004 to 56% in 2020 (Lugovskyy and Terner, 2021).

The data on the share of the fuel costs in the operational and total costs is sparse. Based on the data from Delhaye et al. (2010), highlighted by Bernacki (2021), we report the following.

**Stylized Fact 7.** _In 2010, the fuel costs in the total costs constituted 54% for vessel sizes between 500 and 2,000 TEU and 42% to 46% for vessel sizes between 5,000 and 12,000 TEU. The calculations are based on the speed of 14 knots for the first (smaller) type of vessels and on the speed of 18 knots for the second type._

# **2.3 Freight Rates**

The research on international trade pays special attention to transportation costs as they represent a sizeable part of the overall trade costs. Transport costs affect the trade volume and determine trade patterns, including the organization of just-in-time production and global supply chains (Hummels, 2007; Behar and Venables, 2011). Over time, the contribution of shipping costs to trade costs increased in both developed and developing countries (Clark et al., 2004; Hummels, 2007; Hummels et al., 2009).

The magnitudes and trends differ for the dry-bulkers and containerships, which is not surprising given how different the industries are. Dry-bulkers are used for commodities with lower per tonne value and time sensitivity than manufactured goods transported by containerships. Dry-bulk shipping markets are organized as decentralized spot markets where trading firms hire a ship at the spot market for a specific route—similar to hiring a taxi. The market is characterized by high dispersion 11