rafmacalaba/datause-encoder-data huggingface dataset:
- 31 -

# Table 11 :Trends in Poverty Gap Ratio for Rural Poor by States "1973-74 to 1986-87

||||||Annual Gr|owth Rat|es (%)||
|---|---|---|---|---|---|---|---|---|
||Poverty|Gap Ra|tio(%)||73-74|77-78|83|73-74|
|States|||||to|to|to|to|
|____|__73-74|77-78|83|86-87|77-78|83|86-87|86-87|
|ANDHRA PRADESH|14.1|11|6.5|6|-6|-9.1|-2.3|-6.4|
|ASSAM|13.8|14.5|8.7|8.7|1.2|-8.9|0|-3.5|
|BIHAR|23.3|21.5|21.8|14.2|-2|0.3|-11.5|-3.7|
|GUJARAT|12.2|12.1|5.5|8.6|-0.2|-13.4|13.6|-2.7|
|HARYANA|8.8|7|3.9|4.1|-5.6|-10.1|1.4|-5.7|
|JAMMU-KASHMIR|12.9|10|4.3|5.3|-6.2|-14.2|6.2|-6.6|
|KARNATAKA|16.7|15.8|10.6|10.7|-1.4|-7|0.3|-3.4|
|KERALA|20.4|17.2|10.1|8.3|-4.2|-9.2|-5.5|-6.7|
|MADHYA PRADESH|21.6|21.6|14|12.6|0|-7.6|-3|-4.1|
|MAHARASHTRA|18.6|22.1|12.5|12.05|4.4|-9.8|-1|-3.3|
|ORISSA|31.3|28|17.55|16.4|-2.7|-8.1|-1.9|-4.9|
|PUNJAB|6.4|4|2.8|2.7|-11.1|-6.3|-1|-6.4|
|RAJASTHAN|12.1|11|10|7.4|-2.4|-1.7|-8.2|-3.7|
|TAMIL NADU|18.2|19.5|17.9|12.6|1.7|-1.5|-9.5|-2.8|
|UTTAR PRADESH|16.8|14.1|13.4|<br>9.5|-4.3|-0.9|-9.4|-4.3|
|WEST BENGAL|29.1|25.5|21.6|12.6|-3.2|-3|-14.3|-6.2|
|ALL INDIA|_18.8|17.4|13.4|10.5|-1.9|-4.6|-6.7|-4.4|

# Table 12:Trends in Poverty Gap Ratio for Rural Ultra Poor by StaTES "1973-74 to 1986-87

||||||Annual Gr|owth Rate|s (%)||
|---|---|---|---|---|---|---|---|---|
||Poverty|Gap Rat|io(%)||73-74|77-78|83|73-74|
|States|||||to|to|to|to|
||73-74|77-78|83|86-87|77-78|83|86-87|86-87|
|ANDHRA PRADESH|7.2|5.5|2.8|2.7|-6.5|-11.6|-1|-7.3|
|ASSAM|6.2|5.7|2.9|3.2|-2.1|-11.6|2.9|-5|
|BIHAR|14.1|12.5|12.6|6.6|-3|0.1|-16.9|-5.7|
|GUJARAT|5.5|6.2|1.9|4.4|3|-19.3|27.1|-1.7|
|HARYANA|3.8|3|1.4|1.4|-5.7|-12.9|0|-7.4|
|JAMMU-KASHMIR|5.5|3.81|1.3|1.8|-8.8|-17.8|9.7|-8.2|
|KARNATAKA|8.9|8.9|5.4|5.4|0|-8.7|0|-3.8|
|KERALA|12.3|10.3|4.7|3.9|-4.3|-13.3|-5.2|-8.-|
|MADHYA PRADESH|12.7|12.9|7.1|5.9|0.4|-10.3|-5.2|-5.7|
|MAHARASHTRA|10.2|13.2|6|6|6.7|-13.4|0|-4|
|ORISSA|21.2|18.3|10|8.5|-3.6|-10.4|-4.5|-6 8|
|PUNJAB|2.9|1.9|0.96|0.94|-10|-11.7|-0.6|-8.3|
|RAJASTHAN|6|6|5.2|3.3|0|-2.6|-12.2|-4.5|
|TAMIL NADU|10|11.3|10.4|6.7|3.1|-1.5|-11.8|-3|
|UTTAR PRADESH|8.3|7.1|6.8|4.2|-3.8|-0.8|-12.9|-5.1I|
|WEST BENGAL|19.3|<br>16|12.9|6.1|-4.6|-3 8|-19 3|-85a|
|ALL. INDIA|10.6|<br>9.9|7.1|5|<br>-1.7|-5.9|-9 5|-5 6|
|Computed<br>fromn|||||||||
**Figure 2** The global equilibrium real interest rate – robustness checks

<!-- Start of picture text --> Percent<br>5<br>4<br>3<br>2<br>1<br>0<br>-1<br>-2<br>1983 1986 1989 1992 1995 1998 2001 2004 2007 2010 2013 2016 2019<br>Benchmark Including China<br>Ex-post rate Ex-post rate w/o replacing<br><!-- End of picture text -->

# _d. Equilibrium interest rates in advanced economies and EMDEs_

Equilibrium rates of course follow different paths in different countries. One potentially useful categorization is to focus on differences between advanced economies, which are economically and financially more integrated, and EMDEs, which are still integrating themselves into the world economy. Figure 3 shows the equilibrium real interest rates for these two groups separately. While the advanced economies are in the minority in numbers, they account for a higher share of global gross domestic product (GDP) than EMDEs. It is hence not surprising that the shape of the equilibrium rate in advanced economies is very similar to the one for the world shown in Figure 1. The equilibrium interest rate in EMDEs, on the other hand, shows some specificities: first, their rates were lower in the 1980s; second, they declined slower in the 1990s; third, while it declined during the global financial crisis as well, it went below zero only briefly and started recovering faster and stronger. In 2019, the last observation of the sample, the rate stood at -0.9 percent in advanced economies but at 0.5 percent in EMDEs.<sup>23</sup> Over the last 20 years, the equilibrium interest rate in EMDEs was higher than in advanced economies.

> 23 The divergence between advanced economies and EMDEs has already been noticed by Rachel and Smith (2015), who analyze the nominal yield on 10-year sovereign bonds minus 1-year-ahead inflation expectations in 17 emerging markets.

11
CAMEROON’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE Cameroon has managed to attract private sector participation into some sections of the ports. APM Terminals is a stakeholder in the Douala container terminal, having been selected to manage and operate the facility on a concession basis for a period of 15 years beginning in July 2004. The Port Authority of Douala is also a stakeholder. The private sector also has a strong presence in the ownership, management, and operation of the specialized liquid-bulk export terminals. Despite the strong presence of the private sector in container operations in Douala, greater involvement of the private sector could be achieved as part of wider institutional reforms.

Traffic in Cameroon’s ports increased substantially in the period 1996–2005 but remained relatively light in comparison with other ports along the western coast of Africa. Container traffic in the port of Douala nearly doubled between 1996 and 2005, rising to a volume of 190,700 TEU in the last year (figure 9a), still short of the capacity of 270,000 TEU. General-cargo traffic also grew substantially in Douala, rising from a level of 3.8 million tonnes in 1995 to 5 million in 2006. Dry-bulk traffic in the port, while smaller in volume than other cargo types, chalked up the highest rate of growth—from 100,000 tonnes in 1995 to 500,000 tonnes in 2006. The cargo handled by the port of Douala is comparable to that of the port of Cotonou but behind the level of the ports of Abidjan (Côte d’Ivoire), Lomé (Togo), Tema (Ghana), Luanda (Angola), and Apapa (Nigeria) (table 5). Nonetheless, Douala is the most important regional port of Central Africa, as it handles transit traffic for Central African Republic and Chad.

**Table 5. Port indicators for selected ports**

||Douala<br>Cameroon|Abidjan<br>Côte d’Ivoire|Apapa<br>Nigeria|Cotonou<br>Benin|Harcourt<br>Nigeria|Lomé<br>Togo|Luanda<br>Angola|Pointe Noire<br>Congo|Tema<br>Ghana|
|---|---|---|---|---|---|---|---|---|---|
|Containers handled (TEU per year)|190,700|500,119|336,308|158,201|7,900|460,000|377,208||420,000|
|Crane productivity, container<br>(containers per hour)|18.5|18|12||||6.5|6.5|13|
|Crane productivity, general cargo<br>(tonnes per hour)||16|9|15|8|22.5|16|7.5|13.5|
|Dwell time, container (days)|12|12|42|12||13|12|18|25|
|Turnaround time, truck processing time<br>for receipt and delivery of cargo (hours)|12|2.5|6|6||4|14|12|8|
|Pre-berth waiting time, general cargo<br>(hours)||2.9|36|48|38.4||144|43.2|9.6|
|Pre-berth waiting time, bulk dry (hours)|1.5|1||48|||||6|
|Pre-berth waiting time, container<br>(hours)|1.6|1|12|24||1|96|38.4|12.4|
|Handling charge, bulk dry ($ per tonne)|6|5||5||5|5|2.75|3|
|Handling charge, cargo container ($ per TEU)|220|260|155|180||220|320|140|168|
|Handling charge, general cargo ($ per<br>tonne)|6.5|13.5|8|8.5|8|9|8.5|5.5|10|

_Source_ : AICD ports database downloadable from http://www.infrastructureafrica.org/aicd/tools/data. _Note_ : Data are as of 2006. —= data not available.

TEU = 20-foot equivalent units.

17
THE IMPACT OF INFRASTRUCTURE SPENDING IN SUB-SAHARAN AFRICA

# **Uganda > Telecom investments > Macro results**

||||Reduction in<br>h bli|T|elecom<br>I|Fi|I|
|---|---|---|---|---|---|---|---|
|||||||||
|Variable|Definition|Reference|<br>expenditure|VAT|<br>duties|<br>aid|<br>tax|
|Ym|Aggregate household income|875,000.00|0.09|0.12|0.12|0.14|0.12|
|EV|Equivalent variation||0.09|0.11|0.12|0.17|–0.40|
|s|Wage|1.00|0.06|0.20|0.21|0.32|0.21|
|yg|Government income|163,000.00|0.13|2.73|2.73|2.73|2.73|
|g|Total government expenditure|103,000.00|–2.28|1.84|1.84|1.84|1.84|
|ye|Firm income|113,000.00|0.22|0.03|0.03|–0.11|0.02|
|It|Total private investment|105,000.00|0.30|0.13|–0.44|–3.46|–0.20|
|GDP|GDP|834,000.00|0.13|0.13|0.13|0.12|0.13|
|e|Nominal exchange rate|1.00|0.12|0.11|–1.75|–11.38|–0.05|

# **Uganda > Telecom investments > Sectoral results**

||||Reduction in|Te|lecom|||
|---|---|---|---|---|---|---|---|
|Variable|Sector|Base|other public<br>expenditure|VAT|Import<br>duties|Foreign<br>aid|Income<br>tax|
||Crop agriculture|270,000.00|0.10|0.02|0.03|0.08|–0.18|
||Export agriculture|112,000.00|0.18|–0.07|–0.09|–0.40|–0.14|
|Va|Mining and gas|577.00|0.08|–0.02|0.17|0.92|0.02|
|Value added or|Industries|94,100.00|0.26|–0.33|–0.37|–1.33|–0.01|
|output|Construction|66700.00|1.62|1.19|1.28|1.92|1.39|
||Private services|260,000.00|–0.02|0.04|0.02|0.04|0.08|
||Public services|31,600.00|–2.23|1.50|1.62|3.65|1.80|
||Crop agriculture|1.00|–0.05|0.09|0.07|–0.15|–0.12|
||Export agriculture|1.00|–0.02|0.01|–0.03|–0.46|–0.17|
||Mining and gas|1.34|0.07|0.24|1.59|–9.60|–0.05|
|pq<br>Market prices|Industries|1.10|0.04|0.83|0.11|–4.39|–0.05|
||Construction|1.01|0.41|0.80|0.46|–1.52|0.38|
||Private services|1.02|–0.16|0.35|0.10|–1.82|–0.03|
||Public services|1.00|–0.05|0.33|0.22|–1.74|0.04|
||Crop agriculture|1.00|0.07|0.06|0.08|0.28|–0.31|
||Export agriculture|1.00|0.17|–0.19|–0.24|–0.76|–0.33|
|r<br>|Mining and gas|1.00|0.11|–0.24|0.68|4.32|–0.01|
|Rental rate of<br>capital|Industries|1.00|0.46|–0.65|–0.73|–2.67|0.03|
||Construction|1.00|2.45|1.85|2.01|3.23|2.20|
||Private services|1.00|–0.26|–0.02|–0.05|0.10|0.06|

47
Visits (JD-HV). ProGres records demographic information of each individual refugee and their household at the time of registration with UNHCR including the relationship of each household member with the principal applicant. In this paper, we refer to the self-identified principal applicant as the household head – the term used in the poverty research literature. Self-identification is commonly used as a means of identifying the household head in household surveys of income, consumption and expenditure surveys which are used to estimate poverty rates (see Hanmer et al, (2020)).

Home visits began at the same time as the UNHCR cash assistance program was launched. The program aimed to assist vulnerable Syrian households to meet their basic needs. During the home visits, UNHCR determined whether a household met the criteria to qualify for assistance, considering economic poverty and the presence of members in the household requiring protection. Since 2013, these visits have been made to every registered Syrian refugee household in Jordan outside the camps (UNHCR, 2014).

At registration, each household is assigned a unique registration number that serves to crossreference the initial registration data with Home Visits data. Home Visits data are collected for about one-third of the registered households. In both waves, the selected households to be interviewed are those UNHCR considers to be most vulnerable in terms of education, specific needs and the use of livelihood coping strategies,<sup>7</sup> thus, they are a nonrandom sample of all registered Syrian households in Jordan. During Home Visits, data on household expenditures, aid, income, schooling, and other demographics are collected. Each wave of data is a snapshot of a different nonrandom sample of registered refugees because the pool of registered households changed over time. However, because of data confidentiality, we could not link records across years. For this reason, we treat each wave as an independent sample of cross-sectional data.

We compare 204,941 individuals in 54,900 cases (households) in the 2013 wave, with 195,930 individuals in 43,292 cases in 2018. We focus on comparisons between different types of households; however, we use individual characteristics to determine the household categories. A case is formed when a Principal Applicant (PA) registers at a UNHCR office seeking asylum. Initially the selfidentified PA reports all members of the household and their demographic characteristics, which are recorded in ProGres. Subsequently UNHCR carries out individual interviews with each family

> 7 See UNHCR (2018b) available at https://data2.unhcr.org/en/documents/download/65143.

9
# **Figure 11. Banking sector performance**

# **A. Impact of ECB’s NIRP on banks**

# **B. Impact of ECB’s QE on banks**

<!-- Start of picture text --> Net percent Past six months Next six months Net percent 2015-July<br>30 40 2016-January<br>2016-April<br>Next six months<br>-10 20<br>-50<br>0<br>-90<br>-20<br>-40<br>Enterprises Households for House Profitability Capital Net Interest<br>Purchase Gains/Losses Margin<br>C. Bank equity prices D. Interest margins on household lending<br>Index, Jan 2014 = 100 Percentage points<br>10<br>130 World Euro Area Japan Range from 2010-present<br>8 Pre-NIRP<br>Current<br>110 6<br>4<br>90<br>2<br>70 0<br>50<br>2014 2015 2016<br>E. Interest margins on corporate lending F. Credit growth<br>Percentage points Percent Latest<br>5 10 Pre-Crisis<br>Range from 2010-present Pre-NIRP<br>4 Pre-NIRP 8 Post-NIRP<br>Current 6<br>3<br>4<br>2 2<br>0<br>1<br>-2<br>0<br>rate rate<br>Lending margin Lending Lending volume Lending margin Lending Lending volume<br>income charge charge<br>Net interest Non-interest Non-interest<br>Hungary Denmark Spain France Germany Sweden<br>Spain France Euro Area Sweden Denmark Japan<br>Hungary Germany Denmark Sweden Switzerland<br><!-- End of picture text -->

# **D.  Interest margins on household lending**

Sources: Bloomberg, European Central Bank (2016b), World Bank. A. B. Results from ECB’s Bank Lending Survey in April 2016. Questions start as: “Given the ECB's negative deposit facility rate, did or will this measure, either directly or indirectly, contribute to...” Net percent is calculated as the difference between the sum of responses mentioning “increased considerably” and “increased somewhat” and the sum of responses mentioning “decreased somewhat” and “decreased considerably", divided by the number of responding banks which did not reply “not applicable.” C. Last observation July 2016.

D. E. Lending margins are measured as the difference between rates applied to new household/non-financial corporation loans and a weighted average rate of rates applied to new deposits from households and non-financial corporations. F: Year-on-year growth of credit to households and non-financial corporations. Pre-Crisis stands for average period between Jan. 2003 and Dec. 2007. Pre-NIRP stands for period between Jan. 2010 and beginning of NIRP date. Post-NIRP stands for the period between the beginning of NIRP and last observation. Last observation is March 2016 for Switzerland, May 2016 for Japan, and April 2016 for the other economies.

27
source is information collected by World Bank staff, and Iran, where we use food price data compiled by the Central Bank.<sup>7</sup> To our knowledge there is no consistent information on food prices in Libya, which is hence omitted from the analysis.<sup>8</sup> For some countries food prices refer to urban or capital centers. Overall, because of the great emphasis on consistency checks with other than official sources, we believe the food price dataset provides accurate information.

Food inflation rates for eighteen MENA countries are presented in the Annex, Figures 1A-C. We provide data for almost all MENA economies for the period from December 2006 to October 2011. The beginning of this period is commonly regarded as the start of the first global food price shock. In nearly all countries food inflation registered a peak in 2008, yet there are substantial differences in food inflation patterns across the Arab world, reflecting idiosyncratic factors at the domestic level. In general, domestic food inflation in MENA remained lower than global food inflation, and in most Arab economies food inflation remained positive even during the period when international food inflation turned negative. This fact is consistent with asymmetric transmission of international food price fluctuations to domestic markets in the Arab world – a hypothesis investigated in the second part of this paper.

As a first attempt to understand the exposure of the MENA countries to global food shocks, we present ratios of net imports to domestic consumption of key food commodities, together with their respective import and consumption shares, by country (Table 1A) and country groupings (Table 1B). The ratios of net imports to domestic consumption are indicative of the dependency on foreign imports to satisfy domestic demand for grains, edible oils, meat, and sugar. The analysis covers grain and non-grain imports; the latter account for approximately half of all food imports in developing MENA (Table 1B). In the GCC their share is just under 30 percent.

Dependency ratios, presented in Table 1A, are calculated as: where denotes the net imports of food product _i_ and is the domestic consumption of food product _i_ , with _i_ corresponding to grains, edible oils, meat, and sugar. Dependency ratios ( _Di_ ) are aggregated across the four food groupings for a

> 7 Food prices from Iran‟s national statistical office and annual growth rates are much lower than those reported by the Central Bank.

> 8 Only the CPI is available in the International Financial Statistics (IFS) database until November 2010.

-7-
that there will be something left when the person dies to leave to their beneficiaries. The funds are not being exhausted rapidly and there does not appear to be a fear that people will be left without as they grow older.

The build up of retirement savings will act to some extent as a cushion to the impact of an ageing population. Forecasts in the governments’ intergenerational report indicate that there will be fiscal pressures building from about 2020 onwards, not only due to age pensions, but also due to higher spending on healthcare. The sense is that there is no ‘ageing crisis’, although some changes will have to be made in the level of benefits, taxation or private contributions to superannuation in the longer run. The government seems more than comfortable to leave the investment of retirement income savings to individuals. Most people seem to be investing their funds wisely and the rundown of accumulated assets is only occurring slowly. Although some concerns have been expressed that the current system may lead people to undertake excessive consumption in their early years of retirement and have to rely on the age pension in later life. With changing attitudes towards the need to secure their retirement the opposite may in fact be true. It may be the case that people are too conservative in their spending and spend too little rather than too much. This reduces individual welfare, but it does reduce recourse to the age pension.

There do not appear to be any significant concerns about longevity risk among Australians and hence there is very little demand for products, such as lifetime annuities, which provide a hedge against this risk. Many expect that they will have sufficient resources to ensure that they have a comfortable retirement, while those that do not can rely on the age pension. Finally, for many Australians their home provides an additional buffer, particularly given a growing understanding of equity release products.

Over a number of years the Australian government has taken a number of initiatives aimed at reducing incentives for early retirement. These include the standardization of the retirement age for men and women by phasing in a higher age for women; the increase in compulsory preservation age for occupational superannuation from 55 to 60; the introduction of higher RBL limits for retirement benefits taken at least half in annuity form; and the introduction of a deferred pension bonus plan. The Government appears satisfied that it has its parametric settings broadly correct.

47
Mattoo, Aaditya, Arvind Subramanian, Dominique van der Mensbrugghe, and Jianwu He (2009). “Reconciling Climate Change and Trade Policy” Peterson Institute for International Economic Policy Working Paper 09-15.

Melitz, Marc (2003). "The Impact of Trade on Intra-Industry Reallocations and Aggregate Industry Productivity." _Econometrica_ . 71(6): 1695-1725.

Messerlin, Patrick (2010), “Climate change and trade policy: From mutual destruction to mutual support,” Mimeo. Groupe d'Economie Mondiale , Institut d’Etudes Politiques, Paris.

Moore, Michael O. and Alan Fox (2010). “Why Don’t Foreign Firms Cooperate in U.S. Antidumping Investigations?: An Empirical Analysis,” _Review of World Economics._ 145(4): 597-613.

Moore, Michael O. and Maurizio Zanardi (2009). “Does antidumping use contribute to trade liberalization in developing countries?” _Canadian Journal of Economics._ 42(2): 469-495.

Moore, Michael O. (1996). "The Rise and Fall of Big Steel's Influence on US Trade Policy" in A. Kreuger (ed.) The Political Economy of Trade Protection. Chicago: University of Chicago Press.

Moore, Michael O. (2002). “Commerce Department Antidumping Sunset Reviews: A First Assessment,” _Journal of World Trade_ , 36(2): 675-698.

OECD (2002). “Environmental Policy in the Steel Industry: Using Economic Instruments.” COM/ENV/EPOC/DAFFE/CF A(2002)68/FINAL.

Pauwelyn, Joost (2007). “Competitiveness Concerns: The Limits and Options of International Trade Law” Nicholas Institute for Environmental Policy Solutions Working paper 07-02, Duke University.

Prusa, Thomas (2001). “On the spread and impact of antidumping” _Canadian Journal of Economics._ 34(3): 591-611.

Stiglitz, Joseph (2006). Making Globalization Work. New York: W.W. Norton.

WTO (2009). Trade and Climate Change. Geneva: World Trade Organization.

22
To account for the heteroscedasticity due to the sampling error, all regressions are  1  1  1 weighted by a factor ( _nt_  _nt_  10 ) as described in Borjas (2000), where _nt_ is the number of skilled people by country and half-decade of arrival, in Census year _t_ .

# **_4.2. EMPIRICAL RESULTS_**

This section tests empirically the hypothesis of _β_ -convergence advanced in sub-section 4.1. Table 5 presents the results from estimating the growth rate in occupational placement on the initial level of performance for the 1985 and 1975 arrivals. Columns 1 to 4 provide evidence of convergence as suggested by the negative and significant coefficients for the predicted log of OP in 1980 and 1990 respectively. This result holds for all the alternative specifications of the dependent variable.

# **5. OCCUPATIONAL PLACEMENT IMPROVEMENT AND COUNTRY OF ORIGIN ATTRIBUTES**

# **_5.1. EMPIRICAL FRAMEWORK_**

The third stage of the analysis is an exploration of the extent to which the predicted Occupational Placement Improvement is accounted for by country-of-origin specific factors. Previous literature focusing on earnings found country attributes to be significant determinants of both the entry wages and the growth rates (Jasso and Rosenzweig, 1986, Borjas, 1987, Borjas 2000). Mattoo et al. (2008) identified two types of country specific influences on the initial levels of occupational placement: quality-related, explaining why nominally identical educational qualifications obtained in different countries are valued differently on the US labor market; and selection-related, determining which particular segments of the source country ability distribution are likely to send migrants to the US.

As a first step, I check whether the predicted occupational placement indicators for the two sets of hypothetical individuals, migrating in 1975 and 1985 respectively, are influenced by several quality and selection country specific attributes. The functional form is presented in equation (6).

_predicted_ ln _OP_    '  1' * _GDPpc_    2' * _OPEN_    3' * _ENGLISH_  ' ' ' ' (6)   4 * _DIST_    5 * _CONFLICT_    6 * _COM_    , _t_ ,   _country_ The regression is run separately by hypothetical individual and census year for each OP specification (prestige index, education index 1, education index 2). The dependent variables are 24