rafmacalaba/datause-encoder-data huggingface dataset:
# **Annex Table 7: Distribution channels**

## **Non-life**

|||**Motor **|||**Non Motor **||
|---|---|---|---|---|---|---|
||**Agent**|**Broker **|**Direct**|**Agent**|**Broker **|**Direct**|
|**Bahrain**|5|50|45|5|60|35|
|**Jordan **|79|21|0|36|64|0|
|**Lebanon **|30|40|30|20|60|20|
|**Libya**|40|0|60|10|0|90|
|**Oman **|25|30|45|5|50|45|
|**Qatar **|5|7|88|20|10|70|
|**KSA **|5|47|48|10|20|70|
|**Tunisia* **|26|61|13|37|35|25|
|**UAE**|5|50|45|20|30|50|

Source: AXCO Reports and Country Visits

*Banks also distribute non life

Notes: Most personal lines distribution in Egypt has been placed through agents , and commercial and industrial business have been written directly – corporate brokers were only recently authorized. In Morocco the corporate business is largely placed through brokers – a number of banks have established broking subsidiaries to comply with the law.

## **Life**

|||**Individ**<br>|**ual Life**<br>|||**Grou**<br>|**p Life**<br>||
|---|---|---|---|---|---|---|---|---|
||**Agent**|**Broker**|**Bank**|**Direct**|**Agent**|**Broker**|**Bank**|**Direct**|
|**Kuwait**|90|||10|3|80||17|
|**Lebanon**|25||50|25|20|10|10|60|
|**Libya**|20|0||80|5|||95|
|**Oman**|70|5||25|0|80||25|
|**Qatar **|85|5||15|20|5||75|
|**KSA **|75|5||20|75|5||20|
|**Tunisia**|23|10|40|27|23|26||51|
|**UAE**|55|10||35||50||50|

Source: AXCO Reports and Country Visits Notes: The main distributions systems for Egypt have been agency forces and, increasingly, banks. In Morocco bancassurance now accounts for more than 60% of life sales, although brokers still have a role in the group life market.

39
Financial Regulation and Development Table 5: Taxes and Antebellum USA Banks: Levels, Real 1800

||(1)<br>(2)|(3)|(4)|
|---|---|---|---|
|Variables|Deposits<br>Loans<br>(USD)<br>(USD)|Safe Assets<br>(USD)|Profts<br>(USD)|
||Policy Phase Out|||
|Policy|85,422<br>31,865|47,893|7,358|
||(54,124)<br>(64,387)|(40,700)|(9,688)|
|Bank FE|Yes<br>Yes|Yes|Yes|
|State FE|Yes<br>Yes|Yes|Yes|
|Month FE|Yes<br>Yes|Yes|Yes|
|Year FE|Yes<br>Yes|Yes|Yes|
||Policy and Big Banks|||
|Policy _×_ Big Bank|353,018***<br>424,086***|222,121***|50,395***|
||(80,888)<br>(98,290)|(60,524)|(12,472)|
|Bank FE|Yes<br>Yes|Yes|Yes|
|State FE|Yes<br>Yes|Yes|Yes|
|Month FE|Yes<br>Yes|Yes|Yes|
|State _×_ Year FE|Yes<br>Yes|Yes|Yes|
|Obs.|17,958<br>17,958|17,958|17,958|
|Number of Banks|1,280<br>1,280|1,280|1,280|
|Mean Dep. Var.|299,076<br>969,361|223,475|53,715|
|SD Dep. Var.|771,046<br>1,528,938|677,795|126,821|

_Note:_ This table reports OLS estimates. The unit of observation is bank level, and bank, month, state, year, and state _×_ year fixed effects are included. Standard errors are clustered at bank level. Deposits is a variable reporting the overall deposits at bank level; it is continuous and measured in USD. Safe assets is the amount of liquid assets held by banks in cash, bank-to-bank notes, real estate assets, and other private assets; it is continuous and measured in USD. Loans and Discounts embodies all lending operations at bank level; it is continuous and measured in USD. Profits and Losses is the difference between the asset-generated income and liability-induced costs after taxes; it is continuous and measured in USD. All variables are in real dollars at 1800 prices, and estimates of the consumer price index for these years are from FED Minneapolis, which are based on the Index of Prices Paid by Vermont Farmers for Family Living for 1800 to 1851 and the Consumer Price Index by Ethel D. Hoover for 1851–1890. The means and standard deviations of these variables are reported in the final two rows. The following states are included in the sample because they are treated in some year by the policy: CT, GA, MA, MD, ME, NC, NH, NJ, NY, PA, RI, and VT. However, because of data limitations we observe a bank balance sheet before and after the policy only for the following six states: MA and ME in 1812; NH and RI in 1822; NY in 1839; CT in 1850. ***, **, and * indicate significance at the 1%, 5%, and 10% levels, respectively.

39
# **Figure 6:  Equity Market Volatility**

<!-- Start of picture text --> (a) Stock Market Index, BRICS and US, Jan 2000=100<br>700<br>600 Russian<br>Federation<br>500 Brazil<br>400 India<br>300 South Africa<br>200 China<br>100 United<br>States<br>0<br>2000M01 2001M02 2002M03 2003M04 2004M05 2005M06 2006M07 2007M08 2008M09 2009M10 2010M11 2011M12<br><!-- End of picture text --> <!-- Start of picture text --> (b) "VIX" Daily Index of Expected Market Volatility<br>90<br>80<br>70<br>60<br>50<br>40<br>30<br>20<br>10<br>0<br>01/02/90 01/02/92 01/02/94 01/02/96 01/02/98 01/02/00 01/02/02 01/02/04 01/02/06 01/02/08 01/02/10 01/02/12<br><!-- End of picture text --> Sources: World Bank Global Economic Monitor, CBOE for VIX.

Looking ahead, the international monetary system is likely to face three possible outcomes: (i) continuation of the current “non-system” international monetary system with continuation of capital flows and exchange rate volatility and its outlook punctuated by regional and global financial crisis from time to time, as has been the case since the 1970s with a distinct possibility of the collapse of the system (a la Bretton Woods I and II); (ii) a more likely scenario with the evolution of the current system into a multi-reserve currency system supported by three major currencies – the dollar, the euro and the renminbi, along with smaller currencies, such as the yen and the Swiss franc, with possibly other large emerging economies (e.g. India and Brazil) joining later, with continued volatility of capital flows and exchange rates, mainly among the major reserve currencies (and with blocks of currencies pegged to each of them) in response to their national policy changes and or unexpected events; and (iii) a collective decision by major global economies to reform the IMS and move toward a new supernational currency a la Keynes’s _Bancor_ . We focus here on the stability of the evolving multiple reserve-currency system in this section, given the relatively higher likelihood we attach to it as a possible medium-term scenario in the absence of a multilateral action to reform the entire system.

As shown in section III above, the world economy is in fact moving toward a multi-polar growth system, and this is likely to have important implications for the current international monetary system and its stability over the medium- term. In this context, a key trend that is likely to continue is a declining share of world GDP for the high income countries, as the US, the Eurozone and Japan are likely to grow at a much slower pace than their trend growth and compared to the average growth of emerging economies. In fact, as of end-2011, advanced economies, as a group, were slowing down with parts of the euro area having already entered a recession after a period of anemic recovery. Moreover, concerns about sovereign debt sustainability are now widespread and long-term unemployment is becoming a major issue in a number of these countries. The strong headwinds of deleveraging in the financial and public sectors exert further downward pressure on the pace of economic growth in the advanced countries.

26
TABLE 4. Estimation in first differences using OLS productivities: Productivity in domestic manufacturing firms and reform in upstream services sectors

|Dependent Vari|able: Δln(TFP)|I|II|III|IV|V|VI|VII|
|---|---|---|---|---|---|---|---|---|
|Services input|EBRD|1.062***|||||0.781*|1.108***|
|linkages||(0.400)|||||(0.429)|(0.404)|
||FDI (share)||7.274**||||5.850*||
||||(3.133)||||(3.314)||
||FDI (threshold)|||1.996||||2.521*|
|||||(1.468)||||(1.419)|
||Concentration||||0.188||2.409|1.416|
||||||(3.554)||(3.634)|(3.605)|
||Privatization (share)|||||15.814**|||
|||||||(6.969)|||
|Manufacturing<br>|FDI (share)<br>|-0.143|0.242||-0.279|-0.146|0.120||
|input linkages||(0.571)|(0.634)||(0.640)|(0.584)|(0.636)||
||FDI (threshold)|||0.027||||0.137|
|||||(0.439)||||(0.456)|
||MFN Tariff|-0.211*|-0.369**|-0.225|-0.155|-0.210|-0.368**|-0.323*|
|||(0.120)|(0.185)|(0.196)|(0.124)|(0.131)|(0.180)|(0.186)|
|Within sector|FDI (share)|-0.046|-0.237||0.034|-0.011|-0.191||
|effects||(0.257)|(0.290)||(0.291)|(0.272)|(0.287)||
||FDI (threshold)|||0.015||||-0.069|
|||||(0.189)||||(0.199)|
||MFN Tariff|0.087*|0.155*|0.084|0.058|0.075|0.161*|0.135|
|||(0.052)|(0.083)|(0.089)|(0.055)|(0.057)|(0.081)|(0.082)|
||No. of obs.|4197|4197|4197|4197|4197|4197|4197|
||R<sup>2</sup>|0.02|0.02|0.019|0.018|0.02|0.02|0.02|

The Inputs Linkage variables are weighted by the respective I/O coefficient. All regressors are differenced one year and then lagged one year. All models include region and year fixed effects. Robust standard errors clustered on industry-year combinations are presented in parentheses. *, **, *** indicate statistical significance at the 10, 5 and 1% level.

Given the tight supervision of progress in policy reform by the European Commission in preparation for EU accession, services policy choices can be considered largely exogenous. Nevertheless, in order to present some evidence that reverse causality is not likely to be a problem for the outcome variables in our estimation, we extend the lags of the independent variables in our equation to two years. The results from these specifications are presented in table 6 for OLS TFP measures and table 7 for Olley-Pakes TFP estimates. They demonstrate that our basic conclusion regarding the importance of foreign entry into services sectors holds when we expand the number of lags with which the independent variables enter the regressions. Since it is unlikely that the productivity of manufacturing firms today is relevant for FDI inflows into services sectors two years ago, we feel reasonably confident that reverse causation is not a problem in this specification.

16
# **9. Annex II**

In order to check for the robustness of the different decile groups used from the household survey we compared specific characteristics such as household size, age and gender of head of household among income deciles. As shown in the following table, only small variations among the decile groups were found.

**Table AII– Descriptive Statistics of Income Deciles per year**

## Household Size

|Decile|2012<br>Average|SE|2013<br>Average|SE|2014<br>Average|SE|2015<br>Average|SE|
|---|---|---|---|---|---|---|---|---|
|1|3.82|0.09|3.64|0.10|3.56|0.09|3.53|0.08|
|2|3.17|0.07|2.98|0.07|3.15|0.08|3.15|0.08|
|3|2.93|0.07|2.77|0.07|2.73|0.07|2.84|0.08|
|4|2.62|0.06|2.53|0.06|2.62|0.07|2.58|0.06|
|5|2.50|0.06|2.38|0.07|2.43|0.07|2.35|0.06|
|6|2.47|0.06|2.22|0.05|2.16|0.05|2.22|0.06|
|7|2.26|0.06|2.27|0.06|2.17|0.06|2.25|0.06|
|8|2.28|0.06|2.20|0.06|2.20|0.06|2.10|0.06|
|9|2.15|0.05|2.16|0.06|2.13|0.06|2.04|0.05|
|10|1.87|0.05|1.84|0.05|1.79|0.04|1.85|0.05|
|Household|Head Gender||||||||
||2012||2013||2014||2015||
|Decile|Average|SE|Average|SE|Average|SE|Average|SE|
|1|1.35|0.03|1.29|0.03|1.30|0.03|1.29|0.02|
|2|1.35|0.02|1.33|0.02|1.34|0.03|1.31|0.02|
|3|1.33|0.02|1.36|0.02|1.40|0.03|1.31|0.02|
|4|1.37|0.02|1.38|0.02|1.38|0.03|1.34|0.02|
|5|1.42|0.02|1.44|0.03|1.43|0.03|1.41|0.02|
|6|1.44|0.02|1.44|0.02|1.45|0.02|1.46|0.02|
|7|1.47|0.02|1.45|0.02|1.45|0.03|1.42|0.02|
|8|1.42|0.02|1.45|0.03|1.39|0.02|1.45|0.02|
|9|1.46|0.02|1.45|0.03|1.43|0.03|1.46|0.02|
|10|1.50|0.02|1.49|0.03|1.47|0.03|1.48|0.03|
|Household|Head Age||||||||
||2012||2013||2014||2015||
|Decile|Average|SE|Average|SE|Average|SE|Average|SE|
|1|52.47|0.85|52.39|0.86|52.34|0.85|51.82|0.78|
|2|55.38|0.81|54.83|0.78|55.19|0.82|54.08|0.82|
|3|55.41|0.83|56.85|0.72|57.42|0.80|56.92|0.74|
|4|58.08|0.73|57.27|0.69|57.12|0.72|58.04|0.76|
|5|57.25|0.70|57.52|0.78|57.19|0.80|58.38|0.71|
|6|56.00|0.74|57.78|0.66|58.08|0.72|58.30|0.77|
|7|57.04|0.70|55.95|0.79|56.61|0.78|56.41|0.69|
|8|53.33|0.69|54.65|0.85|55.34|0.80|56.55|0.75|
|9|50.63|0.80|51.17|0.81|52.72|0.79|53.64|0.75|
|10|45.14|0.86|47.67|0.87|47.99|0.80|47.95|0.80|

_Source:_ Based on data from the 2015 Household Budget Survey.

_Note:_ The table shows the share of total consumption for each decile.

21
# **Table 1 (continued): Asset Share of Banks in MENA, by Ownership and Other Categories, 2001-2008**

State (private) banks are defined as banks in which the government (private sector) is a majority shareholder. Foreign (Domestic) banks are defined as banks in which foreign (domestic) entities are majority shareholders. Listed banks are defined as banks that are listed in a stock market. Shares are calculated as simple country averages. GCC countries are Bahrain, Kuwait, Oman, Qatar, Saudi Arabia and UAE. Non GCC private-led banking systems are Egypt, Jordan, Lebanon, Morocco, Tunisia, and Yemen. Non-GCC stateled banking systems include Algeria, Libya, and Syria.

|**Non-GCC Private-led**|**2001**|**2002**|**2003**|**2004**|**2005**|**2006**|**2007**|**2008**|
|---|---|---|---|---|---|---|---|---|
|State Banks|42%|40%|39%|37%|35%|30%|29%|29%|
|Private Banks|58%|60%|61%|63%|65%|70%|71%|71%|
|Private Domestic|47%|49%|49%|51%|52%|52%|49%|48%|
|Private Foreign|10%|11%|12%|12%|13%|18%|22%|22%|
|Private Foreign International|4%|4%|5%|5%|6%|11%|15%|14%|
|Private Foreign Regional|7%|7%|7%|7%|7%|7%|7%|8%|
|Listed Banks|39%|39%|46%|53%|55%|65%|67%|66%|
|Listed Private Banks|39%|39%|41%|42%|46%|56%|58%|58%|
|ListedState Banks|0%|0%|5%|11%|9%|9%|8%|8%|
|**Non-GCC State-led**|**2001**|**2002**|**2003**|**2004**|**2005**|**2006**|**2007**|**2008**|
|State Banks|98%|98%|97%|96%|92%|90%|90%|86%|
|Private Banks|2%|2%|3%|4%|8%|10%|10%|14%|
|Private Domestic|1%|1%|1%|1%|1%|2%|2%|2%|
|Private Foreign|1%|1%|2%|3%|6%|8%|9%|13%|
|Private Foreign International|0%|0%|1%|1%|2%|3%|3%|4%|
|Private Foreign Regional|1%|1%|1%|2%|4%|6%|6%|9%|
|Listed Banks|0%|1%|2%|3%|5%|6%|12%|14%|
|Listed Private Banks|0%|1%|2%|3%|5%|6%|7%|11%|
|ListedState Banks|||||||5%|3%|

**Table 2: Asset Share of State Banks in Non-GCC Countries, 2001-2008**

||**2001 **|**2002 **|**2003**|**2004 **|**2005 **|**2006**|**2007 **|**2008**|
|---|---|---|---|---|---|---|---|---|
|Algeria|97%|98%|96%|95%|92%|91%|93%|90%|
|Egypt|79%|77%|77%|75%|72%|58%|55%|57%|
|Jordan|0%|0%|0%|0%|0%|0%|0%|0%|
|Lebanon|0%|0%|0%|0%|0%|0%|0%|0%|
|Libya|95%|92%|93%|97%|95%|95%|94%|94%|
|Morocco|79%|81%|82%|80%|44%|43%|38%|37%|
|Syria|100%|100%|100%|97%|90%|82%|74%|69%|
|Tunisia|||53%|46%|44%|44%|42%|43%|
|Yemen|31%|23%|20%|17%|16%|15%|14%|13%|

35
Policy Research Working Paper 10121

# **Abstract**

The combined forces of population aging, weakening family and village risk-sharing networks, and low formal pension coverage will make financing elderly consumption a major challenge for the future. This study examines whether households in high-informality settings, where participation in pension schemes is rare, accumulate wealth over the life cycle and what mix of assets and liabilities composes that wealth. Pakistan is an ideal setting, with 88.5 percent of the population in informal employment and limited wide-scale social protection targeting the elderly. Data on housing wealth, land holdings, financial wealth, household durables, and owned businesses are assembled from eight rounds of representative household surveys that span 18 years (2001–18). Changes associated with age are disentangled from differences between cohorts and year effects by applying decomposition analysis. The average informal Pakistani household accumulates 4.2 years’ worth of consumption between the head’s ages of 25 and 65, mostly in the form of residential housing. Wealth accumulation is slower early in the life cycle and picks up speed between ages 40 and 65. Land is an important part of rural households’ portfolio but grows little over the life cycle (10 months’ worth). More liquid forms of wealth such as financial wealth also grow with age, but in much more modest amounts. Overall, consistent with improving living standards and expectations that family support may be less available than in the past, the fraction that reaches old age with significant net worth has increased over the period analyzed, suggesting a potential demand for long-term saving schemes designed for the informal sector.

This paper is a product of the Development Research Group, Development Economics. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at cjoubert@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
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32
AET (2005) sensitivity analysis thus confirms the conclusion based on OLS regressions that there have been remarkable improvements in occupational mobility in rural China from 1988 to 2002. In terms of occupational mobility, the transition and economic reform thus have been nothing short of magical.

The evidence from the multivariate OLS regressions and AET sensitivity analysis is informative; it provides strong indication that, although occupational mobility has improved dramatically in rural China, educational mobility seems to have deteriorated, especially for sons. There is strong evidence that parental education affects children’s education in 2002, but is there a causal effect of parental education? Unfortunately, the AET analysis does not provide us with an estimate of the causal effect of parental education on children’s education. Evidence from alternative specifications of the Klein and Vella (2009) estimator shows that there is robust evidence of a causal effect of parental education on a son’s years of schooling in 2002. For a daughter’s education in 2002, the evidence in favor of a causal effect is much weaker; although the point estimate is large in magnitude, it is not statistically significant at the 10 percent level. In contrast, there is no evidence of a causal effect of parental education on children’s education in 1988, after 10 years of market oriented economic reform that began in 1978. The persistence in educational attainment across generations (especially for sons) in 2002, after a decade and a half of the 9 years compulsory education law seems puzzling.<sup>16</sup> We provide some possible explanations for the dramatic divergence between the trends in intergenerational occupational and educational mobility from 1988 to 2002.

The rest of the paper is organized as follows. Section 2 discusses the data and construction of variables. Section 3 and 4, arranged in a number of sub‐sections, presents the empirical results

> 16 A law mandating 9 years of compulsory education was passed in 1986. However, the implementation of the law has not been uniform across provinces and counties (Tsang (2000), Hannum and Park (2007)).

9
have been far more important in Africa over the past half-century (see Croser and Anderson 2011).

**_Commodity export price index._** We construct a country-specific international commodity export price index for agricultural and natural resource commodities as:

_ComPIi_ , _t_  _ComPricec_ , _t_  _i_ , _c_  _c_  _C_ where _ComPricec,t_ is the international price of commodity _c_ in year _t_ , and _θi,c_ is the average (timeinvariant) value of exports of commodity _c_ in the GDP of country _i_ .<sup>9</sup> We obtain data on annual international commodity prices from UNCTAD Commodity Statistics and our data on the value of commodity exports are from the NBER-United Nations Trade Database. The commodities included in the agricultural commodity export price index are beef, coffee, cocoa, cotton, maize, rice, rubber, sugar, tea, tobacco, wheat, and wood. The commodities included in the natural resource export price index are aluminum, copper, gold, iron, and oil. In case there were multiple prices listed for the same commodity a simple average of all the relevant prices is used.

**_Rainfall._** The annual rainfall data are from Matsuura and Willmott (2007). The rainfall data come at a high resolution (0.5°x0.5° latitude-longitude grid) and each rainfall observation in a given grid is constructed by interpolation of rainfall observed by all stations operating in that grid. We aggregate rainfall data to the country level by assigning grids to the geographic areas of countries.

**_GDP per capita and other data._** Data on real per capita GDP, trade openness (exports plus imports over PPP GDP), private consumption per capita, investment, and the government expenditure share are from the Penn World Table, version 6.3 (Heston et al., 2009). Data on political institutions are from the Polity IV database (Marshall and Jaggers, 2009). The data on civil war incidence are from the PRIO/UPPSALA database on armed conflicts (CSCW, 2010). Data on GDP of agricultural

> 9 This functional form of the commodity export price index follows common practice in the literature. See for example, Collier and Goderis (2007) and the references cited therein.

8