rafmacalaba/datause-encoder-data huggingface dataset:
group with the lowest average educational attainment.

Controlling for the bias of the estimated return to home country experience induced by self-selection in the ...rst-stage regression requires to account for the truncation of the error term. Adding a selection correction term in the form of the inverse Mills ratio to the Mincerian earnings equation in the ...rst step may solve this issue if the unobservables have a joint normal distribution. This Mills ratio can be constructed by using the estimated migration rates conditional on age and male gender. Since education determines potential experience as well as age at arrival, I also use migration rates of male immigrants for three levels of schooling: less then seven years, seven to twelve years and more than twelve. These migration rates have been taken from Bratsberg and Terrell (2002).<sup>30</sup> Controlling for selection does not change the e¤ect of openness on returns to experience when controlling for log Open, log GDP per capita and signi...cant regional dummies. This can be seen by comparing speci...cation (I) of Table 10 with speci...cation (IV ) of Table 5. Once GADP and the share of manufacture are added to the regression in speci...cation (II) the coe¢ cient on log Open decreases by about 10 percent in the entire sample and by 30 percent in the non-OECD sample relative to the uncorrected coe¢ cients if migration rates conditional on age are used to control for selfselection. Migration rates conditional on schooling are only available for a subset of countries. Adding them to the ...rst step regressions reduces the sample size signi...cantly as can be seen in the last two columns of Table 10: Despite the change in the sample, the coe¢ cients on log Open are similar in speci...cation (I) independent on whether migrations rates conditional on age or conditional on education are added to the ...rst step regression.

# 6.2 Returns to Experience and Schooling

Log earnings experience pro...les tend to be steeper for better schooled workers. An OECD study, for example, suggests that participation in jobrelated training programs is correlated with educational attainment (OECD

> 30 Relaxing the assumption of joint normality in order to identify the US return to home country experience in the home country population requires to satisfy an exclusion restriction,i.e. a regressor that a¤ects the migration probability of immigrants from a given country di¤erently, but does not determine earnings. As age at arrival does not only determine the probability to migrate but also earnings, the exclusion restriction is not satis...ed. Immigrants may leave their country for political reasons. Wars or political turmoil are likely to a¤ect the decision to migrate, but not wages. If these variables a¤ect immigrants who immigrate at di¤erent years of immigration in a di¤erent way, they may be used in order to control for selection.

22
15 status. Often, the household surveys' reported income is biased because the survey methodology in use in the transition economies has not yet been adapted to the new economic structure. For example, when income and expenditures are compared in this survey, expenditures greatly exceed income for three hard-to-track groups: farmers, self-employed, and social income recipients. These groups operate to a greater degree in the informal or grey economy, and data are apparently less accessible to the survey gatherers - just as they are to the tax administration."<sup>2</sup> Data on energy consumption was also collected as part of the Polish household budget survey. There is a small caveat as to its representativeness: The 1992/1993 winter was slightly warmer than average (Meyers, Schipper, and Salay, 1994) and the first six months of the year when the survey was conducted are in general warmer than the second six months, so that energy expenditure may be slightly lower in this sample than overall _(Understanding Poverty in Poland)._ Still, our concentration is primarily on the _distribution_ of energy consumption among different groups in the population, and there is no reason to believe that this distribution is skewed in any way.

_Mo consumes the most energy in Poland?_ _Consumption by income group._ The first important finding is that energy consumed a larger portion of the budget of the non-poor than of the poor (Table 3). While the poorest 20 percent of the population spent 7.4 percent of the their total expenditures on energy, the top 40 percent spent more than 10 percent. So, not only did the better off spend a larger absolute amount on energy than the poor, they also consumed a larger proportion of their expenditures as energy. This suggests there is a _positive and strong income elasticity_ of energy demand (see below). This is different from the pattern in industrial market economies where evidence suggests that long-run income elasticities are close to unity (Pindyck 1979).

12/ See _Understanding Poverty in Poland,_ World Bank 1995 for a further description of the survey.
Policy Research Working Paper 5002

# **Abstract**

This paper provides a description of the macroeconomic aftermath of natural disasters. It traces the yearly response of gross domestic product growth—both aggregated and disaggregated into its agricultural and non-agricultural components—to four types of natural disasters— droughts, floods, earthquakes, and storms. The paper uses a methodological approach based on pooling the experiences of various countries over time. It consists of vector auto-regressions in the presence of endogenous variables and exogenous shocks (VARX), applied to a panel of cross-country and time-series data. The analysis finds heterogeneous effects on a variety of dimensions. First, the effects of natural disasters are stronger, for better or worse, on developing than on rich countries. Second, while the impact of some natural disasters can be beneficial when they are of moderate intensity, severe disasters never have positive effects. Third, not all natural disasters are alike in terms of the growth response they induce, and, perhaps surprisingly, some can entail benefits regarding economic growth. Thus, droughts have a negative effect on both agricultural and non-agricultural growth. In contrast, floods tend to have a positive effect on economic growth in both major sectors. Earthquakes have a negative effect on agricultural growth but a positive one on non-agricultural growth. Storms tend to have a negative effect on gross domestic product growth but the effect is short-lived and small. Future research should concentrate on exploring the mechanisms behind these heterogeneous impacts.

This paper—a product of the Development Research Group and the Global Facility for Disaster Reduction and Recovery—is part of a larger effort in the department to study the main sources of vulnerability and to disseminate the emerging findings of the forthcoming joint World Bank-UN Assessment of the Economics of Disaster Risk Reduction. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The authors may be contacted at yikeda1@ worldbank.org and nloayza@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
Estrades, Maliszewska, Osorio-Rodarte, and Seara E Pereira. (2021) Figure 6. Macroeconomic impacts: Percentage change relative to the business-as-usual scenario, 2035 <!-- Start of picture text --> 2.98<br>2.50<br>1.98<br>0.98<br>0.49<br>0.21<br>0.07 -0.01 0.00 -0.01 0.07<br>-0.02<br>Real income RCEP Real income RoW<br>Tariffs alone RCEP ROO liberalization Productivity kick<br><!-- End of picture text -->

# Source: World Bank staff calculations using ENVISAGE model.

Note: Real income is measured as equivalent variation i.e. the expenditure to attain utility in year t in any given simulation using base year prices.

All participating countries benefit from the RCEP, although the gains are not distributed equally and depend on the trade policy changes (Figure 7). Considering the full scenario, with reductions in tariffs, non-tariff measures, and trade costs, Lao PDR, Thailand, Cambodia, Vietnam, and Malaysia benefit the most. These positive gains are magnified when a productivity kick is assumed. Under this scenario, the real income in Vietnam and Malaysia increases almost 5 percent. In Japan, the country that gains less under this scenario, the real income increases by 0.5 percent. Interestingly for Japan, the impact of the four RCEP scenarios is similar, which suggests that most gains are associated with a fall in tariffs, in contrast to the rest of the countries, where the fall in tariffs leads to very small impacts, or even a negative impact as in Cambodia and Vietnam. The negative impact in those countries is associated to a fall in tariff revenue and negative terms of trade with prices of exports dropping faster than import prices. In most countries, there is a significant welfare gain when trade costs are reduced. In Lao PDR, significant gains result from a fall in non-tariff measures.

11
Africa while the rest of Africa experiences drought conditions. In 2100, farms in many parts of the continent continue to suffer from reduced rainfall except for the humid central region and parts of eastern Africa. Niger, Mali, Somalia and other traditionally dry regions benefit from this scenario, because CCSR predicts large increases in rainfall in these regions. The beneficial impacts of the added rain in traditionally dry regions can outweigh the harmful effects of higher temperatures. In the CCC scenarios (Figures 11a–c) much of Africa is vulnerable to adverse impacts except for the West African coast, the humid center, and the northeast. The damages in regions that are harmed get more severe over time with the CCC scenario as temperatures are predicted to rise rapidly.

It is also helpful to remember where the people are living in order to judge which impacts will be the most severe in human terms. Figure 12 provides a population density map of Africa (CIESIN 2004). In conjunction with Figures 9, 10, and 11, one can see where climate change may affect the most people. The population map indicates that West Africa (south of the Sahel), the Mediterranean coastline, and a band across central Africa and a north to south band in Eastern Africa are among the most densely populated. These areas coincide with regions that the CCC and CCSR climate predictions suggest will be harmed. Even the impacts from the relatively more favorable climate scenario based on the PCM models suggest that populated regions such as the Mediterranean coastline, southern Africa and central Africa will be severely affected. The results suggest that the impacts on rural populations in Africa from climate change are likely to be significant.

# **5. Conclusions and implications for policy**

This study is a cross-sectional analysis of the net revenues of African farms, relying on the Ricardian method to investigate the impact of climate on net revenues, and building on a massive data effort to collect information about farmers in 11 African countries. Surveys of over 9000 African farms were combined with detailed measurements of soils, climate, hydrology and elevation from a number of sources.

The study found that African farms are indeed sensitive to climate and especially temperature. It predicts that farm values will decline if temperatures rise. Specifically, the temperature elasticity with respect to net revenue of African farms is estimated to be -1.3. The precipitation elasticity is estimated to be 0.4. The sensitivity is the greatest for dryland farms with a temperature elasticity of -1.6 and a precipitation elasticity of 0.5. Irrigated 19
they first pay the PIT to the GoI and then it is transferred to the PA as part of clearance revenues. East Jerusalem residents pay PIT to the GoI, unless they work in the West Bank, in which case they pay to the PA. Gaza residents do not pay PIT, except for PA workers in Gaza since the tax is deducted at source.

**Taxable income is composed of income from all sources after deducting expenses.** Unless specifically exempt by law, all income is included, less allowable expenses incurred to produce the income and the standard deduction (carryover losses, exemptions, and donations). The individual income tax rate charged is progressive, ranging from 5 to 15 percent (Table 2-3) in the Palestinian Territories and from 1 to 5 percent in Israel. In Israel a 10 percent flat rate is applied to rental income.

**Table 2-3 The income tax framework in the West Bank and Gaza has only three brackets, and**

**statutory rates range between 5 and 15 percent**

Statutory income tax rates for Israel and the Palestinian Territories

|**Israel**||**Palestinian Territ**|**ories**|
|---|---|---|---|
|<br>**Bracket**|**Rate**|**Bracket**|**Rate**|
|1 1 - 62,640|0.1|1 - 75,000|0.05|
|2 62,641 - 107,040|0.14|75,000 - 150,000|0.1|
|3 107,041 - 166,320|0.21|> 150,000|0.15|
|4 166,321 - 237,600|0.31|||
|5 237,601 - 496,920|0.34|||
|6 496,921 - 803,520|0.48|||
|7 > 803,521|0.5|||

Source: Harris Consulting & Tax Ltd. (2017); PWC (2021)

# **Property tax**

**Property tax is collected according to the PA General Administration of Property Tax and then transferred to municipalities.** The Jordanian Property Tax Law no. 11 of 1954 regulates the property tax for properties in the West Bank, while the Property Tax Law in Cities No. 42 of 1940 and the Property Tax Law in Villages, No. 5 of 1942 regulate properties in Gaza. Property tax is imposed on owners of the property or building, both resident and nonresident, and varies for land (unconstructed property) and building. The tax rate varies by municipality and by occupation status of the building (rented, prepared in preparation for rental, and inhabited by the owner). For buildings, property tax is imposed at a rate of 17 percent of the annual rental value of the building after depreciation/lease cost (20 percent of value). The tax on unconstructed buildings is 10 percent of the property valuation, based on the unit value of the land.

# **2.3.2 Indirect taxes**

14
- i) the tradable sector and non‐tradable sector, and

- ii) the formal/modern business sector (chiefly of SMEs and larger establishments) and informal activities (mainly of micro enterprises)

- iii) manufacturing vs. commercial agriculture vs. modern services within the traded sector

- iv) comparative advantage industries vs. other industries within the traded sector

# 2.2.1. The traded and non‐traded sectors

Because employment in subsistence agriculture is largely excluded from the UBR we cannot say whether or not there has been a shift of employment between agriculture as a whole and the rest of the economy in 2000s simply by comparing different waves of the census. The most we can glean from the UBR in terms of reallocation of manpower between agriculture and the rest of the economy relates only to commercial farming and agribusiness as a single digit (ISIC) component of the traded sector.

Manufacturing is the second component. While there is a strong case for doing some of the analysis in terms of a broader industrial sector understood to include (beyond manufacturing) construction as its non‐traded component, the construction sector and mining are both quite small in terms of their combined share in aggregate employment in Uganda. Focusing our analysis of structural change in terms of manufacturing and commercial agriculture as the main components of the traded sectors therefore seems to be appropriate. We also broadly equate the non‐traded sector with services in the present context.

Although there is significant amount of service exports from Uganda and even more service imports the sector in Uganda is overwhelmingly non‐traded for all practical purposes. It is also three times as large in terms of employment as the traded sector as a whole and accounts for 75 percent of the aggregate employment captured by the UBR (Table 2). It will therefore be useful to look at the breakdown of any reallocation of labor between services and the traded sector (i.e., commercial agriculture and manufacturing) in terms flows to and from the main service subsectors. A useful categorization from this point of view would include the following five subsectors: Utilities and transport and communication services, domestic trade and hotels and restaurants, finance and real estate, education and health services, and community services.

# 2.2.2. Comparative advantage industries

Uganda’s revealed comparative advantage at present lies in primary commodities consisting mainly of agricultural ones and fishing and to a lesser extent mining, in the form of gold at this point but expected to include oil and gas over the coming years.<sup>9</sup> But it is also widely believed that the country has scope for

> 9 For example in 2006, the latest year on which data are citied in the current Uganda Export Strategy , Uganda exported just under a billion US dollars’ worth of goods and services two‐third of which represented exports of agricultural commodities.

12
transfers (FODES) per capita received in t -1<sup>11</sup> The econometrics model includes municipality and year fixed effects. Thus, the empirical analysis explores the relationship between individuals’ attitudes and deviations of the municipality income from the national average. Since the treatment comes at the municipality*year level the error term, ε( _m_ , _t_ ), is allowed to be correlated within a municipality*year. I also perform a heterogeneity analysis by restricting the same across different groups based on rural-urban status and weak vs strong state capacity municipalities.<sup>12</sup>

# **4 Results**

I organize results in two parts. First, I analyze data from LAPOP for the time period 2004-2018. Second, and for the same time period, I analyze data from Latinobar ́ometro. As discussed above the main difference between the two surveys is that the latter is exclusively urban while the former interview individuals in both urban and rural areas. In spite of that, the two samples do not appear to substantially differ in terms of other observable demographic characteristics.

I use LAPOP to analyze the link between income at the municipality-year level and individuals’ attitudes towards the president of El Salvador and the municipal government. I also study how attitudes towards other institutions such as national legislature, judicial system, mass media, and the Catholic Church vary with the business cycle. Finally, I explore whether these empirical associations mask any heterogeneity based on location characteristics (i.e., rural vs urban and strong vs weak state capacity of the municipality).

Finally, I use Latinobar ́ometro to explore the link between income (proxied by NTL density) and perceptions of fairness in income distribution, generalized trust, views on country’s economic situation, and attitudes towards democracy. I also perform a heterogeneity analysis based on the strength of municipal state capacity.

## _4.1 LAPOP Analysis (2004-2018)_

Table 3 presents the main results on the relationship between income and trust in the president and the municipal government. Regardless of whether I use income data (for a smaller sample of municipalities in columns 1 to 3) or NTL density as proxy for income in an extended sample (in column 4), individuals’ trust towards the president positively correlates with higher level of development at the municipality level. Specifically, the empirical association between income and trust in the president is virtually unaffected by the inclusion of individual levels controls (column

> 11I used 1992 population counts to normalize both the number of homicides and the amount of government transfers

> 12A municipality is defined as weak (strong) state capacity if the average share of household with access to electricity and water is below (above) 80 percent.

14
Russia). The countries with the largest decline in their initial connectivity are those that are strongly connected to the country experiencing the shock and do not have strong connections to other partner countries. These countries tend to be in the middle‐range of centrality and receive their connectivity through a few well‐connected countries. A shock to one of these well‐connected countries would do the largest damage to their global connectivity.

Figure 12. Simulated impact on individual countries’ connectivity measure (modified PageRank) from a 10% decline in trade, FDI and migration in Germany, Russia and the United States.

<!-- Start of picture text --> Shock originating in Germany<br>(10 Percent fall in all types of connectivity)<br>0 0 0.2 0.4 0.6 0.8URYPAN 1NZL HKGAUS1.2 SGP CAN1.4 1.6<br>‐1‐2 AFG BFAATG TGO BLZBEN BRN ETH AZE TJK KGZARMGEOSWZBWAGUYPRYNAMBRBGABKWTMOZCMRLBNKENOMNJAMALBGHAECUMUSJORBHSQATGTMSLVDZADOMCYPPAKLVACRITTOTUNBLRNGAESTLTUCOLEGYSAUAREPERMARISRCHLARGZAF NORPRTFINLUXDNKTHAIDNINDMYSBRASWEIRLCHEJPNMEXESPITABELFRAGBRCHNUSA<br>BHR BIH BGR NLD<br>RUS<br>MKD<br>‐3 KAZ GRC SVK<br>CZE<br>HUN<br>‐4<br>MLT<br>SYR HRV TUR AUT<br>MDA<br>BGD<br>‐5 LKA POL<br>UKR<br>‐6 Initial level of connectivity<br>Change in level of connectivity<br><!-- End of picture text --> <!-- Start of picture text --> Change in level of connectivity<br><!-- End of picture text --> 31
<!-- Start of picture text --> 15<br>——— ES |<br>—' 7]<br>=")<br>ie<br>10<br>as<br>00<br>05<br>“1.0<br>rxchange_2010“15 rPerimeter_2010 Fullnes_2010 Greuity_avg_2010 Intersection_density_2010 Street_density_2010 Sprawl_2010<br><!-- End of picture text -->

~~SS~~<sup>~~e_—~~</sup>
# **BENCHMARKING FINANCIAL DEVELOPMENT**<sup>**1**</sup>

**Thorsten Beck, Erik Feyen, Alain Ize, and Florencia Moizeszowicz**

> 1 This paper is based on a more detailed document on “Core Financial Development Indicators” that was prepared as part of an FSAP Stock-Taking study co-financed by the Swiss Secretariat for Economic Affairs (SECO) and the World Bank. We thank George Clarke for his guidance on econometric issues at the start of the project, Asli Demirguc-Kunt, Roberto Rocha, and Constantinos Stephanou for their comments, and Monica Dedich for her editorial assistance. This paper’s findings, interpretations, and conclusions are entirely those of the authors and do not necessarily represent the views of the World Bank, its Executive Directors, or the countries they represent.

1