rafmacalaba/datause-encoder-data huggingface dataset:
# 3. Impact of Shocks on Household Welfare

## 3.1. Methodology

### 3.1.1 Data

We estimate the impact of shocks on household welfare using the latest round of the NBHS collected in 2014/15. It contains information on expenditure and consumption, welfare, poverty status, assets, and demographics of Sudanese Households. A representative sample of 13,800 households was drawn from all 18 States of Sudan, of which 11,953 households were surveyed during three rounds of data collection with a response rate of 87 percent (Central Bureau of Statistics 2017).

### 3.1.2 Estimation Strategy

Our empirical strategy is conceptually based on the permanent income and full insurance models of consumption dynamics (Morduch 1995). These models argue that with access to insurance, credit, and/or liquid assets, prevalence of shocks (which hitherto will affect household income) will have a low or no correlation with household consumption (Bardhan and Udry 1999). In the absence of these mechanisms or where they are incomplete, households resort to various coping strategies (ex-ante and/or ex post) to manage and mitigate the impact of shocks and ensure consumption smoothing. Therefore, the effectiveness of these strategies can be examined through the extent to which exposure to shocks results in fluctuations in household consumption.

Since we have one round of data, we rely on the cross-sectional variation in indicators of household welfare to understand the effect of idiosyncratic and covariate shocks on Sudanese households. In our analysis, idiosyncratic shocks are self-reported by household heads and include exposure to floods/droughts, loss of crops/livestock, illness, death of a household member, robbery/assault, and damage to household dwelling. We combine these data with those on rainfall (deviation from the longrun average), price (deviation of the predicted from the actual price level), and incidence of conflict/crime as proxies for covariate shocks. Using this approach, various indicators of household welfare such as consumption, education, asset accumulation, and nutrition levels are used to account for the multidimensionality of the impact of shocks.

In the basic model, we estimate the following specification: ln(yij) = Xijβ+ Sijα+ γj + εij where yij is an indicator of household welfare such as consumption, food consumption score (FCS), dietary diversity, and poverty status for household i in community/state j ; Xij is the vector of household characteristics such as demographics, assets, and employment for household i in community/state j ; Sij is a vector of shocks; γj is a vector of community characteristics or fixed effects; and εij is the error term. Our main parameter of interest in this specification is α . The extent to which shocks affect the welfare of Sudanese households can be examined by assessing whether α is significantly different from zero.

While this specification allows us to estimate the effect of shocks on household welfare, it does not allow us to capture the heterogeneity in the impact of shocks across space and households—how does the 27
liberalisation also allowed unhedged foreign borrowings, which, as Lim (1998) notes, was attractive because the central bank's policy of a stable currency made sure that domestic interest rates were above foreign rates. Financial liberalisation without sufficient prudential regulation and enforcement by the central bank led also in the Philippines to an overexposure to risky loans, especially real estate loans, foreign borrowings and consumer loans to high risk groups.

# **4. Data**

The data used in this study are taken from BankScope, which is a rich source for balance sheet and profit & loss account data for individual banks across the world. In order to estimate the bank efficiencies we have gathered the following variables: interest expenses on deposits and borrowings, personnel expenses, operating expenses (on equipment, building, machinery, etc.), net loans (equal to gross loans or credit plus accrued interest minus loan losses), and investments in securities (including securities purchased with a resale agreement). In addition, we have collected data on total assets, deposits, equity capital, loan loss provisions, short-term borrowings (domestic and foreign) and ownership structures. The data is on 54 commercial banks in Indonesia, 25 in Korea, 34 in Malaysia, 29 in the Philippines, and 29 in Thailand for the years 1992-963 (see Appendix, Tables 1-4, for summary statistics of the data).

The coverage of the data is quite good. In terms of bank loans, the coverage of the total commercial banking system by our sample of commercial banks is about 70% in Indonesia, 90% in the Philippines, 98% in both Korea and Malaysia, and 100% in Thailand. In terms of number of commercial banks, the coverage by our sample is 23% in Indonesia, 96% in Korea, 97% in Malaysia, 57% in the Philippines and 100% in Thailand (see Appendix, Tables 1 and 2).

We consider five different ownership forms: state-owned, family-owned, companyowned, foreign-owned, and widely-owned (dispersed ownership). The ownership category stateowned means that the state is a majority shareholder. Family-owned means that a handful of families own the majority of the voting rights. Majority ownership is defined as having over 33% of the voting rights, which in general is identical to owning more than 33% of the shares, although in some cases there is a large difference between ownership of control rights and ownership of cash flow rights. Similarly, company-owned means that a handful of companies own the majority of the voting rights and foreign-owned means that the financial institution is

> 3 However, data are not available for all banks for all five years. For Malaysia, no data was available for 1993, and for 1994 only for a few of the large banks.

13
fostering macroeconomic stability. A minimum of macro stability, combined with sound management of the public debt and actions to the develop the local currency debt market are key in the formation of a reliable yield curve, which is in turn crucial to the development of interest rate derivatives. Similarly, macro stability and a flexible exchange rate regime are necessary in the development of the market for currency derivatives. In small and undiversified economies, however, these policies might not suffice to give rise to markets for hedges and certain insurance products. In such cases, a premium should be placed on accessing the vast, yet vastly unexploited, risk pooling and diversification opportunities offered by international capital markets—which range potentially from catastrophic insurance to commodity price hedges, from weather and crop insurance to securitization of export receivables, and from currency swaps to GDP-indexed securities.<sup>50</sup> Changes in the state variables involve changes in fundamental institutions and, thus, take a long time to materialize. That does not necessarily mean, however, that that there is no scope for policy in the shorter-run. To the extent that a financial system is operating below the possibilities frontier, there is room for _market-enabling policies_ that may foster the broadening of access even in the absence of perceptible changes in state variables. Where the main reason for being below the possibilities frontier is the demand problem of self-exclusion, the appropriate policies would emphasize raising financial literacy. If – as is more likely - the main problems reside with sub-optimization in credit supply, by contrast, a wider range of policy options can be considered, starting with competition policy.

> 50 For examples of weather insurance, see Hess (2003) and Bie Lillleor (2006). See OECD (2005) for a collection of papers on catastrophe insurance and Auffret (2003) for a discussion of the catastrophic insurance market in the Caribbean.   Schiller (2003) provides a grand vision of the potential for financial markets to aid in long-term and international risk management, including reducing “gratuitous inequality”, the feasibility of which is within reach given recent development in financial theory, information technology, and the science of psychology.

43
three. We use the annual observations from 1974-1982 for 48 countries.

The results from including these either of these two measures of political regime type in the base specifications are emphatically ambivalent (table 10). The lowest ranking group of countries by IIRT, the "autocracy", tends to have a lower ERR than other categories (as evidenced by the positive signs for most of the others). In specifications A and C (without regional dunmmies), IRT categories 3 and 5 (most democratic) tend to have higher returns, while in specification D none of the differences are empirically or statistically significant. Using the ADI, we also find in specifications A and C some weak evidence that more democratic regimes tend to have higher returns, but this is reversed by the inclusion of regional controls.

Moreover, except for a comparison between the "best" and "worst" regime type, there is no clear pattern to the results. For instance, the increment to returns over the autocracies is nearly as high when IIRT=3 as when IIRT=5, and countries with IIRT=4 (partial democracy) are predicted to have an ERR _lower_ by 3.4 percentage points than even autocracies. The most democratic (ADI= 1) countries are also predicted in specification D (at a very low level of significance) to have lower returns than autocracies.

- 30 -
<!-- Start of picture text --> 40 Assets Basic utilities<br>Housing Access to credit<br>Employment Education<br>30<br>20<br>10<br>0<br>.5 1 1.5 2 2.5 3 3.5 4 4.5 5 5.5<br>Cutoff d<br>Contribution to gender differences (%)<br><!-- End of picture text -->

# **_4.5 Comparisons between age groups_**

Another important issue to explore is the correlation between deprivation and gender inequality by age. Table 8 presents the results of this analysis for both Burkina Faso and Togo. When the measure of deprivation headcount (H) is considered, the women’s deprivation rate increases with age in Burkina Faso. In fact, this rate, which represents 73.5% among people aged 15 to 19 years, rises to 92% for people aged 55 years and over. The same pattern is observed with the MPI even though it seems to be less monotonic. Gender inequality remains present in all age groups but it is more acute in the middle aged population (people aged 30 to 44 years). By contrast, gender differences are relatively less considerable for extreme age groups.

<u>Table 8: Multidimensional deprivation rates by gender and age group, for a cutoff</u> _<u>d</u>_ = 3

|Country|Age<br>groups|Pop<br>sha|ulation<br>res(%)||Headc|ount H (%|)||M|PI (%)||
|---|---|---|---|---|---|---|---|---|---|---|---|
||(years)|Men|Women|All|Men|Women|Gender<br>diff.|All|Men|Women|Gender<br>diff.|
||15–19|9.8|10.0|70.8|68.1|73.5|-5.3*|52.8|50.3|55.2|-4.9*|
|**ina**<br>**o**|20–24|6.6|8.5|74.5|66.3|80.8|-14.5*|55.6|48.7|61.0|-12.3*|
|**rk**<br>**Fas**|25–29|5.5|7.9|79.1|72.6|83.5|-10.9*|58.8|51.4|63.9|-12.5*|
|**Bu**<br>|30–34|4.6|6.9|77.5|68.6|83.4|-14.8*|57.2|46.5|64.3|-17.7*|
||35–39|4.0|5.4|79.4|68.4|87.5|-19.1*|58.7|46.1|68.1|-22.0*|

28
carbon emissions to respond to changes in energy prices, including those caused by taxes. In what follows, we describe the model, including how the economy of South Africa is represented and how CO2 emissions and a carbon tax are incorporated into the framework.

**_The economic structure of South Africa._** South Africa is the largest middle income country in Sub-Saharan Africa, its gross national income accounting for about 40 percent of the region’s total income.<sup>7</sup> It has a population of about 47 million and a per capita income of $ 5,390. Being more advanced, its economy is also more diversified than many developing economies.  Agriculture accounts for only 4 percent of total value added while the service sectors are together the largest share of value-added (69 percent) followed by industry sectors (27 percent).  The economic structure is represented by 43 sectors in the framework, three of which are energy sectors: coal, petroleum and electricity & gas (see Annex Table 1). Crude oil for domestic consumption is mainly imported and there is no tariff on crude oil.<sup>8</sup> The data in the model are from a 2003 social accounting matrix (SAM). 9

**Figure 1: CO2 emission by energy input, 2003**

<!-- Start of picture text -->
1.1%<br>17.1%<br>Coal (342 million metric tons)<br>Oil    ( 72 million metric tons)<br>Gas   (  5 million metric tons)<br>81.8%<br><!-- End of picture text -->

_Source:_ IEA (2006).

**_Energy use and CO2 emissions in South Africa._** CO2 emissions depend primarily on energy use and the CO2 coefficient or intensity of each energy input. Because of its heavy energy usage and reliance on coal, South Africa’s energy-related

> 7 The share is in terms of values in US dollars. Alternatively, the share is 32 percent in term of comparable purchasing parity power among countries. Figures are 2006 numbers from the WDI 2008 and calculated using the World Bank Atlas method.

> 8 This is an extension of the initial data base which does not include the sector crude oil; we disaggregate crude oil imports from the category other minerals.

> 9 The data construction was commissioned by the World Bank in 2002/2003 and developed by Claude Van der Merwe from Quantec. See Van Der Merwe (2002) for a description of the sources used.

6
- (ii) Students move for education, perhaps being charged higher for their education than the fees applicable to nationals in the host country;

- (iii) The high skilled and talented move to boost global innovation; (iv) High skilled professionals (doctors and engineers) move to deliver medical and other services.

# North-to-South mobility

- (i) Students move to acquire at least the standardized components of skilled professional education;

- (ii) The sick move to acquire medical services, with the proviso that the fees that they are charged for services could be higher than that for host country patients;

- (iii) The elderly move to enjoy better standards of living and perhaps even cheaper medical care in response to reduced pension benefits;

- <u>(iv)</u> The skilled unemployed move in search of job opp

In a world of asymmetric migration pressures, politics in the rich countries have been a serious impediment. Is it possible to move from the desirable to the practical? The political costs of immigration into industrial countries are well known. Votaries of labor mobility have all focused on and invoked the large efficiency gains as providing the offset to the political costs. But revealed preference suggests that this argument has had only limited traction.

Liberalizing inflows of labor will continue to be a challenge in many industrial countries especially in the aftermath of the recent financial crises. In Europe, there is some evidence that attitudes to migration have become more negative especially in countries worst affected by crises such as Spain and Greece.<sup>17</sup> In the richer countries such as Netherlands, Austria, Italy, and France, parties that are opposed to immigration remain an electoral force to contend with. In the United States, efforts to reform immigration have gained some momentum but still face serious hurdles, especially where it comes to allowing more liberal entry of new immigrants. The current bill passed by the Senate needs to clear the House of Representatives. Even where it does improve access the bill claws it back by requiring skilled immigrants to be compensated at closeto-US wage rates, undermining their competitive advantage.

Calls to increase one-way labor flows have always received an unsympathetic hearing. But what if developing countries were to receive an increasing stream of retirees, elderly, skilled unemployed, and students from the industrial world? The flow of retirees may create its own political problems in poorer countries. These countries might welcome tourists but if the magnitudes get large and if the duration of the stay merges into semi-permanence, the result may be unease and even hostility. With growing north-to-south flows, the politics of realizing these flows will be symmetrically difficult.

> 17 See, European Commission’s “Standard Barometer, 77,” available at: http://ec.europa.eu/public_opinion/archives/eb/eb77/eb77_anx_en.pdf

18
- **Korpe, Poonum S. and William A Petri, Jr..** 2012. “Environmental enteropathy: critical implications of a poorly understood condition.” _Trends in Molecular Medicine_ , 18(6): 328–336.

- **Kosek, Margaret, et al., and the MAL-ED network.** 2013. “Fecal Markers of Intestinal Inflammation and Permeability Associated with the Subsequent Acquisition of Linear Growth Deficits in Infants.” _American Journal of Tropical Medicine and Hygiene_ , 88(2): 390–396.

- **Lin, Audrie, Benjamin F. Arnold, Sadia Afreen, Rie Goto, Tarique Mohammad Nurul Huda, Rashidul Haque, Rubhana Raqib, Leanne Unicomb, Tahmeed Ahmed, John M. Colford Jr., and Stephen P Luby.** 2013. “Household Environmental Conditions Are Associated with Enteropathy and Impaired Growth in Rural Bangladesh.” _American Journal of Tropical Medicine and Hygiene_ .

- **Miguel, Edward and Michael Kremer.** 2004. “Worms: Identifying Impacts on Education and Health in the Presence of Treatment Externalities.” _Econometrica_ , 72(1): 159–217.

- **Mondal, Dinesh, Juliana Minak, Masud Alam, Yue Liu, Jing Dai, Poonum Korpe, Lei Liu, Rashidul Haque, and William A. Petri, Jr.** 2011. “Contribution of Enteric Infection, Altered Intestinal Barrier Function, and Maternal Malnutrition to Infant Malnutrition in Bangladesh.” _Clinical Infectious Diseases_ .

- **Munshi, Kaivan and Mark Rosenzweig.** 2009. “Why is Mobility in India so Low? Social Insurance, Inequality, and Growth.” working paper, Brown University.

- **Nelson, Charles A., Charles H. Zeanah, Nathan A. Fox, Peter J. Marshal, Anna T. Smyke, and Donald Guthrie.** 2007. “Cognitive recovery in socially deprived young children: The Bucharest early intervention project.” _Science_ , 318(5858): 1937–1940.

- **Pattanayak, Subhrendu K, Jui-Chen Yang, Katherine L Dickinson, Christine Poulos, Sumeet R Patil, Ranjan K Mallick, Jonathan L Blitstein, and Purujit Praharaj.** 2009. “Shame or subsidy revisited: social mobilization for sanitation in Orissa, –

- India.” _Bulletin of the World Health Organization_ , 87: 580 587.

32
opportunities which is not concerned only in the outcomes of preparatory-level enrollees. Dropping out is extremely likely to be a selective process, in the sense that it is correlated with family and student characteristics that also affect test scores.

In this paper, we have attempted to correct for the selection biases described above. We follow Ferreira and Gignoux (2011a), and use a simple two-sample non-parametric mechanism for assessing the sensitivity of our inequality measures to alternative assumptions about the sample selection process. The procedure relies on using information on preparatory school graduates from the HIECS Egyptian budget survey from 2004-05. This large-sample survey allows estimating the numbers of preparatory-school graduates and drop-outs in groups defined by similar gender, father’s education and region of residence (the two later variables have consistent coding in the two surveys). The procedure then consists in putting back observations and imputing test-scores for drop-outs in the TIMSS sample. Two extreme assumptions are used for the imputations. The first assumes selection on observables whereby drop-outs would achieve similarly to children taking the TIMSS tests that have similar characteristics. The second assumes selection on both observables and (strongly) unobservables and attributes a test-score in the lower tail (we use the 5th quintile) of the conditional distribution of scores achieved by test takers with similar characteristics. In practice, the corrections are implemented by a re-weighting of the TIMSS sample.

# _Learning achievement inequality: low mean and high dispersions_

By applying the measures above to the TIMSS data, we first document the levels of inequality in learning achievement observed in Egypt and put them in an international perspective. **Figure 2** plots the country-level standard deviations of test scores against the mean scores in Math in the TIMSS 2007 data. Egypt, with a mean score of 391 and a standard deviation of 100, lies in the top left quadrant, i.e. has both a low mean and a high dispersion in learning achievements.

before completing preparatory.

18
# **C. GOVERNMENT PROGRAMS**

Government spending on science, technology and research declined dramatically for most of the past ten years, both in real absolute terms and as a percentage of GDP and total government spending. Nevertheless, government spending still accounts for nearly 50% of total science and technology spending in Russia, a much larger share than in other OECD countries where business spending is the predominant source of R&D spending. Given the relative paucity of business spending and the relatively large share of government spending, it is more important than ever to ensure that scarce budget resources are expended in ways that contribute to the evolution of a modem, knowledge intensive, high tech economy.

Unfortunately, a recent analysis of federal expenditures on R&D noted that a substantial portion of budget expenditures do not contribute to this objective. On the contrary, the report explains, existing Government R&D spending supports prestige basic research over more commercially relevant applied research, subsidizes existing research institutions irrespective of whether their research agenda is congruent with national priorities and objectives, promotes the institutional status quo, fosters the continued separation of research institutes from enterprises, does little to promote private R&D spending, and allocates only a small percentage of total funding on the basis of competitive standards of merit. For example, the report notes:<sup>63</sup> "[U]sing the need to maintain the competitive strength of the economy as a reason for government interventions in the R&D sector implies not so much the support of fundamental research but incentives for the applied science and innovations.... [T]he existing budget classification virtually precludes planning and control of expenditures both from R&D regulation purposes and objectives' perspective and from the standpoint of scientific and technological policy priorities as set by the government.... A strongest case for government intervention in the civil science and R&D sector from the standpoint of the economy is to ensure a stronger competitive edge of the national economy within the framework of international division of labor. However, the above objective of state aid provision to science in Russia has not been yet formulated as a government priority or government policy objective. Moreover, to a certain extent it runs counter to the proclaimed priority of the fundamental research support over aid to applied science....Government financing of R&D to a larger extent aims to maintain the existing infrastructure of scientific organizations rather than to obtain scientific results." Thus, whether intentionally or unintentionally, Government R&D expenditures, would appear to be promoting and preserving the least effective features of the Soviet S&T system. The following recommendations are designed to increase the likelihood that

> 63 Fiscal Policy Center, ANALYSIS OF FEDERAL EXPENDITURES ON R&D. MOSCOW-2001, prepared under U.S. Agency for International Development Contract OUT-PER-I-00-99-00003-00, p7, 16, 45, 48 of the English translation.

- 36 -