rafmacalaba/datause-encoder-data huggingface dataset:
shocks experienced by households over the preceding 12 months and the type of coping mechanisms that were adopted.

Several patterns emerge. While households in Bangladesh faced a wide variety of shocks in 2008-09, we find that health related shocks dominate, followed by climatic shocks. More importantly, a large number of households said they were unable to cope with climatic shocks presumably due to their covariate nature, while they were better able to handle health issues. Moreover the poor are least able to cope with shocks though surprisingly the non-poor appear to face a larger incidence of shocks.

The paper is organized as follows. Section 2 provides details on the data used for the paper and Section 3 provides a descriptive analysis of the type of shocks faced by households in Bangladesh and the main coping strategies. Section 4 discusses results from a multivariate analysis assessing the factors associated with a household being able to cope with shocks. Section 5 concludes.

# **2. Data and household characteristics**

In this paper, we use a nationally-representative survey of 2000 households. The data were collected by the Power and Participation Research Center (PPRC), a Dhaka based research institute. The sampling frame for this study uses the Integrated Multi-Purpose Sample (IMPS) design of the Bangladesh Bureau of Statistics. The IMPS was updated on the basis of the Population Census data of 2001 and now consists of 1000 Primary Sampling Units (PSUs) across the country of which 640 PSUs are rural and 360 urban with the urban further sub-divided into other urban and statistical metropolitan area (SMA). Each PSU consists of 200 households. A total of 16 strata were formed: 1 rural strata from each of the 6 administrative divisions of the country, 1 urban strata from each of the 6 administrative divisions, and 1 SMA (statistical metropolitan area) strata from each of the 4 divisions having SMA (Barisal and Sylhet have no SMAs).

The survey was implemented in 100 PSUs within the 16 strata described above. This was done on the principle of probability proportionate to size with size defined here as the number of households in each strata. Using this principle, the allocation of PSUs to each of the 16 strata is shown in Table 1. The next step was to generate the actual list of the 100 PSUs for the 16 strata 3
(2005). Figure B3 shows the resulting index distribution.

In the Eritrean setting, the construction of a wealth index with PCA might face the problems of clumping and truncation (Vyas and Kumaranayake, 2006).<sup>11</sup> In Gash Barka, asset ownership is very limited and the range of owned asset is quite narrow, most dwelling are similar, most households do not have toilets and almost no one has electricity. To address this issue, we make use of all available information on assets contained in our dataset.

We divided households by asset index quintile and we checked whether ownership of assets with socio-economic status. From Tables B11-B12 we can generally observe that, as wealth increases: water sources improve; households have better toilets and use bushes less often; they use not only firewood to cook, but also electricity and fuels; they have more solid walls (not made in wood or cane but more often in cement, bricks or stone) and roofs (made in cement or stone rather than leafs); own electronics, especially a radio, and hence have better access to information; they also have some vehicles, mainly bikes and carts. Finally, the number of persons per room does not change much.

These results show that the simple wealth index obtained from PCA does a pretty good job in terms of explaining variation in socio-economic status among sampled households, even if the index explains only 5% of the variance in asset ownership. In other cases, ownership initially increases and then decreases as households become wealthier, while monotonicity is expected. The main explanation lies in that the FP procedure works with dichotomous variables only and does not exploit the ordinal information available in the data.

# **References**

Filmer, D. and L. H. Pritchett (2001). Estimating Wealth Effects without Expenditure Data Or Tears: an Application to Educational Enrollments in States of India. _Demography 38_ (1), 115–132. B.2, B3, B11, B12

> 11 Clumping (or clustering) occurs when the wealth index groups households into a limited number of groups. Truncation arises from limited variation in asset ownership, which may makes it hard to distinguish groups with small wealth differences.

8
30 MFP of which was well below 1 percent during 1973-93 (and only 1.8 percent in 199398).

Clearly, farmers are capable not only of surviving without subsidies, but of becoming more productive with their removal – and not with any obviously faster rate of decline in the total number of farmers or farms than occurs with normal economic growth.<sup>27</sup> Three important aspects of the Australian reform success need to be underlined. One is that it helps if assistance to non-agricultural sectors is cut at the same time. In Australia those other micro- and macro-economic reforms made it easier for farmers to adjust and raise their productivity. This is relevant for those many developing countries that still have industrial protection and behind-the-border restrictions on domestic nonfarm market activities. For other high-income countries, where manufacturing protection rates are already low and the macro economy is well managed, the reduction of high agricultural supports would be more painful unless coupled with adjustment assistance measures.

Second, adjustment to cuts in agricultural assistance is easier the greater the heterogeneity within the agricultural sector. This can take at least two forms.

Heterogeneity in rates of industry assistance within the sector, as captured by the standard deviation of farm industry NRAs shown near the bottom of Table 2, ensures that as high rates of assistance are lowered, resources will find it profitable to move to lightly assisted farm industries. Such transfers are far easier than transferring mobile resources to other

> 27 Adjustment has been sharper within individual industries of course, especially those that faced dramatic cuts in subsidies such as Australia’s dairy industry (see footnote 24).
Policy Research Working Paper 8186

# **Abstract**

This paper examines the relationship between spatial and economic mobility in Uganda using longitudinal data from 2005 through 2012. The study relies on a detailed panel tracking survey and exploits exogenous variation in the spatial intensity of violent conflict, rainfall shocks, distance from the regional capital, and ethnic networks in urban areas. The analysis finds significant welfare gains of 58 percentage points due to migration. However, the returns to migration vary with the direction of the move. Moving to a rural destination yields welfare returns of 56 percentage points; the returns to urban moves, at 65 percentage points, are markedly higher. Policies to capture the welfare gains from migration to cities should focus on further urbanization, the development of road infrastructure, and investments in education for men and women in rural areas.

This paper is a product of the Africa Region and the Gender Cross Cutting Solution Area. 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://econ.worldbank.org. The authors may be contacted at mosullivan@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
**Table 3. The** _SEQT_ ( _l_ + 1 _|l_ ) **Test for a VAR with no Linear Restrictions with X=C**

||Statistic|Critical value<br>at 5% level|Signifcant break|
|---|---|---|---|
|Seq( 2_|_1|)<br>39.32|32.60|-|
||||1970q2 (1967q4-1972q4);|
|Seq( 3_|_2|)<br>38.82|33.96|1985q1 (1983q4-1986q2);<br>and 1999q1 (1998q2-1999q4)|
|Seq( 4_|_3|)<br>0.000|34.89|-|

The estimated break-dates in the first part of this section are submitted to a robustness exercise in this sub-section. We would like to dismiss the possibility that the breaks found are not primarily driven by changes in the effect of house prices on the macroeconomy. For instance, the new monetary policy paradigm of the Federal Reserve -nominal policy interest rates reacting more than proportionaly to inflation rate changes- could be the main factor causing the rupture detected in the mid-80s. To avoid this issue, we constrain our VAR to only allow a change in the set of parameters related directly to the impact of house prices in the macrovariables. Thus, the Qu and Perron’s algorithm is applied to the following VAR model: where _m_ denotes the unknown breaks and _εt_ = _{εt_ 1 _, εt_ 2 _, εt_ 3 _}_ is the error term with mean _0_ and constant variance-covariance matrix. Then, in the above VAR with linear restrictions, only the parameters _β_ 3 _j_ , _β_ 7 _j_ and _β_ 11 _j_ are allowed to change. As proposed by Qu and Perron (2007), the _SupLRT_ test is performed to obtain the number and the location of breaks in a VAR with linear constraints.

Let us consider the case of a VAR with X=GDP. The null hypothesis of no break is tested against the previously estimated three breaks. Results (Table 4) show that the _SupLRT_ test does not allow us to reject the null hypothesis of no structural break against the alternative of three breaks, as the value of the _SupLRT_ statistic is 29 _._ 92 while the critical value at the 5% level is 33 _._ 71. Thus, we cannot associate each of the three previously detected breaks as structural changes in the effect of house prices on the macroeconomy. We proceed to examine if two of the estimated breaks are robust to the imposed restrictions. The _SupLRT_ test allows us to reject the null hypothesis of no structural break against the alternative of two breaks, as the value of the _SupLRT_ statistic is 29 _._ 54 while the critical value at the 5% level is 28 _._ 17. The test gives us 1971q2 and 1983q1 as structural breaks. The confidence intervals associated to these two breaks, 1968q2-1973q2 and 1980q2-1985q4, cover the first two break-points obtained in the no restricted
The EU BCA uses Monte Carlo simulation to characterize parameter uncertainty in the composition of mineral output, the prices of minerals, fixed and operating costs, and the price of CO2 offsets (See Table 1). The economic cost of removing and/or disturbing benthic communities is computed in what the Cardno BCA regards as a bounding analysis that, in the view of the analysts, gives an overestimate of possible losses. This judgement is based on computing the value of ecosystem services lost from benthic habitats based on studies on the costs of disturbing terrestrial habitats that have higher gross productivities than benthic ecosystems. A bounding analysis is also used to characterize the possible damages from low probability, high impact ecosystem disturbances. In this case, the cost of the Deepwater Horizon accident is taken as the high boundary reference point.

# **Table 1. Variables included in the Monte Carlo Simulation for the EU BCA**

|Parameter|Range<br>|s for Randomized P<br>|arameters<br>|Source|
|---|---|---|---|---|
||Low Bound|Midpoint|High Bound||
|Production<br>composition<br>(Percent mineral)|No<br>Information|No Information|No Information|Study: SKC 2010|
|Mineral Prices|-50%|Current Price|+50%|No Source Cited|
|Fixed Capital Costs|-20%|Current<br>Estimated Cost|+%20|No Source Cited|
|Operational Costs|-20%|Current<br>Estimated Cost|+20%|No Source Cited|
|Price of carbon<br>offsets from mining|$3.7|$5.80|$7.4|Study: Report on the State<br>of the Carbon Market|

Source: Cardno (2016).

Using this methodology, the analysis finds that the gross profit earned from mining SMS deposits and manganese nodules is likely to be positive, while the gross profits earned from mining cobalt-rich crusts is likely to be negative. The probability that profits from SMS mining are positive is 83%. The range is -$75 million to $260 million, with an expected value of $80 million. The study finds that there is no chance that profits from mining manganese nodules will be negative. The range is $150 million to over $1 billion, with a median value of $500 million.<sup>18</sup> These gross profit estimates are significantly larger than the boundary cost estimates for unavoidable ecological impacts to benthic environments caused by mining activities, which are $0.5 million for SMS and $18.3 million for manganese nodules. The study concludes that cultural impacts on Papua New Guinea from SMS mining will be _de minimis._ Cultural impacts on the Cook Islands from manganese mining will be significant, but the study concludes that it is unclear whether these effects will be regarded as positive or negative. The study also considers the expected cost of unplanned fuel spills as $40,000 and $260,000 for SMS and manganese

> 18 These numbers are from a discussion in Wakefield and Meyers (2018) using Figure 1 on pp 2 and Table 1 on pp

> 8. They are based on a 5% discount rate. These numbers are somewhat different than the numbers in the Cardno (2016) study, which are reported for a 7% discount rate.

18
<!-- Start of picture text --> Child received all immunizations q<br><!-- End of picture text --> Burth assisted by trained health staff Blood sample during pregnancy | 9 Burth at public or privatehealth facility [8 | Postnatal check within 2 months of birth | 8 Composite indicatorfor access to health services 6);3

Human20 Opportunity-. . idex2 40 60 80 100 BHOTin 2000) =OScaleeffect ONistributional effect
# _Public entities_

The public entity issues (e.g., SNCF, EDF) have been growing quickly. This is partly due to Maastrich criteria.<sup>59</sup> According to these criteria, the French government debt ratios must be contained in a certain level. Therefore, instead of refunding some public entities, the government has pushed them to fund themselves on the bond market.<sup>60</sup> There is no specific regulatory framework for public entities. They are subject to the same securities regulation as corporate issuers. However, they benefit from an adaptation of the content of the prospectus to their particular nature if necessary (accounting standards, some public entities have no auditors). The French securities regulator has also established a specific schedule of prospectus for territorial communities (Schedule P).

**Table 2:  Figures for 2002 including tap issues (sources: dialogic and bondware)**

||Amount. millions<br>Pe|rcentage<br>I|ssues|
|---|---|---|---|
||(Euro)|||
|**Central government**|**91,276**|**47.38**|**41**|
|Central government|91,276|47.38|41|
|**Corp/Util (public and private)**|**31,953**|**16.59**|**131**|
|**_public entities_**|_8,639_|_4.48_|_35_|
|Public corporate|4,677|2.43|11|
|Public utility|3,962|2.06|24|
|**_privates entities_**|_23,314_|_12.10_|_96_|
|Private corporate|18,622|9.67|83|
|Private utility|4,692|2.44|13|
|**Banks/Finance**|**59,257**|**30.76**|**460**|
|Private bank|37,131|19.27|343|
|Private finance (other)|7,901|4.10|27|
|Finance vehicle (private|1,000|0.52|1|
|bank/finance)||||
|Public bank|7,722|4.01|71|
|Public finance (other)|5,504|2.86|18|
|**Authorities**|**70**|**0.04**|**2**|
|Local authority|70|0.04|2|
|**Others**|**10,095**|**5.24**|**52**|
|Supranational institution|3,319|1.72|22|
|SPV|6,776|3.52|30|
|Total|**192,652**|**100.00**|**686**|

_Mortgage bonds development_ Until 1999, the mortgage bond market was a "niche" with a unique issuer since 1995, the Credit Foncier de France. The French government decided to create a new type of issuer: les sociétés de crédit foncier (SCF) as an alternative to Pfandbriefe. SCF are credit institutions supervised by the French Banking Commission. Their financial statements must be certified by regular auditors and an independent specific auditor in charge of controlling the compliance of

> 59 Established in order to "harmonize" European economics for the purpose of the introduction of euro in 1999. 60 Due to their top ratings, some public entities issues can be considered as a benchmark for private issuers.

56
cohort of students who learned the two subjects in English from the beginning of their schooling years; this cohort of students never had the chance to learn either subjects in their native language.<sup>2</sup> The availability of the 2007 and 2011 data sets enables us to differentiate the impact of the switch in the language of instruction and the use of non-native tongue as the language of instruction in schools respectively.

Although the data from TIMSS do not provide specific information on a student’s ethnicity, we can infer whether a student is ethnic Malay or non-ethnic Malay by checking the language spoken at home. In particular, students who speak the language of test at home are ethnic Malay and students who do not speak the language of test at home are non-ethnic Malay and we assume the mother tongue of ethnic Malay students is BM while the mother tongue of non-ethnic Malay students is neither BM nor English.<sup>3</sup> Data from other countries are also extracted from TIMSS to serve as the comparison sample. This sample includes 19 other countries that also participated in the eighth graders’ assessments in 1999, 2003, 2007 and 2011 and did not have any language policy change that affected participating students during the relevant period. These countries are Australia, Chinese Taipei, England, Hong Kong SAR, China, Hungary, Indonesia, the Islamic Republic of Iran, Israel, Italy, Jordan, the Republic of Korea, Lithuania, Morocco, Romania, the Russian Federation, Singapore, Slovenia, and Tunisia.

The outcome variables for this study are students’ mathematics and science test scores. We use a set of correlates of test scores derived from the student, school and teacher background questionnaires as pre-language-policy-change measures. Because not all questions from the

> 2 With the exception of the majority of ethnic Chinese students attending a Chinese National-type school, as they received supplementary classes with Chinese language as the medium of instruction in mathematics and science subjects.

> 3 Appendix B provides technical details on the robustness of this approach.

10
CÔTE D’IVOIRE’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE

**Table 14 Financial flows to Côte d’Ivoire’s infrastructure, mid 2000s**<sup>*****</sup>

<u>$ millions per year</u>

|O&M||Ca|pital expenditure<br>Non-overnmental|||
|---|---|---|---|---|---|
|||Non-|g|||
|Public<br>sector|Public<br>sector|ODA<br>OECD<br>financiers|Private<br>sector<br>Households|Total<br>CAPEX|Total<br>spending|
|Information and communication<br>technologies<br>0|0|0<br>4|53<br>0|57|57|
|Irrigation<br>nav|nav|0<br>0|0<br>0|0|0|
|Power<br>476|15|0<br>1|0<br>0|16|492|
|Transport<br>26|50|3<br>10|14<br>0|77|103|
|Water supply and sanitation<br>2|2|1<br>1|0<br>92|4|6|
|Total<br>504|67|4<br>16|67<br>92|246|750|
|*Budget figures are annual average over period 2001/05, wh<br>Source: Derived from Foster and Briceño-Garmendia 2009.<br>O&M = operations and maintenance; ODA = official develop<br>expenditure; OECD = Organisation for Economic Co-operati<br>**Figure 14 ODA flows between 1995 and 2008**<br>**US$ millions**|ile SOE fig<br>ment assist<br>on and Dev|ures are annual average<br>ance; PPI = private parti<br>elopment.|for period 2004/08.<br>cipation in infrastructure;|CAPEX = c|apital|
|WSS<br>transport<br>t|elecom|energy||||

Source: OECD 2010.

Côte d’Ivoire’s current spending on infrastructure spending is comparatively low as a share of GDP —around 5 percent of GDP, or only $35 per person per year. By comparison, other low-income countries in West Africa spend around 10 percent of GDP on infrastructure (figure 17). This effort translates into only $35 per capita per year in infrastructure spending. Furthermore, the pattern of investment is different in Côte d’Ivoire than in other low-income countries in Africa (figure 18). Côte d’Ivoire’s public sector invests more in transport and less in power than its African peers. For all sectors, Côte d’Ivoire receives much less ODA financing than the peer group.

33
# **1. Introduction**

Did the financial sectors of developing countries that received assistance from the World Bank perform better than those in countries that did not? And did the countries that received World Bank assistance enjoy more financial sector development than they otherwise would have? Based on financial sector data in the 1990s, the answer to the first question appears to be yes. Despite severe financial crises in multiple countries that received World Bank assistance to support financial sector reform (such as Argentina, Brazil, Mexico, and Russia), the growth rates for borrowing countries for multiple standard measures of financial sector development were greater than for countries that did not receive such support.

A skeptic could argue that such results are driven by selection – aside from a few crisis countries that received loans, borrowers tended to be countries that were most apt to improve their financial sectors on their own. To answer our second question, whether borrowing countries developed their financial sectors more quickly than they otherwise would have, we use both treatment effects regressions that explicitly account for selfselection and propensity score matching techniques. We also offer robustness checks that examine whether our findings are specific to a region(s) and whether improvements in financial indicators preceded or followed lending projects – if improvement preceded those projects, it would seem unlikely that they had a large causal impact in borrowing countries. However, these multiple estimation techniques provide evidence that reinforces our main findings. Borrowing countries tended to experience substantial postloan improvement in their financial indicators, significantly more than the typical improvement in non-borrowing countries, even after accounting for self-selection.

This paper contributes to a broader literature on the effects of economic reform programs in developing countries. Much of that literature focuses on the impact of IMF and World Bank adjustment lending on broad macroeconomic aggregates, most notably real per capita growth.<sup>1</sup> Much has been made of selection problems in that literature, and

> 1 Easterly (2005) describes this literature as including Barro and Lee (2002), Conway (1994), Corbo and Goldstein (1987), Corbo and Fischer (1995), Devarajan, Dollar and Holmgren (2001), Dicks-Mireaux, Mecagni, and Schadler (2000), Goldstein and Montiel (1986), Haque and Khan (1998), Hutchison (2001), Kapur, Lewis and Webb (1997), Khan (1990), Killick, Gunatilaka, and Marr (1998), Knight and Santaella

2